Tag Archives: Amazon Redshift

How United Airlines uses Amazon Redshift and AWS Glue Data Catalog federation to query Databricks-managed data

Post Syndicated from Vaibhav Agrawal original https://aws.amazon.com/blogs/big-data/how-united-airlines-uses-amazon-redshift-and-aws-glue-data-catalog-federation-to-query-databricks-managed-data/

This post was co-written with Ankit Aggarwal and Raja Kalluri from United Airlines.

United Airlines processes billions of events daily across its data platform, which spans Amazon Redshift and Databricks with Unity Catalog. To bridge these platforms without duplicating data, the team turned to AWS Glue Data Catalog federation.

In this post, we walk through how to configure AWS Glue Data Catalog federation to connect with Databricks Unity Catalog, so you can run live SQL queries from Amazon Redshift without moving or duplicating data.

Why United Airlines needed catalog federation

United Airlines curates petabytes of data through a medallion architecture (bronze to silver to gold) on Amazon Simple Storage Service (Amazon S3). The airline user interaction data layer alone is several double-digit terabytes of near real-time streamed data. Teams use it to measure customer engagement patterns, feature adoption, and conversion behavior across web and mobile touchpoints. Analysts need to query this curated data through Amazon Redshift Serverless. As part of the existing data platform architecture these data tables are cataloged in Databricks Unity Catalog, not in the AWS Glue Data Catalog. As a result, Amazon Redshift has no native visibility into them. Without catalog federation, the only way to make this data queryable from Amazon Redshift would have been to duplicate it into Amazon Redshift Managed Storage (RMS) and build pipelines to keep it in sync.

AWS Glue Data Catalog federation removed this need. Amazon Redshift users now query the gold layer stored in Amazon S3 directly, with Iceberg metadata resolved from Unity Catalog at query time and no data movement. AWS Glue Data Catalog federation connects Amazon Redshift to external catalogs like Unity Catalog, so analysts query cross-platform data without building sync pipelines or duplicating storage.

Amazon Redshift Serverless is powered by the same Graviton-based query engine used in the new RG instance family, which delivers up to 2x faster data lake query performance compared to prior generations. This engine is purpose-built for reading Apache Iceberg tables directly from Amazon S3, making it well-suited for such federated query workloads.

United Airlines is taking a phased approach to adopting AWS Glue Data Catalog federation across its data platform. The initial focus is the most heavily used user interaction data tables, with 30 tables currently federated in production and 70 more in active rollout. Several hundred additional tables across different business domains are planned for production in the coming months.

Solution overview

AWS Glue Data Catalog federation bridges these platforms at the metadata layer. Here’s how the architecture works.

The architecture follows a four-layer federation chain:

  • Databricks Unity Catalog exposes tables through its Iceberg REST API endpoint. For Delta tables, you can turn on UniForm format to make them Iceberg compatible.
  • AWS Glue Data Catalog creates a federated catalog that connects to Databricks Unity Catalog, making metadata visible within AWS without data movement.
  • A resource link database in the default AWS Glue catalog acts as a bridge, pointing to the federated catalog database. This is required for Amazon Redshift compute.
  • Amazon Redshift Serverless references the resource link database through an external schema. When a query runs, Amazon Redshift traverses the link, calls AWS Glue Federation, and reads the Iceberg data through the Databricks Unity Catalog REST API. AWS Lake Formation governs permissions throughout this chain.

Key services or service features used in this solution:

Figure 1: Federation chain from Databricks Unity Catalog to Amazon Redshift Serverless through AWS Glue and Lake Formation

The architecture follows a six-step flow:

  1. A SQL analyst submits a query to Amazon Redshift Serverless.
  2. Amazon Redshift resolves the external schema through the AWS Glue Data Catalog (resource link to federated catalog).
  3. The AWS Glue federated catalog calls the Databricks Unity Catalog Iceberg REST API to retrieve current table metadata.
  4. The namespace IAM role calls AWS Lake Formation GetDataAccess to obtain scoped, temporary S3 credentials.
  5. Lake Formation evaluates fine-grained access policies and vends credentials for the authorized data files.
  6. Amazon Redshift Serverless reads the Iceberg data files directly from S3 and returns results to the analyst.

Prerequisites

Before you begin, make sure the following are in place:

  • A Databricks workspace with Unity Catalog enabled and at least one catalog, schema, and table. Databricks uses UniForm to generate Iceberg metadata on Delta Lake tables on Amazon S3.
  • An AWS account with permissions to manage AWS Glue, AWS Lake Formation, Amazon Redshift Serverless, and IAM.
  • An Amazon Redshift Serverless workgroup and namespace already provisioned.
  • AWS Lake Formation set up with a data lake administrator.
  • AWS Command Line Interface (AWS CLI) configured with appropriate credentials.
  • Familiarity with Amazon Redshift Query Editor v2 or a SQL client.

Note: For setting up the Databricks Unity Catalog side (Phase 1), follow the steps in the AWS blog post Access Databricks Unity Catalog data using catalog federation in the AWS Glue Data Catalog. This walkthrough picks up after the federated catalog has been created in AWS Glue.

Solution walkthrough

The walkthrough is organized into six steps covering Lake Formation configuration, the resource link pattern, IAM role setup, and querying Databricks tables from Amazon Redshift.

Step 1: Configure AWS Lake Formation

1a. Add a data lake administrator

  • In Lake Formation, choose Administration, then choose Administrators and add your admin IAM user or role.

1b. Confirm the federated catalog is registered

  • Choose Data Catalog, then Catalogs and verify that databricks-federated-catalog is visible and registered.

This step is the key architectural detail in the walkthrough. Amazon Redshift resolves CREATE EXTERNAL SCHEMA only against the default AWS Glue Data Catalog. The federated catalog (databricks-federated-catalog) is a separate, non-default catalog object. To give Amazon Redshift a path to the federated data, you create a resource link database in the default catalog that points to the federated catalog’s database.

A resource link does not copy data or metadata. It’s a pointer that Lake Formation resolves at query time.

To create the resource link in the Lake Formation console:

  • Choose Data Catalog, Databases, Create database. Then select Resource link.
  • For Resource link name, enter databricks_federated_db_link.
  • For Target catalog, enter databricks-federated-catalog.
  • For Target database, enter the database name that was discovered by the AWS Glue crawler (for example, databricks_federated_db).

Alternatively, use the AWS CLI:

aws glue create-database \
  --database-input '{
    "Name": "databricks_federated_db_link",
    "TargetDatabase": {
      "CatalogId": "<account-id>:databricks-federated-catalog",
      "DatabaseName": "databricks_federated_db"
    }
  }'

Step 3: Configure the Amazon Redshift Serverless namespace IAM role

When Amazon Redshift queries through the resource link, it uses the IAM role attached to the Amazon Redshift Serverless namespace to call the Lake Formation GetDataAccess API. Lake Formation permissions must be granted to this namespace role.

Choose one of these two approaches:

  • Option A – Update your existing namespace role by adding the following policy inline.
  • Option B – Create a new dedicated role (named RedshiftServerlessNamespaceRole) and attach it to the namespace alongside existing roles.

Attach the following IAM policy to the role:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "glue:GetDatabase",
        "glue:GetDatabases",
        "glue:GetTable",
        "glue:GetTables",
        "glue:GetPartitions",
        "glue:GetCatalog",
        "glue:GetCatalogs"
      ],
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": "lakeformation:GetDataAccess",
      "Resource": "*"
    }
  ]
}

Note: The Resource: “*” in this policy is shown for simplicity. In production, scope resources to specific AWS Glue catalog ARNs, database ARNs, and table ARNs based on your use case.*

After creating or updating the role, associate it with your Amazon Redshift Serverless namespace:

  • In the Amazon Redshift Serverless console, choose Namespaces, select [your namespace], then choose Security and encryption, then Manage IAM roles.
  • If you use Option A, the existing role already has the new permissions, so no change is needed.
  • If you use Option B, add the new role alongside the existing roles.

Step 4: Grant Lake Formation permissions to the Amazon Redshift namespace role

4a. Grant DESCRIBE on the resource link database (default catalog)

  • In Lake Formation, choose Permissions, Data lake permissions, then Grant.
  • Principal: RedshiftServerlessNamespaceRole.
  • Resources: Named Data Catalog resources, Default catalog, databricks_federated_db_link (resouce link).
  • Database permissions: DESCRIBE.

4b. Grant SELECT and DESCRIBE on the target tables (Grant on Target)

Resource links permit only DESCRIBE and DROP permissions on the link itself. To allow Amazon Redshift to actually read data, you must separately grant SELECT on the target tables in the federated catalog. This is the Lake Formation Grant on Target pattern.

  • Principal: RedshiftServerlessNamespaceRole.
  • Resources: Named Data Catalog resources, databricks-federated-catalog, databricks_federated_db, then Tables.
  • Table permissions: SELECT, DESCRIBE.
  • Catalog permission: DESCRIBE.

Important: SELECT must be granted on the TARGET tables in the federated catalog, not on the resource link. Granting SELECT only on the resource link won’t work. This is a common configuration error.

Step 5: Create an external schema in Amazon Redshift

With the resource link in place and permissions granted, you can now create an external schema in Amazon Redshift that points to the resource link database. The external schema is the query interface. When a user runs SQL against it, Amazon Redshift traverses the link to the federated catalog and retrieves metadata and data from Databricks Unity Catalog.

The DATABASE parameter must reference the resource link database name in the default AWS Glue catalog (databricks_federated_db_link), not the federated catalog name directly. The CATALOG_ARN parameter isn’t required here because the resource link lives in the default catalog and Amazon Redshift resolves it automatically.

Connect to your Amazon Redshift cluster as a superuser (for example, using Amazon Redshift Query Editor v2) and run:

CREATE EXTERNAL SCHEMA databricks_schema
FROM DATA CATALOG
DATABASE 'databricks_federated_db_link'
IAM_ROLE '<iam-role-arn>'
REGION '<region>';

A key design principle in this architecture is the clear separation between data physically stored in Amazon Redshift and data accessed externally through federation. External schemas provide a transparent abstraction layer, so Amazon Redshift users can query data stored in S3 without ingestion. For consistency and clarity, United Airlines follows a standard naming convention for all federated schemas in Amazon Redshift: {domain}_iceberg. This convention makes it immediately clear that the data isn’t natively stored within Amazon Redshift but is accessed by using federation through AWS Glue and Lake Formation. This distinction is critical for analysts and engineers, because it improves discoverability, avoids ambiguity between storage layers, and reinforces architectural discipline when working across hybrid data environments.

The User Interactions domain exposes curated datasets representing customer interaction activity, engagement behavior, and channel usage patterns. Operational datasets follow the same pattern, providing governed access to supporting business events and reference information through a common federation framework.

You create a view layer over each external schema using WITH NO SCHEMA BINDING, so that analysts always resolve the freshest schema on each query execution. For example:

CREATE VIEW analytics.clickstream_events AS
SELECT * FROM {domain}_iceberg.interaction_events
WITH NO SCHEMA BINDING;

Step 6: Verify and query Databricks tables from Amazon Redshift

After creating the external schema, verify that the Databricks tables are visible and run a test query.

Verify table visibility

-- Confirm federated tables are visible in Redshift
SELECT * FROM SVV_EXTERNAL_TABLES
WHERE schemaname = 'databricks_schema';

Query a Databricks Unity Catalog table

-- Query a Databricks Unity Catalog table via the federated catalog
SELECT *
FROM databricks_schema.<table_name>
LIMIT 10;

When a query runs, Amazon Redshift calls Lake Formation GetDataAccess using the namespace IAM role to obtain temporary credentials. It then contacts the AWS Glue federated catalog, which in turn calls the Databricks Unity Catalog Iceberg REST API to retrieve metadata and read table data. The result is returned to the Amazon Redshift user transparently.

For SAML-authenticated users, connect using your IdP JDBC plugin:

jdbc:redshift:iam://<workgroup-name>.<account-id>.<region>.redshift-serverless.amazonaws.com:5439/<database>
?plugin_name=com.amazon.redshift.plugin.<YourIdPPlugin>
&idp_host=<your-idp-host>
&preferred_role=arn:aws:iam::<account-id>:role/RedshiftSAMLUserRole
&ssl=true

The Amazon Redshift JDBC driver handles authentication automatically. It authenticates with your IdP, receives a SAML assertion, and calls sts:AssumeRoleWithSAML for temporary IAM credentials. It then calls redshift-serverless:GetCredentials to connect as the mapped database user.

Business impact

AWS Glue Data Catalog federation delivered measurable architectural and operational improvements for United Airlines:

Area Before After Impact
Data access Delta Lake and Amazon Redshift data were completely siloed, so Amazon Redshift users had no access to curated datasets on Databricks-managed S3 data Amazon Redshift users get real-time access to Databricks-managed data through AWS Glue Data Catalog federation ~100 analysts gained access to user interaction data tables in the first phase without adding new pipelines.
Disaster recovery Cross-Region DR relied on Amazon Redshift snapshots every 3 hours (recovery point objective, or RPO, of 3 hours or more) Amazon S3 cross-Region replication on the Delta Lake provides a near-continuous RPO. A new Amazon Redshift Serverless workgroup in the DR Region can federate to the same S3 data More resilient architecture. Reduces cost for Amazon Redshift snapshot and copy maintenance across Regions
Architecture simplification Data processing happened in both Databricks and Amazon Redshift, requiring manual catalog synchronization between the two platforms which was operationally expensive and prone to drift With the federated architecture, data processing is consolidated in Databricks, and Amazon Redshift acts solely as a query engine powering user queries and dashboards through catalog federation Single processing platform, zero sync pipelines, single source of truth
Infrastructure cost Running dedicated Amazon Redshift ETL cluster with RMS storage, snapshots, and compute for data processing For this use case with federation, Amazon Redshift is not needed for ETL but only as a query engine. No RMS storage duplication, no snapshot replication required ~$30K/month in redundant ETL infrastructure cost reduced

Security considerations

At United Airlines, identity governance is unified through Azure Active Directory groups. On the AWS consumption side, users authenticate to Amazon Redshift Serverless through SAML federation. AD group membership determines database-level access to federated schemas. On the Databricks side, the same AD groups govern access to Unity Catalog schemas. This single-identity model provides consistent access control across both platforms without requiring separate user provisioning. Lake Formation handles credential vending for S3 data access during federated queries, while schema-level access decisions are managed through the AD group mappings on each platform.

The architecture also provides multiple layers of security controls built into the federation chain:

  • AWS Lake Formation governs fine-grained access control throughout the federation chain, so that principals can only access authorized databases, tables, and columns.
  • IAM roles follow least-privilege principles. The Amazon Redshift namespace role is scoped only to AWS Glue metadata operations and Lake Formation GetDataAccess.
  • SAML-based authentication integrates enterprise identity providers, so that users authenticate through existing SSO infrastructure before accessing federated data.
  • All Amazon Redshift connections enforce TLS encryption (ssl=true), protecting data in transit between clients and the Amazon Redshift endpoint.
  • Lake Formation permission vending issues short-lived, scoped credentials for each query execution rather than long-lived static credentials.

Other considerations

Review the catalog federation service limitations before deploying. Key requirements:

  • Delta Lake tables must have UniForm enabled to expose Iceberg-compatible metadata.
  • We recommend that source tables be well-partitioned and regularly compacted, because the federated query performance reflects how efficiently the data is organized at write time.

Clean up

To avoid ongoing charges for resources created in this walkthrough, remove them in the following order. This teardown doesn’t affect Databricks metadata or your underlying data stored in Amazon S3.

  • Drop the external schema in Amazon Redshift: DROP SCHEMA databricks_schema;.
  • Delete the resource link database in the default AWS Glue catalog (databricks_federated_db_link).
  • Revoke Lake Formation permissions granted to the Amazon Redshift namespace role on both the resource link database and the target tables in the federated catalog.
  • Delete the federated catalog in AWS Glue (databricks-federated-catalog).
  • Deregister the AWS Glue connection for the Databricks Unity Catalog if no longer needed.
  • Optionally, remove the IAM role (RedshiftServerlessNamespaceRole) if it was created solely for this walkthrough.

Conclusion

In this post, we showed how United Airlines uses AWS Glue Data Catalog federation to give Amazon Redshift Serverless analysts real-time access to double-digit terabytes of curated user interaction data on Amazon S3, without duplicating a single byte or building sync pipelines.

The architecture uses the Iceberg REST API, resource link databases, and Lake Formation credential vending to create a governed query path between Amazon Redshift and Unity Catalog. For United Airlines, this eliminated redundant ETL infrastructure costs, removed the need for catalog synchronization, and turned Amazon Redshift Serverless into a dedicated high-performance query engine for analysts and dashboards.

For questions or feedback, leave a comment on this post.


About the authors

Vaibhav Agrawal

Vaibhav Agrawal

Vaibhav Agrawal is a Senior Analytics Specialist Solutions Architect at AWS, focused on helping enterprise customers design and implement modern data architectures using AWS Analytics services.

Ankit Aggarwal

Ankit Aggarwal

Ankit Aggarwal is a Principal Enterprise Architect at United Airlines, where he leads the United Data Hub (UDH) platform architecture—a petabyte-scale data platform built on AWS and Databricks. He brings over 15 years of experience in data engineering and enterprise architecture.

Raja Kalluri

Raja Kalluri is a Principal Architect at United Airlines, where he leads enterprise-scale data architecture and modernization initiatives. He specializes in building cloud-native data platforms, enabling real-time analytics and AI, and transforming legacy ecosystems.

Every team is a data team — bring Amazon Redshift analytics to ChatGPT Work

Post Syndicated from Naresh Chainani original https://aws.amazon.com/blogs/big-data/every-team-is-a-data-team-bring-amazon-redshift-analytics-to-chatgpt-work/

Today, AWS is announcing the AWS Data Analytics plugin for the new Data agent in ChatGPT Work. The plugin helps teams across an organization ask questions in natural language, analyze governed data across their Amazon Redshift data warehouse and data lakes, and create shareable dashboards. All this happens from a conversation in ChatGPT Work.

Tens of thousands of customers choose Amazon Redshift every day to run their most demanding workloads, because it delivers analytics at scale with industry-leading price performance. They love how Amazon Redshift provides access to their data warehouses and data lakes together in one place. Teams can combine curated business data with the broader operational, historical, and third-party data stored in open formats like Apache Iceberg in their data lakes. This gives them a complete picture to make business-critical decisions across their data.

Customers have asked AWS for a way to put that trusted data in the hands of more of their people. That means not only the analysts and engineers who write SQL, but also the sales leaders, operations managers, and finance teams who depend on the results. A sales leader wants to know how the customer pipeline has changed this quarter. An operations manager wants to understand why fulfillment times changed over the past month. That’s why we built the AWS Data Analytics plugin, bringing the power of Amazon Redshift and AWS analytics to ChatGPT Work.

“Business teams can make decisions faster when they can source their own analytics and build the dashboards they need. Our work with AWS gives more people that ability, helping them understand changes in performance and decide where to focus. The AWS Data Analytics plugin connects Amazon Redshift to the Data agent in ChatGPT Work, so employees can analyze trusted company data simply by asking, with their organization’s existing access controls in place.”

— Arpan Shah, General Manager, Technology at OpenAI

The new plugin helps shorten the path from question to decision for everyone. Using the Data agent in ChatGPT Work, employees can explore the data they are authorized to access in Amazon Redshift by asking questions in everyday language. They can then refine the analysis, investigate changes, and turn the results into a dashboard without leaving ChatGPT Work. The plugin works with both Amazon Redshift provisioned clusters and Serverless workgroups. Customers can integrate it into their existing multi-cluster or multi-workgroup environments and benefit from the cost and security controls they’ve already set up.

Consider Maya, a business analyst supporting a revenue operations team. She wants to understand the revenue performance across various segments and regions.

Maya starts by loading the AWS Data Analytics plugin in ChatGPT Work, and then asking:

What are the revenue metrics for the past 30 days compared to the previous 30-day period?

ChatGPT Work conversation asking for revenue metrics over the past 30 days compared to the previous 30-day period

Figure 1: Asking for revenue metrics in ChatGPT Work using the AWS Data Analytics plugin

The plugin translates her question into SQL, or a sequence of queries if needed, and runs them against the relevant data in Amazon Redshift. It returns key revenue performance metrics based on the same curated revenue data that her analytics team maintains.

Table of revenue performance metrics the plugin returned from Amazon Redshift

Figure 2: Revenue performance metrics returned from Amazon Redshift

Maya notices that gross margin is declining and asks a follow-up question:

What is my revenue breakdown by product category and region for the past 90 days?

Revenue results segmented by product category and region for the past 90 days in ChatGPT Work

Figure 3: Revenue breakdown by product category and region for the past 90 days

The plugin carries the context forward, segments the results, and helps Maya understand each segment’s performance for the past 90 days. She can inspect the analysis and ask additional questions to drill down even further to understand why certain regions are lagging or why certain segments are outperforming others.

This conversational workflow doesn’t replace the data models, metric definitions, or governance practices that the analytics team has established. It helps more employees use that data directly, giving analysts more time for high-value work.

The AWS Data Analytics plugin connects ChatGPT Work to Amazon Redshift and uses the context of the connected analytics environment to help answer questions with the Data agent. During a conversation, it can:

  • Discover the schemas, tables, columns, and data types available to the user.
  • Translate a natural-language question into Amazon Redshift SQL.
  • Run the query against the customer’s Amazon Redshift environment.
  • Present the results in a table or concise explanation.
  • Use follow-up questions to filter, compare, or drill into the results.
  • Turn an analysis into an interactive dashboard that teams can share and explore.

Because the analysis runs against the customer’s existing data, teams can continue to use the curated datasets and business definitions they already maintain in Amazon Redshift. Customers whose Amazon Redshift environments query data in both a warehouse and a data lake can also make that data available through the governed datasets exposed to the plugin. The AWS Data Analytics plugin also supports our broader AWS data and analytics services. This includes the ability to work with AWS Glue Data Catalog, Amazon S3 Tables (a capability of Amazon Simple Storage Service (Amazon S3)), Amazon Athena, and vector search on AWS.

Natural-language analytics requires more than passing a prompt to a database. The agent needs to understand SQL specific to Amazon Redshift, discover metadata, choose the right tables and columns, and construct queries that follow service best practices. The plugin was built using Amazon Redshift skills from the Agent Toolkit for AWS. These skills provide tested procedures and service-specific guidance that agents can use when working with Amazon Redshift.

To get started, install the AWS Data Analytics plugin in ChatGPT Work to connect it to Amazon Redshift. Give your teams a conversational path to governed insights across your data warehouse and data lake today.

To learn more, see the following resources:


About the author

Naresh Chainani

Naresh Chainani

Naresh is a Director of Engineering at AWS, where he leads Amazon Redshift, one of the world’s most widely used cloud data warehouses. With over 20 years of experience across IBM and AWS, he is a recognized leader in high-performance database systems, holding more than a dozen patents and numerous publications at top venues including SIGMOD and VLDB. Naresh is passionate about advancing the state of the art in analytics and developing the next generation of engineering talent.

AWS Weekly Roundup: Claude Fable 5.1 on AWS, Amazon Linux 2027 preview, AWS Certified AI Business Strategist, and more (September 7, 2026)

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-claude-fable-5-1-on-aws-amazon-linux-2027-preview-aws-certified-ai-business-strategist-and-more-september-7-2026/

Last week, Claude Fable 5.1 became available on AWS. According to Anthropic, Claude Fable 5.1 delivers frontier intelligence for ambitious tasks across coding, scientific research, and enterprise workflows. Claude Fable 5.1 is built for long-running, high-stakes work that runs for hours and spans many applications. It can own more of a software project on its own, handling features across an entire codebase, code review, and performance work over extended sessions.

Anthropic has designated Fable 5.1 a Covered Model, a category of Claude models that carry additional data retention, safety review, and access policies wherever they’re offered. Claude Fable 5.1 is subject to data retention for up to 30 days and human review by Amazon personnel, with a new aws_review data retention mode. In this mode, AWS retains your prompts and outputs for human safety review within the AWS boundary. The provider_data_share mode is legacy, and Amazon Bedrock does not share your data with the model provider. In addition, Enterprise Frontier Safeguards (EFS), built in partnership between AWS and Anthropic, will let eligible customers use Covered Models while keeping their data in a cloud environment they control.

You have two ways to access Claude Fable 5.1: Amazon Bedrock and Claude Platform on AWS. To learn more, see the Claude Fable 5.1 model card on Amazon Bedrock and Claude Platform on AWS.

Last week’s launches
Here are some launches that got my attention:

  • Amazon Linux 2027 (AL2027) in public preview: AL2027 is the next version of the Amazon Linux operating system. It runs on kernel 7.1+, purpose-built for cloud-native workloads on AWS with performance, scale, and security in mind. Built on AL2023’s baseline, AL2027 is designed for customers who need a secure, stable, and AWS-native operating system running web applications, databases, containerized microservices, AI/ML workloads, and large-scale infrastructure.
  • Amazon EC2 R9g and R9gd memory-optimized instances: These instances are powered by AWS Graviton5 processors, delivering the best price performance for memory-intensive workloads running on Amazon EC2. R9g and R9gd instances deliver up to 25% better compute performance compared to AWS Graviton4-based R8g and R8gd instances. They are up to 30% faster for databases, up to 35% faster for web applications, and up to 35% faster for machine learning. To learn more, read Daniel’s blog post.
  • AWS Lambda SnapStart for container image functions: Lambda SnapStart is an opt-in capability that makes it easier for you to build highly responsive and scalable applications without provisioning resources or implementing complex performance optimizations. Previously, SnapStart was only supported for managed runtimes (Python, .NET, and Java). You can now use SnapStart for container images to reduce startup times from several seconds to as low as sub-second for latency-sensitive workloads such as ML inference and interactive APIs.
  • AWS Agent Registry now generally available: AWS Agent Registry provides a private, governed catalog and discovery layer for agents, tools, skills, MCP servers, and custom resources within your organization. In addition to the capabilities launched in preview (manual and URL-based record creation, approval workflows, semantic and keyword search, and AWS CloudTrail audit trails), Registry now adds new enterprise features. To learn more, visit the AI Blog post.
  • Amazon Redshift now supports Apache Iceberg v3 tables: You can read from and write to Apache Iceberg v3 tables in your data lake of Amazon Redshift. With this launch, Amazon Redshift introduces support for default column values, row lineage, and deletion vectors. Amazon Redshift’s Graviton based provisioned and serverless clusters support the new v3 format. To learn more, visit Apache Iceberg v3 features in Redshift.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news
Here are some additional projects and news items you may find interesting:

  • AWS named a Leader in the 2026 Gartner Magic Quadrant for Strategic Cloud Platform Services: For the 16th consecutive year, Gartner has recognized AWS as a Leader in the 2026 Gartner Magic Quadrant for Strategic Cloud Platform Services, and once again placed AWS highest on the Ability to Execute axis. We believe this recognition reflects our commitment to delivering the broadest and deepest set of cloud capabilities from infrastructure and AI to security and operations, so you can build, innovate, and scale with confidence.
  • AWS Certified AI Business Strategist: This new certification targets professionals who evaluate, champion, and scale AI initiatives in their organizations: line-of-business leaders driving adoption across their teams, sales professionals articulating AI value to customers, consultants guiding client strategy from experimentation through production, program managers aligning AI investments to business outcomes. Beta exam registration opened September 1, 2026, with exam delivery beginning September 29.
  • Agentic Security: Detection and Response at Machine Speed: We believe security should evolve ahead of AI adoption, not behind it. That belief drove our team to collaborate with the SANS Institute on a new chapter in the 2026 Cloud Security Exchange eBook, where we lay out a practical framework for securing agentic workloads at enterprise scale. Our chapter goes deeper on securing agentic workloads, with specific architectural patterns, implementation guidance, and frameworks for security teams at every stage of agentic AI maturity, whether you’re evaluating, piloting, or operating at scale.

For a full list of AWS blog posts, be sure to keep an eye on the AWS Blogs page.

Learn more about AWS, browse and join upcoming AWS-led in-person and virtual events, startup events, and developer-focused events including AWS re:Invent, AWS Summits, and AWS Community Days. Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development.

That is all for this week. Check back next Monday for another Weekly Roundup!

— Channy

How Moovit achieved 33% cost optimization through architectural modernization

Post Syndicated from Saar Porat original https://aws.amazon.com/blogs/big-data/how-moovit-achieved-33-cost-optimization-through-architectural-modernization/

Moovit, part of Mobileye (Nasdaq: MBLY), is a leading Mobility-as-a-Service (MaaS) solutions provider and the creator of a leading urban mobility app. Moovit’s iOS, Android, and web apps offer users a smart mobility experience to get to their destination using any mode of public and shared transportation. Transit riders can benefit from mobile ticketing to plan, pay, and ride with transit services. Introduced in 2012, Moovit now serves over 1.7 billion users in more than 3,500 cities across 112 countries, in 45 languages.

Behind these user-facing experiences is a data platform that processes large volumes of mobility, application, and operational data to support product analytics, business intelligence (BI), monitoring, and data science. As the platform grew, Moovit needed to keep analytical workloads reliable and cost-efficient without slowing down teams that depend on fresh data every day.

Over several years, Moovit’s Amazon Redshift cluster grew continuously. It started with an expanding fleet of DC2 nodes, migrated to RA3 nodes, and scaled multiple times to keep pace with growing data demands, ultimately becoming the backbone of their entire data platform.

To address this growth, Moovit transformed their data architecture by building an optimal multi-engine lakehouse architecture and assigning each workload to the most suitable option. This modernization reduced their Amazon Redshift cluster by 50 percent, while establishing a flexible, multi-engine architecture ready for future use cases.

In this post, we share how Moovit gained visibility into workload patterns, cleaned up unnecessary load, selected candidates for offloading, and ran a successful proof of concept (POC) on Amazon EMR Serverless. Moovit ultimately divided the workload between multiple engines, building a modern and cost-optimized data platform that combines provisioned Amazon Redshift, Amazon Redshift Serverless, and Amazon EMR.

The challenge: Outgrowing a single-engine data platform

The Amazon Redshift engine handled a wide variety of workloads, including:

  • Heavy ETL processing: Raw data ingestion from Amazon Simple Storage Service (Amazon S3) followed by complex aggregation pipelines (daily user-aggregation running once per day with a 3-day lookback, and weekly 10-day-lookback jobs).
  • Near-real-time operational monitoring: Queries executing every 20 minutes against raw data for system-health dashboards.
  • Business-intelligence reporting: Tableau extracts and live dashboards.
  • Data-science workloads: Exploratory analysis and model-feature engineering.
  • Ad-hoc analysis: Non-recurring queries done by analysts and engineers.

With business growth, storage grew by orders of magnitude over the past decade as the platform expanded. All these varied workloads competed for the same engine and pushed it to its limits. Jobs experienced increasing queue times, service level agreements (SLAs) were at risk, and adding nodes provided minimal performance gains, creating a need to isolate workloads.

Gaining visibility: Measuring workload impact

Moovit’s first modernization milestone was to create a trusted measurement foundation before changing any workloads. Instead of treating warehouse activity as a single opaque stream, the team implemented automated query attribution that continuously classified each query by workload owner and execution context. The classification combined multiple signals: who executed the query (user or service account), recognizable query-signature patterns, and metadata emitted by orchestration frameworks and scheduled processes.

This produced a historical, query-level map of platform usage that answered three critical questions: who is generating load, what kind of workload is running, and how expensive each workload is in runtime and resource terms. With that baseline in place, the team made offload decisions from evidence rather than assumptions. This approach prioritized the largest and most stable optimization opportunities first and reduced the risk of moving business-critical workloads without visibility.

These classifications and workload metrics were reflected in a Tableau report that aggregated query activity by classification label and execution context. The view exposed operational dimensions such as classification, time granularity, service class, execution-time bucket, unload flags, and sample-query context, supporting both trend monitoring and root-cause drill-down.

The worksheet was parameterized to support multiple measurement modes over the same grouped workload population: total execution time, execution plus queue time, total CPU time, average execution time per query, and ratio-based efficiency views (execution/CPU and CPU/execution). This let the team compare “heavy by volume” workloads against “inefficient by behavior” workloads without creating separate artifacts.

For decision-making, CPU time was used as the primary impact metric because it best represented sustained compute pressure. Execution time, queue time, query-count normalization, and workload-management segmentation were treated as secondary evidence to distinguish:

  • compute-heavy but healthy workloads
  • queue-constrained workloads
  • high-frequency/low-cost workloads
  • noisy or weakly classified workloads that required attribution cleanup first

Using this framework, prioritization became systematic: first improve classification coverage, then rank workloads by CPU contribution, then validate with queue and workload management (WLM) signals, and finally choose the action path per workload (optimize SQL, reschedule, isolate, retire, or move to another engine).

The following figure shows an example of one of the dashboard widgets (CPU time by query).

Dashboard widget showing CPU time consumed by each query

Figure 1: CPU time by query, highlighting the most resource-intensive queries and their usage patterns

Cleanup: Reducing unnecessary data warehouse load

With a long-running data platform, in most cases the workloads will start accumulating, some of which become irrelevant at some point. For example, a report which was created and scheduled, yet it became irrelevant after a few years, but still running since no one disabled it. It’s important to indicate these workloads in general to reduce unnecessary load, yet even more critical before doing any significant architectural changes or migrations. Before migrating any workloads, Moovit first reduced unnecessary warehouse load.

The team:

  • Removed unused processes that were still consuming cluster resources.
  • Reduced unnecessary frequency where possible: some jobs ran more often than downstream consumers needed.
  • Reviewed workload-management guardrails to verify resource allocation matched actual priorities.

This cleanup phase was a prerequisite to migration. By removing waste first, the team verified that the workloads eventually selected for offloading were genuinely heavy rather than simply unoptimized or unnecessary.

The no-longer-relevant processes consumed around 7 percent of overall CPU time and were removed before the optimization work began.

Workload selection: Choosing what to offload

With a clear picture of workload patterns, Moovit faced a common decision point: continue scaling the existing Redshift cluster, or re-architect towards a multi-engine approach. The team evaluated two main paths:

  1. Re-architect with Redshift multi-cluster and data sharing: Identify workloads that could benefit from resource isolation, then redistribute processing and queries between multiple Redshift clusters, combining both serverless and provisioned options. This would redistribute load across use-case-optimized clusters and potentially save costs through better resource use.
  2. Re-architect with purpose-built engines: Identify workloads that could benefit from alternative processing frameworks and offload them to more suitable engines. This would reduce pressure on Amazon Redshift while building a more flexible, cost-efficient architecture.

Moovit decided to do both, because while some workloads benefited from being offloaded, others benefited from isolated Amazon Redshift compute.

The measurement data revealed a primary candidate for offloading: raw-data aggregation pipelines. This workload loaded raw data into Amazon Redshift from Amazon S3, then performed heavy sessionization and aggregation transformations. Raw tables were still used for ad-hoc and exploratory analysis, but recurring production consumers primarily depended on aggregated outputs, making these transformations strong candidates for offloading.

Proof of concept: Offloading to EMR Serverless with Spark SQL

With target workload identified, Moovit initiated a POC using Amazon EMR Serverless with Spark SQL. The choice of EMR Serverless was driven by several factors:

  • Spark SQL compatibility: The existing Redshift SQL logic could be ported with minimal changes to Spark SQL syntax.
  • Serverless simplicity: No cluster-management overhead during the evaluation phase.
  • Data-lake native: Processing could occur directly on data in Amazon S3.

The POC defined quantified success criteria measured over five or more consecutive runs:

  • Runtime reduction: Greater than or equal to 40 percent reduction for the transform portion of selected pipelines.
  • Amazon Redshift cost reduction: Greater than 30 percent reduction in Redshift RA3 compute with no performance degradation for remaining workloads.
  • Data-quality parity: Exact match between Spark and Amazon Redshift outputs on row counts, distinct users, and all published metrics over a frozen parity window.

Overcoming initial performance challenges

The first POC attempts exposed significant challenges. Early Spark jobs with 100 executors took approximately 4 hours, far exceeding the 30–40-minute baseline on Amazon Redshift. Beyond raw performance, the team encountered memory pressure, data-parity gaps between Spark and Amazon Redshift outputs, and subtle SQL behavior differences between the two engines.

The team systematically diagnosed and resolved these issues:

  1. Execution-plan analysis: Reviewing the Spark execution plan revealed suboptimal query patterns that generated excessive data shuffles.
  2. Query rewrites: Rewriting specific SQL constructs to align with Spark’s distributed processing model, including splitting large monolithic logic into staged transformations.
  3. Reducing or rewriting expensive DISTINCT patterns: Identifying and eliminating unnecessary DISTINCT operations that created heavy shuffle pressure.

After applying these optimizations, execution time dropped from 4 hours to approximately 10 minutes, and the required executors dropped to fewer than 50, surpassing the original performance.

Validation: Ensuring data parity before cutover

Before transitioning any workload to production, Moovit implemented a rigorous validation process. The new Spark output was compared with the previous Amazon Redshift output using multiple dimensions:

  • Row counts: ensuring no data was lost or duplicated.
  • Distinct users: verifying entity-level completeness.
  • Metric parity: all published business metrics matched.
  • Daily trends: time-series patterns remained consistent.
  • Row-level checks: spot-checking individual records for correctness.

Only after all validation checks passed consistently over multiple consecutive runs did the team proceed with cutover for each workload.

Moving to production: Expanding workload offloading

With a successful POC demonstrating both performance gains and cost savings, Moovit progressively moved additional workloads from Amazon Redshift to EMR:

  • Heavy-aggregation jobs: The primary daily and weekly aggregation pipelines transitioned fully to EMR.
  • Data-transformation stages: Preprocessing steps that previously consumed Redshift compute moved to Spark, with only final aggregated results loaded back into Amazon Redshift for BI consumption.
  • Weekly batch workloads: Large batch jobs that previously created resource contention during weekend processing windows.

The transition used a measured approach: each workload was migrated individually, with data-quality validation confirming parity before decommissioning the equivalent jobs which were running on Redshift.

Additional optimizations: Redshift Serverless, workload isolation, and Amazon EMR on Amazon EC2

Beyond EMR offloading, Moovit implemented further architectural improvements to isolate workloads and optimize costs.

Amazon Redshift rightsizing: Iterative cluster optimization

With heavy workloads successfully offloaded and isolated, Moovit proceeded to right-size the Redshift cluster. Rather than a single resize, the team reduced the cluster incrementally, two nodes at a time, using elastic resize. At each step, they validated that:

  • Existing BI workloads maintained acceptable performance.
  • Queue wait times remained within SLA thresholds.
  • No workload degradation was observed under peak loads.

This iterative approach minimized risk and allowed the team to find the optimal cluster size with confidence.

Workload isolation with Redshift Serverless

Amazon Redshift persisted as the engine of choice for serving curated BI data. However, not all Amazon Redshift workloads needed provisioned capacity:

  • Ad-hoc analyst queries: Moved to Redshift Serverless, isolating unpredictable workloads from the provisioned cluster through data sharing.
  • Data-science workloads: Transitioned to Redshift Serverless for flexible exploration without impacting production.

This workload isolation through Redshift Serverless provided resource separation without requiring additional provisioned capacity. The architecture now used data sharing to provide a unified view across provisioned and serverless clusters.

Operational isolation refinements

Moovit also refined workload isolation by rebalancing WLM priorities on the provisioned cluster. Because the ETL queue mainly handled raw data loading from Amazon S3 (which was not the bottleneck after heavy aggregations moved to Spark), its priority was reduced. At the same time, with most human users moved to Redshift Serverless, Tableau serving workloads on provisioned Redshift were prioritized higher to keep dashboard performance predictable. The final result: a 50% reduction in provisioned Redshift capacity.

Transitioning to EMR on EC2

EMR Serverless proved efficient for the POC phase: it allowed fast iteration without cluster management overhead. However, for longer-term recurring production workloads, Moovit moved to EMR on EC2 to better fit their production cost and infrastructure model, using existing compute reservations.

The transition between EMR deployment options required zero application code changes, demonstrating the flexibility of the EMR deployment options.

AI-assisted SQL translation

Additionally, Moovit used AI-assisted development tools, Claude Code and Cursor, to accelerate parts of the SQL transition process. These tools helped engineers identify Redshift SQL and Spark SQL syntax differences, suggest rewrites, and debug migration issues, while validation and production approval remained under engineer review.

Results: A modern multi-engine architecture

The architectural modernization delivered measurable outcomes:

  • Cluster size reduction: Redshift cluster size reduced to 50 percent of the initial capacity.
  • Performance improvement: Key aggregation jobs ran faster and more consistently on EMR (50 percent execution time reduction for p90).
  • Workload isolation: No single workload type could impact others through resource contention.
  • 33 percent overall data pipeline cost reduction: Combined savings from cluster reduction, transition to EMR, and efficient serverless usage.
  • Future flexibility: The multi-engine architecture provided pathways for additional use cases without architectural changes.

The following figures compare aggregation-job performance before and after the transition.

Chart comparing aggregation-job execution times before and after the transition, with longer, inconsistent runtimes before and shorter, stable runtimes after

Figure 2: Aggregation-job execution times before and after the transition

Chart comparing wall-clock time for job executions across percentiles, with p90 at 5.48 hours before the transition and 2.77 hours after

Figure 3: Wall-clock time for job executions by percentile, before and after the transition

The resulting architecture assigned each workload to the engine that fits it best:

Workload type Engine Rationale
Heavy ETL and aggregation Amazon EMR (Spark SQL) Distributed processing on Amazon S3. No data warehouse load required
Ongoing processing and BI reporting Amazon Redshift provisioned 24/7 running processes
Ad-hoc queries Amazon Redshift Serverless Burst capacity with workload isolation
Data science Amazon Redshift Serverless Flexible exploration without impacting production

Lessons learned

The Moovit modernization journey produced several key insights applicable to similar architectural transitions:

  1. Measure before you move: Establishing baseline metrics and automated classification was essential for identifying true offloading candidates. Without granular workload-level measurements, the team would not have identified which specific processes were exhausting the cluster.
  2. Clean up before you migrate: Reducing unnecessary load first verified that migration efforts targeted genuinely heavy workloads rather than simply unoptimized or unused processes.
  3. Small SQL changes, big impact: Moving from Redshift SQL to Spark SQL required relatively minor syntax adjustments. The core business logic remained intact, and most transformations translated directly with minimal refactoring.
  4. Optimize for the engine: Porting SQL queries to Spark without optimization produced initially poor results for some workloads. Understanding Spark’s distributed execution model and optimizing for it was critical for achieving target performance.
  5. Validate rigorously: Multi-dimensional data-parity checks (row counts, distinct users, metrics, daily trends, and row-level spot checks) gave the team confidence to cut over without data-quality regressions.
  6. Moving between EMR options is straightforward: EMR Serverless proved very efficient for starting fast and evaluating Spark. When Moovit needed to move to EMR on EC2 to use existing reservations, the transition required no application code changes.
  7. Iterative cluster rightsizing: Rather than a single resize, Moovit reduced the Redshift cluster incrementally (two nodes at a time) using elastic resize, validating performance at each step before proceeding further.

Conclusion

Looking ahead, as another potential optimization, Moovit will be evaluating the new Amazon Redshift RG instances for provisioned clusters, providing up to 2.2x better price performance and priced 30% lower than RA3, powered by AWS Graviton.

The broader takeaway is that AWS provides multiple purpose-built engines that can be used in a single data platform. In Moovit’s case, the biggest improvement came from assigning each workload to the engine that fit it best: Amazon Redshift for curated analytical serving, Redshift Serverless for isolated exploratory workloads, and Amazon EMR for large-scale transformations over data in Amazon S3. This architecture gives Moovit a foundation for future optimization and flexibility as data volumes grow and new analytical use cases emerge.

 


About the authors

Saar Porat

Saar Porat

Saar is the Director of BI & Data Engineering at Moovit, where he has spent more than a decade building and scaling the company’s data engineering capabilities. With nearly 20 years of experience in BI, analytics, and data platforms, he focuses on designing reliable, maintainable, and cost-efficient systems that translate complex data into meaningful business impact. Saar led Moovit’s initiative to migrate major workloads from Amazon Redshift to Apache Spark, improving scalability, performance, and infrastructure efficiency while expanding the team’s engineering capabilities beyond SQL-based processing.

Vova Nevski

Vova Nevski

Vova is a Senior Analytics Specialist Solutions Architect at AWS with more than 15 years of experience in the big data and analytics domain, including data lakes, batch and stream processing, both on premises and in the cloud. He partners with AWS customers to design and build solutions best suited to their unique needs.

Integrate Amazon Redshift and IAM Identity Center with enhanced VPC routing

Post Syndicated from Maneesh Sharma original https://aws.amazon.com/blogs/big-data/integrate-amazon-redshift-and-iam-identity-center-with-enhanced-vpc-routing/

You can now use AWS IAM Identity Center authentication with enhanced VPC routing on Amazon Redshift clusters and Amazon Redshift Serverless workgroups. Your users get single sign-on with their existing corporate credentials, and the authentication traffic originates from within your virtual private cloud (VPC) through a VPC endpoint, staying on the AWS private network.

We covered the IAM Identity Center integration end to end in a previous post, Integrate Identity Provider (IdP) with Amazon Redshift Query Editor V2 and SQL Client using AWS IAM Identity Center for seamless Single Sign-On. That post shows how users sign in through Query Editor V2 and third-party SQL clients, and how their identity is propagated to the AWS analytics services.

Many organizations also require that this traffic doesn’t traverse the public internet. Enhanced VPC routing sends everything between your cluster and other AWS services through your VPC, where you can govern it with security groups, network ACLs, and endpoint policies, and observe it in VPC Flow Logs. For teams with data residency, regulatory, or network isolation requirements, it’s often mandatory.

In this post, we show how the new IAM Identity Center VPC endpoints provide a private network path for authentication traffic when enhanced VPC routing is enabled. We walk through the endpoint setup and validate the flow from Query Editor V2 and a SQL client. To use this feature, your Amazon Redshift cluster must be running patch 204 or later, and you must create the VPC endpoints described in the following steps.

Solution overview

When a user signs in with IAM Identity Center from Query Editor V2 or a SQL client, Amazon Redshift doesn’t simply accept the token the client presents. It validates the token with IAM Identity Center and resolves the caller’s identity before the session is established. These calls originate from Amazon Redshift, not from your client, and enhanced VPC routing changes the network path they take.

Authentication flow with enhanced VPC routing

With enhanced VPC routing enabled, the calls Amazon Redshift makes to IAM Identity Center traverse your VPC and follow your networking configuration. The flow is as follows:

  1. The user signs in through Query Editor V2 or a SQL client and authenticates against your identity provider through IAM Identity Center.
  2. IAM Identity Center issues an access token, which the client presents to Amazon Redshift on the database connection.
  3. Amazon Redshift validates the access token against the IAM Identity Center OpenID Connect (OIDC) endpoint, confirming the token’s scopes and the user’s entitlement to the Amazon Redshift application. It doesn’t trust the token the client presented without verification.
  4. Amazon Redshift calls the same OIDC endpoint again to exchange that token for one scoped to Amazon Redshift.
  5. Amazon Redshift calls the IAM Identity Center identity store to resolve the user and their group membership.
  6. Amazon Redshift maps the resolved identity to a database identity, applies role-based access control, and establishes the session.

The following diagram illustrates this authentication flow, showing how each call from Amazon Redshift to IAM Identity Center traverses the VPC through interface endpoints.

Authentication flow showing Amazon Redshift reaching the IAM Identity Center OIDC and identity store endpoints through VPC endpoints

Figure 1: IAM Identity Center authentication flow with enhanced VPC routing enabled

As shown in the diagram, steps 3–5 represent calls that Amazon Redshift makes through your VPC to the IAM Identity Center OIDC and identity store endpoints.

Because a cluster with no public IP address doesn’t use an internet gateway route, Amazon Redshift has no path to IAM Identity Center by default. You provide one with interface VPC endpoints for the two services it needs, the IAM Identity Center OIDC endpoint and the identity store endpoint, which keep the traffic on the AWS network over AWS PrivateLink.

This solution covers the following steps:

  1. Enable enhanced VPC routing.
  2. Verify the DNS attributes on your VPC.
  3. Create the interface VPC endpoints required for IAM Identity Center authentication.
  4. Create interface VPC endpoints for AWS Glue and AWS Lake Formation (optional, if you query a data lake or lakehouse).
  5. Create an Amazon Simple Storage Service (Amazon S3) gateway endpoint.
  6. Validate that the endpoints are available and using private DNS.
  7. Test single sign-on with Amazon Redshift Query Editor V2.
  8. Test single sign-on with a SQL client using the Amazon Redshift JDBC driver.
  9. Verify the calls on AWS CloudTrail.

Prerequisites

You should have the following prerequisites:

  • An AWS account with an Amazon Redshift provisioned cluster. Amazon Redshift Serverless also supports enhanced VPC routing, and the same endpoints apply, but you substitute the equivalent workgroup commands and settings.
  • A working IAM Identity Center integration with Amazon Redshift, as described in Integrate Identity Provider (IdP) with Amazon Redshift Query Editor V2 and SQL Client using AWS IAM Identity Center for seamless Single Sign-On.
  • A cluster running patch 204 or later, which is the minimum maintenance version that supports IAM Identity Center authentication with enhanced VPC routing.
  • Permissions to create VPC endpoints in the VPC where the cluster runs, specifically ec2:CreateVpcEndpoint and ec2:DescribeVpcEndpoints.
  • Optionally, an Amazon Elastic Compute Cloud (Amazon EC2) instance inside the same VPC with SQL Workbench/J and the Amazon Redshift JDBC driver, version 2.1.0.30 or later with its dependent libraries, to test the SQL client flow.

Walkthrough

The examples in this post use the Canada (Central) AWS Region (ca-central-1). Replace all placeholder values with your own.

Step 1: Enable enhanced VPC routing and turn off public access

To control network traffic with Amazon Redshift enhanced VPC routing, you enable enhanced VPC routing in Amazon Redshift. The cluster or workgroup must also not be publicly accessible, so that traffic to IAM Identity Center and other services goes through your VPC endpoints rather than an internet gateway. Follow the instructions in Enable enhanced VPC routing to enable it for a new provisioned cluster or serverless workgroup. For an existing cluster or workgroup, follow these steps:

  1. Sign in to the AWS Management Console and open the Amazon Redshift console at https://console.aws.amazon.com/redshiftv2/.
  2. Open the provisioned cluster or serverless workgroup you want to modify:
    1. For an existing provisioned cluster – choose the Properties tab.
    2. For an existing serverless workgroup – choose the Data access tab.
  3. In the Network and security section, choose Edit.
    1. Select Turn on enhanced VPC routing to route network traffic through the VPC.
    2. If Turn on Publicly accessible is enabled, clear it so the cluster or workgroup is not publicly accessible.
  4. Choose Save changes.

The following screenshot shows the Network and security section with enhanced VPC routing enabled and public accessibility turned off.

Amazon Redshift Network and security section with enhanced VPC routing turned on and public access turned off

Figure 2: Enable enhanced VPC routing in Amazon Redshift

Note: Amazon Redshift restarts the cluster automatically when you change enhanced VPC routing. Make this change during a maintenance window.

Step 2: Verify the DNS attributes on your VPC

Private DNS is what redirects the public AWS service hostnames to your interface endpoints, and it depends on two VPC attributes. Follow these steps:

  1. Navigate to Amazon Redshift and choose the Properties tab for Amazon Redshift provisioned, or the Data access tab for Amazon Redshift Serverless.
  2. Under Network and security setting, choose the associated VPC.
  3. Your VPC details open in a new browser tab.
  4. Review the Details section, where the attributes appear as DNS hostnames and DNS resolution. Make sure that both properties are set to Enabled. The following screenshot shows the VPC Details page with both DNS attributes set to Enabled.
VPC Details page showing DNS hostnames and DNS resolution both set to Enabled

Figure 3: DNS hostnames and DNS resolution enabled on the VPC Details page

  1. If either of the properties is Disabled, choose Actions, choose Edit VPC settings, select Enable on the attribute you need, and choose Save. The following screenshot shows the Edit VPC settings page where you enable these DNS attributes.
Edit VPC settings page with the DNS hostnames and DNS resolution attributes being enabled

Figure 4: Enable DNS hostnames and DNS resolution

Step 3: Create the interface VPC endpoints for IAM Identity Center authentication

Create the two interface endpoints that the authentication flow needs. Each corresponds to one of the two IAM Identity Center calls in the authentication flow described earlier:

Service endpoint Used for
com.amazonaws.<region>.sso-oauth Validating and exchanging the IAM Identity Center access token
com.amazonaws.<region>.identitystore Resolving the user and their group membership

To create an interface endpoint for an AWS service

  1. Open the Amazon Virtual Private Cloud (Amazon VPC) console at https://console.aws.amazon.com/vpc/.
  2. In the navigation pane, choose Endpoints.
  3. Choose Create endpoint.
  4. For Type, choose AWS services.
  5. IAM Identity Center is a Regional service, so these endpoints must reach the AWS Region where your IAM Identity Center instance is available. If you’re using IAM Identity Center multi-Region replication (your instance is replicated to the Region where your Amazon Redshift cluster runs), leave Enable Cross Region endpoint unchecked.
  6. For Service name, search for sso-oauth and select the service for your Region (com.amazonaws.<region>.sso-oauth). The following screenshot shows the top section of the Create endpoint page with the sso-oauth service selected.
Create endpoint page with the sso-oauth service selected for the Region

Figure 5: Create an interface VPC endpoint, part 1

  1. For VPC, select the VPC from which you will access the AWS service. In our use case, we choose the Amazon Redshift VPC.
  2. To enable private DNS support, select Additional settings and choose Enable private DNS name.
  3. For Subnets, select the subnets in which to create endpoint network interfaces. You can select one subnet per Availability Zone. You can’t select multiple subnets from the same Availability Zone. For more information, see Subnets and Availability Zones.
  4. For IP address type, choose IPv4. This assigns IPv4 addresses to the endpoint network interfaces. This option is supported only if all selected subnets have IPv4 address ranges and the service accepts IPv4 requests.
  5. For Security groups, select the security groups to associate with the endpoint network interfaces. For this post, we have selected default security group associated with Redshift. The following screenshot shows the VPC, subnet, and security group selections for the endpoint.
Create endpoint page showing the VPC, subnet, and security group selections

Figure 6: Create an interface VPC endpoint, part 2

  1. For Policy, to allow all operations by all principals on all resources over the interface endpoint, select Full access. To restrict access, select Custom and enter a policy. This option is available only if the service supports VPC endpoint policies. For more information, see Endpoint policies.
  2. (Optional) To add a tag, choose Add new tag and enter the tag key and the tag value.
  3. Choose Create endpoint. The following screenshot shows the policy and tag settings before you create the endpoint.
Create endpoint page showing the policy set to Full access and the tag settings

Figure 7: Create an interface VPC endpoint, part 3

Repeat steps 1–14 for the identity store endpoint, search for identitystore and select the service for your Region (com.amazonaws.<region>.identitystore).

Two settings in the preceding steps are important:

  • Enable private DNS name is required. Amazon Redshift resolves the public service hostname, for example, oidc.<region>.amazonaws.com. Private DNS is what points that hostname at your interface endpoint, so the traffic stays inside your VPC.
  • Use the cluster’s security group, because the cluster is the caller. The endpoint’s security group must allow inbound HTTPS on port 443 from the cluster. Reusing the cluster’s own security group is the simplest approach when it already allows traffic from itself. A dedicated security group needs an explicit port 443 inbound rule from the cluster’s security group.

(Optional) To create an interface endpoint using the command line

Step 4 (optional): Create endpoints for AWS Glue and AWS Lake Formation

Complete this step only if your cluster queries external data through the AWS Glue Data Catalog and AWS Lake Formation. Common examples include Amazon S3 Tables, a capability of Amazon S3, and data lakes registered with Lake Formation. If you only need single sign-on, you can skip to Step 5. Amazon Redshift calls the AWS Glue Data Catalog to enumerate databases and tables, and calls AWS Lake Formation to check permissions and vend temporary credentials for the underlying data. Like the authentication calls, these are made by the cluster, so with enhanced VPC routing enabled they travel through your VPC and need a path of their own.

Repeat steps 1–14 from Step 3 for:

  • com.amazonaws.<region>.glue.
  • com.amazonaws.<region>.lakeformation.

With these endpoints in place, Amazon Redshift routes external catalog operations such as listing external tables through the VPC endpoints rather than the public internet, keeping metadata traffic on the AWS network.

Step 5: Create an Amazon S3 gateway endpoint

With enhanced VPC routing enabled, anything the cluster does against Amazon S3 (COPY, UNLOAD, and Amazon S3 Tables) also travels through your VPC. Create a gateway endpoint and associate it with the route table(s) used by your cluster’s subnets:

  1. Open the Amazon VPC console at https://console.aws.amazon.com/vpc/.
  2. In the navigation pane, choose Endpoints, then choose Create endpoint.
  3. For Type, choose AWS services.
  4. For Service name, search for s3 and select the service for your Region with Type: Gateway (com.amazonaws.<region>.s3). The following screenshot shows the Create endpoint page with the Amazon S3 gateway service selected.
Create endpoint page with the Amazon S3 gateway service selected

Figure 8: Create an Amazon S3 gateway endpoint, part 1

  1. For VPC, choose your Amazon Redshift VPC.
  2. For Route tables, select the route table(s) associated with the subnets your cluster runs in.
  3. Choose Create endpoint. The following screenshot shows the VPC and route table selections for the S3 gateway endpoint.
Create endpoint page showing the VPC and route table selections for the S3 gateway endpoint

Figure 9: Create an Amazon S3 gateway endpoint, part 2

Step 6: Validate the endpoints

Confirm in the Amazon VPC console that every endpoint you created is available, and that private DNS is enabled on the interface endpoints.

  1. Open the Amazon VPC console at https://console.aws.amazon.com/vpc/.
  2. In the navigation pane, choose Endpoints.
  3. In the endpoints list, use the filter bar to filter by VPC ID (choose VPC ID and select your Amazon Redshift VPC). Then locate the endpoints you created for this walkthrough, sso-oauth, identitystore, the Amazon S3 gateway endpoint, and (if you created them) glue and lakeformation.
  4. Confirm each endpoint shows a Status of Available.
  5. Select each interface endpoint (sso-oauth, identitystore, glue, lakeformation) and, on the Details tab, confirm Private DNS names enabled is Yes. The following screenshot shows the completed endpoints list with each endpoint in the Available state.
VPC endpoints list showing the interface and gateway endpoints in the Available state

Figure 10: VPC endpoints created in this walkthrough, in the Available state

Step 7: Test single sign-on with Amazon Redshift Query Editor V2

  1. On the Amazon Redshift console, choose Query editor v2.
  2. Choose your cluster and then choose IAM Identity Center as the connection method.
  3. Sign in with your corporate credentials when prompted.
  4. Expand the cluster in the tree view to list databases, schemas, and tables.

The database list populates within a few seconds. Confirm the login on the server side by querying the connection log. Run this as a user who does not use IAM Identity Center, for example a database user with a password, or through the Amazon Redshift Data API:

SELECT record_time, user_name, auth_method, driver_version, remote_host, event
FROM sys_connection_log
WHERE auth_method LIKE '%Idc%'
AND record_time > dateadd(minute, -15, getdate())
ORDER BY record_time DESC;

A successful sign-in shows user_name as <idc_namespace>:<[email protected]> with event of authenticated, which confirms that the identity was resolved through the endpoints you created. The following screenshot shows the sys_connection_log query results, where each IAM Identity Center sign-in appears with a user_name in the <idc_namespace>:<[email protected]> format.

Query Editor V2 connected through IAM Identity Center, showing the expanded database list

Figure 11: Query Editor V2 connected with IAM Identity Center, showing the database list

Step 8: Test single sign-on with a SQL client

Testing from a SQL client on an EC2 instance inside your VPC is the stronger validation, and we recommend doing both. Query Editor V2 connects through an Amazon Redshift managed proxy, so its connections are recorded with a loopback address. A client running inside your VPC connects to the cluster endpoint directly, which is exactly the path the endpoints you created are there to serve.

Set up SQL Workbench/J

SQL Workbench/J connects through the Amazon Redshift JDBC driver. On an EC2 instance in the same VPC as your cluster, download and install SQL Workbench/J.

  1. Download the latest Amazon Redshift JDBC driver together with its dependent libraries, and extract the archive to a folder on the instance.
  2. Start SQL Workbench/J, and choose File, then Manage Drivers.
  3. Choose the Create a new entry icon, and for Name, enter Amazon Redshift.
  4. For Library, choose the folder icon, and select the driver JAR file along with every JAR file in the dependent libraries folder. Keep only one version of the driver in the list, and remove any previous entries.
  5. Choose File, then Connect window, and choose the Create a new connection profile icon. Enter a name for the profile, such as redshift-idc.
  6. For Driver, choose the Amazon Redshift driver that you created.
  7. For URL, enter your cluster endpoint in the form jdbc:redshift://<cluster endpoint>:5439/<database>, for example jdbc:redshift://my-redshift-cluster.abc123xyz789.ca-central-1.redshift.amazonaws.com:5439/dev.
  8. Leave Username and Password empty. The browser plugin obtains the identity interactively.
  9. Choose Extended Properties, and add the following three properties:
Property Value
plugin_name com.amazon.redshift.plugin.BrowserIdcAuthPlugin
issuer_url https://identitycenter.amazonaws.com/ssoins-<instance-id>
idc_region The Region of your IAM Identity Center instance, such as ca-central-1
  1. Clear Separate connection per tab, so that each editor tab reuses the same physical connection rather than prompting you to sign in again.
  2. Choose Test. Your default browser opens. Sign in with your corporate credentials, and then choose Allow access so that the Amazon Redshift JDBC driver can access your data.
  3. If the connection succeeds, you see a prompt confirming the connection to your Amazon Redshift endpoint, as shown in the following screenshot.
SQL Workbench/J connection profile for Amazon Redshift using the IAM Identity Center browser plugin

Figure 12: SQL Workbench/J connection for Amazon Redshift using the IAM Identity Center browser plugin

In the browser, you will see the following message once the authentication is successful.

Congratulations! You have IAM Identity Center single sign-on working on an Amazon Redshift cluster with enhanced VPC routing enabled.

Step 9: Verify the calls on AWS CloudTrail

You can confirm from AWS CloudTrail that these calls travel through your interface endpoints rather than the internet. Each event includes a vpcEndpointId field naming the endpoint the call traversed, along with a vpcEndpointAccountId field identifying the account that owns it.

The following table maps each interface endpoint to the CloudTrail event you’ll see:

Service endpoint Event source CloudTrail event
com.amazonaws.<region>.sso-oauth sso-oauth.amazonaws.com CreateTokenWithIAM
com.amazonaws.<region>.identitystore identitystore.amazonaws.com DescribeUser, ListGroupMembershipsForMember, BatchDescribeGroup

The following screenshot shows the snippet from the CloudTrail logs showing the CreateTokenWithIAM event that Amazon Redshift generates when it exchanges the IAM Identity Center access token. The eventSource is sso-oauth.amazonaws.com, and the vpcEndpointId field confirms the call traversed your interface VPC endpoint rather than the public internet. The invokedBy field shows the call originated from Amazon Redshift (redshift.amazonaws.com), not from the client.

CloudTrail CreateTokenWithIAM event with event source sso-oauth.amazonaws.com and a vpcEndpointId field

Figure 13: CloudTrail CreateTokenWithIAM event traversing the sso-oauth interface endpoint

Similarly, the following screenshot shows a DescribeUser event (event source identitystore.amazonaws.com) generated when Amazon Redshift resolves the authenticated user against the identity store. As with the previous event, the invokedBy field shows the call originated from Amazon Redshift, and the vpcEndpointId field confirms it traversed the identitystore interface endpoint.

CloudTrail DescribeUser event with event source identitystore.amazonaws.com and a vpcEndpointId field

Figure 14: CloudTrail DescribeUser event traversing the identitystore interface endpoint

Note: where the events appear depends on how your IAM Identity Center instance is deployed:

  • CreateTokenWithIAM (event source sso-oauth.amazonaws.com) is recorded in the same account as your Amazon Redshift cluster.
  • Identity Store API calls (DescribeUser, ListGroupMembershipsForMember, BatchDescribeGroup) are recorded in the account that owns your IAM Identity Center instance. The event source is identitystore.amazonaws.com. If you use a centralized instance in a delegated administrator or management account, these events appear in that account and not in the account running your cluster. Searching the cluster’s own account returns nothing, even when authentication is working normally. To confirm which account to look in, run aws sso-admin list-instances and check OwnerAccountId.

Clean up

To avoid incurring future charges, delete the resources you created for this walkthrough. These endpoints provide the network path for single sign-on while enhanced VPC routing is enabled, so remove them only if you no longer need the integration.

  • On the Amazon VPC console, choose Endpoints.
  • Select the sso-oauth and identitystore interface endpoints you created, and choose Actions, then Delete VPC endpoints.
  • Select the glue and lakeformation interface endpoints, if you created them, and delete them.
  • Select the Amazon S3 gateway endpoint and delete it. This also removes its route table entries.
  • Terminate the EC2 instance you used to test the SQL client connection, if you created one for this walkthrough.
  • If you no longer need the integration, remove the IAM Identity Center application assignment for Amazon Redshift and delete the associated IAM role and policy.

Conclusion

In this post, we showed you how to enable AWS IAM Identity Center authentication for Amazon Redshift on clusters with enhanced VPC routing enabled. Your users get single sign-on with their corporate credentials, and the authentication traffic stays private to your VPC. The key concept is that Amazon Redshift, not your client, validates the access token. Because enhanced VPC routing is enabled, Amazon Redshift routes that validation call through your VPC. On a cluster running patch 204 or later, interface endpoints for sso-oauth and identitystore give Amazon Redshift a private path over AWS PrivateLink. Adding endpoints for AWS Glue, AWS Lake Formation, and Amazon S3 extends the same benefit to data lake and lakehouse queries.

Try this setup in your own environment and let us know what you think in the comments. For more information, see the following resources:


About the authors

Maneesh Sharma

Maneesh Sharma

Maneesh is a Senior Analytics Specialist Solutions Architect at AWS with more than 15 years of experience designing and implementing large-scale data warehouse and analytics solutions. He works with FSI and Enterprise customers to implement modern analytics architectures using Amazon Redshift, Amazon SageMaker Unified Studio, Amazon S3 Tables, AWS Glue, AWS Lake Formation, and AWS IAM Identity Center.

Laura Reith

Laura Reith

Laura is an Identity Solutions Architect at AWS, where she thrives on helping customers overcome security and identity challenges. In her free time, she enjoys wreck diving and traveling around the world.

Suchintya Dandapat

Suchintya Dandapat

Suchintya is a Principal Product Manager for AWS where he partners with enterprise customers to solve their toughest identity challenges, enabling secure operations at global scale.

Jonathan Glaser

Jonathan is a Software Development Engineer on the Amazon Redshift Connectivity team, where he works on authentication and the Redshift client drivers. He focuses on secure authentication for Redshift, including its integration with IAM Identity Center in Enhanced VPC Routing environments. Jonathan holds master’s degrees in biotechnology and computer science, and in his spare time enjoys reading fiction and exploring NYC’s food scene.

Nishtha Mehrotra

Nishtha is a Senior Software Development Engineer on the Redshift Connectivity team at AWS. With over 10 years of software engineering experience, she specializes in database driver development, performance optimization, and identity integration. Nishtha works on Amazon Redshift’s ODBC, JDBC, and Python drivers, ensuring reliable and performant connectivity for customers at scale. She is passionate about improving developer experience and building secure, high-performance data infrastructure that powers analytics workloads across AWS.

Razor Group’s journey to a modern data lakehouse on AWS

Post Syndicated from Yaswanth Kothainti original https://aws.amazon.com/blogs/big-data/razor-groups-journey-to-a-modern-data-lakehouse-on-aws/

Razor Group is one of Europe’s leading ecommerce aggregators, operating 250+ brands across multiple global marketplaces. With a portfolio exceeding $400M in revenue, the company relies on data to power every critical business decision, from dynamic pricing and inventory optimization to advertising spend and supply chain orchestration.

At the heart of this operation sits the Razor Operating System (ROS), a proprietary platform that processes 370M+ API calls monthly through 9,300+ data pipelines, transforming marketplace signals into automated actions at scale.

In this post, we share how Razor Group optimized their data platform by implementing a lakehouse architecture on AWS. We cover the architectural decisions, the phased migration approach, and the measurable business outcomes. Whether you’re looking to optimize workload performance, reduce infrastructure costs, or unlock multi-engine flexibility for your analytics, this blueprint provides actionable insights you can adapt for your organization.

The business challenge: Scaling data infrastructure for hypergrowth

As Razor Group’s brand portfolio expanded rapidly, the demands on their data platform grew significantly. The company needed their analytics infrastructure to keep pace with the speed of ecommerce, where pricing decisions, stock replenishment, and advertising bids happen in near real time.

Their existing architecture, built on Amazon Redshift provisioned clusters, had served them well during earlier growth stages. As workloads diversified and data volumes surged, several optimization opportunities emerged:

Razor Operating System data architecture before the migration: signal sources such as Amazon Selling Partner API, Shopify, NetSuite, Walmart, and Target ingested through AWS Lambda and Amazon MSK, stored in Amazon S3 and Amazon DynamoDB, modeled in Amazon Redshift, and consumed by ML notebooks, ML jobs on AWS Batch, and Tableau dashboards, orchestrated by Apache Airflow

Figure 1: The Razor Operating System data architecture before the migration

  • Workload contention: Over 1,000 SQL models for ETL, transformation, and analytics competed for the same compute resources, creating resource contention during peak processing windows.
  • Cost-to-utilization mismatch: Always-on clusters ran 24/7, but workload analysis revealed that 98% of compute demand came from batch ETL rather than interactive analytics, which resulted in significant idle capacity during off-peak hours.
  • Data freshness gaps: Batch-oriented pipelines delivered data with 4–6 hour latency, limiting the team’s ability to react to fast-moving marketplace dynamics.
  • Scaling constraints: As concurrent users and pipeline complexity grew, vertical scaling alone couldn’t address the need for workload isolation and elastic capacity.

These weren’t failures of any single service. They were signals that the architecture needed to evolve to match the scale and diversity of Razor Group’s workloads.

Why a lakehouse architecture?

Rather than replacing their existing investments, Razor Group recognized the opportunity to optimize workload placement by adopting a modern lakehouse architecture. The core principles driving this decision:

  • Open table formats: Apache Iceberg provides ACID transactions, time travel, and schema evolution. Data is stored once and accessed by any compatible engine without duplication.
  • Elastic, per-workload scaling: With data persisted on Amazon Simple Storage Service (Amazon S3), each engine independently scales compute to match its workload. Each engine spins up for peak processing and scales to zero when idle, without over-provisioning shared infrastructure.
  • Multi-engine flexibility: Different workloads have different requirements. Heavy ETL benefits from distributed Spark processing, ad hoc exploration from serverless queries, and business intelligence (BI) dashboards from high-performance warehouse engines, each optimized for its purpose.

This approach allowed Razor Group to right-size each workload to the best-fit engine while maintaining a single, governed copy of data accessible across the entire platform.

Solution overview

Razor Group partnered with AWS to implement a comprehensive lakehouse architecture that brings together multiple AWS services, each playing a complementary role:

New lakehouse architecture on AWS: the same signal sources ingested through AWS Lambda and Amazon MSK, stored and modeled as Bronze, Silver, and Gold Apache Iceberg tables using Apache Spark Connect on Amazon EC2 with AWS Lake Formation and AWS Glue Data Catalog, served through Amazon Redshift, and consumed by ML notebooks, ML jobs on AWS Batch, and Tableau dashboards

Figure 2: End-to-end lakehouse architecture on AWS

Designing for scale: The lakehouse vision

The core insight driving Razor Group’s new architecture was simple: build a single, open format data lake that any engine can query. In the old model, each tool maintained its own copy of the data. In the new model, a single open-format data lake on Amazon S3 serves as the source of truth, and multiple purpose-built compute engines read from it based on the workload at hand.

This shift, commonly called a lakehouse architecture, combines the cost economics and scalability of a data lake with the query performance and governance of a data warehouse. Its open table format, Apache Iceberg, provides ACID transactions, schema evolution, time travel, and no vendor lock-in.

Storage and governance: The open data foundation

  • Amazon S3 Tables (a capability of Amazon S3) with Apache Iceberg — The primary storage layer, providing open-format tables with ACID transactions, partition evolution, and time travel. Data is stored once and accessible by any Iceberg-compatible engine.
  • AWS Glue Data Catalog — A unified metadata repository for consistent data discovery across all compute engines.
  • AWS Lake Formation — Fine-grained access control with column-level and row-level security so that governance scales with the platform.

Compute: Right engine for the right workload

  • Apache Spark on Amazon Elastic Compute Cloud (Amazon EC2) — Elastic, distributed compute for heavy ETL and transformation workloads. It uses AWS Graviton instances and Amazon EC2 Spot Instances for cost optimization.
  • Amazon Athena — Serverless SQL for ad hoc exploration and lightweight queries directly on Iceberg tables, with no infrastructure to manage.
  • Amazon Redshift Serverless — High-performance serving layer for BI dashboards, Tableau workloads, and interactive analytics. Amazon Redshift Serverless automatically scales to meet demand and pauses when idle, so it stays cost-efficient for the analytics workloads it serves best.

Orchestration and observability

  • Apache Airflow — Pipeline orchestration that manages 9,300+ data pipelines with dependency tracking and service level agreement (SLA) monitoring.
  • Comprehensive observability stack — Cost attribution, pipeline health monitoring, and data quality checks across all layers.

Note: When the architecture was originally designed, Amazon Redshift lacked Iceberg write support, making self-managed Spark the only viable ingestion path. This constraint has since been removed. Amazon Redshift now supports full Apache Iceberg DML (UPDATE, DELETE, MERGE), complementing its earlier CREATE/INSERT capabilities and AWS Glue Iceberg materialized views. This makes it a complete read/write Iceberg engine.

Migration approach

Rather than a risky big-bang cutover, Razor Group adopted a phased migration of five stages, each delivering standalone value while building the foundation for the next. Both Amazon Redshift and Spark pipelines ran in parallel during the transition, which maintained business continuity and let the team compare outputs with confidence. At no point was a production pipeline paused or a dashboard unavailable.

The migration journey: Five phases

The migration unfolded across five structured phases, each building on the previous one and delivering incremental value before the next began.

Phase 1: Establish the lakehouse foundation

Before migrating a single query, Razor Group needed to answer three questions: where does the data live, how is it managed, and how do we query it?

Why S3 Tables over self-managed Iceberg

Razor Group had already committed to Apache Iceberg as the table format: open, engine-agnostic, and equipped with ACID transactions and time travel. The question was whether to self-manage Iceberg on standard S3 buckets or use Amazon S3 Tables.

Self-managed Iceberg is powerful but operationally expensive. Someone has to run compaction jobs to prevent small-file proliferation. Someone has to expire old snapshots before metadata bloat degrades query planning. Someone has to clean up orphaned data files after interrupted writes. With 700+ models running across 40+ schemas, many of them materializing multiple times per day, that maintenance burden would scale with the platform rather than shrink.

S3 Tables eliminated this entire category of work. Compaction, snapshot management, and unreferenced file removal run continuously and automatically. The integrated Iceberg REST Catalog API means any compatible engine, such as Spark, Trino, Athena, Amazon Redshift, and Flink, can discover and query tables without maintaining a separate metastore. Discovery is unified through AWS Glue Data Catalog, which now exposes the Iceberg REST Catalog protocol as its access interface. Because tables are first-class AWS resources, access control, encryption, and lifecycle policies operate at the table level rather than through complex S3 bucket policies layered on top of file-path conventions.

For a company that didn’t want the operational burden of self-managing open table format maintenance, this was the deciding factor.

AWS Glue Data Catalog provides unified metadata discovery across all tiers. Lake Formation handles column- and table-level access control, with AWS Identity and Access Management (IAM) roles that follow least-privilege principles and AWS CloudTrail turned on for a full audit trail.

Choosing the query protocol

Prior to the rearchitecture, the Amazon Redshift cluster was 98% ETL, and only a fraction of compute hours were analyst SELECT queries. The replacement engine needed to handle both heavy batch transformations and interactive ad hoc queries.

Traditional Spark (spark-submit) handles batch ETL well, but couples clients to the cluster. Every job requires packaging driver JARs, managing classpaths, and submitting from within the cluster. For a platform running 200+ production directed acyclic graphs (DAGs) that process massive data volumes daily, this operational friction was a non-starter.

Spark Connect is the gRPC-based client-server protocol introduced in Spark 3.4, and it solved the coupling problem entirely. The cluster runs a persistent gRPC endpoint. Clients connect remotely and submit queries over the wire. Airflow operators become thin clients: they open a session, submit SQL, and get results, with success and failure mapping directly to task states. There are no driver JARs and no polling. Multiple consumers, including pipeline orchestrators, the web application, and developer notebooks, share one cluster without any of them needing Spark installed locally.

Deploying Spark Connect

Razor Group deployed a self-hosted Spark cluster on Amazon EC2: an on-demand AWS Graviton leader node, Spot workers at about 70% cost savings, and the Spark Connect endpoint exposed through an internal Network Load Balancer. Custom Amazon Machine Images (AMIs) bake in the full Spark, Iceberg, and S3 Tables stack, so private-subnet nodes have everything they need without internet access at runtime.

This phase produced no immediate business value, but it made everything that followed possible.

Phase 2: Migrate data ingestion

Razor Group’s ingestion layer pulls data from Amazon Selling Partner API, Seller Central portals, NetSuite ERP, and custom web scrapers. In the previous architecture, all of this landed in Amazon Redshift through COPY commands, which meant data freshness was dictated by batch job schedules and competed for resources on the same cluster that served analytical queries.

Razor Group migrated these pipelines to AWS Lambda functions orchestrated by Apache Airflow, writing data directly to S3 Tables in Iceberg format. The shift from schedule-driven to event-driven significantly improved freshness. Lambda functions spin up only when there’s data to process, and Airflow sensors trigger downstream transformations the moment new data lands. This replaced rigid hourly batch windows with data freshness measured in minutes.

The orchestration layer manages 200+ DAGs across 90+ flows and processes data from dozens of sources at scale. The migration required rewiring destinations from Amazon Redshift COPY to Iceberg writes, but the orchestration logic itself carried over with minimal changes.

This phase alone eliminated roughly 40% of compute costs by severing the always-on cluster dependency for ingestion.

Phase 3: Transform processing pipelines

This was the most technically demanding phase, and where Razor Group learned the most. The team migrated 1,000+ SQL models from Amazon Redshift to Apache Spark, working incrementally up the dependency chain across 40+ schemas. The models moved through a medallion structure: Bronze for raw ingested data, Silver for cleaned and conformed data, and Gold for business-ready aggregates.

Razor Group built automated conversion tooling and a validation framework that ran both Amazon Redshift and Spark outputs in parallel, comparing results row-by-row before decommissioning anything. Several categories of transformation pushed the limits of what automation could handle:

  • Window functions: The QUALIFY clause in Amazon Redshift has no Spark equivalent. Each instance required wrapping in a subquery with explicit row numbering, which affected dozens of models in the inventory schema alone.
  • JSON serialization: The most time-consuming category. Complex columns stored as JSON STRING in Amazon Redshift needed from_json() with hand-written STRUCT definitions in Spark. Every nested payload column across ads, orders, and transaction pipelines required schema introspection, with no shortcuts.
  • Function dialect: More than 20 function-level conversions, including NVL to COALESCE, DATEADD to interval arithmetic, and LISTAGG to ARRAY_JOIN(COLLECT_LIST()).
  • Snapshot elimination: The single biggest hidden cost. Full table copies that ran multiple times daily only to preserve point-in-time state consumed more than 35 hours of weekly Amazon Redshift compute. With Iceberg’s native time travel, these became zero-cost operations overnight.

When migrating 1,000+ SQL models, automated tooling handles the mechanical syntax conversions well. But roughly 30% of the models required human judgment: those with complex JSON payloads, deeply nested window functions, or cross-schema snapshot dependencies. These models consumed 70% of the migration effort.

Razor Group built a structured migration workflow that used Claude to accelerate this work: read source SQL, identify dependencies, convert syntax, resolve missing base tables, add JSON parsing, validate outputs, and write to the lakehouse. The system did more than translate SQL. It applied schema context, traced cross-model dependencies, and flagged edge cases that would have taken engineers hours to find manually. What could have been a multi-year effort became a systematic, repeatable process measured in weeks. This approach fundamentally changed the speed of migration.

Phase 4: Unify the serving layer

With data flowing through Iceberg tables, Razor Group collapsed the serving layer. End users query Gold-layer Iceberg tables through Amazon Redshift Serverless, and internal exploration and machine learning (ML) workloads read the same tables through Spark Connect. This removed the need to maintain separate data copies, materialized views, or extract jobs for different consumers.

This is the strategic payoff of an open table format. Iceberg tables on S3 are engine-agnostic: Spark for batch transforms today, Trino for interactive queries tomorrow, Flink for streaming next quarter. Any engine that speaks Iceberg can read the data without conversion or migration. Razor Group went from being locked into a single vendor’s SQL dialect to having the freedom to adopt new engines without touching the storage layer.

Phase 5: Operationalize and observe

The final phase made the lakehouse production-grade. Razor Group built a comprehensive observability stack that aggregates metrics, traces, and logs from every pipeline component into a unified view. This view supports centralized log search, anomaly detection, and automated alerting that correlates failures across the entire data platform.

This observability layer did more than provide visibility. It gave the team confidence. When you’re running thousands of pipeline executions daily, you need to know within minutes when something breaks, what caused it, and which downstream consumers are affected. That’s the difference between reactive firefighting and proactive operations.

Pipeline orchestration consolidated around three patterns: a daily pipeline (ingestion to materialization to export to AI agent analysis), an operations worker polling every 15 minutes, and weekly scraper jobs.

The cutover was zero-downtime by design: both schedulers ran in parallel for two weeks. Automated comparison checks validated that every pipeline produced identical outputs before the prior architecture system was disabled.

Results and business impact

The lakehouse architecture delivered measurable improvements across every dimension:

Metric Before After Improvement
P95 query runtime 180 seconds 63 seconds 65% faster
Infrastructure cost Always-on provisioned clusters Elastic, workload-optimized 63% reduction
Data freshness 4–6 hour batch cycles Event-driven pipelines 15-minute freshness
Concurrent capacity Limited by cluster size Elastic, independent scaling Unlimited
Engine flexibility Single engine Multi-engine (Spark, Athena, Amazon Redshift) Open format portability

The 63% reduction compares the lakehouse run-rate (January–March 2026) with the pre-rearchitecture run-rate (October–December 2025), the trailing three months before the rearchitecture. The figure is an apples-to-apples blended infrastructure number that includes compute and storage across both architectures. The before column covers Amazon Redshift cluster compute and managed storage. The after column covers Amazon EC2 (Spark workers, both on-demand and Spot), AWS Lambda, AWS Glue, Amazon Athena, Amazon Redshift Serverless, and S3 Tables storage. Data-transfer and ancillary services are excluded because they were not materially different between the two periods. Workload mix (the number of pipelines, models, and end-user query volume) was held broadly comparable across the two windows.

Lessons learned

Start with the decision loops, not the tools, and know your workload before you replace your warehouse.

The most valuable activity of the entire migration wasn’t writing a line of code. It was the Amazon Redshift workload analysis we ran before making any architectural decisions. Discovering that 98% of compute was ETL, with only a sliver going to analyst queries, validated the move to on-demand Spark. It also prevented us from over-provisioning the replacement infrastructure for interactive workloads that barely existed. Architecture decisions should always trace back to core business requirements: pricing accuracy, promotional responsiveness, intraday P&L visibility. Start there, not with the technology.

Design for multiple compute engines, and choose the right engine per workload.

One of the clearest lessons from running a single-engine architecture is what you give up. Avoid locking yourself into one compute layer for BI, ingestion, backfills, and ML alike, because they have fundamentally different cost and performance profiles. Iceberg, Spark, and S3 Tables work well together out of the box once you make the shift. The technology isn’t the hard part. The hard part is mapping 1,000+ models across 40+ schemas, tracing dependencies through 200+ DAGs, and discovering that a column is actually a JSON string silently serialized differently between two engines. Migration is as much an excavation project as an engineering one.

Automate conversion, but budget for the 30%.

Automated tooling handles mechanical syntax conversions well, and it should be the first tool you reach for. But models with complex JSON payloads, deeply nested window functions, or cross-schema snapshot dependencies require human judgment, and that work doesn’t compress. Roughly 30% of our models needed significant manual intervention, and those models consumed 70% of the total migration effort. Plan for it honestly from the start.

Observability must include cost attribution, and watch out for hidden cost bombs.

Snapshot operations were our biggest surprise. Full table copies that ran multiple times daily to preserve point-in-time state were costing more than 35 hours of weekly compute, and nobody questioned it because “that’s how snapshots work.” Iceberg’s time-travel capability eliminated their cost, and that single feature justified a meaningful portion of the migration on its own. More broadly, you cannot optimize what you cannot see, so track query-level usage and attribute it to teams and functions. Cost observability is not a nice-to-have. It’s foundational.

Governance isn’t optional. Build it into the foundation, and align stakeholders from day one.

Catalog and access control need to come first, before you scale adoption, not after. The same principle applies to people: migration is a cross-functional program, not an infrastructure project. Our two-week parallel run caught edge cases that row-level validation missed entirely: time zone differences between Amazon Redshift and Spark, partition pruning behavior under concurrent writes, and subtle ordering differences in non-deterministic window functions. That parallel run wasn’t a safety net. It was where the migration actually proved itself. None of it works without the right stakeholders involved and aligned from the very beginning.

Conclusion

Razor Group’s journey offers valuable lessons for organizations looking to optimize their data architectures:

  1. Analyze your workload mix first. Understanding that 98% of compute was ETL rather than interactive queries guided the decision to offload heavy processing to elastic Spark, while preserving Amazon Redshift Serverless for the interactive analytics it handles best.
  2. Design for multi-engine flexibility. Open table formats like Apache Iceberg eliminate the need to choose a single engine. Each workload runs on the engine best suited to its access pattern, cost profile, and performance requirements.
  3. Automate migration, but budget for complexity. Automated transpilation handled 70% of SQL models, but the remaining 30% consumed 70% of engineering effort. Plan accordingly.
  4. Observability must include cost attribution. Without per-workload cost visibility, optimization is guesswork. Razor Group discovered that Iceberg snapshot maintenance alone consumed more than 35 hours of compute weekly, a hidden cost that observability surfaced and automation resolved.
  5. Build governance into the foundation. AWS Lake Formation and AWS Glue Data Catalog provided fine-grained access control from day one, not retrofitted after the migration.
  6. Validate with parallel systems. A two-week parallel run between old and new architectures caught edge cases that automated testing missed, which supported a confident production cutover.

The road ahead

With the lakehouse foundation in place, Razor Group is positioned to accelerate innovation, from real-time pricing models to AI-driven inventory optimization, all powered by a unified, open, and governed data platform on AWS.

The company’s transformation demonstrates that modern data architectures aren’t about choosing between services. They’re about placing each workload where it performs best, using open formats to eliminate silos, and scaling each layer independently as the business grows.

To learn how other organizations are implementing similar lakehouse architectures on AWS, see How BigBasket uses the Iceberg-based lakehouse architecture on AWS to power lightning-fast grocery delivery across India.


About the authors

Yaswanth Kothainti

Yaswanth is VP of Data Engineering & Platform at Razor Group, a $400M+ ecommerce enterprise, where he built the company’s data platform from the ground up and leads a 65-member global engineering organization. His core expertise spans enterprise data platforms, data governance, FinOps, and agentic AI systems, with a track record of translating complex platform investments into measurable business outcomes.

Shubham Purwar

Shubham Purwar

Shubham is an Analytics Specialist Solutions Architect at AWS. He helps organizations unlock the full potential of their data by designing and implementing scalable, secure, and high-performance analytics solutions on AWS. In his free time, Shubham loves to spend time with his family and travel around the world.

Ravi Kompella

Ravi Kompella

Ravi is Principal Analytics Specialist with experience in driving adoption of modern data architectures, enterprise data lakehouses, and real-time data systems across multiple industry verticals in India across all segments including startups and SaaS providers.

Long-term system tables retention in Amazon Redshift with Amazon S3 Tables

Post Syndicated from Nidhi Nayak original https://aws.amazon.com/blogs/big-data/long-term-system-tables-retention-in-amazon-redshift-with-amazon-s3-tables/

Amazon Redshift system tables capture a continuous stream of operational signals: every query that runs, every connection that is made. This data powers observability, performance analysis, and compliance auditing across your data warehouses. Until now, the system tables retained this critical data for only 7 days, making long-term compliance and auditing difficult without custom workarounds.

Amazon Redshift system table integration with Amazon S3 Tables, a capability of Amazon Simple Storage Service (Amazon S3), automatically delivers your system table logs data to Amazon S3 Tables and stores them in Apache Iceberg format. You can configure retention periods for Amazon Redshift system table beyond the current 7-day limit, giving you extended compliance, auditing, and cross-warehouse observability without custom ETL pipelines or cluster resource consumption. Your data is open, durable, and queryable from Amazon Redshift, Amazon Athena, AWS Glue, Amazon EMR, or other Apache Iceberg-compatible engines.

In this post, we walk through how the Amazon Redshift system table integration delivers log data to Amazon S3 Tables. This feature is supported on RA3 and RG provisioned clusters and Amazon Redshift Serverless workgroups.

The challenge

If you run Amazon Redshift, you often face operational challenges driven by the 7-day system table retention limit:

  1. Limited query trend visibility: You want to compare how the same query performed 30 days ago compared to today. When performance shifts gradually, extended baselines enable data-driven root cause analysis rather than reactive troubleshooting.
  2. Enable before-and-after comparisons: When you add a new workload, change instance type, or adjust Workload Management (WLM) queues, you want to measure the impact precisely. Extended retention preserves the baseline data you need.
  3. Unlock seasonal capacity planning: Month-end spikes, quarter-close surges, and annual peaks require months of historical data to identify and plan. Extended retention reveals seasonal patterns across months and years.
  4. Custom ETL pipeline overhead: To work around the retention limit, teams build custom pipelines that copy system table data hourly/daily into persistent tables within Amazon Redshift Managed Storage. These pipelines consume cluster resources, compete with production workloads, and require ongoing engineering maintenance. When Amazon Redshift updates system table schemas and data sharing configurations, these pipelines require manual intervention and create gaps in records.
  5. Compliance requirements: Regulated industries are required to maintain audit trails spanning months or years. The 7-day limit requires custom infrastructure to meet these requirements. Amazon S3 Tables integration for Amazon Redshift system tables now addresses this.

How it works

Amazon Redshift system tables integration with Amazon S3 Tables is a fully managed capability that automatically writes Amazon Redshift system table data to Amazon S3 tables in Apache Iceberg format. AWS handles partitioning, compression, and retention management automatically. The log writing process runs in an isolated background process that alleviates resource contention with production workloads. AWS manages the pipelines for you.

The feature supports over 25 system views at launch – see the supported system views documentation.

Setting up

Follow these steps to enable system table integration with Amazon S3 Tables from the Amazon Redshift console:

  1. Open the Amazon Redshift console and navigate to the System table integrations page. You can also access this from the detail page of your provisioned cluster or Serverless workgroup.
  2. Choose Create System table integration. This launches the configuration wizard.
  3. Select the Amazon Redshift Provisioned cluster or Amazon Redshift Serverless workgroup that you want to enable the feature on.

    Amazon Redshift console data warehouse selection step in the create System table integration wizard

    Figure 1: Selecting the Amazon Redshift data warehouse in the System table integration wizard

  4. Choose the system views to publish from the Available system tables list. Select individual SYS_* views, or choose Select all supported system tables to publish all current and future supported views. If you select all, new views added in the future are automatically included without requiring a configuration change.

    Available system tables list in the System table integration wizard with SYS views selected for publishing

    Figure 2: Choosing the system views to publish from the Available system tables list

  5. Select the deployment model. Choose how data is organized in Amazon S3 Tables:
  • Individual S3 table per system table per data warehouse to keep this warehouse’s data in its own set of tables.
  • Shared S3 table per system table across data warehouses to consolidate data from multiple warehouses in the account into a shared set of tables.
  1. Optionally configure encryption with an AWS Key Management Service (AWS KMS) customer managed key. By default, data is encrypted with Amazon S3-managed key (SSE-S3) encryption.
  2. Save your changes. Amazon Redshift begins publishing the selected views to Amazon S3 Tables and continues adding new records on a fixed frequency.

To verify the integration is active:

  • Navigate to your cluster or workgroup detail page.
  • Check the integration status and the last ingestion time for each view.
  • You can also view the published data from the Amazon S3 Tables console.

After it’s enabled, Amazon Redshift writes log data to Amazon S3 tables periodically through an isolated background process, separate from production workloads. To start querying the retained logs, you will need to perform a one-time setup that connects your Amazon Redshift environment to Amazon S3 Tables data through AWS Glue Catalog. Complete the following steps:

  1. Set up an AWS Identity and Access Management (IAM) role with the necessary permissions for AWS Glue Data Catalog and Amazon S3 Tables access, then associate it with your Amazon Redshift cluster or Amazon Redshift serverless namespace.
  2. In AWS Glue Data Catalog, create a resource link that points to the Amazon S3 Tables database where your logs reside.
  3. In Amazon Redshift, create an external schema that references the resource link:
    CREATE EXTERNAL SCHEMA <schema_name>
    FROM DATA CATALOG
    DATABASE '<resource_link_database>'
    IAM_ROLE '<iam_role_arn>';

  4. With this in place, you can query your historical system table data using familiar 2-part notation:
    SELECT * FROM <schema_name>.<table_name>;

Because access to Amazon S3 Tables is read-only, the integrity of your audit trails is inherently preserved.

For detailed setup instructions including IAM policy examples, see Registering the S3 Tables bucket with AWS Glue Data Catalog.

Your data is now in Apache Iceberg

Your system table data is stored in Apache Iceberg, an open table format, so you have the freedom to choose a compatible query engine. Your observability and auditing data works with the tool you already use.

You can analyze your operational data using:

  1. Amazon Redshift: After the S3 table bucket is integrated with AWS Glue Data Catalog, create an external schema in Amazon Redshift pointing at the resource link to query the retained tables.
  2. Amazon Athena: Run serverless SQL queries against historical logs with zero infrastructure provisioning.
  3. AWS Glue: Build automated data processing and transformation jobs on top of your operational data.
  4. Amazon EMR: Run Spark-based analytics at scale for complex cross-warehouse analysis.

Because the data is stored in open Apache Iceberg format in Amazon S3 Tables, you can query it with Amazon Redshift, Amazon Athena, AI agent skills for natural-language queries, Amazon SageMaker Unified Studio, an Iceberg-compatible engine, business intelligence (BI) tools, and observability systems.

Cost efficiency

Log delivery from Amazon Redshift to Amazon S3 Tables incurs no additional cost. You only pay for Amazon S3 Tables storage, maintenance, and querying the data with the engine of your choice.

Solution overview

The following scenarios illustrate how Amazon Redshift system tables integration with Amazon S3 Tables addresses common operational, compliance, and observability challenges across your Amazon Redshift environment. We also built a dedicated skill, querying-aws-redshift, for this feature and embedded it into the AWS MCP Server so you can query Amazon Redshift system tables from Amazon S3 Tables.

With months or years of SYS_QUERY_HISTORY data retained, you can trace how individual queries perform over extended periods. You can compare execution time, queue time, and resource consumption for a query across days, weeks, or months.

You can pinpoint exactly when performance started degrading and correlate it with what changed: a new schema, a spike in data volume, or an additional concurrent workload. Extended retention turns troubleshooting into proactive, data-driven root cause analysis.

Scenario 2: Assess workload impact before and after changes

Every workload change affects your system: a new ETL pipeline, an instance type change, a Workload Management (WLM) queue adjustment, or a new team of analysts running ad hoc queries. The question is always: how did this change affect performance?

With Amazon S3 Tables integration for Amazon Redshift system table, you can make data-driven decisions with confidence. Query SYS_QUERY_HISTORY to compare execution times, queue wait durations, and concurrency scaling events from the weeks before a change versus the weeks after. If you onboarded a new reporting workload two weeks ago and want to understand its effect on existing queries, the data to confirm that is already there, with zero custom pipeline required.

Scenario 3: Build observability dashboards

Your system table data is stored in Apache Iceberg and cataloged in AWS Glue, which means an observability or business intelligence (BI) tool that reads Apache Iceberg can connect directly to it. Visualize workload distribution trends in Amazon Quick Sight for executive reporting. Use Amazon SageMaker Unified Studio for deeper analytical exploration or to power AI-driven insights from your operational data. Beyond AWS services, connect your preferred third-party observability systems and BI tools to track query volumes, monitor connection patterns, set up alerts for anomalies, or correlate Amazon Redshift operational data alongside application-level logs.

Your observability and auditing data works with tools that you already use. Direct access to durable, structured operational data, with a tool you prefer.

Scenario 4: Plan capacity with seasonal context

Workload demand varies throughout the year. Month-end close, quarter-end reporting, annual planning cycles, and promotional events all create predictable usage spikes, but only if you have enough historical data to see the pattern.

With extended retention, you can analyze utilization trends across multiple business cycles. Identify when you consistently approach capacity limits, measure how demand shifts quarter over quarter, and validate whether your provisioned resources align with actual usage.

Scenario 5: Maintain compliance audit trails

For regulated industries, extended retention delivers a fully managed audit trail with built-in integrity.

SYS_CONNECTION_LOG records every authentication attempt. SYS_USERLOG captures user account changes. SYS_QUERY_HISTORY documents every query executed against your warehouse.

Configure retention to match your organization’s data retention policies: whether that is 90 days, one year, or multiple years. The read-only access policy helps prevent records from being altered after they are written, including by administrators.

Scenario 6: Centralize fleet observability across your warehouse

If you run multiple Amazon Redshift warehouses, you benefit from a unified view of operational data. The feature supports two deployment patterns to match your organizational structure:

  1. Individual tables per warehouse: Each warehouse writes to its own dedicated Amazon S3 tables, providing complete data isolation for compliance-sensitive environments. To query multiple warehouses, a UNION operation is required.
  2. Shared tables: Warehouses across the same account and same AWS Region write to a single shared set of Amazon S3 tables, with data distinguished by the warehouse_name column. Filter by warehouse for instant cross-cluster analysis.

Best practices

  1. Identify warehouses with logs requiring isolation for privacy reasons and select the individual table per warehouse option for those. For the remaining warehouses, use the Shared tables (consolidated) option for ease of management.
  2. Align retention duration with your compliance requirements. Configure the minimum retention period that satisfies your compliance requirements to reduce storage costs.
  3. When querying retained system tables, filter on metadata columns such as warehouse_account_id, warehouse_region_name, warehouse_namespace_arn, warehouse_name, and s3_tables_ingestion_time to reduce scan scope and improve performance. This is particularly important when querying large volumes of historical data across multiple warehouses.
  4. Rely on the built-in read-only access for audit trail integrity. Use the Amazon S3 Tables configuration APIs to manage retention and encryption settings.
  5. Plan your encryption strategy early. Choose your encryption key carefully at setup, as changes require recreating the integration. If you anticipate consolidating warehouses in the future, choose a shared AWS KMS key from the start.

Conclusion

Amazon Redshift system table integration with Amazon S3 Tables replaces custom ETL pipelines with a fully managed solution to preserve your Amazon Redshift operational data. With automatic Apache Iceberg-based storage, open format queryability, and built-in audit integrity, you get months or years of observability data, fully managed. You can enable it through the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS SDKs.

To learn more, visit the Amazon Redshift system tables documentation.


About the authors

Nidhi Nayak

Nidhi Nayak

Nidhi is a Senior Technical Account Manager with AWS, she helps enterprise customers build scalable, high-performance cloud applications and optimize cloud operations. With over a decade of experience in Data Analytics, Nidhi currently focuses on Redshift & Generative AI integration with Redshift.

Raza Hafeez

Raza Hafeez

Raza is a Senior Product Manager, Technical at Amazon Redshift. He has 15+ years of experience building and optimizing enterprise data warehouses and is passionate about making cloud analytics accessible and cost-effective for customers of all sizes.

Shubham Purwar

Shubham is an AWS Analytics Specialist Solution Architect. He helps organizations unlock the full potential of their data by designing and implementing scalable, secure, and high-performance analytics solutions on the AWS platform. With deep expertise in AWS analytics services, he collaborates with customers to uncover their distinct business requirements and create customized solutions that deliver actionable insights and drive business growth. In his free time, Shubham loves to spend time with his family and travel around the world.

Amrita Singh

Amrita Singh

Amrita is a Senior Technical Account Manager at AWS, based in Salt Lake City, USA. She specializes in Amazon Redshift, helping enterprise customers optimize their data warehouse environments for performance, scalability, and cost efficiency. Amrita works directly with AWS customers to provide guidance and technical assistance on their cloud journeys, helping them achieve higher flexibility, scale, and resiliency with AWS services.

Amazon Redshift multi-Region disaster recovery

Post Syndicated from Werner Gunter original https://aws.amazon.com/blogs/big-data/amazon-redshift-multi-region-disaster-recovery/

Modern enterprises trust Amazon Redshift to power their most demanding analytics workloads and increasingly require multi-Region disaster recovery to protect those workloads against Regional disruptions. From real-time fraud detection and regulatory reporting to customer-facing dashboards processing millions of transactions daily, organizations are designing for resilience from day one. In financial services, for example, regulatory frameworks increasingly mandate geographic redundancy for data infrastructure, making cross-Region disaster recovery (DR) not only a technical consideration but a compliance requirement. A well-designed DR strategy keeps your analytics infrastructure available and responsive regardless of Regional disruptions, protecting revenue streams, maintaining regulatory standing, and preserving customer trust.

In our previous blog post, Implement disaster recovery with Amazon Redshift, we covered node-level recovery, Availability Zone (AZ) recovery, Multi-AZ deployments, cross-Region backup setup, CNAME implementation, Amazon Redshift Spectrum and Redshift Data sharing considerations.

In this post, we walk through the core concepts of cross-Region disaster recovery, introduce a framework for assessing your requirements, and then dive deep into three primary DR strategies for Amazon Redshift: Active-Passive, Active-Active, and a Hybrid approach. For each strategy, we cover architecture, trade-offs, implementation guidance, and cost considerations so you can make an informed decision for your workload.

What is disaster recovery?

Disaster recovery includes the set of policies, tools, and procedures that enable an organization to restore critical systems and data after an incident. It helps maintain business continuity during events such as a regional AWS outage, accidental data deletion, infrastructure failure, or a security event.

Any DR strategy depends on two key metrics:

  • Recovery Point Objective (RPO): The maximum acceptable amount of data loss, measured in time. An RPO of 30 minutes means you can tolerate losing up to 30 minutes of data that you can reproduce from your source systems.
  • Recovery Time Objective (RTO): The maximum tolerance for downtime, before restoring business operations after a disaster is declared. An RTO of 30 minutes means your systems must be fully operational within 30 minutes of a failure.

These two numbers drive all architectural decisions for DR and understanding them helps clarify the trade-offs between various DR strategies.

Assessing your DR requirements

Before selecting a strategy, you need to assess your workload’s criticality and your organization’s tolerance for data loss and downtime. Ask yourself:

  • What is the business impact of downtime? If your Amazon Redshift cluster powers customer-facing applications, regulatory reporting, or real-time risk calculations, even an hour of downtime might be unacceptable. If it powers internal dashboards refreshed daily, a 2-hour RTO might be acceptable.
  • Can data be backfilled from upstream sources? If your data pipeline originates from Amazon Managed Streaming for Apache Kafka (Amazon MSK) or Amazon Simple Storage Service (Amazon S3), you might be able to replay events after a failover, relaxing your RPO requirements. If data is generated in-place or cannot be replayed, you need tighter replication.
  • What are your regulatory obligations? Financial services, healthcare, and government workloads often have explicit RPO/RTO requirements mandated by regulators. These are non-negotiable floors.
  • What is your cost tolerance? Active-active architectures can double your infrastructure spend. Active-passive approaches offer significant savings at the cost of slightly longer recovery times.

The following table serves as a quick reference to match your requirements to a DR strategy:

Requirement Recommended strategy
RPO: 10–30 min, RTO: 1–2 hours, cost-sensitive Active-Passive
RPO: Near-zero, RTO: Minutes, mission-critical Active-Active
Mixed criticality across data tiers Hybrid

The following decision tree helps you select the right disaster recovery strategy based on your workload’s RPO and RTO requirements.

Decision tree for choosing a Redshift DR strategy based on RPO and RTO requirements

Cross-Region best practices

Regardless of which strategy you choose, the following practices apply universally to Amazon Redshift DR implementations.

Use multi-Region AWS KMS keys: Encrypt your Amazon Redshift clusters and S3 data with multi-Region AWS Key Management Service (AWS KMS) keys. This avoids the need to re-encrypt data during failover, which can add significant time to your RTO. Note that AWS KMS allows only one replica of a multi-Region key per AWS Region within the same partition. This is a service-level constraint. In most DR scenarios, a single multi-Region key per Region is sufficient since all resources in that Region can share the same key.

Automate with infrastructure as code: Define all DR Region infrastructure with infrastructure as code (IaC), such as Terraform, AWS CloudFormation, or AWS Cloud Development Kit (AWS CDK). IaC supports consistency between Regions, removes manual configuration errors, and enables rapid provisioning during failover. For organizations using Terraform Enterprise, verify that your workspace configuration supports multi-Region deployments.

Implement comprehensive monitoring. Use Amazon CloudWatch alarms where possible:

Early detection of replication failures is critical. A silent replication failure discovered during a disaster is far worse than one caught proactively. For detailed metrics monitoring configuration, see the Amazon CloudWatch alarms user guide.

Test quarterly. DR plans that aren’t tested regularly are more likely to fail during an actual disaster. Conduct quarterly failover tests that measure actual RTO and RPO against your targets. Validate data consistency post-failover. Document lessons learned and update your runbooks accordingly.

Use Amazon Redshift Spectrum. For cold and warm data tiers, you can query data directly in Amazon S3 without loading it into Amazon Redshift. This can reduce your data restoration requirements during failover. Remember that your cluster and S3 bucket must be in the same Region. Recreate external schemas in the DR Region pointing to your replicated S3 data. For Amazon Redshift Serverless endpoints and Redshift provisioned clusters without Spectrum, the DR strategy relies on snapshot replication and cross-Region restore. The same principles apply regardless of whether you use RA3 or RG (Graviton) node types.

Strategy 1: Active-Passive with snapshot replication

In an active-passive configuration, your primary AWS Region runs the end-to-end workload, including data ingestion, processing, and serving data through Amazon Redshift. Amazon Redshift replicates data to the DR Region using its built-in cross-Region snapshot feature. During a disaster, you restore clusters from replicated snapshots in the DR Region.

RPO: 15 minutes plus time for data replication | RTO: 1–2 hours | Cost: Low

Active-Passive architecture with Amazon Redshift cross-Region snapshot replication to the DR Region

Snapshots in Amazon Redshift provisioned clusters

By default, Amazon Redshift provisioned clusters take a new snapshot every 8 hours, or whenever 5 GB of data changes are detected on any single node, whichever comes first. The 5 GB threshold is evaluated per node independently.

Amazon Redshift offers automated snapshots of your cluster at no extra storage cost in both your primary and DR Regions. You will incur charges for the data transfer when Amazon Redshift copies snapshots across Regions. The initial cross-Region copy is a full snapshot transfer. Subsequent copies are incremental, transferring only the changed blocks since the last snapshot, which significantly reduces transfer time and cost.

When to customize the automatic snapshot schedule

You can override the default and set a custom schedule, with a minimum frequency of once per hour. However, this is only useful in one scenario:

Cluster type Recommendation
≥ 5 GB of changes per node per hour Keep the default — already snapshotting frequently enough
< 5 GB of changes per node per hour Customize the schedule to take snapshots more often

When to use manual snapshots

If you need a guaranteed RPO of less than 1 hour (for example, every 15 minutes), or need to retain backups beyond 35 days, use manual snapshots scheduled at the frequency you want. Manual snapshots incur additional storage charges but are retained until explicitly deleted.

Comparing automatic and manual snapshots

 

Automatic snapshots Manual snapshots
Frequency Every 8 hours or 5 GB change (customizable to run hourly) Any frequency you choose
Best for RPO ≥ 1 hour RPO < 1 hour (for example, 15 min)
Cost No additional cost (included with cluster) Additional storage charges.
Retention 1–35 days (configurable) Until explicitly deleted
Cross-Region copy Supported (incremental) Supported (incremental)

Architecture

The following diagram illustrates the Active-Passive DR architecture.

The Active-Passive strategy keeps compute resources in the DR Region ready to be spun up from snapshots when needed. When replicating data, consider the other services that are part of your end-to-end data pipeline. In the Amazon Redshift data sharing model, the producer cluster creates and owns the data, while consumer clusters read from the producer through data shares. In a DR context, the producer is restored first in the DR Region, then consumer clusters are resumed to serve read workloads.

  • Amazon S3 is frequently used with Amazon Redshift. For complete data resiliency, replicate data in Amazon S3 as well using Amazon S3 Cross-Region Replication (S3 CRR). It continuously replicates your S3 data lake to the DR Region with near-zero lag. For Apache Iceberg tables, we recommend using replication for Amazon S3 Tables, a capability of Amazon S3, to guarantee that both the data and the associated metadata (manifests, snapshots) are replicated consistently to the DR Region.
  • Customers use AWS Glue Data Catalog and AWS Lake Formation to catalog and maintain permissions. Read this post on how to build multi region resilient data architecture using AWS Glue and AWS Lake Formation.
  • Customers often use Amazon DynamoDB alongside Amazon Redshift in data pipeline architectures to track pipeline orchestration state, such as job IDs, processing timestamps, batch completion flags, and ingestion checkpoints that tell your pipeline which data has been processed. Amazon DynamoDB Global Tables replicate this state across both Regions, so pipeline state is available in the DR Region and you know exactly where to resume processing after failover.

DR Region (Passive) components:

  • Amazon Redshift clusters ready to restore from snapshots.
  • AWS Lambda functions with data transformation pipelines code deployed and ready.
  • Amazon MSK infrastructure defined in IaC but not provisioned.
  • Amazon EMR job definitions ready but not running.

Failover sequence (20–60 minutes):

  1. Restore the Amazon Redshift cluster in DR Region, from the latest cross-Region snapshot (this is typically the longest step).
  2. Provision and start Amazon MSK clusters in the DR Region.
  3. Disable S3 event triggers for AWS Glue Catalog (to prevent split-brain metadata updates).
  4. Stand up Amazon EMR and resume data processing.
  5. Resume paused Amazon Redshift consumer clusters.
  6. Recreate external schemas pointing to the DR Region’s AWS Glue Catalog. Note: External schemas, external schema-level permissions, and references to external resources (for example, S3 paths, AWS Glue Catalog databases) included in the Amazon Redshift snapshot, contain references to primary Region resources. Plan to recreate these in your DR Region as part of your failover runbook. Database users, groups, and their internal permissions are replicated with the snapshot. Plan to script external schema recreation as part of your failover runbook.
  7. Update query or application service endpoints to the DR Region.
  8. Update Lambda data transformation pipelines to point to the new producer endpoint.

When to choose Active-Passive

  • You can tolerate 15–20 minutes of data loss.
  • A 1–2 hour RTO is acceptable for your business.
  • Cost optimization is a priority.
  • Data can be backfilled or replayed from upstream sources (for example, Amazon MSK topic retention).

Strategy 2: Active-Active multi-Region

In an Active-Active configuration, both your primary and DR Regions run fully operational data pipelines simultaneously. Data is ingested, processed, and served in both Regions at all times. Failover becomes a matter of redirecting traffic rather than restoring infrastructure. This reduces RTO to minutes.

RPO: Near-zero | RTO: < 1 hour (often minutes) | Cost: High

Architecture

The following diagram illustrates the Active-Active DR architecture. Active-Active requires mirroring your entire pipeline, from ingestion through serving, across both Regions.

Real-time replication layer:

  • Amazon MSK Replicator: Mirrors Kafka topics in real time from the primary Region to the secondary Region. This is the earliest point of replication in the pipeline, so the DR Region processes the same events with minimal lag.
  • Amazon DynamoDB Global Tables: Active state tracking across both Regions keeps pipeline controls and job state synchronized.
  • Active Amazon EMR processing: Both Regions continuously process incoming data, maintaining fresh state in their respective S3 data lakes and AWS Glue Catalogs.
  • Active Amazon Redshift producer clusters: Both Regions continuously ingest processed data, maintaining near-identical warehouse state.
  • Mirrored data transformation pipelines: Data transformation events are actively processed in the DR Region through DynamoDB replication, keeping derived data consistent. In the Active-Active model, both Regions maintain their own Amazon Redshift cluster that independently ingests the same source data, so the DR Region’s Amazon Redshift already has current data. The mirrored pipeline supports the transformation logic and derived datasets stay synchronized.

DR Region (Active) components:

  • Amazon Redshift clusters paused but ready (can be activated in minutes).
  • Any Amazon Redshift data shares synchronized regularly between Regions.
  • External schemas active and synchronized.
  • Query or application service endpoints pre-configured and tested.

Failover sequence (minutes):

  1. Failover Amazon MSK consumers to the DR Region’s Amazon MSK cluster.
  2. Resume Amazon Redshift consumer clusters in the DR Region.
  3. Update query or application service endpoints to point to the DR Region.
  4. Promote the DR Region’s Lambda data transformation pipelines functions to act as primary.

Because the DR Region’s pipeline is already running, there is no infrastructure provisioning delay. Failover is primarily a configuration change.

Cost considerations

Active-Active essentially doubles your infrastructure costs. You are running full Amazon MSK, Amazon EMR, and Amazon Redshift clusters in both Regions simultaneously. For large-scale deployments (1+ PB), this represents a significant ongoing investment. The business case rests on the cost of downtime exceeding the cost of duplicate infrastructure. This is a calculation that often favors Active-Active for customer-facing or regulatory workloads.

When to choose Active-Active

  • You require near-zero RPO with no tolerance for data loss.
  • RTO must be measured in minutes, not hours.
  • Your analytics infrastructure directly impacts customer-facing operations or regulatory compliance.
  • The cost of downtime (financial, reputational, regulatory) exceeds the cost of duplicate infrastructure.
  • You have strict Service Level Agreements (SLAs). For example, zero RPO and full-service functionality within 4 hours including data ingestion.

Strategy 3: Hybrid — tiered DR by data criticality

Not all data in your warehouse is equally critical. Some real-time insights and regulatory reports demand near-zero RPO, while historical trend analyses and archived compliance data can tolerate hours of recovery time. A Hybrid approach applies different DR strategies to different data tiers, optimizing cost while protecting what matters most.

RPO: Varies by tier | RTO: 30 minutes – 2 hours | Cost: Medium

Architecture

The following diagram illustrates the Hybrid DR architecture.

The Hybrid strategy requires a data model that supports clear separation at the schema or table level, with different recovery objectives applied per tier.

Tier 1: Hot data (Active-Active):

  • Real-time dashboards, regulatory reporting, customer-facing analytics.
  • Near-zero RPO through Amazon MSK Replicator and active Amazon Redshift producer in both Regions.
  • RTO: Minutes.

Tier 2: Warm data (Active-Passive):

  • Daily reports, historical trend analysis, internal operational data.
  • RPO: 1 hour through hourly Amazon Redshift snapshots replicated cross-Region.
  • RTO: 1–2 hours.

Tier 3: Cold data (S3 replication only):

  • Archived data, long-term compliance storage, infrequently accessed history.
  • RPO: Hours (S3 CRR with standard replication lag).
  • RTO: 2+ hours (restore from S3 into Amazon Redshift Spectrum or a new cluster).
  • No active Amazon Redshift infrastructure in DR Region for this tier.

Implementation considerations

  • Your data model must support clear separation at the schema or table level to apply different recovery strategies. To achieve different RPO/RTO per data tier, while avoiding unnecessary table level maintenance complexities, consider using separate clusters or namespaces for each tier, or use a combination of cluster snapshots and S3-based backups (UNLOAD) for finer-grained table-level recovery.
  • Workload Management (WLM) queues or separate clusters may be needed to isolate hot, warm, and cold workloads.
  • Monitoring must track replication latency independently for each tier.
  • Failover runbooks must be tier-aware. Operators need to know which systems to restore first.

When to choose Hybrid

  • You have clearly defined data tiers with meaningfully different criticality.
  • Your data model already supports or can be refactored to support hot/cold separation.
  • You want to protect mission-critical data with Active-Active while managing costs for less critical workloads.
  • Your organization has the operational maturity to manage tiered failover procedures.

Testing your DR strategy

Schedule quarterly DR tests that include:

  1. Failover execution following your documented runbook.
  2. RTO measurement from disaster declaration to full operational status.
  3. RPO validation to verify data consistency.
  4. Application testing to confirm connectivity.
  5. Failback procedure documentation.
  6. Lessons learned and runbook updates.

Conclusion

Disaster recovery for Amazon Redshift is not a one-size-fits-all problem. The right strategy depends on your RPO and RTO requirements, your data’s criticality, your ability to replay data from upstream sources, and your cost tolerance.

  • Active-Passive offers a cost-effective path to 10–20 minute RPO and 1–2 hour RTO, suitable for most analytics workloads.
  • Active-Active delivers near-zero RPO and minute-scale RTO for mission-critical services where downtime cost exceeds infrastructure cost.
  • Hybrid lets you apply the right level of protection to the right data, optimizing cost without compromising on what matters most.

Whichever strategy you choose, the fundamentals remain the same: replicate early in the pipeline, automate your infrastructure, monitor replication health continuously, and test your failover procedures regularly. DR is not a project you complete. You maintain it as an ongoing practice.

Next steps


About the authors

Werner Gunter

Werner Gunter

Werner is a Principal Specialist Solutions Architect at Amazon Web Services, based in Berlin, Germany. As a seasoned data professional, he has helped large enterprises worldwide over the past 2 decades, to modernize their data analytics estates.

Nita Shah

Nita Shah

Nita is a Sr. Analytics Specialist Solutions Architect at AWS based out of New York. She has been building enterprise data platforms, data warehousing, and analytics solutions for over 20 years and specializes in Amazon Redshift. She is focused on helping customers design and build enterprise-scale well-architected analytics and decision support platforms

Upgrade Amazon Redshift DC2 clusters to the new Amazon Redshift RG

Post Syndicated from Ricardo Serafim original https://aws.amazon.com/blogs/big-data/upgrade-amazon-redshift-dc2-clusters-to-the-new-amazon-redshift-rg/

When you upgrade your Amazon Redshift DC2 (Dense Compute) clusters to RG instances powered by AWS Graviton, you gain access to capabilities that were never available on DC2. These include managed storage, data sharing, zero-ETL integrations, streaming ingestion, and faster query compilation. You also gain availability zone (AZ) features such as cross-AZ cluster relocation for disaster recovery (DR) and concurrency scaling for writes. RG also adds a built-in data lake engine for querying Apache Iceberg and Parquet tables directly on your cluster nodes.

This post covers the new features you gain when upgrading from DC2 to RG, the node mapping guidance for sizing your new cluster, the upgrade methods available, and validation options including Amazon Redshift Test Drive.

Why upgrade from DC2 to RG instances

As data volumes grow, DC2 customers face a choice: add extra compute nodes only to get more storage, or offload data elsewhere. The local SSD capacity on each node is fixed, and there is no managed storage tier to absorb growth. Both RA3 and RG instances solve this with Amazon Redshift Managed Storage, which decouples storage from compute. You can scale data volume independently of node count, paying only for the storage you use with no fixed ceiling per node. This means you no longer need to over-provision compute to accommodate data growth.

RG is the recommended upgrade path over RA3. RG instances run on AWS Graviton processors, delivering higher throughput for data warehouse and data lake workloads at a lower price per vCPU compared to RA3. Because both RA3 and RG share the same managed storage architecture and feature set, RG provides more performance for less cost. For current pricing details, visit Amazon Redshift pricing.

Amazon Redshift RG instances run on AWS Graviton processors. These processors provide more compute cores and lower memory latency compared to the previous-generation hardware behind DC2. This can translate to faster query execution for data warehouse workloads, particularly for large scans where memory throughput is the bottleneck. Exact performance improvements depend on workload characteristics, cluster size, and query complexity. Use Redshift Test Drive to measure the difference for your specific workload.

Data lake access: New with RG

DC2 clusters can query data in Amazon Simple Storage Service (Amazon S3) through Amazon Redshift Spectrum. However, Spectrum adds a per-TB scanning cost on top of your cluster pricing, and does not support enhanced VPC routing on DC2 provisioned clusters (requiring additional configuration for secure S3 access).

RG addresses these constraints with an integrated data lake engine that processes queries directly on your cluster’s dedicated compute nodes:

DC2 (Spectrum) RG (Integrated Engine)
Data lake query cost Extra $5/TB scanned on top of cluster cost Included in node pricing, no extra charge
Apache Iceberg Queries via Spectrum Native queries on cluster compute, no Spectrum needed
Apache Iceberg Statistics Manual collection JIT-Analyze auto-collects statistics
VPC routing Not compatible with enhanced VPC routing No conflict, runs on the cluster itself

With RG, you can consolidate warehouse and data lake workloads on a single cluster with no extra per-query charges for data lake access.

Features available with RG

Upgrading from DC2 to RG gives you access to the full set of modern Amazon Redshift capabilities. Three of the most impactful for DC2 customers are data sharing, zero-ETL integrations, and managed storage. With data sharing, you can query live data from other Amazon Redshift clusters or accounts without copying or moving data, reducing storage duplication and keeping consumers always up to date. Zero-ETL integrations automatically replicate data from Amazon Aurora, Amazon Relational Database Service (Amazon RDS), and Amazon DynamoDB into Amazon Redshift without building or maintaining ETL pipelines. This reduces operational overhead and data freshness lag. Managed storage scales independently from compute, so you can grow your data without adding nodes and only pay for the storage you use.

Additional capabilities available with RG:

  • Streaming ingestion – ingest data from Amazon Kinesis Data Streams and Amazon Managed Streaming for Apache Kafka (Amazon MSK) in near real-time, so you can build dashboards and analyze the latest data without batch delays.
  • Concurrency scaling for writes – automatically add transient capacity during burst write workloads, so ingest operations don’t slow down your analytical queries.
  • Cross-AZ cluster relocation – relocate your cluster to another Availability Zone with no endpoint changes, supporting disaster recovery without the cost of a standby cluster.
  • Multi-AZ deployments – run your cluster across multiple Availability Zones as a single database delivering high availability (HA) and automatic failover without a passive standby.
  • Faster query compilation – queries compile faster on Graviton processors, reducing cold-start latency for new or modified queries.

RG instance details and node mapping

This table shows the available RG instance configurations:

RG Instance vCPUs Memory
rg.large 2 16 GiB
rg.xlarge 4 32 GiB
rg.4xlarge 16 128 GiB
rg.12xlarge 48 384 GiB

For current pricing, visit Amazon Redshift pricing for more information.

DC2 to RG node mapping guidance

Use this table to determine the recommended starting configuration when upgrading from DC2:

Current Node Type Node Ratio RG Node Type Guidance
dc2.large (1–3 nodes) 1:1 rg.large 1 rg.large for every 1 dc2.large
dc2.large (4 nodes) 4:3 rg.large 3 rg.large for 4 dc2.large
dc2.large (5–15 nodes) 8:3 rg.xlarge 3 rg.xlarge for every 8 dc2.large
dc2.large (16–32 nodes) 10:1 rg.4xlarge 1 rg.4xlarge for every 10 dc2.large
dc2.8xlarge (2–15 nodes) 2:3 rg.4xlarge 3 rg.4xlarge for every 2 dc2.8xlarge
dc2.8xlarge (16–128 nodes) 2:1 rg.12xlarge 1 rg.12xlarge for every 2 dc2.8xlarge

Extra nodes might be needed depending on workload requirements. Add or remove nodes based on the compute requirements of your required query performance. Validate your specific configuration using Redshift Test Drive before migrating production workloads.

Prerequisites

Before starting the upgrade, confirm the following:

  • Snapshot availability — a recent snapshot of your DC2 cluster is required for all upgrade methods. If automated snapshots are disabled, create a manual snapshot before starting. Visit Amazon Redshift snapshots for more information.
  • Network configuration — verify that your virtual private cloud (VPC), subnet groups, and security groups are configured to support the new RG cluster. If you use enhanced VPC routing, confirm your S3 endpoint and route table configuration. Visit Enhanced VPC routing for more information.
  • Cluster version — your DC2 cluster must be running a supported Amazon Redshift version. Check the release notes for minimum version requirements.

Upgrade methods

Three methods are available for migrating from DC2 to RG instances. The right choice depends on your operational constraints: whether you need write access during migration, whether the target configuration supports elastic resize, and how much downtime your workload can tolerate.

Elastic resize is the fastest and most efficient path. Amazon Redshift creates a snapshot, provisions the RG cluster, and redirects the endpoint automatically. The cluster remains in read-only mode for a few minutes during the operation, and the endpoint doesn’t change, meaning no application-side updates are required. This is the recommended method when the target configuration is supported by elastic resize.

Classic resize

Use classic resize when the target configuration is not available through elastic resize, or when you need data slice rebalancing. Downtime is similar to elastic resize (a few minutes of read-only mode in Stage 1). In Stage 2, data redistributes to its original distribution patterns in the background without blocking queries. The advantage of classic resize is that it rebalances data slices evenly across nodes. This matters when you move to a different node type that might require a different number of slices. Stage 2 can take time on busy clusters, and the duration depends on data volume, cluster utilization, and target cluster size. Queries might run slower until redistribution completes.

Snapshot and restore with cluster identifier swap

This method uses snapshot and restore of the existing DC2 cluster to provision a new RG cluster with a different identifier. After validating the new cluster, you swap the cluster identifiers to redirect application traffic without changing the endpoint. This approach provides these benefits:

  • Test and validate the RG cluster while the DC2 cluster continues serving production traffic.
  • Roll back by reversing the identifier swap if issues arise.
  • No application-side endpoint changes required after the swap.

The trade-off is that data written to the source cluster after the snapshot requires manual synchronization before the cutover. If your migration plan includes a write-freeze window, you can take the final snapshot at the start of that window and avoid synchronization entirely.

This AWS Command Line Interface (AWS CLI) command illustrates restoring a DC2 snapshot to an RG cluster:

aws redshift restore-from-cluster-snapshot \
    --cluster-identifier my-cluster-rg \
    --snapshot-identifier my-dc2-snapshot \
    --node-type rg.4xlarge \
    --number-of-nodes 3 \
    --cluster-subnet-group-name my-subnet-group \
    --vpc-security-group-ids sg-abc123 \
    --cluster-parameter-group-name my-param-group \
    --port 5439 \
    --no-publicly-accessible \
    --enhanced-vpc-routing \
    --iam-roles 'arn:aws:iam::111122223333:role/RedshiftRole'

After restoring, validate your workload on the new cluster. When ready, swap the cluster identifiers:

aws redshift modify-cluster \
    --cluster-identifier my-cluster \
    --new-cluster-identifier my-cluster-dc2-old

aws redshift modify-cluster \
    --cluster-identifier my-cluster-rg \
    --new-cluster-identifier my-cluster

Validating your target configuration

Before migrating production clusters, validate that your target RG configuration meets performance requirements. There are several ways to approach this depending on your needs:

Run your existing QA process on a test cluster. Create an RG cluster from a snapshot, then execute the same test suites and validation scripts you would use for any code or infrastructure change. This approach helps confirm basic compatibility and catch regressions.

Use lower environments first. Migrate your development or staging clusters to RG before production. This gives your team hands-on experience with the new instance type and surfaces any configuration differences in a low-risk setting.

Replay production workloads with Redshift Test Drive. For production-level validation with real traffic patterns, Redshift Test Drive is an open source utility that automates workload replay across multiple target configurations. It extracts queries from your source cluster’s audit logs and replays them against the target, then provides a comparison UI for latency, errors, and deviation.

For a detailed walkthrough, read Find the best Amazon Redshift configuration for your workload using Redshift Test Drive.

Best practices

Before migrating, run Amazon Redshift Advisor on your current cluster to identify optimization opportunities such as unused tables, missing sort keys, or distribution style changes. Drop unnecessary tables to reduce data transfer time, and schedule the migration during off-peak hours for minimal business impact. Removing tables that are no longer used (for example, tables with suffixes like _bkp, _tmp, or _old) also speeds up classic resize. These unused tables would otherwise be rebalanced across nodes during Stage 2, adding time to a process that delivers no value for data no one queries.

During migration, communicate the cutover window to stakeholders. Because the DC2 cluster remains active until the identifier swap, coordinate a brief write-freeze period before the final snapshot to minimize data synchronization effort.

After migration, monitor the cluster for 48–72 hours to identify any performance deviations and adjust node count if needed. Update your runbooks and operational documentation with the new cluster details, node types, and any endpoint changes if you used the snapshot and restore method. Once the migration is considered successful you may delete the DC2 cluster.

Conclusion

Upgrading from Amazon Redshift DC2 to RG instances powered by AWS Graviton gives you a Graviton-based architecture with managed storage and improved query performance. It also gives you access to the full suite of Amazon Redshift features that were never available on DC2: data lake queries, data sharing, zero-ETL, faster query compilation, and cross-AZ relocation. The snapshot restore and cluster identifier swap method provides a safe migration path with built-in rollback. Use Redshift Test Drive to validate your target configuration with real workload data before committing.

To get started, review the RG instance availability and pricing, determine your target configuration using the node mapping guidance, and run Redshift Test Drive against your production workload.


About the authors

Ricardo Serafim

Ricardo Serafim

Ricardo is a Senior Analytics Specialist Solutions Architect at AWS. He has been helping companies with Data Warehouse solutions since 2007.

Nita Shah

Nita Shah

Nita is a Sr. Analytics Specialist Solutions Architect at AWS based out of New York. She has been building enterprise data platforms, data warehousing, and analytics solutions for over 20 years and specializes in Amazon Redshift. She is focused on helping customers design and build enterprise-scale well-architected analytics and decision support platforms.

Ankit Sahu

Ankit Sahu

Ankit brings over 18 years of expertise in building innovative data products and services. His diverse experience spans product strategy, go-to-market execution, and digital transformation initiatives. Currently, as Sr. Product Manager at Amazon Web Services (AWS), Ankit is driving the vision and strategy for Amazon Redshift.

Govern Amazon Redshift Data Warehouses Data Across Accounts using Amazon SageMaker Unified Studio

Post Syndicated from Bandana Das original https://aws.amazon.com/blogs/big-data/govern-amazon-redshift-data-across-accounts-with-sagemaker-unified-studio/

Managing data governance across multiple Amazon Redshift clusters in different AWS accounts presents significant challenges. Organizations operating multiple Amazon Redshift clusters across AWS accounts often rely on manual processes for secure data sharing, which increases operational overhead and governance requirements. In this post, we show you how to use Amazon SageMaker Unified Studio to implement cross-account data sharing in Amazon Redshift using data mesh principles. We demonstrate how to build a scalable data mesh architecture that supports secure, auditable data sharing across AWS accounts while reducing operational burden.

Amazon SageMaker Unified Studio as the backbone of our data mesh

Amazon SageMaker Unified Studio is a data and AI development service which brings together functionality and tools from existing AWS Analytics and AI and machine learning (ML) services, including Amazon EMR, AWS Glue, Amazon Athena, Amazon Redshift, Amazon Bedrock and Amazon SageMaker AI. With the service, organizations can catalog, discover, share, and govern data stored across Amazon Web Services (AWS) without relying on manual coordination between AWS accounts.

A data mesh is an architectural approach that treats data as a product, with decentralized ownership by data producers while maintaining centralized governance. This architecture separates source systems, data producers (data publishers), data consumers (data subscribers), and central governance. The solution we present is tailored for cross-AWS account usage, creating a foundation for data governance so you can share data across Amazon Redshift clusters in different AWS accounts.

Our proposed solution addresses the following common challenges that organizations face when sharing data across AWS accounts:

  • Manual, ad-hoc data sharing processes are replaced with automated, event-driven data publishing to the SageMaker Unified Studio catalog.
  • Inconsistent governance across different use cases is resolved through a consistent governance framework with proper access controls.
  • High load on producer Amazon Redshift clusters is reduced through decoupled publishing that lowers the operational burden on data producers.
  • Complex credential management is simplified using AWS Secrets Manager and AWS KMS encryption.
  • Lack of auditable data publishing is addressed with full traceability of access and permissions supported by the SageMaker Unified Studio service.

With this approach, you can help reduce the time and effort required for cross-account data sharing while maintaining security and governance standards.

Architectural overview

The architecture spans three AWS accounts, each with a distinct role in the data mesh:

Central Data Governance Account (Account A) hosts the Amazon SageMaker Unified Studio domain, which serves as the unified catalog and governance layer for data discovery, access control, and subscription management across accounts.

Data Producer (Account B) hosts the source of data and processing workflows. Raw data lands in an Amazon Simple Storage Service (Amazon S3) source bucket and is processed through AWS Glue extract, transform, and load (ETL) jobs or Amazon Redshift auto copy into the Amazon Redshift source database. Amazon Redshift credentials are securely stored in AWS Secrets Manager.

Data Consumer (Account C) hosts the target Amazon Redshift database and analytics workflows. After access is granted, consumers can query shared data and connect downstream visualization tools.

While this diagram shows a single producer and consumer for simplicity, in a real-world deployment there might be hundreds of producer and consumer accounts connecting through the central governance layer. Amazon SageMaker Unified Studio scales to support this by providing a single place for managing data products regardless of the number of participating accounts.

The data sharing workflow is driven by Amazon SageMaker Unified Studio. The data owner publishes data to the catalog, where it becomes discoverable by consumers across accounts. Consumers browse the catalog, subscribe to data products, and the data owner approves the request. After approval, Amazon SageMaker Unified Studio handles the cross-account sharing, granting the consumer access without requiring direct connectivity between producer and consumer Amazon Redshift clusters.

Publishing Amazon Redshift data assets to the data mesh

In a data mesh architecture, data producers need to make their data products discoverable and accessible across the organization. Amazon SageMaker Unified Studio provides a centralized catalog where data assets can be published for consumer subscription.

In practice, this means registering your data sources with the catalog so they can be discovered, governed, and subscribed to by consuming teams. This section walks through the steps required to register Amazon Redshift data sources with SageMaker Unified Studio.

Before you can publish data assets from your producer account, you need to complete several configuration steps across your Amazon Redshift cluster, AWS Secrets Manager, and Amazon SageMaker Unified Studio.

Prerequisites

  • Install the AWS Command Line Interface (AWS CLI) (v2.15+ recommended).
  • Obtain temporary credentials with permissions to administer each account (producer, consumer, and domain account)
  • IAM permissions required: redshift:* on the relevant clusters, secretsmanager:CreateSecret / PutResourcePolicy / TagResource, kms:CreateKey / PutKeyPolicy / TagResource, datazone:* for subscription-target creation, and iam:PassRole for the Amazon Redshift cluster role.
  • Amazon Redshift clusters must use RA3 node types (ra3.xlplus, ra3.4xlarge, or ra3.16xlarge). Data sharing is not supported on other node types.
  • Amazon SageMaker Unified Studio domain must already be created in Account A with the Tooling and LakeHouseCatalog blueprints available.
  • All resources must be in an AWS Region where Amazon SageMaker Unified Studio is available.

Step 1: Account association and blueprint enablement

To implement the data mesh architecture described in the previous section, you need to set up the following accounts and enable the required blueprints. This ensures that the central governance layer can discover and manage data assets across your producer and consumer accounts.

This post uses three separate AWS accounts to illustrate the cross-account data sharing pattern. However, Amazon SageMaker Unified Studio also supports publishing and subscribing to data within a single account or across any number of accounts depending on your organizational setup. Additionally, this walkthrough uses a provisioned Amazon Redshift cluster, but Amazon SageMaker Unified Studio also supports Amazon Redshift Serverless for both publishing and subscribing to data assets.

Step 2: Configure your Amazon Redshift cluster and credentials

  • In the producer account (Account B), the data to be shared resides in an Amazon Redshift cluster.
  • Verify that your Amazon Redshift cluster uses node types from the RA3 family.
  • Add the following tags to your Amazon Redshift cluster.

Amazon Redshift console showing tags added to the cluster

  • Create a superuser in Amazon Redshift for Amazon SageMaker Unified Studio. For the Amazon Redshift cluster, the database user you provide in AWS Secrets Manager must have superuser permissions. With superuser permission, your Amazon Redshift cluster can publish data and subscribe from the data mesh created with Amazon SageMaker Unified Studio, and it manages the subscriptions (access) on your behalf. For reference, see the note section in this QuickStart guide with sample Amazon Redshift data.

Tag key-value pairs configured on the Amazon Redshift cluster

  • Store the user’s credentials in Secrets Manager. Select the credential type, enter the credential values, and choose the AWS Key Management Service (AWS KMS) key with which to encrypt the secret

QuickStart guide note about providing superuser credentials for Amazon SageMaker Unified Studio

Tags on the AWS Secrets Manager secret including the Amazon Redshift cluster ARN

Resource policy added to the AWS Secrets Manager secret for Amazon SageMaker Unified Studio access

  • If your secret is encrypted with a customer managed AWS KMS key, append the key policy with the following statement and add a tag to the key: AmazonDataZoneEnvironment = All. You can skip this step if you’re using an AWS managed KMS key.
{
    "Sid": "AllowSMUSRolesSecretsAccess",
    "Effect": "Allow",
    "Principal": {
        "AWS": "*"
    },
    "Action": [
        "kms:Decrypt",
        "kms:DescribeKey",
        "kms:GenerateDataKey"
    ],
    "Resource": "*",
    "Condition": {
        "StringEquals": {
            "kms:ViaService": "secretsmanager.<<AWS_Region>>.amazonaws.com"
        },
        "StringLike": {
            "aws:PrincipalArn": [
                "arn:aws:iam::<<Data_Producer_Acct_Id(Account B)>>:role/aws-service-role/redshift.amazonaws.com/AWSServiceRoleForRedshift",
                "arn:aws:iam::<<Data_Producer_Acct_Id(Account B)>>:role/<<Redshift_Cluster_IAM_Role_Name>>",
                "arn:aws:iam::<<Data_Producer_Acct_Id(Account B)>>:role/datazone*",
                "arn:aws:iam::<<Data_Producer_Acct_Id(Account B)>>:role/service-role/AmazonSageMaker*"
            ]
        }
    }
},
{
    "Sid": "AllowSMUSRolesCreateGrant",
    "Effect": "Allow",
    "Principal": {
        "AWS": "*"
    },
    "Action": "kms:CreateGrant",
    "Resource": "*",
    "Condition": {
        "Bool": {
            "kms:GrantIsForAWSResource": "true"
        },
        "StringEquals": {
            "kms:ViaService": "secretsmanager.<<AWS_Region>>.amazonaws.com"
        },
        "StringLike": {
            "aws:PrincipalArn": [
                "arn:aws:iam::<<Data_Producer_Acct_Id(Account B)>>:role/aws-service-role/redshift.amazonaws.com/AWSServiceRoleForRedshift",
                "arn:aws:iam::<<Data_Producer_Acct_Id(Account B)>>:role/<<Redshift_Cluster_IAM_Role_Name>>"
            ]
        }
    }
}

Note: Enable automatic rotation. Configure Secrets Manager automatic rotation for this secret with a rotation interval appropriate to your security policy (for example, every 30 days). When implementing rotation, verify that the rotation Lambda function updates the credentials in both Secrets Manager and Amazon Redshift database users simultaneously. Note that Amazon SageMaker Unified Studio retrieves the secret at connection time, so rotation must produce credentials that are valid immediately upon storage: use the alternating-users rotation strategy if you need to avoid downtime during rotation. See the Secrets Manager rotation documentation for setup instructions.

Using Amazon Redshift Serverless?

  • Add the following Tags to the Amazon Redshift Serverless namespace and workgroup.

Tags added to the Amazon Redshift Serverless namespace and workgroup

  • In the Secrets Manager secret, verify the host points to your Serverless endpoint.

AWS Secrets Manager secret showing the host pointing to the Serverless endpoint

  • Add the following tags to the AWS Secrets Manager secret.

Tags added to the AWS Secrets Manager secret for the Redshift Serverless credentials

Publish Amazon Redshift data to the data mesh

With prerequisites complete, you can now register your Amazon Redshift cluster as a data source in Amazon SageMaker Unified Studio.

Step 1: Create an Amazon Redshift type connection

  • Sign in to Account B, navigate to your Amazon SageMaker Unified Studio associated domain, and open the Amazon SageMaker Unified Studio URL.

Amazon SageMaker Unified Studio associated domain sign-in page

Add an Amazon Redshift connection form in Amazon SageMaker Unified Studio

  • The newly created Amazon Redshift connection appears here.

Newly created Amazon Redshift connection listed in Amazon SageMaker Unified Studio

Step 2: Create the data source for your Amazon Redshift data warehouse

Add an Amazon Redshift data source form in Amazon SageMaker Unified Studio

Amazon Redshift data source configuration in Amazon SageMaker Unified Studio

  • For Publishing settings, choose whether assets are immediately discoverable in Amazon SageMaker Catalog.

Publishing settings controlling asset discoverability in the Amazon SageMaker catalog

Using Amazon Redshift Serverless?

When creating the connection and data source, use your workgroupName instead of clusterName. The rest of the data source configuration remains the same.

Step 3: Run the data source and publish the data asset to the data mesh

Data source run configuration in Amazon SageMaker Unified Studio

Data source run results in Amazon SageMaker Unified Studio

  • During creation of data source if you choose Publishing settings such as assets are immediately discoverable, the Amazon Redshift tables and views appear in the catalog as Published, ready for discovery and subscription by data consumers.

Published Amazon Redshift tables and views in the Amazon SageMaker catalog

Data discovery view in the Amazon SageMaker Unified Studio portal

Subscribe Amazon Redshift data through the data mesh

To complete the end-to-end test, you need to set up a consumer Amazon Redshift cluster in Account C.

Step 1: Setting up the consumer cluster

  • Follow the prerequisites from Steps 1 and 2 in the previous section, make sure the cluster and secret are properly tagged as in the following screenshots:
  • Amazon Redshift cluster tags:

Tags applied to the consumer Amazon Redshift cluster

  • Tags for the AWS Secrets Manager secret that stores the user credentials for the Amazon Redshift cluster:

Tags on the AWS Secrets Manager secret storing the consumer cluster credentials

Step 2: Connect the consumer cluster to the data mesh

  • Log into Amazon SageMaker Unified Studio and navigate to your consumer project.
  • In the Compute section of your project, choose Add compute, then choose Connect to existing compute resources.
  • Choose Amazon Redshift Provisioned.
  • Select your consumer Amazon Redshift cluster from the dropdown list and enter the Secrets Manager name.
  • Choose Add compute.
  • Your newly added Amazon Redshift cluster should now show as available.

Consumer Amazon Redshift cluster added as compute in Amazon SageMaker Unified Studio

  • The newly added Amazon Redshift cluster shows an Available state.

Consumer Amazon Redshift cluster showing an Available state

  • In the Data section you can see that objects (table/views) from Amazon Redshift cluster are visible and you can query them.

Data section showing Amazon Redshift tables and views available to query

Step 3: Creating a subscription target

  • Find the tooling environment ID: in your local terminal after obtaining correct credentials as a project member, run this command to find the tooling environment ID.
export REGION='<your-region>'
export SUBSCRIBER_PROJECT_ID='<your-project-id>'
export DOMAIN_ID='dzd-xxxxxxx'

aws datazone list-environments \
  --domain-identifier $DOMAIN_ID \
  --project-identifier $SUBSCRIBER_PROJECT_ID \
  --region $REGION
  • In the response, find and copy the tooling environment ID as shown in the following example.
{
    "items": [
        {
            "projectId": "<PROJECT_ID>",
            "id": "<ENVIRONMENT_ID>",
            "createdBy": "SYSTEM",
            "createdAt": "<TIMESTAMP>",
            "updatedAt": "<TIMESTAMP>",
            "name": "Tooling",
            "awsAccountId": "<AWS_ACCOUNT_ID>",
            "awsAccountRegion": "eu-west-1",
            "provider": "Amazon SageMaker",
            "status": "ACTIVE",
            "environmentConfigurationId": "<ENVIRONMENT_CONFIGURATION_ID>"
        }
    ]
}
  • Locate the Manage Access Role: In Account C, navigate to SageMaker Unified Studio and find the Tooling blueprint. In the Provisioning Tab you will find the Manage Access role and copy the value, as it is needed for the next CLI call.

Provisioning tab showing the Manage Access role for the Tooling blueprint

  • Create the Subscription Target.

With all the information collected, you can create the subscription target for the Amazon Redshift cluster as shown by the CLI call.

export TOOLING_ENV_ID='<tooling-environment-id>'
export AUTHORIZED_PRINCIPAL='datazone_env_<tooling-env-id>'
export MANAGE_ACCESS_ROLE='arn:aws:iam::<account-id>:role/service-role/AmazonSageMakerManageAccess-<domain-id>'

aws datazone create-subscription-target \
  --domain-identifier $DOMAIN_ID \
  --environment-identifier $TOOLING_ENV_ID \
  --name "RedshiftCluster-default-target" \
  --subscription-target-config '[{
    "formName": "RedshiftSubscriptionTargetConfigForm",
    "content": "{\"databaseName\":\"<db-name>\",\"secretManagerArn\":\"arn:aws:secretsmanager:<region>:<account>:secret:<secret-name>\",\"clusterIdentifier\":\"<cluster-id>\",\"schemaName\":\"<schema-name>"}"
  }]' \
  --applicable-asset-types RedshiftViewAssetType RedshiftTableAssetType \
  --manage-access-role $MANAGE_ACCESS_ROLE \
  --provider "Amazon SageMaker" \
  --type RedshiftSubscriptionTargetType \
  --authorized-principals $AUTHORIZED_PRINCIPAL

Using Amazon Redshift Serverless?

Use RedshiftServerlessSubscriptionTargetType as the --type and RedshiftServerlessSubscriptionTargetConfigForm as the formName in the subscription target config. Replace clusterIdentifier with workgroupName in the content JSON.

  • Verify the Subscription Target.

To verify that the subscription target was created successfully, make a last CLI call. You should find in the return a new subscription target with the name RedshiftCluster-default-target.

aws datazone list-subscription-targets \
  --environment-identifier $TOOLING_ENV_ID \
  --domain-identifier $DOMAIN_ID \
  --region $REGION

Step 4: Subscribing to data assets

  • Open the data catalog inside SageMaker Unified Studio and search for the assets you want to subscribe to.

Data catalog search for assets to subscribe to in Amazon SageMaker Unified Studio

Adding multiple databases and schemas

To publish assets from entirely different databases on the same Amazon Redshift cluster, you need to create a separate data source for each database, meaning repeating the steps mentioned in the section before. Each data source points to the same cluster connection but specifies a different database name. This approach gives you independent control over scheduling, publishing settings, and metadata generation for each database’s assets.

On the consumer side, each subscription target is bound to a specific database and schema combination. This is the target location where SageMaker Unified Studio will create views that give the consumer access to subscribed assets. To receive subscribed data in multiple databases or schemas, you create one subscription target per database-schema combination. For example, different teams within the consumer account might want the data materialized in their own schema. The following example shows this pattern:

# Subscription target for the sales schema
aws datazone create-subscription-target \
  --domain-identifier $DOMAIN_ID \
  --environment-identifier $TOOLING_ENV_ID \
  --name "RedshiftCluster-sales-target" \
  --subscription-target-config '[{ "formName": "RedshiftSubscriptionTargetConfigForm", "content": "{\"databaseName\":\"consumer_db\",\"secretManagerArn\":\"arn:aws:secretsmanager:<region>:<account>:secret:<secret-name>\",\"host\":\"<endpoint>\",\"port\":\"5439\",\"schemaName\":\"sales\"}" }]' \
  --applicable-asset-types RedshiftViewAssetType RedshiftTableAssetType \
  --manage-access-role $MANAGE_ACCESS_ROLE \
  --provider "Amazon SageMaker" \
  --type RedshiftSubscriptionTargetType \
  --authorized-principals $AUTHORIZED_PRINCIPAL

# Subscription target for the marketing schema
aws datazone create-subscription-target \
  --domain-identifier $DOMAIN_ID \
  --environment-identifier $TOOLING_ENV_ID \
  --name "RedshiftCluster-marketing-target" \
  --subscription-target-config '[{ "formName": "RedshiftSubscriptionTargetConfigForm", "content": "{\"databaseName\":\"consumer_db\",\"secretManagerArn\":\"arn:aws:secretsmanager:<region>:<account>:secret:<secret-name>\",\"host\":\"<endpoint>\",\"port\":\"5439\",\"schemaName\":\"marketing\"}" }]' \
  --applicable-asset-types RedshiftViewAssetType RedshiftTableAssetType \
  --manage-access-role $MANAGE_ACCESS_ROLE \
  --provider "Amazon SageMaker" \
  --type RedshiftSubscriptionTargetType \
  --authorized-principals $AUTHORIZED_PRINCIPAL

Verifying the audit trail

To substantiate the governance and traceability claims in this architecture, enable AWS CloudTrail in all three accounts with data events for Secrets Manager and KMS. Enable Amazon Redshift audit logging on clusters to capture connection and query activity through STL_CONNECTION_LOG and STL_QUERY. Subscription approvals and rejections are recorded by SageMaker Unified Studio and emitted to CloudTrail under the datazone.amazonaws.com event source. Look for CreateSubscriptionRequest, AcceptSubscriptionRequest, and RejectSubscriptionRequest events.

Clean up

If you deployed this solution for testing or evaluation purposes and no longer need the resources, we recommend cleaning up to avoid unnecessary costs. Amazon Redshift clusters, Secrets Manager secrets, and SageMaker Unified Studio projects all incur charges when left running. The following steps guide you through a structured teardown in the correct order: subscriptions first, then data assets, and finally the infrastructure itself. This order verifies that no orphaned resources remain.

  • Remove all subscriptions
  • Delete your data assets
  • Delete the projects
    • Delete the project within your SageMaker Unified Studio Domain after all subscriptions are removed. Make sure to delete both the consumer and producer projects.
  • Delete the SageMaker Unified Studio Domain in Account A.

Conclusion

In this post, we demonstrated how Amazon SageMaker Unified Studio simplifies cross-account data governance for Amazon Redshift. By implementing this solution, organizations can move away from ad-hoc, non-auditable data sharing processes to a secure, scalable, and fully governed approach. Amazon SageMaker Unified Studio serves as the central governance layer that data producers and consumers use to publish, discover, and subscribe to data products across AWS accounts. This turns a fragmented data landscape into a well-governed data mesh without the need for custom tooling or manual coordination.

With cross-account data sharing and governance in place, the natural next step is to use this well-governed data for machine learning and generative AI workloads. Because Amazon SageMaker Unified Studio brings together data, analytics, and AI capabilities in a single environment, teams can more efficiently transition from discovering and subscribing to data products to building ML models and generative AI applications, all within the same environment. This reduces the traditional friction between data engineering and data science, accelerating time to value. To get started with establishing your organization’s data mesh using Amazon SageMaker Unified Studio, follow the guidance for Setting up Amazon SageMaker Unified Studio.


About the authors

Bandana Das

Bandana Das is a senior Data Architect in Amazon Web Services and specializes in Data and Analytics. She builds event-driven data architectures to support customers in Data management and data-driven decision making. She is also passionate about enabling customers on their Data management journey to the cloud.

Sindi Cali

Sindi Cali is a ProServe Consultant with AWS Professional Services. She supports customers in building data driven applications in AWS.

Anirban Saha

Anirban Saha is a DevOps Architect at AWS, specializing in architecting and implementation of solutions for customer challenges. He is passionate about well-architected infrastructures, automation, data-driven solutions and helping make the customer’s cloud journey as smooth as possible.

Stoyan Stoyanov

Stoyan Stoyanov works for AWS as a DevOps Engineer. He has more than 10 years of experience in software engineering, cloud technologies, DevOps, data engineering, and security.

Viral Thakkar

Viral Thakkar is a Software Engineer at AWS, working on Amazon DataZone and Amazon SageMaker Unified Studio with a primary focus on distributed systems and data governance with deep expertise in building large-scale data analytics and pipelining solutions. He is passionate about tackling complex distributed systems challenges while also creating tools and automated scripts that simplify day-to-day workflows and improve productivity.

Patch perfect: Automating Amazon Redshift patch testing

Post Syndicated from Eva Donaldson original https://aws.amazon.com/blogs/big-data/patch-perfect-automating-amazon-redshift-patch-testing/

Amazon Redshift continuously innovates to deliver improved performance and advanced features. In some releases, Amazon Redshift patches might introduce behavior changes. Testing patches in a non-production environment confirms that production workloads continue to function and you can maintain your applications’ service level agreements. As a best practice, keep Dev/QA clusters on the Current patch track and Production on the Trailing track. Test on Dev/QA when a patch lands, allowing 1–6 weeks of review before the scheduled production deployment.

In this post, we demonstrate an automated test suite that validates your Amazon Redshift cluster automatically after any patch, reboot, or modification. It uses standard drivers against real workload patterns to provide a verified gate between a patch landing and that patch reaching production.

Architecture

The solution uses native AWS services to create an automated validation pipeline.

Architecture diagram of the patch testing pipeline: Amazon EventBridge triggers AWS Lambda, which runs an AWS Fargate task that tests the cluster and reports to Amazon S3 and Amazon SNS

Figure 1 — High-level architecture diagram

Process overview showing the four stages: event detection, orchestration, test execution, and reporting

Figure 2 — Process overview

  1. Event Detection: When your Amazon Redshift cluster receives a patch, reboot, or modification, the Amazon Redshift cluster event notifications fire. Amazon EventBridge rules match these events automatically.
  2. Orchestration: A lightweight AWS Lambda function receives the event from the Amazon EventBridge rule and launches an AWS Fargate task. The task runs in a subnet within the same Amazon Virtual Private Cloud (VPC) as your Amazon Redshift cluster, giving the test runner direct network connectivity to the cluster endpoint.
  3. Test Execution: A Docker container runs a comprehensive test suite in four phases:
    • JDBC Driver Tests – Validates the official Amazon Redshift JDBC driver, testing DatabaseMetaData API calls, connection handling, and queries that tools like SQL Workbench/J depend on.
    • ODBC Driver Tests – Validates the PostgreSQL ODBC driver with SQLTables, SQLColumns, and other ODBC API calls that RStudio and similar tools use.
    • Catalog SQL Queries – Runs approximately 35 queries against pg_catalog, information_schema, and svv_* views, organized by client (SQL Workbench, DBeaver, RStudio, JDBC metadata API).
    • Performance Benchmarks – Executes your custom workload queries and compares execution time against known baselines, flagging regressions. For convenience, the solution includes sample queries to be replaced with performance validation queries from your workloads.
  4. Reporting: Detailed JSON results land in Amazon Simple Storage Service (Amazon S3) for historical analysis. An Amazon Simple Notification Service (Amazon SNS) notification sends your team an email immediately with a pass/fail summary. Full JSON results are written to Amazon S3 with timing data for every individual query, row counts, error details, and the Amazon EventBridge event that triggered the run. If tests fail, you have specific, actionable evidence (which queries broke, which drivers failed, which benchmarks regressed) to open a support case requesting a rollback and defer maintenance until the case is resolved. When tests succeed, you can move forward with confidence to production.

For real-time feedback while the tests are running, a quick command tells you the current state:

aws lambda invoke --function-name my-redshift-tests-trigger \
--payload '{}' --cli-binary-format raw-in-base64-out /dev/stdout

What gets tested

The test suite covers two critical areas: client tool compatibility and query performance.

Client compatibility queries

The test suite replicates the connection behavior of popular SQL clients by issuing the same metadata API calls and queries they perform when connecting to your cluster.

Client What’s tested
SQL Workbench/J Connection queries, schema browsing, metadata enumeration
DBeaver Database object discovery, catalog traversal
RStudio (DBI/odbc) ODBC-specific catalog queries, column type mapping
JDBC Metadata API getTables(), getColumns(), getPrimaryKeys(), and other DatabaseMetaData method equivalents

The package contains the exact queries these clients execute upon connection.

Performance regression detection

The benchmark phase of the suite automatically detects whether it has been run before. On the first execution, it captures baseline query execution times as the “known good” state for your pre-patch environment. On every subsequent run, it compares current query timings against the stored baseline and flags any regressions. If a query that previously completed in 2 seconds now takes 15, the report calls it out immediately. This phase is designed to test your most performance-sensitive queries.

Prerequisites

Before deploying, make sure your environment meets the following requirements:

Docker installed. Consider building the image with AWS CloudShell, which comes with Docker pre-installed. You can do this either by uploading the customized repo to Amazon S3 and then downloading it to AWS CloudShell, or by cloning and customizing the repo directly within AWS CloudShell.

Getting started

The full solution is available on GitHub. It includes the AWS CloudFormation template, Docker build scripts, test suite, and documentation.

Clone the GitHub repo, customize it for your workload, deploy it against a Dev/QA cluster.

Detailed instructions are included in the package README.md. Reference those for deployment.

Step 1: Clone the repo

Clone the GitHub repo.

Step 2: Customize the scripts for your environment

The test suite ships with comprehensive default queries. After cloning and before deployment, edit the scripts as described in the following sections for each phase.

Add your performance-critical queries

Edit bundle/run_tests.py and replace the example queries with queries where performance is critical:

BENCHMARK_QUERIES = {
    "daily_patient_summary": """
SELECT department, COUNT(DISTINCT patient_id), AVG(los_days)
FROM clinical.encounters
WHERE admit_date >= CURRENT_DATE - 30
GROUP BY 1
""",
    "revenue_rollup": """
SELECT payer_type, SUM(total_charges)
FROM billing.claims
WHERE service_date >= DATE_TRUNC('month', CURRENT_DATE)
GROUP BY 1
""",
}

Add client-specific catalog queries

If your team uses custom views or schemas, add them to bundle/client_catalog_queries.py:

"custom_view_check": {
    "description": "Verify our reporting view works after patching",
    "sql": "SELECT * FROM analytics.monthly_kpis LIMIT 10",
},

Step 3: Build the Docker image

Execute build-image.sh, which creates an Amazon ECR repository, builds the Docker image (with JDBC and ODBC drivers bundled), and pushes it, outputting the image URI for the next step.

# Upload project to S3, then build in CloudShell
./build-image.sh --stack-name my-redshift-tests

Step 4: Deploy the stack

Use the AWS Command Line Interface (AWS CLI) to deploy the AWS CloudFormation stack with your environment-specific parameters. The stack creates the required components: Amazon Elastic Container Service (Amazon ECS) cluster, AWS Fargate task definition, security groups, VPC endpoints (to keep AWS Secrets Manager and Amazon SNS traffic off the NAT gateway), Amazon S3 bucket, Amazon SNS topic, AWS Lambda trigger, and Amazon EventBridge rules.

aws cloudformation deploy \
--template-file template.yaml \
--stack-name my-redshift-tests \
--parameter-overrides \
RedshiftSecretArn=arn:aws:secretsmanager:... \
RedshiftHost=my-cluster.xxxx.us-east-2.redshift.amazonaws.com \
RedshiftClusterIdentifier=my-cluster \
VpcId=vpc-xxxxxxxx \
VpcSubnetIds=subnet-aaa,subnet-bbb \
RedshiftSecurityGroupId=sg-xxxxxxxx \
EcrImageUri=123456789012.dkr.ecr.us-east-2.amazonaws.com/my-redshift-tests-runner:latest \
[email protected] \
--capabilities CAPABILITY_NAMED_IAM

Key takeaways

Here are the core principles that make automated patch testing effective:

  1. Dev/QA on Current track, Production on Trailing: This separation creates the buffer window between when a patch is available and when it reaches production. Without it, there’s no opportunity to catch regressions before they affect users.
  2. Automate the validation: The track split is most effective if the test suite runs after every patch. Event-driven automation helps confirm no patch goes untested during the buffer window.
  3. Test with real drivers: Simulated queries aren’t sufficient. The test suite exercises the Amazon Redshift JDBC and PostgreSQL ODBC drivers that your SQL clients depend on. This validates the same code paths your tools use in production.
  4. Event-driven, not scheduled: Tests run the moment a patch is applied. They don’t run on a fixed cron schedule. Patch applied, then test executed, then results delivered in minutes.
  5. Low operational overhead, minimal cost: The entire solution is serverless (AWS Lambda and AWS Fargate). There are no instances to manage and no agents to install. The Fargate task spins up only when a patch event fires, runs the test suite, and shuts down. You pay only for the compute each test run consumes.

Clean up

When you no longer need the automated test suite, delete the associated resources so you don’t incur ongoing costs.

  1. Delete any created prerequisites, if not needed.
    1. Amazon Redshift cluster (removes the managed secret).
    2. NAT gateway.
    3. VPC.
  2. Empty the Amazon S3 results bucket (AWS CloudFormation cannot delete non-empty buckets).
  3. Delete the image you installed in the Amazon ECR repository in step 1 of getting started.
  4. Delete the AWS CloudFormation stack to remove the Amazon ECS cluster, AWS Fargate task definition, security groups, VPC endpoints, Amazon S3 bucket, Amazon SNS topic, AWS Lambda function, and Amazon EventBridge rules created by the deployment.
    aws cloudformation delete-stack --stack-name my-redshift-tests

Conclusion

Automated patch testing ensures consistent and predictable performance of your production workloads. By deploying Dev/QA clusters on the Current track with event-driven validation, you gain weeks of advance notice before patches reach production. The solution presented here provides comprehensive testing of JDBC drivers, ODBC drivers, catalog queries, and performance benchmarks. It requires zero manual intervention. Deploy it once, customize it for your workload, and gain confidence that the next Amazon Redshift patch will be validated before it matters.

To learn more about Amazon Redshift, explore the following resources:


About the author

Eva Donaldson

Eva Donaldson

Eva is a Senior Technical Account Manager (TAM) at AWS, specializing in Healthcare & Life Sciences customers. With 20+ years of experience as a data architect, engineer, and team manager, she focuses on designing automated data platforms and solutions that solve real business problems.

How BigBasket uses the Iceberg based lakehouse architecture on AWS to power lightning-fast grocery delivery across India

Post Syndicated from Annie Mattoo original https://aws.amazon.com/blogs/big-data/how-bigbasket-uses-the-iceberg-based-lakehouse-architecture-on-aws-to-power-lightning-fast-grocery-delivery-across-india/

Delivering fresh groceries to millions of customers across India in a few minutes demands a radically modern data architecture and resilient processes to help the business make faster decisions. This is what BigBasket was able to achieve by building a lakehouse architecture on AWS.

In this post, we demonstrate how BigBasket implemented the lakehouse architecture on AWS, including their architecture decisions, implementation approach, and the measurable business results you can expect from a similar modernization. Whether you’re facing scalability challenges or planning your own lakehouse implementation, this blueprint provides actionable insights you can adapt for your organization.

About BigBasket

BigBasket (Innovative Retail Concepts Private Limited) is India’s largest online supermarket, serving millions of customers across over 60 cities. Founded in 2011, the company offers groceries, fresh produce, household items, and personal care products through its mobile app and website, operating subscription services (BBDaily) and quick commerce (bbnow). For BigBasket, the ability to deliver groceries on time isn’t only a competitive advantage. It’s the foundation of customer trust, where every minute counts.

However, rapid business growth brought significant operational challenges:

  • Inability to consistently meet on-time delivery adherence because of high order volumes, extended travel times, and more, directly impacting key metrics like on-time rate (OTR)-10 mins and OTR-15 mins.
  • Struggling to meet on-time delivery targets because of picking inefficiency, high order volumes, and extended travel times, directly impacting key metrics like OTR-10 mins and OTR-15 mins.
  • Delays in stock availability impacting vendor fill-rates, inter-distribution center orders, and warehouse operations.
  • Inaccurate stock forecasting for top-selling stock keeping units (SKUs), assortment variety, event SKUs, store capacity, and buying cycles.
  • Lower dark store productivity across picking, stacking, order processing, and goods receipt notes (GRN).

Behind these business challenges lay a fundamental technology problem: the existing data infrastructure couldn’t keep pace. The company experienced rapid store growth, expanding 4x in a short timeframe, which exposed several limitations within their existing data architecture that needed attention.

Understanding the technical bottlenecks

BigBasket’s initial architecture relied heavily on a single data warehouse built on Amazon Redshift to meet all reporting and dashboarding needs. While this traditional approach had served them well initially, several important limitations emerged:

  • Stale data: Extract, transform, load (ETL) pipelines delivered only day-old (D-1) data, making near real-time analysis impossible for dashboard requirements.
  • Extended recovery times: Pipeline failure recovery processes took several hours, causing significant delays in data availability for business users.
  • Schema rigidity: Schema changes in source databases frequently triggered pipeline failures because of a lack of schema evolution support.
  • Scalability constraints: The infrastructure struggled to handle the sudden load increase from 13,000 to over 35,000 transactions for reports and dashboards with more than 1,000 dataset refreshes.
  • Cost implications: Increasing data volumes demanded additional compute resources, driving up costs.

Diagram of the scalability and cost limitations of BigBasket’s legacy Amazon Redshift data warehouse

It became clear that the existing data infrastructure wasn’t able to meet the evolving business requirements and a redesign of their data architecture is needed.

Why lakehouse architecture?

A modern data lakehouse architecture addresses these issues with near real-time data processing, flexible schema evolution, and scalable analytics, capabilities necessary for fast-moving commerce operations. The lakehouse approach combines the flexibility and cost-effectiveness of data lakes with the performance and governance features of data warehouses, combining the strengths of both. The design of a data lakehouse provides interoperability across storage systems for combined analytics activities.

Solution overview

BigBasket partnered with AWS to implement a comprehensive lakehouse architecture using a combination of AWS native services and open-source technologies.

The following diagram shows an elaborated view of Bigbasket’s modernized architecture on AWS.

Detailed lakehouse data flow across bronze, silver, and gold medallion layers on AWS

Data ingestion: Enabling continuous replication

AWS Database Migration Service (AWS DMS) ingests data from online transaction processing (OLTP) databases running on Amazon Relational Database Service (Amazon RDS) into the lakehouse on AWS.

This method continuously replicates data with minimal latency, so your analytics reflect near real-time business operations.

Storage and governance: Building a solid foundation

The lakehouse is built on Amazon Simple Storage Service (Amazon S3) and Amazon Redshift, which serve as the centralized data lake and warehouse following a medallion architecture.

The architecture persists all analytical data using Apache Iceberg as the open table format. Iceberg provides a robust foundation for large-scale analytics with the following capabilities:

  • ACID transactions: Guarantees data consistency and correctness across concurrent read and write operations.
  • Time travel: Supports querying historical table versions for auditing, troubleshooting, and recovery.
  • Schema evolution: Allows schema changes without disrupting existing queries or downstream pipelines.

The medallion architecture structures data across three logical layers within the lakehouse:

  • Bronze layer: Implements change data capture (CDC)-based source replication using AWS DMS. Raw change events flow into Amazon S3 as Apache Parquet files in their original format from source systems, preserving the complete change history. The data pipeline processes and deduplicates these events using Apache Spark on Amazon EMR to create and maintain Apache Iceberg tables that act as replicated source tables.
  • Silver layer: Represents the conformed data model, where data is cleansed, standardized, and validated with enforced quality checks. This layer contains core dimension and fact tables, modeled for analytical consistency and reuse across domains. Data is stored as Apache Iceberg tables on Amazon S3, making it reliable and performant for downstream analytics and transformations.
  • Gold layer: Provides business-ready data marts and wide tables optimized for reporting, dashboarding, and domain-specific use cases. These datasets are curated to align with business metrics and key performance indicators (KPIs) and are served from Amazon Redshift, using Iceberg-backed tables to deliver fast, scalable analytics for business intelligence (BI) tools and end users.

This layered approach maintains a clear separation of concerns across raw ingestion, analytical modeling, and business consumption, while supporting scalability and flexibility across the organization. AWS Lake Formation enforces fine-grained data access controls, and the AWS Glue Data Catalog centrally manages metadata across Amazon S3 and Amazon Redshift, ensuring consistent data discovery and governance across the analytics ecosystem.

Data processing: Flexibility and performance

For data processing and transformations, BigBasket uses Amazon EMR with Apache Spark and dbt, orchestrated by Apache Airflow running on Amazon Elastic Kubernetes Service (Amazon EKS) as the core compute layer of the lakehouse. Apache Spark on Amazon EMR handles large-scale distributed processing, including CDC deduplication, incremental transformations, and complex data reshaping. Apache Iceberg serves as the open table format, which provides several critical capabilities.

dbt is used to define and execute transformation logic using SQL, managing the build of data models such as staging, intermediate, and final tables on top of the raw data. dbt uses the dbt-Trino adapter to run these transformations using the Trino engine, materializing the results as Apache Iceberg tables in Amazon S3. This approach provides a simple, modular, and governed way to manage transformations while taking advantage of Iceberg’s transactional guarantees.

These features are necessary for production lakehouse implementations and help you avoid vendor lock-in while maintaining enterprise reliability.

Online analytical processing (OLAP) and analytics: Hybrid approach for cost optimization

The analytics layer uses a hybrid approach that you can adapt based on your query patterns:

  • Amazon Redshift: For querying of active, frequently accessed data from the Gold layer.
  • Amazon Athena: For ad-hoc queries on historical data.
  • Apache Trino: For federated queries across multiple data sources while powering dbt-driven transformations directly on Apache Iceberg tables.

This hybrid strategy optimizes costs by keeping frequently accessed data in Amazon Redshift while querying historical data directly from Iceberg tables in Amazon S3. Amazon Redshift data sharing supports a multi-warehouse architecture for cross-team collaboration, allowing different teams to access shared datasets without data duplication.

Orchestration: Managing complex workflows

Apache Airflow running on Amazon EKS orchestrates and schedules data pipelines across the entire environment, providing visibility and control over complex workflows. This gives you a unified view for monitoring and managing your data operations.

Machine learning integration

Amazon SageMaker AI powers machine learning workloads for predictive analytics and model training directly on lakehouse data, from demand forecasting to delivery optimization. This tight integration means your data scientists can work with the same governed data that powers your analytics.

Visualization: Making insights accessible

Amazon Quick Sight provides data visualization and business intelligence reporting capabilities, making insights accessible to business users across the organization without requiring technical expertise.

Special focus: Clickstream data processing

BigBasket implemented a sophisticated dual-path architecture for processing clickstream data from mobile apps and web interactions:

  • Real-time path: Data flows through Scala stream collectors on Amazon Elastic Compute Cloud (Amazon EC2) (behind Elastic Load Balancing) to Amazon Kinesis Data Streams and Amazon OpenSearch Service for immediate insights into customer behavior. This path is necessary when you need to react to user actions within seconds, for example detecting fraud or personalizing experiences in real time.
  • Batch path: The batch path validates data, stores it in Amazon S3, processes it through Amazon EMR, and loads it into Amazon Redshift for comprehensive historical analysis. This path handles data quality checks, enrichment, and aggregation for long-term analytics.

The trade-off between these approaches is latency versus completeness. Real-time processing gives you speed but may sacrifice some data quality checks, while batch processing provides accuracy but introduces delay. This dual approach achieves both immediate operational insights and deep analytical capabilities, letting you optimize for different use cases.

The following diagram shows how the clickstream data is handled and effectively processed today.

BigBasket’s dual-path clickstream processing architecture with real-time and batch paths on AWS

The results: measurable business impact

The data platform transformation achieved significant results across multiple dimensions:

Technical improvements

  • Near real-time data: Achieved near real-time data availability for dashboards within 3–5 minutes, replacing previously day-old data.
  • Rapid failure recovery: Pipeline failure re-runs now complete in minutes instead of hours.
  • Comprehensive governance: Full control over data governance with robust observability, lineage, data accuracy, and consistency.
  • Enhanced scalability: Successfully handling over 35,000 reports and dashboards with over 1,000 dataset refreshes.

Business outcomes

  • On-time delivery: Improved monitoring with real-time insights on low-performing stores.
  • Stock availability: Reduced operational issues with visibility into key bottlenecks.
  • Stock forecasting: Improved accuracy and availability of top-selling SKUs.
  • Dark store productivity: Enhanced productivity of warehouse executives across all operations.

Key takeaways: lessons for modern data platforms

BigBasket’s journey offers valuable insights for organizations facing similar challenges:

  1. Quick commerce needs quick observability. In the fast-paced world of quick commerce, faster decision-making directly improves business metrics. Real-time data isn’t a luxury. It’s a necessity.
  2. Embrace ELT for real-time needs. Shifting from traditional ETL to an extract, load, transform (ELT) pattern within a lakehouse architecture is important to unlock near real-time analytics capabilities.
  3. A lakehouse delivers speed and governance. Modern lakehouse architectures don’t force trade-offs. You can achieve both fast data availability and comprehensive control, lineage, and accuracy.
  4. Focus on operational resilience. Designing for rapid failure recovery (re-runs in minutes, not hours) is necessary for maintaining data availability and business trust, especially in customer-facing operations.
  5. Incremental migration. You don’t need to rebuild everything. Evolve your current Amazon S3 data lake or reuse your existing investments in Amazon Redshift to build the data lakehouse capabilities.

The road ahead

BigBasket continues to innovate, now moving to adopt Amazon SageMaker Unified Studio to access all lakehouse components in a simplified manner across the enterprise. This next evolution will further streamline data access and accelerate insights across teams.

The company’s transformation demonstrates that with the right architecture and AWS services, organizations can turn data infrastructure challenges into competitive advantages, delivering not only better analytics but better customer experiences.

As you plan your own lakehouse implementation, use these patterns and lessons learned to accelerate your journey and avoid common pitfalls.


About the authors

Naga Sandeep Grandhi

Naga Sandeep Grandhi

Sandeep is an engineering leader at BigBasket, driving data platform and cloud architecture initiatives, including the next-gen data lake built for scale, reliability, and real-time insights.

Vikram Kumar

Vikram Kumar

Vikram is a Principal Engineer at BigBasket, where he leads the data engineering team. He specializes in designing and scaling modern data platforms on AWS, enabling BigBasket to process large-scale data efficiently and power data-driven decision-making across the organization.

Annie Mattoo

Annie Mattoo

Annie is a Sr. Analytics Specialist at AWS, bringing over 15+ years of expertise in helping customers with their DATA & AI journeys. She has successfully led customer teams to seamlessly adopt AWS Data & AI services and has worked with Fortune 500 customers across the globe in her previous roles.

Vineet Thapliyal

Vineet Thapliyal

Vineet is an Enterprise Account Manager at Amazon Web Services (AWS) in Bengaluru, India, where he manages strategic cloud and generative AI engagements across some of India’s largest conglomerates spanning energy, retail, and technology. He is passionate about helping enterprises unlock business value through AI/ML, cloud modernization, and industry-specific innovation — from renewable energy analytics to retail transformation at scale.

Anirudh Chawla

Anirudh Chawla

Anirudh is an Analytics Solution Architect at AWS. He helps organization empowers businesses to harness their data effectively through AWS’s analytics platform. His interest lies in building highly available distributed systems.

Deploy modern data platforms in minutes with MDAA

Post Syndicated from Sudeshna Dash original https://aws.amazon.com/blogs/big-data/deploy-modern-data-platforms-in-minutes-with-mdaa/

Modern Data Architecture Accelerator (MDAA) is an open source framework that replaces infrastructure code with concise YAML configuration, so your team can deploy a governed, production-ready data architecture, reducing deployment time from months to weeks (depending on complexity and team experience).

Organizations building modern data architecture on AWS face a critical challenge: deploying production-ready, governed infrastructure traditionally requires 6–12 months of custom development, thousands of lines of infrastructure code, and continuous remediation cycles to maintain security and compliance. Governance is often added incrementally, treated as an afterthought that creates compliance gaps and engineering rework.

MDAA addresses this by replacing infrastructure code with concise YAML configuration, achieving up to 97.6 percent code reduction (from approximately 1,800 lines of AWS CloudFormation to 45 lines of MDAA YAML) while embedding governance from the start. The complete Governed Lakehouse Starter Kit deploys 491 AWS resources across 12 stacks from approximately 450 lines of YAML configuration, representing a 66x verbosity ratio where each line automatically expands into production-ready infrastructure.

In this post, we explore how MDAA transforms data architecture development from months of manual coding to production-ready deployment through configuration-driven infrastructure and embedded governance, examine a real customer transformation, and provide a clear implementation pathway for your own data modernization journey.

Customer use case and challenge

A university system office needed to modernize its analytics architecture across 17 campuses while managing sensitive educational data. Their third-party dependency created bottlenecks that slowed feature implementation from weeks to months, and their IT team lacked the cloud skillsets to build modern infrastructure independently.

With MDAA, they achieved:

  • 95 percent reduction in time-to-value for dashboard and feature implementation (from weeks to hours).
  • 17 campuses integrated into a unified, secure architecture.
  • 7.2TB of data and over 8,000 dashboards migrated successfully.
  • Significant cost savings by removing third-party dependencies and reducing license costs.
  • Enhanced security posture for external stakeholders accessing sensitive educational data.

The team used MDAA to implement a modernization strategy with continuous integration and continuous delivery (CI/CD) for automated deployment. The architecture now supports rapid response to stakeholder requests while maintaining strict data governance through AWS Lake Formation.

Their transformation demonstrates what becomes possible when governance is embedded from launch rather than added incrementally, moving from months-long manual development to weeks of production-ready deployment through configuration-driven infrastructure.

Solution: MDAA and its value propositions

MDAA’s capabilities stem from its modular, composable architecture. The accelerator provides over 40 pre-built modules that encapsulate AWS best practices for security, governance, and operational excellence. Organizations describe the outcomes they want in MDAA-specific YAML configuration files (not CloudFormation or Terraform YAML) and the accelerator automatically translates these configurations into AWS Cloud Development Kit (AWS CDK) constructs, which then deploy via CloudFormation with embedded governance.

Configuration over code. The MDAA framework takes a fundamentally different approach: describe the outcomes you want in YAML, and the accelerator deploys production-ready infrastructure with embedded governance. Consider deploying a governed data lake where fraud detection teams need write access to transaction data, while marketing analytics teams require read-only access to customer behavior data. Traditional approaches require over 1,800 lines of CloudFormation across Amazon Simple Storage Service (Amazon S3) buckets, AWS Key Management Service (AWS KMS) keys, AWS Identity and Access Management (IAM) policies, and Lake Formation permissions. With MDAA, the same governed data lake is expressed in 45 lines of configuration, a 97.6 percent reduction, while helping you apply encryption, least-privilege access, and cross-account governance as built-in defaults.

The configuration deploys multi-zone S3 storage with KMS encryption, Lake Formation permissions with tag-based access control (TBAC) enabled, Amazon SageMaker Unified Studio for data product discovery, and encrypted AWS Glue Data Catalog with automated crawlers. All permissions flow through Lake Formation rather than individual IAM policies.

Embedded governance from day one. Governance is declared in YAML and deployed alongside infrastructure from the first run. Fine-grained access controls, encrypted data catalogs, data quality validation, audit trails, and sensitive data classification are all part of the same configuration. MDAA’s Governed Lakehouse starter kit defines an entire governed data architecture in roughly 450 lines of YAML, which produces approximately 29,700 lines of CloudFormation across 12 stacks (a 98.5 percent reduction in infrastructure code).

Modular, composable architecture. Each module is purpose-built to handle a specific capability within the data architecture. Modules communicate through AWS Systems Manager Parameter Store, passing resource identifiers (Amazon Resource Names (ARNs), IDs, and names) between stacks. This approach removes hardcoded dependencies. A KMS key created in one module can be referenced by another through parameter resolution, with all dependencies resolved automatically at deployment time.

The diagram illustrates the deployed architecture and team-level access flow that MDAA generates from the 45-line configuration.

Progressive architecture patterns. MDAA provides four reference architecture patterns that align to progressive stages of data infrastructure maturity:

  • Basic Data Lake deploys a governed data lake with built-in security controls, data quality checks, centralized metadata management using AWS Lake Formation and AWS Glue.
  • Data Science Platform extends the data lake with Amazon SageMaker notebooks, feature stores, and machine learning (ML) pipelines so data science teams can experiment and train models on governed data.
  • SageMaker Unified Studio adds a single interface for analytics and ML collaboration, connecting data engineers, analysts, and data scientists in one workspace.
  • Generative AI Platform layers Amazon Bedrock and Retrieval Augmented Generation (RAG) capabilities on top of your existing data foundation, so teams can build generative AI applications grounded in enterprise data.

Each pattern builds the one before it. You can start with the Basic Data Lake and adopt additional patterns as your team’s needs grow. MDAA’s modular design means you add capabilities without rearchitecting what you already deployed.

The infrastructure is versioned through GitHub, repeatable across environments, and auditable through comprehensive AWS CloudTrail logging. Data engineers focus on data pipelines and business logic while MDAA manages infrastructure complexity and governance integration. This represents the fundamental shift: from writing infrastructure code to describing the outcomes you want through configuration, with governance embedded from the start.

Use case of MDAA: Governed data architecture

DataOps teams spend significant time on governance tasks, including permissions management, compliance validation, and access control, rather than building pipelines and analytics. These aren’t data problems, they’re governance problems that consume engineering capacity meant for higher-value work. MDAA addresses this at the architectural level. Governance is declared in YAML and deployed alongside infrastructure from the first run.

The following sections walk through how each governance module works in practice.

Publish, discover, subscribe, and consume data products between business units: SageMaker Unified Studio

Amazon SageMaker Unified Studio provides a governed data catalog where data producers publish data products, and consumers discover and subscribe to them. Your deployment with MDAA includes a pre-configured domain, blueprints (managed and custom), projects, and environment profiles, all defined in a single configuration file:

# sagemaker.yaml --- 16 lines that deploy 114 CloudFormation resources
domains:
  domain1:
    dataAdminRole:
      id: ssm:/{{org}}/govern1/generated-role/data-admin/id
    description: SMUS Domain 1
    userAssignment: MANUAL

    tooling:
      vpcId: '{{context:vpc_id}}'
      subnetIds:
        - '{{context:private_subnet_id1}}'
        - '{{context:private_subnet_id2}}'

    groups:
      team1:
        ssoId: '{{context:team1-group-sso-id}}'
      team2:
        ssoId: '{{context:team2-group-sso-id}}'

Behind this configuration, MDAA deploys an Amazon SageMaker Unified Studio domain with dedicated KMS keys, execution and provisioning roles, and single sign-on group profiles for team access. Data producers tag and publish assets with metadata, ownership, and classification. Consumers browse a searchable catalog, see only authorized assets, and request access through a governed workflow. Cross-account and cross-business-unit data sharing flows through a subscription model, ensuring every access grant is tracked, auditable, and revocable.

Use case of MDAA: Restricting access to cardholder data using Lake Formation

AWS Lake Formation provides fine-grained access control at database and table levels, removing manual IAM policy management. MDAA deploys AWS Lake Formation with pre-configured settings that disable IAMAllowedPrincipals, the critical governance setting that ensures all permissions flow through centralized governance:

# lakeformation-settings.yaml --- 6 lines that deploy 25 CloudFormation resources
lakeFormationAdminRoles:
  - id: generated-role-id:data-admin
createCdkLFAdmin: true
createDataZoneAdminRole: true
iamAllowedPrincipalsDefault: false

That last flag is the single most important governance setting in the platform. Without it, an IAM principal with glue:GetTable can read tables in the catalog, bypassing the entire access control model. Most manual setups miss this or defer it.

With the data lake configuration, you declare roles and access policies in YAML where admins get full control, engineers get read access to curated data, extract, transform, and load (ETL) roles get scoped write access, and MDAA compiles them into the correct S3 bucket policies and Lake Formation registrations.

Use case of MDAA: Ensuring data integrity with AWS Glue Data Quality

AWS Glue Data Quality runs automated validation rulesets continuously as part of the pipeline, not as periodic batch checks. MDAA’s data quality module supports over 15 built-in rule types, from completeness and uniqueness checks to statistical thresholds and data freshness validation:

# data-quality.yaml
projectName: example-project

rulesets:
  customer-data-quality:
    description: Validate customer data completeness and uniqueness
    targetTable:
      databaseName: project:databaseName/customer-data
      tableName: customers
    ruleset:
      - ruleType: IsComplete
        column: customer_id
      - ruleType: Uniqueness
        column: email
        comparisonOperator: ">"
        threshold: 0.95
      - ruleType: RowCount
        comparisonOperator: ">"
        value: 100

Quality metrics flow into Amazon CloudWatch for real-time alerting. If anomalies are detected, automated workflows quarantine affected records and alert data engineering teams before issues reach downstream consumers.

Protecting metadata at rest: AWS Glue Data Catalog encryption

Table schemas, column names, and partition structures can reveal sensitive information about an organization’s data architecture, even without access to the underlying data. AWS Glue Catalog Encryption secures metadata at rest using AWS KMS-managed keys. MDAA configures catalog encryption by default, so schema definitions and connection passwords are encrypted from initial deployment without requiring manual key management setup. Access to catalog metadata follows the same Lake Formation governance controls applied to the data itself, so teams see only the schemas that they’re authorized to query.

Auditing every data access event: CloudTrail integration

Every data access event must be logged and attributable to a specific identity. Without a complete audit trail, demonstrating compliance during a regulatory review becomes a manual, error-prone process. AWS CloudTrail captures API-level activity across the data infrastructure, recording who accesses what data, when, and from which service. MDAA configures CloudTrail integration by default, so audit logging is active from initial deployment rather than added retroactively. Log data flows into a centralized, tamper-resistant store, giving compliance teams a single location to query access history across all business units and accounts.

Identifying sensitive data automatically: Macie integration

In large environments, sensitive information spreads across dozens of S3 buckets through pipelines, transforms, and ad hoc data drops, and self-reporting data owners consistently produce gaps. Amazon Macie uses machine learning to automatically discover and classify sensitive data in S3, surfacing findings at the object level without manual tagging. MDAA configures Macie across your S3 buckets during deployment, routing findings to Amazon EventBridge where automated workflows can alert owners or trigger remediation.

Together, these controls form a layered defense: Lake Formation governs access to cataloged data, Glue Data Quality validates integrity on arrival, and Macie identifies sensitive data that lands outside governed pipelines to reduce compliance risk.

Multi-account data mesh

MDAA provides extensive support for multi-account data mesh setups, with decentralized data ownership across business units and centralized governance. The data mesh starter kit supports cross-account data product publishing and consumption, allowing organizations to scale data sharing while maintaining consistent security and compliance controls.

Technical implementation

Ready to deploy your modern data architecture? Here are the resources to get started:

MDAA Implementation Guide provides detailed instructions for deploying all starter packages, including architecture patterns, configuration examples, security best practices, and troubleshooting guidance.

MDAA Hands-on Workshop offers step-by-step guided implementation with AWS experts. The workshop covers configuration management best practices, implementation patterns, hands-on labs with real-world scenarios, and cleanup instructions.

GitHub Repository and Documentation provide source code, module reference, and comprehensive documentation.

Organizations approach MDAA from different starting points. Some modernize existing data architectures, migrating from on-premises infrastructure or legacy cloud architectures. Others build new architectures for artificial intelligence and machine learning (AI/ML) initiatives or generative AI applications. Financial services organizations require PCI-DSS compliance from day one. Healthcare organizations need controls that can help support HIPAA. Each journey benefits from MDAA’s configuration-driven approach and embedded governance.

Conclusion

MDAA transforms data architecture development from months of manual coding to production-ready deployment. Configuration-driven infrastructure reduces development time by 40–60 percent while embedding governance from the start. The university system’s 95 percent reduction in time-to-value demonstrates the outcome: organizations deploy secure, compliant, governed data architectures in weeks rather than months.

Financial services organizations can deploy architectures to help them align with PCI-DSS compliance requirements using Lake Formation access controls, Glue Data Quality validation, SageMaker Unified Studio data discovery, comprehensive CloudTrail audit trails, and automated Macie data classification, all inherited from configuration rather than built manually.

Data architecture journeys need not follow six-month timelines with governance added incrementally. MDAA provides an alternative: describe the outcomes you want through YAML configuration, inherit pre-validated security controls, and deploy production-ready infrastructure with comprehensive governance from initial deployment.

Security and compliance is a shared responsibility between AWS and the customer. For more information, see the AWS Shared Responsibility Model.

Need help or have questions? Contact AWS ProServe for personalized guidance on selecting the right package and deployment strategy for your organization.


About the author

Sudeshna Dash

Sudeshna Dash

Sudeshna is a Data Scientist at AWS Professional Services based in Berlin, Germany. She specializes in data architecture, generative AI, and agentic AI systems on AWS. Sudeshna is a contributor to the Modern Data Architecture Accelerator (MDAA) open-source project and helps customers design and deploy governed, production-ready data and AI/ML architectures on AWS.

John Reynolds

John Reynolds is a Principal Engineer with AWS Professional Services based in Seattle, Washington. He leads the architecture and development of Modern Data Architecture Accelerator (MDAA), focusing on turning proven delivery patterns into reusable, production-ready foundations that customers can adopt and extend at scale.

Amazon Redshift RG: Faster and lower cost, Graviton-powered

Post Syndicated from Stefan Gromoll original https://aws.amazon.com/blogs/big-data/amazon-redshift-rg-faster-and-lower-cost-graviton-powered/

Amazon Redshift recently announced the general availability of a new Graviton-powered instance called RG. Built on Amazon’s own Graviton processors, RG delivers:

  • Up to 2.2x faster performance for data warehouse workloads compared to RA3.
  • Up to 2.4x faster for Iceberg queries and 1.5x faster for Parquet queries through an integrated vectorized data lake engine.
  • No per-TB scan charges for data lake queries, eliminating the Amazon Redshift Spectrum cost applied on RA3 clusters.
  • 30 percent lower cost per vCPU compared to RA3.

RG is both faster and cheaper. While cloud vendors typically charge more for faster performance or newer generation hardware, Amazon Redshift delivers better performance at lower cost.

In this post, we describe the innovations that make RG instances so much faster. We also share benchmark results showing that RG delivers up to 4.2x better price-performance than other leading data warehouses.

What makes RG so fast

The new RG instances are built from the ground up to take advantage of Graviton processors. The vectorized engine of Amazon Redshift is optimized with Graviton-based single instruction, multiple data (SIMD) kernels to deliver accelerated, parallelized execution for analytics workloads. Operations like predicate evaluations over Parquet encodings use Graviton vector comparison, table lookup, and vector manipulation intrinsics. To support these increased processing speeds, RG instances use custom-built Nitro SSDs. This lets RG use faster local storage as a caching layer for Amazon Redshift Managed Storage (RMS), data lake scans, and intermediate result sets for computations that can’t fit in memory. RG’s JIT (Just-In-Time) Analyze feature also collects statistics from data lake files automatically as queries run, so the optimizer can produce significantly better query plans. Together, these represent innovation across the entire stack: hardware acceleration with Graviton, vectorized execution with SIMD kernels, high-speed storage with Nitro SSDs, and intelligent query planning with JIT Analyze.

These optimizations, coupled with RG’s purpose-built high-performance vectorized data lake engine, combine to make Amazon Redshift’s new RG instances up to 2.2x faster than RA3 for analytics workloads at 30 percent lower cost.

Purpose-built high-performance vectorized data lake engine

With RA3, data lake queries offloaded scans to a separate compute fleet known as Amazon Redshift Spectrum. Because data lake queries ran on this separate compute, additional overhead was introduced to transfer query metadata and results between RA3 clusters and the Spectrum fleet. Amazon Redshift RG instances include a completely new built-in scan layer designed from the ground up for data lakes. This new scan layer includes a purpose-built I/O subsystem that incorporates smart prefetch capabilities to reduce data latency. The new scan layer is also optimized to process Apache Parquet files, the most commonly used file format for Iceberg, through fast vectorized scans that use SIMD kernels optimized for Graviton. The scan layer includes sophisticated data pruning mechanisms that operate at both partition and file levels, which significantly reduces the volume of data that needs to be scanned. This pruning capability works with the smart prefetch system to create a coordinated approach that maximizes efficiency throughout the entire data retrieval process.

The new purpose-built vectorized data lake engine is up to 2.4x faster than RA3 for Iceberg queries and 1.5x faster than RA3 for Parquet queries.

Because this new vectorized data lake engine integrates directly with the core execution engine of Amazon Redshift, new performance optimizations are possible compared to RA3. With this architecture, data lake queries on RG now benefit from fast local data caching, improved bloom filters, vectorized Parquet scans, and advanced filtering and pruning.

RG also solves a common problem customers face when querying data in the lake: open-format files like Iceberg in Amazon Simple Storage Service (Amazon S3) often lack useful metadata and statistics, which makes it difficult to run a SQL query optimally.

Statistics are metadata about your data, such as distinct value counts, min/max values, distribution patterns, and row counts. The query optimizer uses this information to choose the most efficient way to run a query. For example, when joining two tables, the optimizer needs to know how many unique values each side produces to pick the right join strategy. Without statistics, it has to guess, which often leads to slower joins and unnecessary data movement across nodes. This is where Amazon Redshift’s new feature called JIT (Just-In-Time) Analyze comes in. RG instances automatically fetch and store statistics of your Iceberg files as queries run, so Amazon Redshift can choose query execution strategies that are far more optimized than it could without these statistics.

These improvements make scans of Iceberg and Parquet data much faster than RA3. Removing Amazon Redshift Spectrum compute also means RG instances remove the $5/TB cost for data lake queries, which makes data lake queries cheaper and costs predictable. This is a triple win for data lake price-performance: faster performance, lower compute cost, and no per-TB scan cost.

Faster insights from faster data loads

Amazon Redshift RG’s fast I/O and Graviton-optimized engine result in faster data loads compared to RA3. To measure this improved performance, we ran the data ingestion step of 10TB TPC-DS and TPC-H on equivalently sized RA3 and RG clusters. RG ingested the TPC-DS dataset 2x faster and the TPC-H dataset 1.4x faster, as shown in the following figure.

Bar chart comparing data ingestion time on RA3 and RG, showing RG loads TPC-DS 2x faster and TPC-H 1.4x faster

The new Graviton-based RG instances are up to 2.0x faster for data loads compared to RA3 instances. This means workloads can see the latest data sooner, and users and agents can get up-to-date insights faster. This faster ingestion on RG comes at 30 percent lower cost compared to RA3, resulting in up to 2.9x better price-performance for data loads compared to RA3 instances.

What customers are saying

Amazon Redshift customers are already seeing performance and cost benefits of switching to RG. Southwest Airlines and tombola tested their business-critical workloads, and found they could get better performance and save on cost:

Southwest Airlines

“Amazon Redshift RG instances have the potential to deliver meaningful business impact for Southwest Airlines. Based on initial testing in our development environment, our data warehouse workloads run 50–60% faster, and data lake analytics are 45% faster—enabling teams to get insights sooner, respond to operational conditions faster, and make data‑driven decisions with less latency. These early results are encouraging, and we are excited to validate and scale these improvements in production. All of this comes without per‑terabyte Spectrum scanning charges, delivering 30% lower cost than RA3 at a time when fuel prices continue to pressure industry margins!!”

— Sean Lynch, Vice President, Data and Architecture, Southwest Airlines

tombola

“The new Graviton-based Amazon Redshift RG instances delivered 1.8x–2x faster write throughput and up to 2.2x faster read speeds compared to RA3 across a diverse set of batch and analytical jobs — enabling us to process 40% more within the same window. Compressed ETL cycles, accelerated time-to-insight, and decision-making no longer bottlenecked by the pipeline — together, these translated directly into fresher data reaching our analysts and business teams sooner. What made this even more compelling was a concurrent 30% reduction in compute spend alongside the gains — delivering more for less is a rare outcome, and one worth highlighting. In a volume-heavy gaming industry at tombola, where query latency and cost compound at scale, this has been one of the more impactful platform decisions we’ve made this year.”

— Akshay Srinivasan, Data Engineer, tombola

Qoala

“After migrating our Amazon Redshift cluster from RA3 to the new Graviton-based RG instances, we saw 60–70% faster query processing times across our BI and analytics workloads. As a growing insurtech platform handling millions of policy transactions, faster time-to-insight means our data team can deliver dashboards and reports to the business sooner. We moved to a larger node configuration to accommodate future growth, and the performance gains far exceeded the incremental investment – making this one of the most impactful infrastructure decisions we’ve made this year.”

— Umar Abdul Aziz, VP of Data, Qoala

Performance results

To see how RG stacks up, we ran benchmarks derived from the industry-standard TPC-DS and TPC-H benchmarks at 10TB scale on the new Amazon Redshift RG instances and on leading alternative data warehouses. These benchmarks are designed to run queries of various operational requirements and complexities, such as ad hoc, reporting, iterative online analytical processing (OLAP), and data mining. We sized each data warehouse at approximately the same on-demand cost ($32/hr) and ran three power runs of each benchmark out of the box, with no special tuning or manual customization. The results are shown in the following charts.

Bar chart of TPC-DS 10TB price-performance showing Amazon Redshift RG leading alternative data warehouses

Bar chart of TPC-H 10TB price-performance showing Amazon Redshift RG leading alternative data warehouses

The new RG instance leads, and by a large margin. Better price-performance means better performance and lower cost.

Conclusion

Amazon Redshift RG instances are the next generation of analytics engine, delivering high performance for data warehouse and data lake workloads. Because RG supports all the same workloads and features as RA3, getting started is straightforward. See our migration guide for how to upgrade and start getting better performance at lower cost.

Find the best price-performance for your workloads

The benchmarks used in this post are derived from the industry-standard TPC-DS and TPC-H benchmarks, and have the following characteristics:

  • We use the schema and data unmodified from TPC-DS and TPC-H.
  • The queries are generated using the official TPC-DS and TPC-H kits with query parameters generated using the default random seed of the kits. TPC-approved query variants are used for a warehouse if the warehouse doesn’t support the SQL dialect of the default queries.
  • The test includes the 99 TPC-DS SELECT queries and 22 TPC-H SELECT queries. It doesn’t include maintenance and throughput steps.
  • Three power runs were run, and the best run is taken for each data warehouse.
  • Price-performance is calculated as the cost per hour (USD) divided by 3,600 seconds/hour times the benchmark geomean in seconds, which is equivalent to the geomean cost per query. The latest published on-demand pricing is used for all data warehouses.

We call this the Cloud Data Warehouse benchmark, and you can reproduce the preceding benchmark results using the scripts, queries, and data available in our GitHub repository. It’s derived from the TPC-DS benchmarks as described in this post, and as such isn’t comparable to published TPC-DS results, because the results of our tests don’t comply with the official specification.


About the authors

Stefan Gromoll

Stefan Gromoll

Stefan is a Principal Engineer with the Amazon Redshift team where he is responsible for Redshift performance. In his spare time, he enjoys cooking, playing with his four boys, and chopping firewood.

Ankit Sahu

Ankit Sahu

Ankit brings over 18 years of expertise in building innovative data products and services. His diverse experience spans product strategy, go-to-market execution, and digital transformation initiatives. Currently, as Sr. Product Manager at Amazon Web Services (AWS), Ankit is driving the vision and strategy for Amazon Redshift.

Mohammed Alkateb

Mohammed Alkateb

Mohammed is an Engineering Manager at Amazon Redshift, leading Software Engineers, Applied Scientists, and Amazon Scholars across query optimization, data lake access, performance engineering, and new instance qualification. Prior to Amazon, he spent over 12 years with the Teradata Optimizer team. Mohammed holds a PhD from The University of Vermont and has many US patents and publications in premier database conferences.

Yousuf Hussain

Yousuf Hussain

Yousuf is a Senior Software Engineer at Amazon Redshift with 11 years of experience in building and operating large-scale cloud data warehouse systems. He is passionate about analytics and focuses on instance strategy, availability, and reliability to deliver a performant experience for Amazon Redshift customers.

Nita Shah

Nita Shah

Nita is a Sr. Analytics Specialist Solutions Architect at AWS based out of New York. She has been building enterprise data platforms, data warehousing, and analytics solutions for over 20 years and specializes in Amazon Redshift. She is focused on helping customers design and build enterprise-scale well-architected analytics and decision support platforms.

Sanket Hase

Sanket Hase

Sanket is an Engineering Manager with the Amazon Redshift team, where he leads query execution teams focusing on data lake analytics, hardware-software co-design, and vectorized query execution. Sanket holds a Master’s in CS from Carnegie Mellon University and has several U.S. patents in the field of database systems

Jingbo Zhang

Jingbo Zhang

Jingbo is a Data Engineer at Amazon Redshift focused on new instance qualification and performance validation. She has contributed to the qualification and launch of multiple Graviton-based Redshift instance families, including RG, r8gd, and r7gd, with a focus on benchmarking, performance analysis, and automation. Jingbo holds a master’s degree in data Analytics from Carnegie Mellon University.

AI-powered performance recommendations for Amazon Redshift

Post Syndicated from Steve Phillips original https://aws.amazon.com/blogs/big-data/ai-powered-performance-recommendations-for-amazon-redshift/

Data platform teams running Amazon Redshift collect performance telemetry across system views like SYS_QUERY_HISTORY, SVV_TABLE_INFO, and SVV_ALTER_TABLE_RECOMMENDATIONS, plus Amazon CloudWatch metrics for capacity, query execution, and storage. The challenge is interpretation. Correlating a spike in QueryRuntimeBreakdown commit time with hundreds of small INSERT statements, or connecting high disk spill with undersized compute, takes deep expertise and hours of manual analysis.

In this post, you learn how to build an AI-powered solution that collects the telemetry, pre-computes performance signals, correlates them with CloudWatch, and uses Amazon Bedrock to generate prioritized recommendations. The source code is in the accompanying GitHub repository: sample-ai-performance-advisor-for-amazon-redshift.

The signal-based design is what makes this solution produce precise recommendations rather than generic advice. Instead of dumping raw system view output into the large language model (LLM) prompt, the collector pre-computes boolean and threshold-based findings, pairs them with CloudWatch correlations, and hands the model a structured context. The model then cross-references specific query IDs, table names, and metric values in its output.

Solution overview

Two AWS Lambda functions run on a 24-hour Amazon EventBridge schedule:

  • The collector Lambda runs 13 diagnostic SQL queries against Amazon Redshift Serverless and reads the workgroup’s Workload Management (WLM) configuration. It also collects CloudWatch metrics across capacity, query execution, WLM, connections, and storage. From these inputs, it computes the performance signals. Finally, it writes a telemetry JSON file to Amazon Simple Storage Service (Amazon S3).
  • The analyzer Lambda reads the telemetry from Amazon S3, builds a structured prompt with inline CloudWatch-to-signal correlations. Using the correlations, the analyzer calls Amazon Bedrock (Anthropic Claude Sonnet 4.6), and writes the resulting recommendations JSON back to Amazon S3.
  • An Amazon Simple Notification Service (Amazon SNS) topic sends an email summary of the top recommendations to subscribers.
AWS architecture diagram showing an automated Redshift analysis pipeline within the AWS Cloud. Amazon EventBridge triggers a “Collector” AWS Lambda function, which interacts bidirectionally with AWS Secrets Manager, Amazon Redshift, and Amazon CloudWatch to gather data. The Collector passes results to an “Analyzer” AWS Lambda function, which exchanges data with Amazon Bedrock and reads/writes to Amazon S3. The Analyzer then publishes to Amazon Simple Notification Service (SNS), which delivers an email notification.

Figure 1 – Architecture diagram

Prerequisites

Before deploying the solution, make sure the following are in place.

  • An Amazon Redshift Serverless workgroup with a database and query history.
  • An Amazon Redshift database administrator user (superuser). The collector reads views that only a superuser can query (SVV_TABLE_INFO, SVV_ALTER_TABLE_RECOMMENDATIONS, SVV_MV_INFO, SYS_SERVERLESS_USAGE, SYS_AUTO_TABLE_OPTIMIZATION).
    Store the admin credentials in AWS Secrets Manager and pass the secret ARN to the collector.
    Alternatively, have an existing superuser run ALTER USER "IAMR:redshift-performance-recommendations-role" CREATEUSER;
    once to grant the Lambda role superuser privileges.
  • Amazon Bedrock model access for the model of choice. For this solution, a us.anthropic.claude-* model is recommended for multi-region inference. The solution doesn’t depend on a single model.
  • The AWS Command Line Interface (AWS CLI) installed and configured, and a clone of the GitHub repository.

Create the supporting resources

You need an Amazon S3 bucket, an Amazon SNS topic, an AWS Secrets Manager secret, and an AWS Identity and Access Management (IAM) role before the Lambda functions can run.

Create the Amazon S3 bucket

The Amazon S3 bucket will host the output report.

  • Open the Amazon S3 console and choose Create bucket.
  • Enter a globally unique name (for example, amzn-s3-demo-bucket), keep the default settings, and choose Create bucket.

The collector writes telemetry JSON under the telemetry/ prefix and the analyzer writes recommendations under the recommendations/ prefix.

Create the Amazon SNS topic and subscription

Use Amazon SNS to generate notifications once reports are created.

  • Open the Amazon SNS console and choose Topics, Create topic.
  • Select Standard, and enter the name redshift-performance-recommendations.
  • Choose Create topic.
  • On the topic detail page, choose Create subscription.
  • Select Email as the protocol, enter your email address, and choose Create subscription.
  • Open the confirmation email from AWS Notifications and choose Confirm subscription.
Amazon SNS “Create topic” console page. The Type is set to Standard (selected over FIFO), and the Name field contains “redshift-performance-recommendations.” Annotation arrows highlight the Topics nav item, the Standard topic type, the entered name, and the “Create topic” button in the lower right. Optional sections for Encryption, Access policy, Delivery policy, Message delivery status logging, Tags, and Active tracing are collapsed below.

Figure 2 – Create SNS Topic

Store the admin credentials in AWS Secrets Manager

To avoid using hard-coded credentials, create an AWS Secrets Manager secret to connect to Amazon Redshift.

  • Open the AWS Secrets Manager console and choose Store a new secret.
  • Select Other type of secret, choose the Plaintext tab, and paste the following, replacing <ADMIN_PASSWORD> with the workgroup’s admin password:
    {"username":"admin","password":"<ADMIN_PASSWORD>"}

  • Choose Next, enter redshift-performance-admin as the secret name, then choose Next, Next, and Store.
  • Copy the secret Amazon Resource Name (ARN) from the secret detail page. You pass it to the collector in a later step.
AWS Secrets Manager “Store a new secret” page, Step 1: Choose secret type. “Other type of secret” is selected, and the Plaintext tab shows the key-value pair {“username”:“admin”,“password”:“”}. The encryption key is set to aws/secretsmanager. Annotation arrows highlight the secret type selection, the plaintext credentials, and the “Next” button in the lower right.

Figure 3 – Create secret

Create the IAM role and attach the policy

The repository includes a trust policy in iam/trust-policy.json (allowing lambda.amazonaws.com to assume the role) and the least-privilege permission policy in iam/lambda-role-policy.json. Replace the <ACCOUNT_ID>, <REGION>, <YOUR_BUCKET>, and SNS topic ARN placeholders in the permission policy with your values, then create the role in the AWS Management Console or with this AWS CLI command:

aws iam create-role --role-name redshift-performance-recommendations-role \
    --assume-role-policy-document file://iam/trust-policy.json

aws iam put-role-policy --role-name redshift-performance-recommendations-role \
    --policy-name redshift-performance-policy \
    --policy-document file://iam/lambda-role-policy.json

The permission policy grants the Amazon Redshift Data API, Amazon S3, Amazon SNS, Amazon Bedrock, AWS Lambda invoke, AWS Secrets Manager, and Amazon CloudWatch Logs permissions that both Lambda functions require.

Deploy the Lambda functions

The collector source is in lambda/collector.py and it loads the SQL files in sql/ at runtime. The deployment package must contain both.

Package the collector

Open a terminal or shell window and execute a command to copy the collector code, supporting SQL into a folder and archive.

mkdir -p build/collector/sql
cp lambda/collector.py build/collector/
cp sql/*.sql build/collector/sql/
(cd build/collector && zip -qr ../collector.zip .)

Create the collector function

Using the AWS Management Console, navigate to AWS Lambda.

  • Choose Create function.

    AWS Lambda “Create function” console page with the “Configure custom execution role” panel open on the right. “Author from scratch” is selected, the function name is “redshift-performance-collector,” and the runtime is Python 3.14. Under Additional settings, the “Custom execution role” toggle is enabled, and the execution role list is set to “redshift-performance-recommendations-role.” Annotation highlights mark the Author from scratch option, function name, runtime, custom execution role toggle, the selected role, the Save button, and the “Create function” button.

    Figure 4 – Create AWS Lambda function

  • Select Author from scratch, enter redshift-performance-collector as the name, and select Python 3.14.
  • Expand Custom settings, toggle Custom execution role, choose an existing role, select redshift-performance-recommendations-role, and choose Save.
  • On the function page, choose Upload from, .zip file, and upload build/collector.zip.
  • In Runtime settings, select Edit, and set the Handler to collector.lambda_handler.

    Lambda console for the “redshift-performance-collector” function, Code tab. The code editor shows collector.py — a Python file that runs diagnostic SQL queries against Amazon Redshift Serverless, collects CloudWatch metrics, writes telemetry to Amazon S3, and invokes the analyzer Lambda. The Runtime settings section below shows the Handler highlighted as “lambda_function.lambda_handler,” with an arrow pointing to the Edit button and the “Upload from .zip file” option highlighted.

    Figure 5 – Set AWS Lambda handler

  • Choose Configuration, Edit, set timeout to 5 minutes, and memory to 256 MB.

    Lambda console for “redshift-performance-collector,” Configuration tab with “General configuration” selected. The panel shows Memory 128 MB, Ephemeral storage 512 MB, and Timeout 0 min 3 sec, with SnapStart set to None. Annotation arrows point to the General configuration menu item and the Edit button.

    Figure 6 – Set AWS Lambda timeout and memory

  • Under Configuration, select Environment variables, and add the following keys:
    • WORKGROUP: your Amazon Redshift Serverless workgroup name.
    • NAMESPACE_NAME: the namespace the workgroup belongs to.
    • DATABASE: dev (or your target database).
    • BUCKET: the Amazon S3 bucket name you created earlier.
    • SECRET_ARN: the AWS Secrets Manager secret ARN you copied earlier.
    • ANALYZER_FN: redshift-performance-analyzer.

Package and create the analyzer

Repeat the same steps for the analyzer, using lambda/analyzer.py with a 15-minute timeout:

(cd lambda && zip -q ../build/analyzer.zip analyzer.py)

Use the Lambda console to create redshift-performance-analyzer with handler analyzer.lambda_handler, timeout 15 minutes, memory 256 MB, the same execution role, and these environment variables:

  • BUCKET: the same Amazon S3 bucket.
  • SNS_TOPIC: the SNS topic ARN.
  • MODEL_ID: us.anthropic.claude-sonnet-4-6.

The analyzer creates the Amazon Bedrock client with read_timeout=600 and max_tokens=16384 to handle large prompts and long responses. Anthropic Claude inference on a full telemetry payload typically takes 2–4 minutes.

How the signals and the prompt work

You don’t write any custom code for signal computation or prompt construction. Both computation and construction live in the repository.

The compute_signals() function in lambda/collector.py scans the telemetry for Boolean and threshold-based anti-patterns. At the table level, it looks for row skew, ghost rows, stale statistics, unsorted data, sub-optimal sort or distribution keys, and oversized VARCHAR columns. It also flags runtime and workload issues such as disk spill, small-insert bursts, high Data Definition Language (DDL) executions, and unoptimized COPY file size. Beyond that, it catches Amazon Redshift Spectrum queries that fail to prune partitions and data sharing materialized views doing full refresh. It also flags WLM configurations that lack Query Monitoring Rules (QMR), such as limits on blocks spilled to disk and query execution time. The full set of signals and thresholds is defined inline in the function. To tune a threshold or add a custom signal, edit this function and redeploy.

The build_prompt() function in lambda/analyzer.py constructs the Amazon Bedrock prompt in four sections. The first section lists the triggered signals. The second adds CloudWatch metrics, annotated with >> CORRELATION lines that pair each signal with its supporting metric. The third includes the filtered supporting data, limited to the table and query rows that triggered a signal. The fourth gives explicit instructions to return a pipe delimited text where every recommendation references specific table names, query IDs, and metric values. This structure is why the model produces targeted output rather than generic best-practice advice.

Schedule daily runs

Use the Amazon EventBridge console to trigger the collector every 24 hours.

  • Open the EventBridge console and choose Schedules under Scheduler, Create schedule.
  • Enter the name redshift-performance-daily for Schedule name, toggle Recurring schedule and Rate-based schedule.
  • Under Rate expression, enter 24 and select hours.
  • For Flexible time window, choose Off, and select Next.
    Amazon EventBridge Scheduler “Create schedule” page, Step 1: Specify schedule detail. The schedule name is “redshift-performance-daily.” Under Schedule pattern, “Recurring schedule” and “Rate-based schedule” are selected, with a rate expression of 24 hours, and the time zone set to (UTC-06:00) America/Denver. Annotation highlights mark the Schedules nav item, the recurring/rate-based selections, the rate expression, and the Next button.

    Figure 7 – Create Amazon EventBridge schedule

     

  • On the Select target page, choose AWS Lambda, select the redshift-performance-collector function, and choose Next.

    EventBridge Scheduler “Create schedule” page, Step 2: Select target. “Templated targets” is selected and the AWS Lambda “Invoke” target is chosen from the grid of target options. In the Invoke section, the Lambda function list is set to “redshift-performance-collector” with an empty JSON payload. Annotation highlights mark the Templated targets toggle, the AWS Lambda Invoke target, the selected function, and the Next button.

    Figure 8 – Select Amazon EventBridge schedule target

  • Accept the defaults for Settings and select Next. EventBridge automatically adds a resource-based permission on the Lambda function so the rule can invoke it.
  • Choose Create schedule.

Run it once and review the output

Invoke the collector manually to confirm the pipeline works end-to-end.

  • In the Lambda console, open the redshift-performance-collector function and choose Test. Create a test event named manual with the body {} and choose Test.

    Lambda console for “redshift-performance-collector,” Test tab. A new test event named “manual” is being configured with Invocation type set to Synchronous, event sharing set to Private, the “Hello World” template selected, and an empty {} Event JSON body. Annotation arrows point to the function in the left nav, the Synchronous option, the event name, the Event JSON field, and the Test button.

    Figure 9 – Test end-to-end workflow

  • The function completes in under a minute. Check the Monitor tab for the invocation log via the CloudWatch live logs link.
  • In the Amazon S3 console, open your bucket. Confirm that the telemetry/ prefix contains a JSON file with the current timestamp.
  • Within 2–4 minutes, the analyzer publishes a message to the SNS topic. Check the email address you subscribed for the summary with the top 10 recommendations. Confirm that the recommendations/ prefix in Amazon S3 contains the full JSON.

Each recommendation has a priority (critical, high, medium, low) and a category (query_optimization, table_design, capacity, wlm, maintenance, or ingestion). It also includes a signal_source that names the signals and CloudWatch metrics that triggered it, a plain-language explanation, a specific SQL or configuration action, and an expected impact estimate.

Email notification from AWS Notifications with the subject “Redshift performance: 3 critical, 5 high, 4 medium, 2 low (8 signals)” highlighted. The body is a plain-text “Amazon Redshift Performance Recommendations” report listing workgroup, namespace, database, analysis time, and 14 recommendations. Two critical items are shown for the game_events table: fixing extreme row-skew via DISTSTYLE ALL, and eliminating non-encoded columns with column compression, each with a category, source, explanation, SQL action, and expected impact.

Figure 10 – Sample analyzer emailed output

Best practices

  • Tune thresholds to your workload. The default thresholds in compute_signals() come from the Amazon Redshift operational review playbook. For high-velocity ingestion or small-cluster environments, consider lowering the small-insert threshold, widening the stale-statistics window, or adding custom signals for your own tables.
  • Keep the signal-to-metric correlations current. When you add a signal, also add a matching correlation in build_correlations(). The inline >> CORRELATION lines are what make the model connect an infrastructure metric to an application-level symptom.
  • Review recommendations before you act. The analyzer produces prioritized suggestions, but VACUUM, ANALYZE, and ALTER TABLE actions change table state. Read the explanation and action on each recommendation, validate the SQL against your schema, and run it during a maintenance window.

Cleaning up

To avoid ongoing charges, delete the resources you created for this solution:

  • The two AWS Lambda functions: redshift-performance-collector and redshift-performance-analyzer.
  • The Amazon EventBridge rule: redshift-performance-daily.
  • The Amazon SNS topic and its email subscription: redshift-performance-recommendations.
  • The Amazon S3 bucket, including the telemetry/ and recommendations/ objects.
  • The AWS Secrets Manager secret: redshift-performance-admin.
  • The IAM role and its inline policy: redshift-performance-recommendations-role.

Conclusion

You now have a daily performance review for Amazon Redshift Serverless that runs entirely on AWS Lambda, stores every run in Amazon S3, and delivers prioritized recommendations by email. The signal-based prompt pattern keeps the Amazon Bedrock cost low and the recommendations specific to your workload.

To learn more, see the following resources:


About the authors

Steve Phillips

Steve Phillips

Steve is a Principal Technical Account Manager and Analytics specialist at AWS in the North America region. Steve currently focuses on data warehouse architectural design, AI/ML data foundations, data lakes, data ingestion pipelines, and cloud distributed architectures.

Richard Raseley

Richard Raseley

Richard is a Senior Technical Account Manager in North America who works with Games customers. He is passionate about applying his background in automation, cloud computing, networking, and storage to help customers build AI solutions.

Scale analytics with Amazon Redshift multi-warehouse enhancements

Post Syndicated from Raza Hafeez original https://aws.amazon.com/blogs/big-data/scale-analytics-with-amazon-redshift-multi-warehouse-enhancements/

Onboard analytics workloads at scale with Amazon Redshift’s improved remote table data definition language (DDL), materialized view improvements, and concurrency scaling enhancements for zero-ETL and auto-copy.

As organizations scale their analytics capabilities, they need the ability to add workloads without disrupting production operation or being constrained by the resources of a single data warehouse. In this post, we introduce new capabilities of Amazon Redshift that enhance our multi-warehouse and scaling capabilities: remote materialized view (MV) operations, remote table DDL support, and concurrency scaling enhancements for zero-ETL and S3 event integration. These features help you build more scalable, performant decentralized analytics architectures on Amazon Redshift.

Let us review how these new features enable you to run analytics at scale.

New remote materialized view operations

New remote table DDL operations

  • ALTER TABLE ALTER DISTSTYLE operations now work on remote warehouses through concurrency scaling and data sharing. You can dynamically optimize data distribution across distributed environments, improving query performance and resource utilization without requiring data migration. This is especially valuable for data engineers fine-tuning performance across multiple warehouses and administrators adapting to changing query patterns.
  • ALTER TABLE APPEND operations now extend to remote warehouses through concurrency scaling and data sharing. This consolidates data across distributed environments, so you can efficiently combine tables without complex data movement or extract, transform, and load (ETL) processes. Organizations managing dynamic table operations across multiple environments can maintain data consistency while reducing operational overhead.

Concurrency scaling improvements

With these new concurrency scaling capabilities, you can maintain consistent data freshness without compromising existing warehouse performance. This eliminates the traditional trade-off between analytics and data loading. Apart from turning on concurrency scaling, no additional changes are required to take advantage of these features.

Customer use cases

This section covers two industry use cases: the first for a financial services customer and the second for a gaming industry customer.

Financial services use case

The following is a sample architecture for a large financial services customer with global operations. This customer uses a multi-warehouse architecture built on Amazon Redshift.

Financial services multi-warehouse architecture using STG, DWH, ETL, and USR Amazon Redshift warehouses

The staging (STG) warehouse serves as a raw zone for data from various sources, like the bronze layer of a medallion architecture. This warehouse also cleanses and standardizes the raw data to the silver layer and makes it available for further processing. The STG warehouse uses MVs to process millions of nested JSON messages and extract attributes into scalar columnar Amazon Redshift tables.

CREATE MATERIALIZED VIEW rawdb.fsi.customer_orders_raw
distkey(c_custkey) sortkey(c_custkey) AS (
    SELECT c_custkey,
        o.o_orderstatus,
        o.o_totalprice,
        o_idx
    FROM customer_orders_lineitem c,
        c.c_orders o AT o_idx
);
REFRESH MATERIALIZED VIEW rawdb.fsi.customer_orders_raw;

The DWH warehouse serves as the primary Amazon Redshift instance and gold layer, providing data to consuming applications like Business Objects and Tableau. The zero-ETL concurrency scaling improvements provide consistent data freshness even when zero-ETL ingestion spikes occur alongside heavy DWH workloads. The DWH MVs provide fast access to aggregated data for Tableau extracts and Business Objects live reports. The DWH warehouse takes advantage of concurrency scaling when multiple MVs need to be refreshed on the DWH instance.

CREATE MATERIALIZED VIEW bodb.final.customer_churn_tbl
AS (
    SELECT state,
        account_length,
        area_code,
        total_charge/account_length AS average_daily_spend,
        cust_serv_calls/account_length AS average_daily_cases,
        churn
    FROM custdb.final.customer_activity_all
);
REFRESH MATERIALIZED VIEW bodb.final.customer_churn_tbl;

The ETL01/02 warehouses serve as dedicated compute environments for running project-specific ETL jobs, while the USR01/02 warehouses handle user workloads such as ad-hoc analysis or model building from dbt. When new objects are required by user workloads, they are created and maintained on the remote producer warehouse (DWH).

ALTER TABLE salesdb.final.sales_report_all
ALTER DISTKEY sales_id;
ALTER TABLE APPEND salesdb.final.sales_report_all
FROM stagingdb.sales.sales_2026_02;

Gaming industry use case

A leading gaming company has built their entire analytics infrastructure on AWS, with their analytics team managing data streaming from games, data warehousing, and business intelligence tools. They standardized Amazon Redshift across the organization, migrating off Vertica running on Amazon Elastic Compute Cloud (Amazon EC2). After overcoming early challenges with cluster resize operations, the team became strong advocates for Amazon Redshift and now runs their primary production cluster on 32 ra3.16xlarge nodes.

As their data ingestion pipeline grew, query workloads began competing with data ingestion processes, creating performance bottlenecks. Rather than scaling up their primary cluster, they implemented a workload isolation strategy using Amazon Redshift data sharing. The customer launched a second 16-node ra3.4xlarge cluster as a data share consumer, with the primary cluster serving as the producer. This architecture allowed them to migrate consumption workloads to the consumer cluster while the producer focused on data ingestion, effectively supporting growth without increasing the primary cluster size.

Gaming company architecture with a producer Amazon Redshift cluster sharing data to a consumer cluster

Recognizing the advantages of this distributed architecture, the gaming company expanded their approach by migrating workloads to Amazon Redshift Serverless, further using the data sharing model for workload isolation. Amazon Redshift’s remote materialized view capability allowed the gaming company to create materialized views directly on the data shared by the producer cluster. Each consumer cluster could now build materialized views optimized for its specific workload patterns. This created pre-aggregated datasets, custom join strategies, and workload-specific data distributions, without impacting the producer cluster’s performance or requiring data duplication. The producer warehouse maintains data distribution and sorting strategies designed for generic enterprise needs, providing consistent data quality across all consumers. Meanwhile, consumer warehouses used remote materialized views to fine-tune query performance for their distinct analytical requirements, whether supporting real-time player analytics, business intelligence dashboards, or ad-hoc data science workloads. This distributed approach to data consumption optimization proved essential for the gaming company. It delivered fast query performance across diverse analytical workloads while maintaining a single source of truth in the producer cluster and avoiding the operational overhead of managing redundant data copies.

Best practices

To get the most out of these new capabilities, consider the following best practices:

  • Enable concurrency scaling on your Amazon Redshift clusters and Serverless workgroups to allow ETLs and user queries to run even faster, providing consistent report and dashboard performance.
  • Set up usage limits for concurrency scaling on both Amazon Redshift provisioned clusters and Serverless workgroups by configuring an appropriate MaxRPU setting. This helps you avoid unexpected additional costs. For more information, see the Amazon Redshift usage limits documentation.
  • Use remote MVs to offload resource-intensive MV creation and refresh operations from your primary warehouse to remote data share clusters.

Conclusion

In this post, we walked through the new MV refresh features, remote table DDL capabilities, and expanded concurrency scaling support for zero-ETL and S3 auto-copy. These features help you move beyond the constraints of a single warehouse. They are particularly valuable for organizations managing distributed data architectures that require dynamic table management across multiple environments while maintaining data consistency and adapting quickly to changing workloads. To get started, make sure you are running the latest Amazon Redshift version. Then visit the Amazon Redshift documentation to learn more about concurrency scaling, data sharing, and materialized views.


About the authors

Raza Hafeez

Raza Hafeez

Raza is a Senior Product Manager, Technical at Amazon Redshift. He has 15+ years of experience building and optimizing enterprise data warehouses and is passionate about making cloud analytics accessible and cost-effective for customers of all sizes.

Ravi Animi

Ravi Animi

Ravi is a senior product leader in the Amazon Redshift team and manages several functional areas of the Amazon Redshift cloud data warehouse service, including spatial analytics, streaming analytics, query performance, Spark integration, and analytics business strategy. He has experience with relational databases, multidimensional databases, IoT technologies, storage and compute infrastructure services, and more recently, as a startup founder in the areas of artificial intelligence (AI) and deep learning, computer vision, and robotics.

Satesh Sonti

Satesh Sonti

Satesh is a Principal Analytics Specialist Solutions Architect based in Atlanta, specializing in building enterprise data platforms, data warehousing, and analytics solutions. He has over 20 years of experience in building data assets and leading complex data platform programs for banking and insurance clients across the globe.

Milind Oke

Milind Oke

Milind is a senior Redshift specialist solutions architect who has worked at Amazon Web Services for three years. He is an AWS-certified SA Associate, Security Specialty and Analytics Specialty certification holder, based out of Queens, New York.

Amazon Redshift delivers faster performance for BI dashboards and real-time analytics

Post Syndicated from Stefan Gromoll original https://aws.amazon.com/blogs/big-data/amazon-redshift-delivers-faster-performance-for-bi-dashboards-and-real-time-analytics/

Business intelligence (BI) dashboards and real-time analytics have become essential tools for making informed decisions quickly. Modern data warehouses must excel at complex, long-running analytical queries and also deliver sub-second response times for the short, ad hoc queries that power interactive and real-time experiences. This matters even more as agents explore and derive new insights from massive amounts of data. From executives monitoring key performance indicators on their morning dashboards to data analysts using agents to explore datasets interactively, the expectation is clear: queries should return results fast and predictably.

Amazon Redshift has long been optimized for these use cases. Over the years, we’ve introduced numerous features designed to improve query performance for BI and real-time analytics workloads, including result caching, materialized views, and automatic workload management (AutoWLM). These capabilities have helped thousands of customers build responsive dashboards and real-time applications on Amazon Redshift. However, we know that when it comes to interactive analytics, every millisecond matters. That’s why we keep focusing on making dashboards load faster and helping exploratory queries return results more quickly.

Today, we’re excited to announce a new performance optimization in Amazon Redshift that improves the response times of low-latency SQL queries, such as those used in real-time analytics applications or generated by BI dashboards. With this enhancement, you can experience improved query latencies because of a reduction in the time Amazon Redshift spends preparing SQL queries for execution. SQL queries start faster, so they return results quicker.

How the optimization works

To understand this improvement, let’s first examine one of Amazon Redshift’s existing core performance capabilities: code generation. Code generation is an optimization technique that analyzes each SQL query and generates query-specific C++ code internally. This code is then compiled and executed in parallel across the available Amazon Redshift compute nodes to deliver results back to you. Code generation has been fundamental to Amazon Redshift query performance, executing complex analytical queries with high efficiency.

While code generation results in performant query execution, new queries can experience a one-time compilation overhead the first time they run. Amazon Redshift already caches compiled code, and more than 99% of queries in the Amazon Redshift fleet execute using this cached generated code and experience no compilation overhead. For queries that haven’t been cached yet, the one-time compilation overhead is most noticeable for fast-running queries (for example, millisecond or single-digit second queries), where it can represent a significant portion of total execution time.

With the optimization we announced, Amazon Redshift reduces this compilation overhead. Here’s how it works: when Amazon Redshift receives a query, it first checks if optimized compiled C++ code already exists in the cache from previous executions of similar queries in the Amazon Redshift fleet. If so, it uses that code for best performance. If not, Amazon Redshift now applies a new query compilation optimization that processes new queries immediately using composition. Composition is a technique that generates a lightweight arrangement of pre-existing logic. At the same time, it creates query-specific optimized code that is compiled and executed across available compute resources to boost performance further. Composition removes compilation from the critical path of query execution and provides immediate execution while compilation proceeds in the background. With this optimization, new queries processed by Amazon Redshift start faster and deliver performance consistent with subsequent runs.

This approach ensures that first-time queries start much quicker, while repeated queries continue to benefit from the same leading price-performance that Amazon Redshift code generation delivers.

The best part? No action is necessary for your queries to start benefiting from this performance optimization. This enhancement is now the default for all SQL queries in Amazon Redshift for all users on provisioned clusters or serverless workgroups in all AWS Regions where Amazon Redshift is available at no additional cost.

Real-world performance results

We analyzed the impact of this new optimization on Amazon Redshift customer clusters. To do so, we measured the compilation time of the 1% of query segments that didn’t get a cache hit in our compilation cache and therefore required compilation. The following chart shows the results. The P50 compilation time before the optimization was 4.3 seconds. With this optimization, the compilation time dropped 25.7x to 170 ms.

Bar chart comparing P50 compilation time on Amazon Redshift before and after the FastCompile optimization, showing a reduction from 4.3 seconds to 170 milliseconds, a 25.7x improvement

With this optimization, BI dashboards load faster, interactive exploration feels more responsive, and real-time analytics applications can deliver insights with lower latency.

What customers are saying

“Following the significant performance improvements that Amazon Redshift demonstrated for cold query execution on our cluster with the FastCompile query performance feature enabled, achieving 2.4x faster query performance with compilation time reduced from 12 seconds to 5 seconds, we have adopted Amazon Redshift as our analytics solution”

— Vijay Hiremath, Group Manager, Business Platforms, Intuit

“As a data platform leader at a leading Chinese liquor company, we rely heavily on Amazon Redshift as our enterprise data warehouse. With diverse analytical query patterns, we faced performance challenges during initial compilation. After testing Redshift’s new cold query compilation enhancement, cold queries now perform nearly as fast as warm queries, with significantly improved speed on diverse queries”

— Yujie Wang, Data Platform Leader, JNC

“In a mid size customer processing about 85 GB of data daily through complex ETL pipelines — multiple tables, mixed DML operations, all landing into our 1.7 TB Amazon Redshift data warehouse, fast compile enhancements accelerated our post-maintenance ETL pipelines by 25%. Now the customer data loads complete faster, data hits analysts sooner for quick decisions”

— Jagan Mohan, Product Engineering Head, Algonomy

Industry-leading price-performance for all of your workloads

To illustrate the impact of this optimization, we simulated a short-running BI-like low-latency workload using a benchmark derived from the industry-standard TPC-DS benchmark. We ran the workload at a relatively small scale of 100 GB on a 3-node RG xlarge Amazon Redshift cluster. At this cluster size and scale, queries finish in milliseconds or single-digit seconds, representing the expected latencies of a typical BI dashboard. The derived TPC-DS benchmark includes 99 different queries that represent a mix of realistic business intelligence workloads, including reporting queries, ad hoc analysis, and data exploration patterns. For this test, we compared a single cold run of these queries on an Amazon Redshift RG cluster with the same run on comparable alternative cloud data warehouses. We launched the warehouses, loaded the data, executed a single run of 99 queries, and measured the total runtime and geometric mean of the queries. No other cluster warm-up or setup was done. This query performance improvement is hardware agnostic. It works on all supported Amazon Redshift hardware instance types, on RA3 and RG on provisioned clusters, and on the hardware that supports serverless workgroups.

The results are shown in table below and summarized in subsequent chart. With this new optimization, Amazon Redshift delivers the fastest runtime and geomean for these short queries at the lowest cost, with up to 8.3x better price-performance than the leading alternative data warehouses for new queries.

. Cost / hr Runtime (sec) Geomean (sec) Runtime comparison Geomean comparison Geomean price-performance
Redshift 3-node RG.xlarge $2.28 235 1.7 baseline baseline baseline
Alternative Warehouse A $3.00 327 2.3 1.4x slower 1.3x slower 1.7x more expensive
Alternative Warehouse B $4.00 538 3.4 2.3x slower 2x slower 3.4x more expensive
Alternative Warehouse C $6.00 907 5.5 3.9x slower 3.2x slower 8.3x more expensive

Bar chart comparing TPC-DS benchmark price-performance for the Amazon Redshift 3-node RG.xlarge baseline against three alternative cloud data warehouses, showing Amazon Redshift fastest at lowest cost and up to 8.3x better price-performance

Conclusion

The new query startup optimization in Amazon Redshift continues our commitment to fast performance across analytical workloads. By reducing compilation overhead, we’ve made BI dashboards and real-time analytics applications more responsive, while maintaining the query execution performance that Amazon Redshift is known for.

Because this optimization is automatically enabled for all Amazon Redshift customers, you can start experiencing these benefits immediately. No configuration changes or query rewrites are required. Your existing queries will run faster.

To learn more, visit Amazon Redshift. To get started, you can try Amazon Redshift Serverless and start querying data in minutes without setting up or managing data warehouse infrastructure. For more details on performance best practices, see the Amazon Redshift Database Developer Guide.

Find the best price performance for your workloads

The benchmark used in this post is derived from the industry-standard TPC-DS benchmark, and has the following characteristics:

  • The schema and data come from TPC-DS unmodified.
  • The queries are used unmodified from TPC-DS. TPC-approved query variants are used for a warehouse if the warehouse does not support the SQL dialect of the default TPC-DS query.
  • The test includes only the 99 TPC-DS SELECT queries. It does not include maintenance and throughput steps.
  • A single power run was run with query parameters generated using the default random seed of the TPC-DS kit. The total runtime and geomean of that single cold run were used for the results in this post.
  • Price performance is calculated as the geomean in seconds divided by 3,600 seconds per hour, multiplied by the cost of the warehouse per hour. The result is equivalent to the geomean cost per query. Published on-demand pricing is used for all data warehouses.

We call this benchmark the Cloud Data Warehouse Benchmark, and you can reproduce the preceding benchmark results using the scripts, queries, and data available on GitHub. It is derived from the TPC-DS benchmark and is not comparable to published TPC-DS results, because our test results do not comply with the specification.

Each workload has unique characteristics. If you’re starting out, a proof of concept is the best way to understand how Amazon Redshift performs for your requirements. When running your own proof of concept, focus on proper cluster sizing and the right metrics: query throughput (the number of queries per hour) and price performance. You can make a data-driven decision by requesting assistance with a proof of concept or by working with a system integration and consulting partner.

To stay current with the latest developments in Amazon Redshift, subscribe to the What’s New in Amazon Redshift RSS feed.


About the authors

Stefan Gromoll

Stefan Gromoll

Stefan is a Principal Engineer with Amazon Redshift where he is responsible for Redshift performance across the stack. In his spare time, he enjoys cooking, playing with his three boys, and chopping firewood.

Ravi Animi

Ravi Animi

Ravi is a Senior Product Management leader in the Redshift Team and manages several functional areas of the Amazon Redshift cloud data warehouse service including performance across the stack, query processing, materialized views, spatial analytics, streaming analytics and migration strategies. He has deep experience with relational databases, multi-dimensional databases, IoT technologies, storage and compute infrastructure services and as a startup founder using AI/deep learning, computer vision, and robotics. He has dual bachelors degrees in physics and electrical engineering from Washington Univ. St. Louis, a masters degree in engineering from Stanford and an MBA from Chicago Booth.

Venkat Govindaraju

Venkat Govindaraju

Venkat is a Principal Engineer in the Amazon Redshift engineering team. He has designed and developed several major features in Amazon Redshift including the feature discussed in this blog. He holds a Ph.D in computer science from the University of Wisconsin, Madison.

Kiran Chinta

Kiran Chinta

Kiran is a Senior Development Manager in the Amazon Redshift engineering team. He has led the delivery of several key features in Amazon Redshift. He has extensive experience leading software engineering teams at Amazon Web Services, IBM and other companies.

Optimize your Tableau integration with Amazon Redshift Serverless

Post Syndicated from Nidhi Nayak original https://aws.amazon.com/blogs/big-data/optimize-your-tableau-integration-with-amazon-redshift-serverless/

This is a guest blog post co-written by Adiascar Cisneros, from Tableau at Salesforce.

Integrating Tableau with Amazon Redshift Serverless gives you high-performance analytics with serverless scaling and minimal capacity planning. Although automatic scaling handles warehouse management for you, optimization requires a strategic approach to data modeling, security, and query management.

In this post, we provide a guide to help you use Tableau’s Relationships and Amazon Redshift Serverless architecture to deliver sub-second insights while maximizing every Redshift Processing Unit (RPU). We also provide guidance on five key areas: data model architecture for optimal query performance, security configuration and access control, performance optimization through smart configuration, cost management strategies, and query and join optimization techniques.

Prerequisites

Before implementing these optimization strategies, make sure you have:

  • Tableau Desktop (version 2022.1 or later) or Tableau Server deployed.
  • An active Amazon Redshift Serverless workspace.
  • AWS Identity and Access Management (IAM) permissions to configure authentication and access controls.
  • Network connectivity configured between your Tableau environment and Amazon Redshift Serverless.
  • The native Amazon Redshift driver installed.

Building the foundation

The success of any analytics system begins with its data model. True scalability starts with the end-user experience. Your data model is more than a storage structure. It’s the foundation of dashboard responsiveness. By aligning your database design in Amazon Redshift with your analytical requirements, you empower Tableau to generate highly efficient queries, reducing costs and keeping your users engaged with the data.

When connecting to Amazon Redshift, we recommend using Tableau’s logical data model, specifically Relationships. With Relationship, you can preserve the native level of detail for each table, so Tableau can perform join culling and dynamically query only the specific tables needed for a particular visualization.

When designing your Amazon Redshift schema, implement a well-structured star or snowflake schema, or one big denormalized table where appropriate. This allows Tableau to optimize query execution automatically. Modern Amazon Redshift deployments benefit significantly from Automatic Table Optimization (ATO), which uses AI and machine learning (ML) to continuously monitor and adjust sort keys and distribution keys. To take advantage of ATO, keep sort keys and distribution styles at their default AUTO setting when you create tables. ATO then continuously monitors workload patterns and adjusts keys to improve query performance.

Start by implementing Relationships in your existing workbooks to take advantage of join culling and improved query performance.

Securing your connection

Native database drivers provide enhanced security features and better integration with Amazon Redshift capabilities compared to generic ODBC or JDBC alternatives.

The integrity of your analytics relies on the quality of the connection between your platforms. Use the native Amazon Redshift driver rather than generic ODBC or JDBC alternatives. The native driver is specifically engineered to use the advanced capabilities of Amazon Redshift and supports modern security protocols, such as AWS IAM Identity Center, out of the box. By prioritizing the native driver, you verify that your connection uses the latest security patches and performance optimizations, establishing a hardened and efficient entry point for your data. For more information, see Integrate Tableau and Okta with Amazon Redshift using AWS IAM Identity Center.

Connection stability for high-scale environments

In Amazon Redshift, cursors are used to retrieve a result set from a query and process the data row-by-row or in smaller chunks rather than loading the entire set into memory at once. For high-scale environments, stable connections depend on how you handle large result sets. In some high-volume scenarios, Amazon Redshift cursors can introduce resource overhead that impacts user concurrency. Monitor your workload and, if necessary, fine-tune your connection configurations using Tableau Data Customization (TDC) files. TDC files are XML configuration files that customize how Tableau connects to your database. Specifically, validate whether disabling cursors improves throughput.

Important: This configuration loads the entire dataset into memory. For large datasets, this might cause performance degradation or out-of-memory errors. Evaluate your dataset size and business requirements before you turn on this setting. This is a key step in tuning your deployment, helping verify that your Amazon Redshift resources remain available and responsive for secure, ad-hoc analysis.

Security best practices

Follow security best practices while deploying Amazon Redshift Serverless. Configure security groups to control inbound access from Tableau Server and Desktop IP ranges. IAM authentication must be the primary method, complemented by SSL/TLS encryption for all connections.

Role-based access control (RBAC) forms the backbone of your security framework:

For authorization, implement a layered security model:

  • Apply explicit GRANT statements.
  • Create distinct database roles aligned with business functions.
  • Use Amazon Redshift system-defined roles judiciously.
  • Apply dynamic data masking for sensitive data.
  • Conduct regular security audits to support ongoing protection.

Audit your current connection types and migrate to the native Amazon Redshift driver if you’re using ODBC or JDBC connections.

Enhancing performance through smart configuration

Smart configuration spans how much data you query, where you push complex logic, how you design dashboards, and how you tune connections. The following sections cover each area.

Managing data volume

To maximize workbook efficiency, start by rigorously managing your data volume. Although Amazon Redshift handles large datasets well, your dashboard should query only what is strictly necessary. Use Tableau Hyper Extracts for production environments to provide a consistent, high-speed cache that offloads repetitive query processing from Amazon Redshift. If a live connection is required, strictly limit your data intake by using Data Source Filters and hiding all unused fields. This helps verify that Tableau generates leaner queries, significantly reducing network latency and processing time.

Shifting complexity to the database

Next, shift the burden of complexity away from the visualization layer. Materialize calculations within your extracts or push complex logic (especially row-level string manipulations and regex) directly down to the Amazon Redshift database level. By pre-calculating these values before the user ever loads the dashboard, you eliminate expensive runtime processing.

Simplify your logic within Tableau by using native features like CASE statements or Sets rather than complex IF/THEN statements. Testing shows these methods perform significantly faster for grouping dimensions.

Streamlining dashboard design

Additionally, optimize the rendering process by streamlining your dashboard design:

  • Limit the number of visualizations per dashboard.
  • Prioritize fixed-size dashboards to maximize server-side caching effectiveness.
  • Avoid high-cardinality filters (fields with thousands of unique values).
  • Don’t use the ‘Show Only Relevant Values’ setting on large datasets, because it forces the system to run extra background queries that slow down your dashboard.

Connection and parameter tuning

Optimize Tableau’s performance by enabling connection pooling tailored to your concurrent user count. Configure datetime handling and parallel query execution settings to match your workload patterns.

You can enhance the automatic resource management of Amazon Redshift Serverless through parameter optimization. Key parameters include:

Choosing between extracts and live queries is a foundational architectural decision. We recommend a hybrid approach tailored to specific use cases rather than a one-size-fits-all policy.

When to use live queries

Live queries are best for real-time analytics. They use Amazon Redshift Serverless automatic scaling to query massive datasets in place. Use this approach for:

  • Up-to-the-minute data requirements.
  • Datasets too massive for extracts.
  • Scenarios requiring database-level row security.
  • Integration with Amazon Redshift Spectrum for Amazon Simple Storage Service (Amazon S3) data.

Keep in mind that live connections rely entirely on the database’s performance, so optimizing your Amazon Redshift tables and using materialization techniques within the database is important for maintaining interactivity.

When to use extracts

For scenarios when data is static or where query performance is critical, Tableau Hyper Extracts provide a high-speed cache that shifts the processing load from Amazon Redshift to Tableau’s data engine. This is valuable for dashboards with complex calculations (such as row-level string manipulations or heavy aggregations) where an extract can pre-materialize results, effectively baking in the logic before the user ever loads the view. By using extracts for these heavy workloads, you reduce the compute load on Amazon Redshift, lowering costs while delivering sub-second response times to end users.

Right-sizing your extracts

To maximize efficiency, right-size your extracts for your dashboard’s specific needs:

  • Avoid the SELECT * mentality.
  • Use data source filters to limit rows.
  • Hide unused fields to remove redundant columns.
  • For higher-level analysis, aggregate your data during the extract process. For example, summarize daily transactions into monthly trends to significantly reduce file size and query time.
  • Schedule refreshes during off-peak hours.
  • Use incremental updates to add only new rows, minimizing Amazon Redshift RPU usage and network overhead.

Balance performance and cost by aligning your connection choice with business freshness requirements and data complexity. Monitor usage patterns to refine this balance over time.

Star schema query and join optimization

Optimize your star schema joins and queries to reduce execution time and compute costs by using Tableau Relationships. Relationships keep tables separate, allowing Tableau to automatically query only the necessary tables for the fields in the view. Relationships are more flexible and often perform better than joins because they don’t force a row-level merge on all fields.

Inefficient joins and poorly optimized queries force Amazon Redshift to scan unnecessary data, increasing both query execution time and compute costs.

Query optimization best practices

Avoid Custom SQL, which forces Tableau to wrap queries in complex sub-selects. Instead, connect directly to tables or views to let the database optimizer function effectively.

Define primary and foreign keys in your Amazon Redshift schema to allow Tableau to assume referential integrity.

Important: Amazon Redshift does not enforce primary or foreign key constraints. They are informational only, and the query optimizer uses them to generate more efficient execution plans. You’re responsible for data integrity at the application or ETL layer. For more information, see Defining constraints. Assume Referential Integrity is a Tableau setting that tells the engine to trust defined key relationships without validating them at query time, reducing query complexity.

Use Materialized Views to pre-compute heavy aggregations, which reduces execution time for frequently accessed data patterns. For example, create materialized views for common date-based aggregations or customer-level summaries.

Optimize Amazon Redshift Serverless by denormalizing data to minimize complex joins. After you apply these changes, use Tableau’s Performance Recorder to regularly validate your query speeds and identify bottlenecks.

Cost optimization and monitoring

Amazon Redshift Serverless charges in RPU-hours on a per-second basis (60-second minimum), so you only pay for the workloads you run.

Optimizing query volumes and resource usage helps you control Amazon Redshift Serverless costs and maintain predictable spending. To help control compute costs, optimize Tableau queries before they reach Amazon Redshift by using Data Source Filters and ‘Hide All Unused Fields.’ This forces the generation of lean SELECT statements that scan only the necessary rows and columns. Because Amazon Redshift Serverless scales resources based on workload, reducing data volume and complexity at the Tableau source layer can help lower RPU consumption and costs.

For more information, see Amazon Redshift Serverless billing.

Using extracts as a cost buffer

Tableau Hyper Extracts act as a cost buffer for high-traffic dashboards. By extracting data into Tableau’s in-memory engine, database costs are typically incurred during scheduled refreshes rather than for every individual user interaction. For live connections, maximize Tableau’s caching architecture by setting server cache policies to “Refresh less often,” ensuring that repetitive dashboard views are served instantly from memory and avoid redundant, billable queries.

Monitoring and alerting

Monitor RPU usage patterns and set billing alerts to maintain cost control:

  • Combine query result caching with strategic scheduling for resource-intensive tasks.
  • Use scaling event data and query patterns to define thresholds.
  • Set up Amazon CloudWatch alarms for RPU consumption spikes.
  • Review Amazon Redshift query monitoring metrics weekly to identify optimization opportunities.

Clean up

To avoid incurring ongoing charges, delete the resources you created while testing the configurations described in this post.

  • Delete the Amazon Redshift Serverless workgroup and namespace if they were created for testing.
  • Remove any IAM roles, policies, and users created specifically for Tableau connectivity.
  • Delete security groups configured for Tableau Server or Desktop IP access.
  • Remove any materialized views, tables, or schemas created during testing.
  • Cancel any scheduled Tableau extract refreshes connected to test workgroups.
  • Delete Tableau data sources and workbooks that reference test environments.
  • Remove any CloudWatch alarms or CloudTrail configurations set up for monitoring test resources.

For more information about managing Amazon Redshift Serverless resources, see Billing for Amazon Redshift Serverless.

Conclusion

This post covered key optimization strategies for Tableau and Amazon Redshift Serverless integration: data model architecture using Relationships, security configuration with native drivers and AWS IAM, performance optimization through extracts and smart configuration, cost management with RPU monitoring, and query optimization techniques.

As AI-driven optimization evolves, staying informed about Amazon Redshift AI features and best practices, including Tableau Pulse, is key. Regularly review your configuration, performance, and security to verify that your Tableau and Amazon Redshift Serverless integration remains secure, cost-effective, and high-performing.

Optimization is an ongoing, iterative process. To keep your environment optimized, regularly review your settings, monitor performance, and adapt as workload patterns evolve. This approach maintains a cost-effective analytics environment that scales with your organization.

Ready to build a secure, high-performance analytics solution that delivers both speed and cost efficiency? Visit the Salesforce and AWS partnership webpage to start scaling your insights today.


About the authors

Nidhi Nayak

Nidhi Nayak

Nidhi is a Senior Technical Account Manager with AWS, she helps enterprise customers build scalable, high-performance cloud applications and optimize cloud operations. With over a decade of experience in Data Analytics, Nidhi currently focuses on Redshift & Generative AI integration with Redshift.

Nita Shah

Nita Shah

Nita is a Sr. Analytics Specialist Solutions Architect at AWS based out of New York. She has been building enterprise data platforms, data warehousing, and analytics solutions for over 20 years and specializes in Amazon Redshift. She is focused on helping customers design and build enterprise-scale well-architected analytics and decision support platforms

Bill Tarr

Bill Tarr

Bill is a Principal Partner Solutions Architect at AWS, specializing in Business Applications including Salesforce, MuleSoft, and agentic AI interoperability. From software builder to architect, he has 20+ years of experience shaping SaaS technology strategies from startup to enterprise. Bill has delivered 12+ sessions at AWS re:Invent and produced 71 episodes of “Building SaaS on AWS.

Adiascar Cisneros

Adiascar Cisneros

Adiascar is a Tableau at Salesforce Sr. Product Manager. Adiascar manages the Tableau technical relationship with Amazon Web Services, coordinating roadmap prioritization, connector improvements, customer events, and publications. Adiascar joined Tableau in 2018 and is based in Atlanta GA.

Multi-Region identity-based access to Amazon Redshift and S3 Tables

Post Syndicated from Maneesh Sharma original https://aws.amazon.com/blogs/big-data/multi-region-identity-based-access-to-amazon-redshift-and-s3-tables/

Organizations with lines of business operating across multiple AWS Regions increasingly run analytics workloads on globally distributed data. These organizations want to manage users and groups centrally, typically in the AWS Organizations management account and in a single Region, while still letting each line of business access data from the Region where its workloads run. Organizations should govern access based on the actual workforce user and their group memberships in the corporate directory.

With multi-Region support for AWS IAM Identity Center, organizations can federate workforce identities into a single organization instance in their primary Region. After you replicate this instance to additional Regions, member accounts running services such as Amazon Redshift or Amazon Athena in those Regions can integrate with IAM Identity Center locally, to resolve the same centrally managed users and groups.

This solution uses Trusted Identity Propagation (TIP), a capability that passes a user’s Identity Center identity and group memberships through a chain of AWS services. With TIP, when a user authenticates through Identity Center, that identity context flows to downstream services like AWS Lake Formation and Amazon S3 Access Grants. With this approach, you get consistent, identity-based access control without additional AWS Identity and Access Management (IAM) role configurations.

In Part 1 of this series, we showed how to simplify enterprise data access using the Amazon Redshift integration with Amazon S3 Access Grants. We demonstrated how to grant Amazon Simple Storage Service (Amazon S3) permissions to AWS IAM Identity Center users and groups using S3 Access Grants, and tested the integration using a federated user to unload and load data between Amazon Redshift and Amazon S3 within a single AWS Region.

In this post, we extend that solution across AWS Regions. We introduce a fictional company, AnyCompany Global, to illustrate how organizations with global operations can use AWS IAM Identity Center Multi-Region to set up consistent, identity-based access to Amazon Redshift and Amazon S3 Tables across Regions.

Specifically, we demonstrate:

  • How IAM Identity Center Multi-Region replicates identity data so that the same users and groups are available in each enabled Region.
  • How AWS Lake Formation grants fine-grained table-level and column-level access to S3 Tables based on group membership.
  • How S3 Access Grants controls UNLOAD/COPY operations to Amazon S3 based on the same identity.

We also show how to connect with your preferred SQL client.

Fictional scenario: AnyCompany Global

AnyCompany Global is a retail analytics company with a centralized IT team and distributed analytics teams. They use the following personas:

  • Alice — IT administrator (manages IAM Identity Center and AWS accounts).
  • Bob — platform engineer (sets up data infrastructure in us-west-2).
  • Ethan — data analyst (member of the awssso-sales group, queries data).

AnyCompany Global has two AWS accounts:

  • Account A (us-east-1) — management account with IAM Identity Center.
  • Account B (us-west-2) — analytics account with Amazon Redshift, Amazon S3, and the AWS Glue Data Catalog.

The same IAM Identity Center user (Ethan) authenticates once and accesses data in Account B (us-west-2) using the same credentials and group memberships — you don’t need additional user provisioning because IAM Identity Center replicates identities to the secondary Region.

Solution overview

The following diagram illustrates the multi-account, multi-Region architecture. Account A (us-east-1) hosts IAM Identity Center, which replicates identities to us-west-2 where Account B runs the analytics workloads.

Multi-account, multi-Region architecture diagram showing IAM Identity Center in us-east-1 replicating to us-west-2, where Amazon Redshift queries S3 Tables through Lake Formation and writes to Amazon S3 through S3 Access Grants

Figure 1: Multi-account, multi-Region architecture with S3 Access Grants, AWS Lake Formation, and IAM Identity Center.

This solution demonstrates two complementary data access patterns, both controlled by the end user identity:

Pattern Access method Permission controlled by
Pattern A SELECT on S3 table bucket through Amazon Redshift Spectrum Lake Formation
Pattern B UNLOAD/COPY to and from Amazon S3 S3 Access Grants

The solution workflow includes the following steps:

  • Ethan connects from Amazon Redshift Query Editor v2 in us-west-2 and authenticates via the IAM Identity Center endpoint (replicated to us-west-2) using his corporate IdP credentials.
  • For Pattern A (SELECT): Amazon Redshift queries the Amazon S3 Tables catalog (s3tablescatalog). Lake Formation evaluates Ethan’s IAM Identity Center group membership and grants access to the cataloged data.
  • For Pattern B (UNLOAD/COPY): Amazon Redshift requests temporary credentials from S3 Access Grants in us-west-2. S3 Access Grants evaluates the request, matches Ethan’s identity and group membership, and vends scoped temporary credentials for the authorized S3 location.
  • Ethan runs SELECT to query data through Lake Formation, and UNLOAD to write data to Amazon S3 through S3 Access Grants. You don’t need an IAM role ARN in the commands.

Walkthrough

The following sections walk you through enabling IAM Identity Center Multi-Region, configuring Amazon S3 Tables with Lake Formation in the secondary Region, testing both access patterns, and verifying the result with AWS CloudTrail. Start with the prerequisites, then complete each step in order.

Prerequisites

You should have the following prerequisites already set up:

  • AWS Organizations enabled with at least two AWS accounts – Centralized Account(Region 1) and Member Account(Region2)
  • IAM Identity Center enabled in the management account (Account A, us-east-1) with a delegated administration account
  • Corporate IdP integrated with IAM Identity Center (users and groups synced, for example, awssso-sales and awssso-finance groups).
  • Resource sharing enabled in your organization with AWS Resource Access Manager (AWS RAM)
  • Complete solution from Part 1 replicated in us-west-2 (Account B), including:
    • Amazon Redshift cluster (in us-west-2) with IAM Identity Center integration enabled (using the replicated Identity Center endpoint in us-west-2).
    • S3 Access Grants instance configured with IAM Identity Center association
    • Amazon S3 bucket (for example, amzn-s3-demo-bucket-west) with folders for each group (for example, awssso-sales/, awssso-finance/).
    • IAM role for S3 Access Grants (for example, iamidcs3accessgrant) with trust policy and permissions policy.
    • S3 Access Grants location registered and grant created for the awssso-sales group.
    • S3 Access Grants enabled on the Amazon Redshift managed application under Trusted identity propagation
    • Cross-account resource sharing via AWS RAM (if Amazon Redshift and S3 Access Grants are in different accounts)
    • Lake Formation enabled on the Amazon Redshift managed application under Trusted identity propagation
    • Lake Formation and Glue permissions added to the IAM role used in the Amazon Redshift managed application (for example, IAMIDCRedshiftRole). For the required permissions, see Querying data through AWS Lake Formation.
  • An AWS account with an IAM role that has administrative access (e.g., Admin role) configured as a Data Lake Admin in Lake Formation

Note: Creating and using AWS resources in this tutorial incurs charges, including AWS Key Management Service (AWS KMS) keys, S3 table buckets, Amazon Redshift clusters, and Amazon S3 storage. See the cleanup section at the end of this post to avoid ongoing charges.

Step 1: Set up IAM Identity Center Multi-Region

Alice performs this step in the management account (Account A, us-east-1). IAM Identity Center uses encryption at rest for identity data. To enable multi-Region, you must first create a multi-Region customer-managed AWS Key Management Service (AWS KMS) key and replicate it to the additional Region.

Create a multi-Region AWS KMS key

  1. On the AWS KMS console in us-east-1, choose Create key.
  2. For Key type, select Symmetric.
  3. For Key usage, select Encrypt and decrypt.
  4. Under Advanced options, select Multi-Region key.
  5. Provide an alias (for example, idc-multi-region-key).
  6. Apply the AWS KMS key policy as documented in Baseline KMS key policy.

Replicate the key to us-west-2

  1. On the AWS KMS console in us-east-1, select the key you created.
  2. Choose the Regionality tab.
  3. Choose Create new replica keys.
  4. Select US West (Oregon) us-west-2.
  5. Choose Replicate key.

For detailed instructions, see Creating multi-Region replica keys.

AWS KMS console Regionality tab showing the multi-Region replica key configured for an additional Region

Figure 2: Replica key configured for the additional Region.

Add us-west-2 to IAM Identity Center

  1. On the IAM Identity Center console in us-east-1, in the navigation pane, choose Settings.
  2. Choose Add Region.
  3. From the Region list, select US West (Oregon) us-west-2. The list shows Regions where you replicated the customer-managed AWS KMS key.
  4. Choose Add Region.

A blue banner indicates that Identity Center is replicating your workforce identities, configuration, and metadata to the new Region. After the initial replication, the Replication Status column changes to Replicated. Your Identity Center endpoints in us-west-2 are now active.

For detailed instructions, see Add the Region in IAM Identity Center.

IAM Identity Center Settings page with the multi-Region replica key added for us-west-2 and replication status set to Replicated

Figure 3: IAM Identity Center settings showing the multi-Region replica key added for us-west-2.

Update your IdP configuration for the additional Region

You’ve successfully replicated your Identity Center instance to the Oregon (us-west-2) Region. Your workforce identities are now available in that additional Region and can use the new AWS access portal endpoint.

To make sure AWS managed application (service provider-initiated) authentication redirect user to respective application, add the ACS URL for the additional Region so that the app contains both Regional ACS URLs.

In the following section highlighted in red, you can view all ACS URL information:

IAM Identity Center settings page with the View ACS URLs section highlighted in red

Figure 4: IAM Identity Center settings showing the View ACS URLs option.

Copy the respective ACS URL as shown in the following figure:

IAM Identity Center settings page listing the ACS URLs for both Regions

Figure 5: IAM Identity Center settings showing the ACS URLs for both Regions.

Use the following instructions to add the ACS URL for the additional Region in your Identity Center application in Okta:

  1. Log in to the Okta portal as an Admin.
  2. Expand the Applications drop-down in the left pane, then choose Applications
  3. Choose your Identity Center Application
  4. Select the Sign-on tab and choose Edit in the Settings windows.
  5. In the AWS SSO ACS URL1 box under Advanced Sign-on Settings – add the additional ACS URL
  6. Choose Save.

Okta application Sign-on tab with the AWS SSO ACS URL1 box configured for the IAM Identity Center application

Figure 6: Okta application for IAM Identity Center Sign-on tab to add ACS URLs.

Create a permission set for the secondary Region

Create a permission set in the management account to grant federated users console access to Amazon Redshift Query Editor V2 in the secondary Region (us-west-2). For more information about permission sets, see Permission sets.

  1. In the management account, open the IAM Identity Center console.
  2. In the navigation pane, under Multi-Account permissions, choose Permission sets → Create permission set.
  3. Choose Custom permission set, then choose Next.
  4. Under AWS managed policies, select AmazonRedshiftQueryEditorV2ReadSharing.
  5. Under Inline policy, add the following policy:
    {
      "Version": "2012-10-17",
      "Statement": [
        {
          "Effect": "Allow",
          "Action": [
            "redshift:DescribeQev2IdcApplications",
            "redshift-serverless:ListNamespaces",
            "redshift-serverless:ListWorkgroups",
            "redshift-serverless:GetWorkgroup"
          ],
          "Resource": "*"
        }
      ]
    }

  6. Choose Next. Enter a permission set name (for example, Redshift-QEV2-West).
  7. Under Relay state, set the default to the Query Editor V2 URL for the secondary Region: https://us-west-2.console.aws.amazon.com/sqlworkbench/home.
  8. Choose Next, then Create.

After creation, assign this permission set to the relevant IAM Identity Center group (for example, awssso-sales) for Account B (us-west-2).

Step 2: Set up Amazon S3 Tables integration with AWS Glue Data Catalog and Lake Formation in Account B (us-west-2)

In this step, the data lake administrator (Bob) sets up Amazon S3 Tables with Lake Formation for fine-grained access control. He completes the following tasks:

  1. Create an S3 tables bucket.
  2. Enable S3 Tables integration with AWS Glue Data Catalog and Lake Formation.
  3. Register the table bucket with Lake Formation (removes default IAM-based access).
  4. Grant Lake Formation permissions to an IAM Identity Center group (awssso-sales) so that only authorized users can query data through Trusted Identity Propagation.

Step 2.1: Remove default Lake Formation permissions

Before creating S3 Tables resources, disable the default IAMAllowedPrincipals grants that Lake Formation applies to new databases and tables. By default, Lake Formation grants IAMAllowedPrincipals access to new resources, which means that standard IAM policies (rather than Lake Formation permissions) control access. For identity-based access through Trusted Identity Propagation, you need Lake Formation to be the sole arbiter of access.

The order matters. If you remove these defaults before registering the S3 Tables resource, Lake Formation will not apply IAMAllowedPrincipals to your S3 Tables catalog or its children. If you register the resource first, you need to manually revoke the IAMAllowedPrincipals grants from each resource.

From the console

  1. Open the Lake Formation console in your target Region (for example, us-west-2).
  2. In the left navigation, choose Administration → Data Catalog settings.
  3. Uncheck both options:
    • Use only IAM access control for new databases
    • Use only IAM access control for new tables in new databases
  4. Choose Save.

Lake Formation Data Catalog settings page with both default IAM access control options cleared

Figure 7: Lake Formation Data Catalog settings with default IAM access control disabled.

Optional: Verify Lake Formation default permissions through the AWS CLI

aws lakeformation get-data-lake-settings --region <REGION>

Confirm both CreateDatabaseDefaultPermissions and CreateTableDefaultPermissions are empty arrays ([]).

Add AWSServiceRoleForRedshift as a read-only admin

If you plan to query S3 Tables from Amazon Redshift Query Editor V2, you must add the Amazon Redshift service-linked role as a Read-Only Admin in Lake Formation. Complete the following steps:

  • In the Lake Formation console, go to Administration → Administrative roles and tasks.
  • Under Data lake administrators, choose Add. Choose Read only administrator.
  • From the menu, choose AWSServiceRoleForRedshift.
  • Choose Confirm.

Important: Without this, Amazon Redshift Query Editor V2 doesn’t display external databases from s3tablescatalog. The Amazon Redshift service-linked role needs read-only admin access to browse the Data Catalog on behalf of users.

Step 2.2: Create the Lake Formation data access role for S3 Tables

Create an IAM role that Lake Formation assumes to generate temporary, scoped credentials on behalf of users requesting access to S3 Tables data. Lake Formation uses this role (instead of its service-linked role) because Trusted Identity Propagation requires sts:SetContext in the trust policy, which is not available on the service-linked role. Without a custom role with this permission, Lake Formation cannot propagate the user’s IAM Identity Center identity when accessing S3 Tables.

Create the role with the trust policy

aws iam create-role \
    --role-name LFAccessRole-S3Tables \
    --assume-role-policy-document '{
        "Version": "2012-10-17",
        "Statement": [{
            "Effect": "Allow",
            "Principal": {
                "Service": "lakeformation.amazonaws.com"
            },
            "Action": [
                "sts:AssumeRole",
                "sts:SetSourceIdentity",
                "sts:SetContext"
            ]
        }]
    }'

Attach the S3 Tables permissions policy

aws iam put-role-policy \
    --role-name LFAccessRole-S3Tables \
    --policy-name S3TablesDataAccess \
    --policy-document '{
        "Version": "2012-10-17",
        "Statement": [
            {
                "Sid": "LakeFormationPermissionsForS3ListTableBucket",
                "Effect": "Allow",
                "Action": ["s3tables:ListTableBuckets"],
                "Resource": ["*"]
            },
            {
                "Sid": "LakeFormationDataAccessPermissionsForS3TableBucket",
                "Effect": "Allow",
                "Action": [
                    "s3tables:CreateTableBucket",
                    "s3tables:GetTableBucket",
                    "s3tables:CreateNamespace",
                    "s3tables:GetNamespace",
                    "s3tables:ListNamespaces",
                    "s3tables:DeleteNamespace",
                    "s3tables:DeleteTableBucket",
                    "s3tables:CreateTable",
                    "s3tables:DeleteTable",
                    "s3tables:GetTable",
                    "s3tables:ListTables",
                    "s3tables:RenameTable",
                    "s3tables:UpdateTableMetadataLocation",
                    "s3tables:GetTableMetadataLocation",
                    "s3tables:GetTableData",
                    "s3tables:PutTableData"
                ],
                "Resource": ["arn:aws:s3tables:<REGION>:<ACCOUNT_ID>:bucket/*"]
            }
        ]
    }'

Step 2.3: Register S3 Tables with Lake Formation

Register the S3 Tables resource with Lake Formation using the data access role. This step lets Lake Formation manage access to S3 Tables through the Data Catalog and creates the s3tablescatalog federated catalog automatically.

Open the Lake Formation console and complete the following steps:

  1. Choose Catalogs in the navigation pane and choose Enable S3 Table integration.

Lake Formation Catalogs page with the Enable S3 Table integration option highlighted

Figure 8: Lake Formation Catalogs page with the Enable S3 Table integration option.

  1. Select the IAM role and select Allow external engines to access data in Amazon S3 locations with full table access. Choose Enable.

Enable S3 Table integration dialog with the IAM role selected and the Allow external engines option enabled

Figure 9: Enable S3 Table integration dialog with the IAM role and external-engine access configured.

Alternative: Register through the AWS CLI

aws lakeformation register-resource \
    --resource-arn "arn:aws:s3tables:<REGION>:<ACCOUNT_ID>:bucket/*" \
    --role-arn "arn:aws:iam::<ACCOUNT_ID>:role/LFAccessRole-S3Tables" \
    --with-federation \
    --region <REGION>

Important: Verify that the --role-arn matches the exact ARN of the role created in Step 2.2 (including the path). A mismatch (e.g., role/service-role/LFAccessRole-S3Tables vs role/LFAccessRole-S3Tables) will cause credential vending failures later.

Optional: Verify the registration

aws lakeformation list-resources --region <REGION>

Confirm the S3 Tables entry shows WithFederation: true and the correct role ARN.

Step 2.4: Create the S3 table bucket and namespace

Create an S3 table bucket and a namespace. Complete the following steps on the Amazon S3 console:

  1. In the navigation pane, choose Table buckets.
  2. Choose Create table bucket.
  3. On the next page, enter the bucket name as <TABLE_BUCKET_NAME>.
  4. Keep the other options as default and choose Create table bucket.
  5. After you create it, the AWS Management Console redirects you to the list of table buckets. Choose the table bucket <TABLE_BUCKET_NAME>.
  6. Choose Create table with Athena.
  7. Create a namespace in S3 Tables (equivalent to a database in AWS Glue Data Catalog). Enter the namespace (database) name as <NAMESPACE_NAME> and choose Create namespace.

You can also perform these steps using the AWS Command Line Interface (AWS CLI). Refer to Creating a table bucket using the AWS CLI for equivalent commands.

Step 2.5: Grant admin role access

After you remove default permissions, you need to give your Admin role explicit Lake Formation permissions to create tables. Because your Admin role is a Data Lake Admin, you can already see s3tablescatalog in the Amazon Athena console, but creating tables requires an explicit grant.

From the console

  • Open the Lake Formation console in your Region.
  • Choose Data permissions → Grant.
  • Under Principals, select IAM users and roles and choose your Admin role.
  • Under LF-Tags or catalog resources, select Named Data Catalog resources.
  • For Catalogs, choose <Account ID>:s3tablescatalog/<Table_Bucket_Name>.
  • For Databases, select your database (for example, customer_ns_db).
  • Select Super for Database permissions and Grantable permissions.
  • Choose Grant.

After this grant, you can create and insert data into tables from the Athena console.

Note: Your Admin role must be a Data Lake Admin (configured in Step 2.1) to browse s3tablescatalog in Athena. You need the explicit database grant for write operations (CREATE TABLE, INSERT).

Step 2.6: Create a table from the Athena console

  1. Open the Amazon Athena console in your Region.
  2. In the Data source menu, select AwsDataCatalog.
  3. For Catalog, choose s3tablescatalog/<Table_Bucket_Name>.
  4. For Database, choose your namespace.
  5. Run a CREATE TABLE statement. For example:
CREATE TABLE <NAMESPACE_NAME>.<TABLE_NAME> (
    customer_id int,
    first_name string,
    last_name string,
    region string,
    membership_tier string
)
TBLPROPERTIES ('table_type' = 'ICEBERG');

INSERT INTO <NAMESPACE_NAME>.<TABLE_NAME> VALUES
  (1, 'Joyce', 'Deaton', 'West', 'Gold'),
  (2, 'Daniel', 'Dow', 'East', 'Silver'),
  (3, 'Marie', 'Lange', 'West', 'Gold'),
  (4, 'Wesley', 'Harris', 'East', 'Bronze'),
  (5, 'Jerry', 'Tracy', 'West', 'Silver');

Step 2.7: Grant permissions to the IAM Identity Center group

Give your IAM Identity Center group access to query tables. This step enables Trusted Identity Propagation (TIP) for this group. When users in the group access data through TIP-integrated services like Amazon Redshift, Lake Formation evaluates their IAM Identity Center group membership and enforces table-level and column-level permissions accordingly.

From the console

Grant DESCRIBE on the database:

  1. Open the Lake Formation console in your Region.
  2. Choose Data permissions → Grant.
  3. Under Principals, select IAM Identity Center and choose your IAM Identity Center group (for example, awssso-sales).
  4. Under LF-Tags or catalog resources, select Named Data Catalog resources.
  5. For Catalogs, choose <Account ID>:s3tablescatalog/<Table_Bucket_Name>.
  6. For Databases, select your database (for example, customer_ns_db).
  7. For Database permissions, select Describe.
  8. Choose Grant.

Grant SELECT and DESCRIBE on tables:

  1. Choose Data permissions → Grant.
  2. Under Principals, select IAM Identity Center and choose your IAM Identity Center group (for example, awssso-sales).
  3. Under LF-Tags or catalog resources, select Named Data Catalog resources.
  4. For Catalogs, choose <Account ID>:s3tablescatalog/<Table_Bucket_Name>.
  5. For Databases, select your database (for example, customer_ns_db).
  6. For Tables, select All tables (or a specific table).
  7. For Table permissions, select Select and Describe.
  8. Choose Grant.

Tip: You can also configure column-level or row-level permissions for fine-grained access control. When granting on a specific table, additional options for Column permissions and Data filters become available.

Step 2.8: Optional: Verify the Lake Formation permissions

Confirm database-level permissions

aws lakeformation list-permissions \
    --resource '{"Database": {"CatalogId": "<ACCOUNT_ID>:s3tablescatalog/<TABLE_BUCKET_NAME>", "Name": "<NAMESPACE_NAME>"}}' \
    --region <REGION>

Confirm table-level permissions

aws lakeformation list-permissions \
    --resource '{"Table": {"CatalogId": "<ACCOUNT_ID>:s3tablescatalog/<TABLE_BUCKET_NAME>", "DatabaseName": "<NAMESPACE_NAME>", "TableWildcard": {}}}' \
    --region <REGION>

You should see:

  • Your Admin role with ALL permissions at the database level.
  • Your IAM Identity Center group with DESCRIBE permissions at the database level.
  • Your IAM Identity Center group with DESCRIBE on ALL_TABLES and SELECT on ALL_TABLES (with ColumnWildcard) at the table level.
  • No IAM_ALLOWED_PRINCIPALS entries.

Step 2.9: Create Amazon Redshift tables and grant permissions

Connect to the Amazon Redshift cluster in us-west-2 as an admin user and create Redshift local tables. Grant permissions on those local resources to IAM Identity Center groups.

Create a schema and table

CREATE SCHEMA IF NOT EXISTS sales_schema;

CREATE TABLE IF NOT EXISTS
sales_schema.store_sales (
  customer_id INTEGER ENCODE az64,
  product VARCHAR(50),
  sales_amount INTEGER ENCODE az64
)
DISTSTYLE AUTO;

-- Insert sample data
INSERT INTO sales_schema.store_sales VALUES
  (1, 'Laptop', 1200),
  (2, 'Phone', 800),
  (3, 'Tablet', 450),
  (4, 'Monitor', 350),
  (5, 'Keyboard', 120);

Grant permissions to the IAM Identity Center group

GRANT USAGE ON SCHEMA sales_schema TO ROLE "awsidc:awssso-sales";
GRANT SELECT, INSERT FOR TABLES IN SCHEMA sales_schema TO ROLE "awsidc:awssso-sales";

-- Grant access to the S3 Tables external database in Redshift (for Lake Formation queries on customer profiles)
GRANT USAGE ON DATABASE "customers3tables@s3tablescatalog" TO ROLE "awsidc:awssso-sales";

Step 3: Test the solution

In the management account, navigate to the IAM Identity Center console and copy the AWS access portal URL (for example, https://d-1234560789.awsapps.com/start) from the dashboard.

  • Log out from the management account and paste the AWS access portal URL in a new browser window.
  • A pop-up redirects you to your IdP login page. Enter Ethan’s IdP credentials.
  • After successful authentication, you’re logged into the AWS console as a federated user. Select the QEV2 permission set for the secondary Region (us-west-2).
  • In Query Editor V2, open the context (right-click) menu on your Amazon Redshift instance, choose Create connection, and for Authentication, select IAM Identity Center.
  • Because your IdP credentials are already cached, the browser reuses them automatically. You’re now connected to Amazon Redshift.

Pattern A: Query the S3 table catalog using Lake Formation permissions

Query the customer profile data through s3tablescatalog. Lake Formation enforces access based on Ethan’s IAM Identity Center group membership:

SELECT *
FROM "customers3tables@s3tablescatalog"."customer_ns_db"."customer_profiles";

Amazon Redshift Query Editor V2 results pane displaying customer profile rows returned from the s3tablescatalog through Lake Formation

Figure 10: Query results from s3tablescatalog returned through Lake Formation in Amazon Redshift Query Editor V2.

This query reads customer profile data from Amazon S3 through Amazon Redshift Spectrum, with Lake Formation controlling who can access which tables and columns.

Pattern B: Unload data to Amazon S3 using S3 Access Grants

Run the UNLOAD command to write data from Amazon Redshift to the S3 bucket:

UNLOAD ('SELECT * FROM "dev"."sales_schema"."store_sales"')
TO 's3://west-idc-amzn-s3-demo-bucket/awssso-sales/';

You don’t need an IAM role ARN in the command. S3 Access Grants handles authorization based on Ethan’s IAM Identity Center identity and group membership, propagated across Regions using IAM Identity Center Multi-Region support.

Verify the data in Amazon S3

On the Amazon S3 console, navigate to s3://west-idc-amzn-s3-demo-bucket/awssso-sales/ and verify that the unloaded data files are present.

Join Lake Formation data with locally loaded Amazon Redshift data

Combine customer profile data (queried via Lake Formation) with sales data (loaded via S3 Access Grants) using the shared customer_id column:

SELECT c.first_name, c.last_name, c.membership_tier,
  s.product, s.sales_amount
FROM "customers3tables@s3tablescatalog"."customer_ns_db"."customer_profiles" c
JOIN  dev.sales_schema.store_sales s ON c.customer_id = s.customer_id
ORDER BY s.sales_amount DESC;

Amazon Redshift Query Editor V2 results joining S3 Tables customer profiles with the local store_sales table

Figure 11: Joined results from S3 Tables and Amazon Redshift local data, ordered by sales amount.

This shows that you can join S3 Tables data with Amazon Redshift using the same IAM Identity Center identity.

Verify access control

To confirm that S3 Access Grants is enforcing access, try accessing a folder Ethan does not have a grant for:

UNLOAD ('SELECT * FROM "dev"."sales_schema"."store_sales"')
TO 's3://west-idc-amzn-s3-demo-bucket/awssso-finance/';

This should return an access denied error, confirming that S3 Access Grants is controlling access based on the user’s identity and group membership.

Step 4: Verify with AWS CloudTrail

You can verify that Amazon Redshift used both S3 Access Grants and Lake Formation for authorization by checking AWS CloudTrail:

  • On the CloudTrail console, choose Event history.
  • Filter by Event source: s3.amazonaws.com. Look for GetDataAccess events (S3 Access Grants).
  • Filter by Event source: lakeformation.amazonaws.com. Look for GetDataAccess events (Lake Formation).

Both event types show Ethan’s IAM Identity Center user identity, confirming trusted identity propagation works end-to-end for both access patterns.

The following table lists related blog posts and integration guides covering additional identity-based access patterns with Amazon Redshift. Although many of these were written for single-Region deployments, you can extend them to multi-Region environments by first enabling IAM Identity Center Multi-Region as described in Step 1 of this post. Use the table to find the guide that matches your identity provider and tooling:

Integration / use case Identity provider What it covers Blog link
Amazon Redshift federated permissions Any Centralize permission management across multiple Amazon Redshift clusters within a Region using IAM Identity Center-linked database roles. Simplify multi-warehouse data governance with Amazon Redshift federated permissions
Amazon Redshift Query Editor V2, DbVisualizer, DBeaver Any Foundational Amazon Redshift and IAM Identity Center setup, role-based access control (RBAC), JDBC single sign-on (SSO) with PKCE. Integrate IdP with Query Editor V2 and SQL client
Amazon Redshift and S3 Access Grants (single Region and cross-account) Any Amazon S3 data access through UNLOAD/LOAD with identity-based permissions. Simplify data access with S3 Access Grants
Amazon SageMaker Unified Studio with Athena and Amazon Redshift Any SQL analytics with Lake Formation governance. Configure SSO with SageMaker Unified Studio
Amazon QuickSight with Lake Formation Any Cross-account Glue Data Catalog, business intelligence dashboards. Cross-account Glue and Lake Formation
Tableau (Desktop, Server, Prep) Okta TTI plus OIDC setup, Tableau OAuth XML configuration. Integrate Tableau with Okta
Tableau (Desktop, Server, Prep) PingFederate TTI plus OIDC setup, JWT access token manager. Integrate Tableau with PingFederate
Tableau (Desktop, Server, Prep) Microsoft Entra ID TTI plus OIDC setup, Entra app registration. Integrate Tableau with Entra ID
ThoughtSpot Okta / Microsoft Entra ID Native OIDC integration, supports both IdPs. Integrate ThoughtSpot

Key considerations

When implementing this multi-Region architecture, keep the following operational and configuration considerations in mind. These reflect common challenges and design decisions encountered during deployment:

  • IAM Identity Center Multi-Region requires a customer-managed multi-Region AWS KMS key replicated to each additional Region before you can add the Region to Identity Center.
  • S3 Access Grants instances are regional. You need a separate instance in each Region where your users access data. A bucket must be in the same Region as the Access Grants instance that manages it.
  • IAM Identity Center Multi-Region provides the same user and group identities across Regions, so you can use the same group IDs in grants across Regions.
  • You must register Lake Formation data locations with a customer-managed role that includes sts:SetContext in its trust policy. For S3 Tables, use aws lakeformation register-resource with the --with-federation flag and the resource ARN format arn:aws:s3tables:<REGION>:<ACCOUNT_ID>:bucket/*. Using the service-linked role causes the error: Cannot vend credentials from service-linked role to Identity Center principal.
  • SELECT and UNLOAD use different permission models. Lake Formation controls query-time access to cataloged data (SELECT through Spectrum). S3 Access Grants controls direct Amazon S3 access (COPY/UNLOAD). Both use the same IAM Identity Center identity.
  • The Amazon Redshift managed application IAM role must include sts:SetContext in its trust policy and have both Lake Formation/Glue and S3 Access Grants permissions.
  • Cross-account setup requires AWS RAM resource sharing for S3 Access Grants and proper IAM Identity Center application configuration in the analytics account.
  • Scoped vs object-level permissions in Amazon Redshift. When granting permissions with GRANT ... FOR TABLES IN SCHEMA, use REVOKE ... FOR TABLES IN SCHEMA to remove them. The REVOKE ... ON ALL TABLES IN SCHEMA syntax only removes object-level permissions, not scoped permissions.
  • The Lake Formation data access role for S3 Tables requires sts:SetContext in its trust policy (for TIP) and s3tables:* permissions on the table bucket resources.
  • AWSServiceRoleForRedshift must be a Read-Only Admin in Lake Formation for Amazon Redshift Query Editor V2 to display external databases from s3tablescatalog.
  • Federated catalog CatalogId format. When using CLI commands for S3 Tables resources in Lake Formation, use the full path format: <ACCOUNT_ID>:s3tablescatalog/<TABLE_BUCKET_NAME>. Using the account ID alone returns empty results.

Clean up

To avoid ongoing charges, clean up the resources created in this post:

  • Delete the S3 table bucket (delete tables → namespaces → bucket using aws s3tables CLI commands).
  • Deregister the S3 Tables resource from Lake Formation (aws lakeformation deregister-resource --resource-arn "arn:aws:s3tables:<REGION>:<ACCOUNT_ID>:bucket/*").
  • Delete s3tablescatalog from Glue (aws glue delete-catalog --catalog-id "s3tablescatalog").
  • Delete the LFAccessRole-S3Tables IAM role and associated policies.
  • Delete the S3 Access Grants instance and grants in us-west-2.
  • Delete the S3 bucket used for UNLOAD/COPY in us-west-2.
  • Delete the iamidcs3accessgrant IAM role and associated policies.
  • Deregister the S3 data location from Lake Formation.
  • Delete the Lake Formation IAM Identity Center integration.
  • Delete the Amazon Redshift cluster in us-west-2 if you created one for testing.
  • Remove us-west-2 from IAM Identity Center Multi-Region (if no longer needed).
  • Schedule deletion of the AWS KMS replica key in us-west-2 (minimum 7-day waiting period).

Conclusion

In this post, we extended the Amazon Redshift and S3 Access Grants integration to a multi-Region setup using IAM Identity Center Multi-Region replication. We demonstrated two complementary data access patterns: SELECT through Lake Formation for fine-grained access control on S3 Tables data, and UNLOAD/COPY through S3 Access Grants for direct Amazon S3 access. Both patterns use the same IAM Identity Center identity for access control. We also showed how to set up a customer-managed multi-Region AWS KMS key, enable IAM Identity Center in an additional Region, configure Amazon S3 Tables with Lake Formation for identity-based access control using Trusted Identity Propagation, and replicate the complete S3 Access Grants setup in a different Region and account.

With this approach, AnyCompany Global’s analysts authenticate once and access data in any enabled Region while Lake Formation and S3 Access Grants enforce per-user, per-group access policies.

For additional guidance, refer to the following resources:


About the authors

Maneesh Sharma

Maneesh Sharma

Maneesh is a Sr. Specialist Solutions Architect in Analytics at AWS, bringing more than 15 years of hands-on experience in designing and implementing large-scale data warehouse and analytics solutions. He collaborates closely with customers to help them build scalable, high-performance analytical data platforms.

Rohit Vashishtha

Rohit Vashishtha

Rohit is a Senior Analytics Specialist Solutions Architect at AWS based in Dallas, Texas. He has two decades of experience architecting, building, leading, and maintaining big data platforms. Rohit helps customers modernize their analytic workloads using the breadth of AWS services and ensures that customers get the best price/performance with utmost security and data governance.

Srividya Parthasarathy

Srividya Parthasarathy

Srividya is a Senior Big Data Architect with Amazon SageMaker Lakehouse. She works with the product team and customers to build robust features and solutions for their analytical data platform. She enjoys building data mesh solutions and sharing them with the community.

Sandeep Adwankar

Sandeep Adwankar

Sandeep is a Senior Product Manager with Amazon SageMaker Lakehouse. Based in the California Bay Area, he works with customers around the globe to translate business and technical requirements into products that help customers improve how they manage, secure, and access data.

Why tombola chose Graviton-powered RG instances for Amazon Redshift

Post Syndicated from Prabhu Pandian original https://aws.amazon.com/blogs/big-data/why-tombola-chose-graviton-powered-rg-instances-for-amazon-redshift/

Part of Flutter Entertainment, the world’s largest online sports betting and iGaming operator, tombola is the world’s biggest online bingo community and has been using Amazon Redshift to run its data analytics workloads. Founded in Sunderland, UK, the company traces its roots to the 1950s, when it began printing bingo tickets during the golden age of the game. tombola launched online in 2006 and has since expanded to Italy, Spain, Denmark, and Sweden. The company builds all of its games in-house, holds the most prestigious Safer Gambling award, and recently partnered with Flutter sibling brand Sisal to bring its bingo application to Italian players.

In this post, you learn how tombola followed a strict engineering principle: no changes to production without evidence. That meant a head-to-head comparison of RA3 versus RG on their actual workload. You also see benchmark results on Amazon S3 Tables and the migration from RA3 to RG instances.

Current data architecture

Amazon Redshift sits at the center of tombola’s data architecture. The production cluster runs on RA3 nodes and serves multiple schemas with hundreds of tables, supporting every analytical workload the business runs, from sub-second application lookups to multi-minute extract, transform, load (ETL) transforms. What makes tombola’s Amazon Redshift workload distinctive is the breadth of what flows through it. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) DAGs orchestrate pipelines across over 14 business domains, including segmentation, fraud detection, marketing, finance, and SafePlay responsible-gaming. Configuration-driven ingestion pipelines land data from SQL Server, Amazon DynamoDB, Amazon OpenSearch Service, Postgres, and external APIs into Bronze and Silver layers on Amazon Simple Storage Service (Amazon S3), before loading it into Amazon Redshift. From there, over 250 dbt models running on Amazon Elastic Container Service (Amazon ECS) transform the data into analytical gold layers. Outputs feed multiple downstream consumers: Amazon SageMaker for fraud scoring and churn prediction, Amazon DynamoDB for low-latency APIs, and region-specific pipelines spanning the UK, Italy, Spain, Denmark, and Sweden. As the application grew, with more domains, more DAGs, and more concurrent users, the team began evaluating ways to reduce steady-state query latency and lower compute cost without rearchitecting the system. When AWS made Graviton-powered RG nodes available for Amazon Redshift, the timing was right.

Benchmark performance results

The benchmark infrastructure was fully defined as infrastructure as code (IaC), making sure every test run was reproducible. The team deployed two test benchmark clusters (one RA3 and one RG) in a like-for-like configuration. They mirrored the settings (Amazon Virtual Private Cloud (Amazon VPC), security groups, AWS Key Management Service (AWS KMS), AWS Identity and Access Management (IAM) roles, and parameter groups) from the production environment to remove configuration drift. The benchmark runner was containerized as an Amazon ECS task (python:3.11-slim-bookworm ARM64 base), providing repeatable, isolated execution for each test round. Benchmark workloads were selected by analyzing production cluster logs and metrics, then classified into three tiers:

  • Heavy: ETL queries with multi-table CTE chains, full-table scans, and aggregation windows.
  • Medium: Business intelligence (BI) queries driving reporting and analytics dashboards.
  • Light: Application queries with sub-second response times.

Architecture

Scenarios tested

To validate the performance of Graviton-powered RG instances against the existing RA3 nodes, tombola designed four benchmark scenarios that progressively increase in complexity and realism. Together, these scenarios provide a comprehensive view of performance from isolated query execution through to sustained, real-world analytical workloads.

Scenario 01: Cold-cache, single-stream execution. This scenario isolates raw compute performance by running queries against a cold cache in a single stream, avoiding caching and concurrency as variables.

Per-query speedups ranged from 1.05× (light lookup queries) to 1.68× (heavy ETL transforms). Zero errors on both clusters (28 attempts each).

Weight Class RA3 p50 (ms) RG p50 (ms) Speedup
Heavy (ETL) 210,372 133,855 1.57×
Medium (BI) 2,193 1,642 1.34×
Light (App) 3.20 2.76 1.16×

The following chart shows per-query speedup ratios for the cold-cache scenario. Heavy ETL queries (left) show the largest gains, with speedups of 1.57–1.68×, and lighter queries still benefit at 1.05–1.16×. The pattern is consistent: RG’s advantage scales with query complexity.

Scenario 02: Warm-cache, single-stream execution. This scenario repeats Scenario 01 with the result cache enabled to confirm that RG maintains its latency advantage even when cached results are in play.

Per-query speedups ranged from 1.04× to 1.64×. Zero errors on both clusters (35 attempts each).

Weight Class RA3 p50 (ms) RG p50 (ms) Speedup
Heavy (ETL) 93,636 61,691 1.52×
Medium (BI) 2,189 1,584 1.38×
Light (App) 3.08 2.58 1.19×

With result caching enabled, the speedup pattern holds for non-cached queries. Cache hits on both clusters land in 118–185 ms, confirming the caching subsystem operates identically regardless of node type. The RG advantage appears exclusively on execution paths that bypass the cache.

Scenario 03: Concurrency sweep. This scenario introduces parallel load by sweeping through 1, 5, 10, and 20 concurrent streams, testing how each node type handles contention and queuing under pressure.

Both clusters used the same Concurrency Scaling configuration (max_concurrency_scaling_clusters=1, WLM-only). RG completed 482 more queries in the same wall-clock window.

Metric RA3 RG Improvement
Total queries completed 1,438 1,920 +33% throughput
Light p50 (ms) 3.44 3.04 1.13×
Medium p50 (ms) 20,784 15,055 1.38×
Errors 0 0 —

Under increasing parallel load (1, 5, 10, and 20 concurrent streams), RG maintained lower latencies and completed 33 percent more queries in the same wall-clock window. Both clusters used the same Concurrency Scaling configuration, so the throughput difference is attributable to per-node compute efficiency.

Scenario 04: Mixed realistic workload. This scenario combines the previous elements into a mixed realistic workload, running 10 streams simultaneously for 30 minutes with a weighted distribution of heavy, medium, and light queries to simulate actual production conditions.

This scenario best simulates production. The headline finding: heavy ETL queries saw speedups of up to 2.27× under concurrent load, and RG completed 46 percent more total queries in the same 30-minute window. Zero errors on both clusters.

Metric RA3 RG Improvement
Total queries completed 405 593 +46% throughput
Heavy p50 (ms) 1,186,572 642,294 1.85×
Medium p50 (ms) 2,319 1,631 1.42×
Light p50 (ms) 3.12 2.90 1.08×
Errors 0 0 —

The mixed-realistic scenario best simulates production. Under 10 concurrent streams over 30 minutes, heavy ETL queries showed speedups of up to 2.27×. RG’s per-vCPU throughput advantage compounds under contention, exactly the condition where production clusters spend most of their time.

Extended benchmark: Amazon S3 Tables (Iceberg) performance

tombola’s future data architecture will integrate with agents and revolves around Apache Iceberg, backed by Amazon S3 Tables. Amazon S3 Tables offer Amazon S3 storage that is specifically tuned for analytics, with built-in capabilities that keep making queries faster and helping lower storage costs for table data. They’re purpose-built to hold tabular datasets, such as daily purchase logs, streaming sensor readings, or ad impression events. In this model, data is organized into rows and columns, similar to how information is structured in a traditional database table. With that direction in mind, tombola also benchmarked Graviton’s performance querying Iceberg tables directly. The dataset includes player profiles, game session history, and geolocation data: a mix of wide tables and high-cardinality columns that stress both compute and I/O.

To evaluate performance across different scenarios, tombola generated queries at varying levels of complexity. Medium queries involve standard analytical functions like ranking and aggregation, and Medium-High queries introduce multi-step transformations with joins and cumulative calculations. At the High tier, queries combine distinct counting, conditional pivoting, and time-window aggregations. Very High queries are the most demanding: self-joins across the full dataset, multi-signal scoring logic, and advanced statistical functions. This tiered approach captures how each node type performs as computational demands increase.

As with the previous benchmarks, the team kept the test as comparable as possible: a true like-for-like evaluation between RG (powered by Graviton) and RA3 nodes of equivalent size.

Testing was split into two phases:

Phase 1: Concurrency. All queries were submitted simultaneously to measure how well each node type handles concurrent workloads. The goal was to understand throughput differences: how much more work RG nodes can push through under pressure compared to similarly sized RA3 nodes.

All queries were run simultaneously across multiple rounds:

Grouped bar chart showing total execution time across 3 rounds for RA3 vs Graviton

Phase 2: Sequential execution. Each query was run in isolation with full compute resources available. This removed concurrency as a variable and gave a clean read on raw query performance. The results were clear: RG outperformed RA3 across multiple query types, showing consistent gains when given dedicated compute.

In sequential execution, Graviton (RG) delivered consistent performance gains across all query complexity levels: Medium-complexity queries ran 45–73 percent faster (average 58 percent), Medium-High queries improved by 42 percent, High-complexity queries achieved 57–66 percent faster execution (average 62 percent), and Very High-complexity queries saw gains of 60–67 percent (average 63 percent). The results demonstrate that RG’s advantage scales with workload complexity, delivering the largest improvements on the most demanding analytical queries.

tombola’s modernization approach

tombola is modernizing its Amazon Redshift cluster using the Elastic Resize path to change from RA3 to RG node types. The operation snapshots the existing cluster, provisions a new RG cluster from that snapshot, and transfers data in the background. During this transfer period, the source cluster remains available in read-only mode. When the resize nears completion, Amazon Redshift automatically updates the endpoint to point to the new RG cluster and drops connections to the source. The team chose this approach because it aligns with their engineering principle of evidence-based changes: no production cutover without proof. The benchmark results, with zero errors across all scenarios against production-representative workloads, provided the confidence needed to proceed. After the resize is complete, the external tables, schemas, and query syntax remain unchanged. With RG’s integrated data lake query engine, tombola also removes its dependency on Amazon Redshift Spectrum. Data lake queries now run directly on cluster nodes within the Amazon VPC boundary, using existing IAM roles, with zero per-TB scanning charges.

Conclusion

The benchmark results make a compelling case for migrating tombola’s Amazon Redshift infrastructure from RA3 (Intel Xeon) to RG (Graviton4) instances. Across every scenario tested, RG delivered significant and consistent performance gains:

  • Cold-cache performance: 1.57× faster on heavy ETL queries, with per-query speedups up to 1.68×.
  • Warm-cache performance: 1.52× faster on heavy workloads, maintaining advantage even with result caching enabled.
  • Concurrency: 33 percent higher throughput under parallel load, with RG sustaining lower latencies as streams increased from 1 to 20.
  • Mixed realistic workload: 1.85× faster on heavy ETL queries and 46 percent more total queries completed, the scenario closest to production traffic patterns.
  • Amazon S3 Tables (Iceberg): Up to 51 percent faster under concurrent load and 57 percent faster in sequential execution, critical for tombola’s future lakehouse architecture.

Beyond raw performance, RG delivers architectural benefits that align with tombola’s strategic direction. The integrated data lake query engine removes Amazon Redshift Spectrum overhead and per-TB scan charges. The 4:3 node mapping (4 ra3.4xlarge nodes to 3 rg.4xlarge nodes) reduces infrastructure costs by 25 percent.

Based on these results, tombola are modernizing their production Amazon Redshift cluster to Graviton4-based RG instances. The work has already started and similar results as above are noticed.  The existing RA3 features, including concurrency scaling, data sharing, and system views, are fully supported on RG. This positions tombola to handle growing data volumes and user concurrency with better performance, greater cost efficiency, and a predictable pricing model as the application scales.

The results and benefits described in this post are specific to tombola’s workload and environment. Although Amazon Redshift RG instances powered by AWS Graviton4 processors can deliver significant performance improvements, actual results will vary based on factors including workload characteristics, data volumes, cluster configuration, and query complexity. We encourage you to evaluate RG instances with your own workloads to determine the benefits for your environment. To learn more, visit the Amazon Redshift marketing page and the Amazon Redshift documentation, or get started in the Amazon Redshift console.


About the authors

Prabhu Pandian

Prabhu Pandian

Prabhu has over 15 years of experience spanning data engineering, business intelligence, and data analytics. He has built a career on turning complex data challenges into actionable insights across industries including retail, healthcare, logistics, iGaming, and the public sector. He has led high-performing teams at organisations architecting data warehouses, building ETL pipelines processing tens of millions of records daily, and delivering analytics. Currently, as the Data Engineering Lead at tombola, he is focused on harnessing the power of AWS services to build scalable, optimised data platforms that drive real business value. He is passionate about engineering data infrastructure that is not just robust and efficient, but one that empowers teams to make faster, smarter decisions.

Akshay Srinivasan

Akshay Srinivasan

Akshay is a Data Engineer at tombola, where he runs the Data Platform & Reliability pod, shaping the architecture, scalability, and resilience of the company’s core data infrastructure across batch, streaming, and machine learning workloads. He favors open source tooling and composable AWS services, building platforms designed to be flexible and operationally sustainable. Over the past eight years he has built data platforms from the ground up across fintech, gaming, and enterprise environments, standing up greenfield infrastructure, automating complex operational workflows, and engineering systems in domains where data reliability directly affects regulatory and business outcomes. Having worked with Amazon Redshift since 2017, he has seen its evolution first-hand, from early node types through to the modern lakehouse capabilities the platform offers today.

Sidhanth Muralidhar

Sidhanth Muralidhar

Sidhanth is a Principal Technical Account Manager at AWS, where he partners with enterprise customers to design, scale, and optimize cloud-focused systems. He specializes in guiding organizations through complex architectural decisions across cost efficiency, reliability, performance, and operational excellence. His work increasingly sits at the intersection of data systems and AI as well, helping customers operationalize modern data architectures and build intelligent, production-ready systems.

Vlad Siniavin

Vlad Siniavin

Vlad is a Sr. Technical Account Manager at AWS with over 15 years of experience in building innovative solutions, products and services. He is driven by delivering measurable outcomes for his customers – whether that’s reducing operational risk, optimising costs, or accelerating cloud adoption. He believes the best technical guidance starts with deeply understanding what matters most to the customer and acting in their best interest.