Tag Archives: Customer Solutions

Building highly available Oracle databases with Amazon FSx for NetApp ONTAP

Post Syndicated from Vignyanand Penumatcha original https://aws.amazon.com/blogs/architecture/building-highly-available-oracle-databases-with-amazon-fsx-for-netapp-ontap/

Oracle databases power mission-critical enterprise applications, making their continuous availability essential for business operations. Traditional Oracle high availability (HA) solutions require complex clustering software, expensive shared storage arrays, and specialized database administration teams. These conventional approaches often introduce single points of failure while demanding significant operational overhead.

Modern cloud architectures offer a transformative approach that combines Amazon FSx for NetApp ONTAP (FSxN) with Amazon EC2 Auto Scaling groups, automated AMI creation, AWS Lambda-driven orchestration, and AWS Systems Manager Parameter Store (SSM Parameter). This solution removes traditional Oracle HA complexities while delivering enterprise-grade availability, automated recovery, and makes sure new instances launch with the latest Oracle configuration.

This post shows how to build a highly available Oracle database architecture using FSxN shared storage, Auto Scaling groups with dynamic AMI updates, and serverless orchestration to help reduce recovery times with current configurations.

Solution overview

The solution uses multiple AWS services working together to create a comprehensive high availability architecture. FSxN Multi-AZ provides persistent shared storage spanning availability zones for Oracle database files, software, and configurations, so that data remains accessible when EC2 instances are replaced. Auto Scaling groups deliver automated instance lifecycle management with the latest AMI configurations, so failed instances are quickly replaced with identical configurations that can immediately access the existing Oracle database files on FSxN. AWS Backup creates AMIs that capture the latest Oracle host configurations including patches and settings, preserving the complete server state for consistent deployments. AWS Lambda extracts the AMI ID from backup recovery points and updates the SSM Parameter, orchestrating the entire configuration management workflow. Systems Manager Parameter Store stores the current AMI ID for Auto Scaling group launch templates, so new instances always launch with the most recent configuration and can immediately connect to the Oracle database on shared storage.

The following diagram shows the complete architecture with all AWS services and their interactions:

AWS architecture diagram showing Oracle Database disaster recovery across two Availability Zones using FSx for ONTAP synchronous replication, AWS Backup automation with EventBridge and Lambda, and Auto Scaling group with SSM Parameter Store for AMI management.

Key benefits include:

  • Recovery Time Objective (RTO): Can help achieve 2–5 minutes with latest Oracle configuration
  • Recovery Point Objective (RPO): Near-zero through synchronous Multi-AZ replication
  • Configuration consistency: New instances launch with identical Oracle host setup
  • Automated AMI management: Scheduled AMI creation with Parameter Store updates

Walkthrough

This walkthrough demonstrates implementing Oracle HA using Amazon FSx for NetApp ONTAP shared storage, AWS Backup-driven AMI creation, Lambda orchestration, and Auto Scaling groups with Parameter Store integration for configuration consistency and automated failover.

Prerequisites

For this walkthrough, you should have the following prerequisites:

  • An AWS account with appropriate permissions for Amazon FSx, Auto Scaling, EC2, Lambda, and Systems Manager
  • A VPC with subnets in at least two Availability Zones
  • Oracle database software

Keep in mind that customers are responsible for their own Oracle licensing compliance.

  • An EC2 instance with Oracle database installed and configured
  • AWS Identity and Access Management (IAM) roles for AMI creation and cross-service communication
  • Basic knowledge of Oracle database administration and AWS automation

Assumptions

This post is a conceptual illustration of the architecture. Your specific implementation will vary based on your VPC layout, Oracle version, storage requirements, and organizational security policies.

We assume the reader is familiar with:

  • Creating and configuring Amazon FSx for NetApp ONTAP file systems through the AWS console
  • iSCSI concepts including initiators, targets, and multipath I/O
  • Oracle database startup and shutdown procedures
  • AWS Backup, Lambda, and Auto Scaling group fundamentals

For detailed step-by-step instructions on specific AWS services, refer to the additional resources section.

Step 1: Create an Amazon FSx for NetApp ONTAP file system

FSxN Multi-AZ provides the persistent shared storage foundation for this architecture. Unlike Amazon Elastic Block Store (Amazon EBS) volumes, which are bound to a single AZ, FSxN Multi-AZ replicates data synchronously across two AZs with automatic failover. This means that when an EC2 instance is replaced (whether in the same AZ or a different one), the new instance can immediately access the existing Oracle database files without restoring from backup.

To create the file system, navigate to the Amazon FSx console and select Amazon FSx for NetApp ONTAP as the file system type.

The critical configuration choice is selecting Multi-AZ deployment, which places an active file server in one AZ and a standby in another.

Amazon FSx console showing oracle-fsxn-multi-az file system configuration with ONTAP Multi-AZ 1 deployment, 1024 GiB SSD storage, 512 MB/s throughput, spanning us-east-1a preferred and us-east-1b standby subnets.

FSxN console showing Multi-AZ deployment type selection with preferred and standby subnets in separate availability zones.

After the file system is created, you need to set up a Storage Virtual Machine (SVM), which acts as a logical storage container providing data access to your Oracle instances. The SVM creation is done from the FSx console under your file system’s details.With the SVM in place, the next step is configuring iSCSI access. FSxN exposes iSCSI endpoints—these are IP addresses (one per AZ) that your EC2 instances use to connect to the storage over the iSCSI protocol. You can find these endpoint addresses in the FSx console under your SVM’s Endpoints tab.

Amazon FSx Storage Virtual Machine configuration page showing oracle-svm with Created lifecycle state, NFS, iSCSI, and management endpoints for Oracle Database storage connectivity.

SVM Endpoints tab showing iSCSI endpoint IP addresses for each availability zone. These addresses are used in the EC2 instance’s iSCSI discovery configuration.

The iSCSI setup involves creating iGroups (which define which EC2 instances can access the storage) and LUNs (logical storage units mapped to those groups) through the NetApp ONTAP CLI. On the EC2 side, you configure the iSCSI initiator to discover and connect to the FSxN endpoints, then mount the resulting block devices. Using multipath I/O with both endpoints makes sure that Oracle data remains accessible even during an AZ failover. For detailed iSCSI configuration steps, see mounting iSCSI LUNs on Linux clients.

A dedicated security group is required for FSxN access. At minimum, the security group must allow inbound traffic on ports 111 (NFS portmapper), 635 (NFS mountd), 2049 (NFS), 3260 (iSCSI), 4045–4046 (NFS lock), 443 (HTTPS for management), and 22 (SSH for ONTAP CLI). Restrict the source to only your Oracle EC2 instances’ security group.

Step 2: Set up AWS Backup for EC2 instance protection

AWS Backup captures the complete state of your Oracle EC2 instance. The key design choice here is using tag-based resource selection rather than specifying instance IDs directly. Because Auto Scaling groups replace instances (and generate new instance IDs), tag-based selection makes sure that any new instance with the correct tags are automatically included in the backup plan.Configure a backup plan with a frequency appropriate for your environment and set the resource assignment to select EC2 instances matching your application tag (for example, ‘Application: Oracle’).

AWS Backup console showing blog-test backup plan with hourly backup rule targeting Oracle EC2 instances identified by the Application:oracle-db tag.

AWS Backup resource assignment configured with tag-based selection. Any EC2 instances tagged with the application tag are automatically included in the backup plan.

Step 3: Configure Lambda for AMI management

When AWS Backup completes an EC2 backup, it creates an AMI as the recovery point. An Amazon EventBridge rule detects this completion event and triggers a Lambda function. The function extracts the AMI ID from the backup recovery point, updates the SSM Parameter Store parameter with the new AMI ID, and cleans up older AMIs to control storage costs.

AWS Lambda function configuration for oracle-backup-handler showing Python 3.11 runtime, EventBridge trigger, and description indicating it processes AWS Backup completion events and updates AMI in SSM.

Lambda function overview showing the EventBridge trigger, Python 3.11 runtime, and function description indicating its role in processing backup completions and updating AMI references in SSM.

This event-driven approach means the latest AMI is available without manual intervention. The Lambda function needs IAM permissions for EC2 (to manage AMIs), SSM (to update the parameter), and Backup (to read recovery point metadata).

Amazon EventBridge rule oracle-backup-completion configured to trigger the oracle-backup-handler Lambda function when AWS Backup completes an EC2 backup job, with event pattern filtering for COMPLETED state.

EventBridge rule configured to match AWS Backup job completion events for EC2 resources, with the Lambda function as the target.

Step 4: Configure the Systems Manager Parameter Store

The SSM Parameter Store holds the current AMI ID that the Auto Scaling group’s launch template references. The parameter is created with the aws:ec2:image data type, which enables the launch template’s resolve:ssm: functionality, a feature that allows the launch template to dynamically resolve the AMI ID at instance launch time without requiring a template version update.

AWS Systems Manager Parameter Store showing /oracle/ec2/ami-id parameter with AMI value ami-0a705a7d5523c555, version 857, last modified by the oracle-backup-lambda-role on April 25, 2026.

SSM Parameter Store showing the /oracle/ec2/ami-id parameter with aws:ec2:image data type. The “Last modified user” confirms the Lambda function is automatically updating this parameter after each backup cycle.

When Lambda updates this parameter after each backup cycle, the next instance launched by the Auto Scaling group will automatically use the latest AMI. This removes the operational burden of manually updating launch template versions.

Step 5: Set up an Auto Scaling Group with dynamic AMI

The launch template references the SSM parameter using the resolve:ssm: prefix for the AMI ID field. This is the mechanism that ties the entire automation pipeline together. The mechanism backups trigger AMI creation, AMI IDs flow into Parameter Store, and the launch template resolves the latest AMI at launch time.

EC2 Launch Template oracle-db-launch-template version 75 showing AMI ID resolved from SSM parameter resolve:ssm:/oracle/ec2/ami-id with r7i.large instance type for Oracle Database deployment.

Launch template AMI configuration showing the ‘resolve:ssm:’ prefix, which dynamically retrieves the latest AMI ID from Parameter Store at instance launch time.

The Auto Scaling group is configured with minimum, maximum, and desired capacity all set to 1. This is not traditional auto-scaling, it’s a self-healing pattern. The sole purpose is to detect when the Oracle instance becomes unhealthy and automatically launch a replacement. The health check grace period should be set to at least 300 seconds (5 minutes) to allow Oracle sufficient time to start before health checks begin evaluating the new instance.

The launch template also includes a User Data script that runs on each new instance. This script configures the iSCSI initiator, discovers and connects to the FSxN endpoints, mounts the Oracle data volumes, and starts the Oracle database through a systemd service. This automation makes sure that a replacement instance is fully operational without manual intervention.

EC2 Auto Scaling group oracle-db-asg configuration showing desired capacity of 1, scaling limits 1-1, r7i.large instance type, oracle-db-launch-template with Latest version, spanning two availability zone subnets.

Auto Scaling group configured with min=max=desired=1 across two availability zones, providing self-healing capability.

Test the complete workflow

To validate the architecture, simulate an instance failure by terminating the current Oracle EC2 instance.

The expected sequence is:

  1. The Auto Scaling group detects the instance is unhealthy (within approximately 30 seconds)
  2. A new instance launches from the latest AMI resolved from Parameter Store (approximately 2 minutes)
  3. The User Data script connects to FSxN using iSCSI and starts Oracle (approximately 2–3 minutes)
  4. The Oracle database is available and accepting connections (total elapsed: approximately 5 minutes)

Auto Scaling group Activity History showing the self-healing sequence — the unhealthy instance is terminated, and a replacement is launched automatically within seconds.

The new instance automatically inherits the application tags from the Auto Scaling group, which means AWS Backup includes it in the next backup cycle without manual configuration.

Cleaning up

To avoid incurring future charges, delete the resources:

  • Delete Lambda functions and EventBridge rules
  • Remove Parameters from Systems Manager Parameter Store
  • Delete AWS Backup plans and backup vault
  • Deregister created AMIs
  • Terminate Auto Scaling group instances
  • Delete the Amazon FSx for NetApp ONTAP file system

Conclusion

This architecture facilitates Oracle high availability with configuration consistency by combining FSxN persistent shared storage with automated AMI management and AWS Backup protection. The Lambda-driven AMI management from backup recovery points and Parameter Store integration helps make sure that replacement instances launched by Auto Scaling groups always use the latest Oracle host configuration and can immediately connect to the existing Oracle database files stored on FSxN. Replacements occur only when health checks fail. Organizations can target high availability while maintaining configuration consistency across instance replacements. The automated AMI management alleviates configuration drift and makes sure that disaster recovery scenarios restore Oracle instances with identical host-level configurations that can immediately access the persistent Oracle database on shared storage. Healthy instances continue running unchanged, with replacements occurring only, when necessary, because of health check failures.Next steps include implementing cross-Region AMI replication, adding AMI validation testing, and developing custom health checks that verify both Oracle database and host configuration consistency.

Additional resources

Automating contract intelligence with Doczy.ai™ on AWS

Post Syndicated from Sanket Nasre original https://aws.amazon.com/blogs/architecture/automating-contract-intelligence-with-doczy-ai-on-aws/

Extracting actionable insights from thousands of contracts and legal documents remains a challenge. For organizations, critical business information is locked in unstructured documents such as contracts, legal agreements, provider arrangements, and vendor invoices. Extracting and operationalizing this information has traditionally been a manual, error-prone, and resource-intensive process. This leads to missed savings opportunities, costly delays, and significant inefficiencies across the enterprise.

AArete, a global management and technology consulting firm specializing in healthcare, recognized this challenge and developed Doczy.ai™, an intelligent contract interpretation solution powered by generative AI on Amazon Web Services (AWS).

In this post, we show you how Doczy.ai™ uses generative AI on AWS to automate contract intelligence at scale, transforming unstructured documents into structured, actionable insights, so organizations can automate critical business processes and unlock the full value of their data.

The challenge: Data trapped in documents

For healthcare organizations, managing and interpreting contracts and documents represents a major operational bottleneck. Manual review processes require deploying teams to extract data from thousands of documents. This is an approach that is neither scalable nor sustainable, highly prone to error, and costly. Organizations relying on institutional knowledge face additional risks: critical information resides with a few key individuals, creating knowledge silos and succession planning challenges. Existing Contract Lifecycle Management (CLM) systems often prove inadequate for capturing the nuanced and complex terms unique to each agreement. These legacy systems can only configure predefined fields, missing the rich detail and contextual information that distinguishes contracts. The downstream impact is substantial: in healthcare, reimbursement terms must be manually translated into claims systems—a slow, error-prone process. Similarly, verifying vendor invoices against contract terms often requires manual effort, leading to payment processing delays and missed contractual savings opportunities. These inefficiencies ultimately leave significant value on the table.

This is where Doczy.ai™ provides significant value.

Doczy.ai™: An intelligent contract interpretation solution

Doczy.ai™ directly addresses these challenges using advanced AI and scalability on AWS. Developed by AArete, Doczy.ai™ pushes the boundaries of document intelligence. The solution automatically interprets complex documents and converts them into a structured, queryable information repository that allows organizations to unlock the full value of their data and drive smarter decisions.The evolution of Doczy.ai™ reflects rapid AI advancement. Prior to 2020, document processing required manual effort, with individuals processing approximately 100 documents per week. Between 2020–2023, the firm implemented rules-based contract processing, achieving approximately 55% accuracy. The breakthrough came in 2024 with an AI-based processing built on AWS achieved 99% accuracy—a dramatic improvement over the 55% accuracy of traditional rules-based systems.

Doczy.ai™ architecture

Doczy.ai™ is built on a comprehensive AWS architecture designed to handle the entire document processing lifecycle: from the moment a file enters the system to the moment it generates actionable business intelligence.

Doczy.ai is built on a comprehensive AWS architecture designed to handle the entire document processing lifecycle: from the moment a file enters the system to the moment it generates actionable business intelligence.

Architecture of Doczy.ai™

External users access the platform through a secure Next.js frontend, with Amazon Cognito managing authentication and authorization behind the scenes. After authentication, users upload documents directly to Amazon Simple Storage Service (Amazon S3), where durable, scalable object storage ensures nothing is lost and everything is accessible at scale. From there, the real intelligence begins.

An AWS Lambda function triggers Amazon Textract to extract text and metadata from documents in various formats. What sets Doczy.ai™ apart at this stage is its patented “smart chunking” algorithm, a proprietary approach that goes far beyond pulling words off a page. Rather than treating a document as a flat sequence of text, smart chunking preserves hierarchical structure and one-to-many relationships within documents. It uses a combination of semantic and keyword search to decompose text into meaningful, context-aware chunks, applying dynamic parameters to maintain logical relationships throughout. Sequential identifiers and metadata-driven grouping organize these chunks into field groups, detecting overlaps and removing duplications while keeping the document’s natural flow intact.

After chunking, the document enters the dual clustering engine of Doczy.ai™. This two-lens methodology analyzes every contract simultaneously from both a semantic and a structural perspective. On the semantic side, extracted text is converted into embeddings, numerical representations of meaning, and similar ideas are grouped together even when they’re expressed in different words. On the structural side, pattern-recognition algorithms identify clause types, formatting conventions, table layouts, and hierarchical organization, understanding. For example, that a three-nested-level exhibit carries fundamentally different implications than a straightforward attached schedule.These two analyses don’t operate in isolation. Projection algorithms compare the semantic and structural clusters side by side, synthesizing them into a unified, enriched document model that captures both meaning and context. It’s this convergence that drives the 99% accuracy rate of Doczy.ai™. The system doesn’t just read the words, it understands the contract. Advanced large language models (LLMs) then generate structured output grounded in this dual-clustered intelligence.Before output is finalized, the system determines each document’s file class and generates prompts tailored to the extracted text, cluster classification, and domain context. Through few-shot and multi-shot prompting, the platform continuously edits the prompt on domain-specific examples and based on real outputs, creating a feedback loop that compounds accuracy improvements over time.

The resulting structured data flows into Snowflake, forming a centralized repository that powers intelligent dashboards with actionable insights and visualizations. Throughout the entire pipeline, Amazon CloudWatch monitors performance in real time and proactively surfaces issues before they escalate, while AWS Secrets Manager safeguards sensitive information, ensuring that security is not an afterthought, but a foundational layer woven into every stage of the system.

The transformative impact of Doczy.ai™

The results of this AI-powered approach are transformative and measurable. By automating contract interpretation and document processing, Doczy.ai™ has demonstrated significant impact at scale for multiple organizations across healthcare and financial services. The scale of operations over the last 22 months demonstrates the maturity and production readiness of Doczy.ai™. This solution has processed 2.5 million contract documents (50 million pages) with 137 million API calls to Amazon Bedrock and 442 billion tokens—a level of automation and accuracy previously unattainable through manual or traditional document processing approaches. Over this same period, Doczy.ai™ has helped clients achieve approximately 330 million dollars in cumulative direct and indirect savings.The 99% accuracy rate represents significant improvement over the approximately 55% accuracy of rules-based systems and far exceeds manual processing, which is typically affected by fatigue and human error. The 97% reduction in manual processing time translates directly to cost savings and enables organizations to reallocate human resources to higher-value activities that require judgment and strategic thinking.

A use case in action: Business process automation for health plans

For health plans, Doczy.ai™ provides a powerful solution to automate and improve contract management across the entire lifecycle. It ingests existing contracts in both paper and digital formats, integrates with contract management systems such as Coupa and Icertis, and processes new contracts and amendments as they’re executed. It then creates a centralized metadata repository that feeds directly into downstream systems, enabling end-to-end business process automation.This automation unlocks critical capabilities: Organizations can continuously analyze and improve contract terms, identifying opportunities to improve financial performance and operational efficiency. The architecture feeds accurate, up-to-date contract data directly into claims systems, automating the configuration process that previously required manual translation of reimbursement terms and removing manual data entry, configuration errors, and delays. Additionally, the platform helps maintain claim payment accuracy by assessing payments against contract terms, identifying discrepancies, and flagging potential overpayments or underpayments before they occur.By automating manual processes, health plans can adapt quickly to new contract terms and regulatory requirements. The intelligent dashboards and actionable insights provided by Doczy.ai™ enable decision-makers to understand contract performance, identify trends, and take proactive action to optimize financial outcomes.

Getting started with Doczy.ai™

Organizations interested in using Doczy.ai™ to transform document processing and contract management can engage with AArete to discuss their specific use cases and requirements. AArete offers the platform as a Software as a Service (SaaS) solution, enabling rapid deployment without significant infrastructure investment. AArete’s team of experts will configure this solution for your specific document types, domain terminology, and business processes, supporting maximum value from day one.

Conclusion

The challenge of unlocking data from unstructured documents is a major hurdle for many businesses, particularly in healthcare and financial services where contracts and agreements govern critical operational and financial relationships. By embracing intelligent document intelligence on AWS, organizations can solve this long-standing operational challenge and unlock a new frontier of strategic advantage, turning their data into their most valuable asset.

Built on a sophisticated architecture that orchestrates Amazon Cognito, Amazon S3, AWS Lambda, Amazon Textract, Amazon Elastic Container Service (Amazon ECS), Amazon Bedrock, Amazon CloudWatch, and AWS Secrets Manager, Doczy.ai™ demonstrates how modern cloud services can solve complex document-heavy business problems. Its advanced hybrid smart chunking, dual clustering, and prompt optimization techniques form the core of a patented contract intelligence engine.

Doczy.ai™ delivers tangible impact, processing up to 250,000 contract documents per week with 99% accuracy, reducing manual processing time by 97%, and helping clients unlock roughly 330 million dollars in cumulative savings over 22 months. By embracing this intelligent document processing, organizations can turn contracts into a strategic data asset, improving efficiency, accuracy, and profitability while freeing teams to focus on higher-value work.

To learn more about how AArete and Doczy.ai™ can help your organization transform document processing and unlock the value of your unstructured data, visit the AArete website.


About the authors

Scaling oncology patient support: How New York Cancer and Blood Specialists transformed customer experience with AWS and Pronetx, now part of Caylent

Post Syndicated from Muni T. Bondu original https://aws.amazon.com/blogs/architecture/scaling-oncology-patient-support-how-new-york-cancer-and-blood-specialists-transformed-customer-experience-with-aws-and-pronetx-now-part-of-caylent/

As one of the United States’ leading oncology and hematology providers, the goal of New York Cancer and Blood Specialists (NYCBS) is to provide comprehensive and compassionate care to patients. The organization handles more than 250,000 patient calls every year across over 100 specialized queues and wanted to optimize its manual call handling process.

This post details how NYCBS partnered with Amazon Web Services (AWS) and AWS partner Pronetx (now part of Caylent) to migrate to Amazon Connect Customer, the AWS cloud contact center service. The migration delivered a 54 percent improvement in patient enrollment and transformed the way NYCBS connects with the patients who need them most.

Solution overview

NYCBS chose to migrate to a dedicated Amazon Connect Customer instance with the help of Pronetx, an AWS partner specializing in Amazon Connect Customer. The 13-week engagement covered three phases: discovery and foundation (2 weeks), build and implementation (8 weeks), and acceptance testing and go-live (3 weeks).

This solution uses Amazon Connect Customer as the core contact center service. At the heart of the customer experience infrastructure lies the call routing and patient communication logic. Within this infrastructure, several key components work together to deliver HIPAA-compliant patient care.

Architecture

The architecture has three main layers, as shown in the following diagram. Each layer is designed to handle a specific aspect of the customer experience operations of NYCBS while maintaining HIPAA compliance throughout.

Architecture diagram showing the three-layer NYCBS contact center solution built on Amazon Connect. The diagram illustrates: (1) CTR management microservice with Amazon API Gateway, AWS Lambda, and Amazon DynamoDB; (2) core contact center services with Amazon Connect instance, AWS Secrets Manager, Amazon Polly, and IVR Lambda functions; (3) call recording and AI/ML pipeline with Amazon S3, Amazon Transcribe, and Amazon Lex. Shared AWS services include AWS CloudFormation, AWS IAM, AWS KMS, Amazon CloudWatch, and Amazon SNS. External integrations include Microsoft Intune ID and NYCBS on-premises systems.

Figure 1: NYCBS customer experience solution architecture on Amazon Connect, showing the CTR management microservice, core contact center services, and call recording and AI/ML pipeline layers, integrated with shared AWS services and external systems.

CTR management microservice

This layer handles contact trace record (CTR) processing through Amazon API Gateway as the entry point. The microservice uses AWS Lambda functions (Get Disposition Code and Update CTRs) to retrieve and process call disposition codes, with Amazon DynamoDB storing disposition code data for quick retrieval.

Core contact center services

The central Amazon Connect Customer instance manages incoming agent flows and call routing across more than 100 specialized queues. AWS Secrets Manager securely stores credentials and sensitive configuration. Amazon Polly provides text-to-speech capabilities for automated voice responses. Configuration is managed through DynamoDB configuration tables and configuration Lambda functions, with IVR Integration Lambda functions handling IVR workflows.

Call recording and AI/ML pipeline

Call recordings and artifacts are stored in Amazon Simple Storage Service (Amazon S3). A dedicated Lambda function handles voicemail processing, with Amazon Transcribe converting voicemails to text. A subsequent Lambda function then creates cases from transcribed voicemails. AI capabilities are powered by Amazon Lex for conversational chatbots and other AWS AI services for intelligent automation, with OMS Lookup providing Order Management System integration.

Shared services and integrations

The architecture integrates with external systems including Microsoft Intune ID for identity management and the on-premises systems of NYCBS. Shared AWS services provide the foundation:

Together, these components helped NYCBS reduce patient enrollment time by 54 percent. The reduction came through automated multi-language routing in English, Spanish, Russian, and Mandarin. NYCBS also added specialty-based queue prioritization for urgent cases and real-time agent monitoring across the more than 250,000 annual patient calls. The implementation maintains HIPAA compliance through role-based access controls, encryption with AWS KMS, and secure credential storage.

Technical highlights

A key engineering decision was moving from a shared multi-tenant environment to a dedicated Amazon Connect Customer instance. This architectural shift helped NYCBS implement several capabilities that had previously been unavailable:

  • Multi-language routing (English, Spanish, Russian, and Mandarin) through language-detection flows.
  • Specialty-based queue prioritization for urgent oncology cases.
  • After-hours coverage logic with automated callback options.
  • HIPAA-compliant call recording with role-based access controls.
  • Real-time agent monitoring and performance dashboards.

Key takeaways

A dedicated Amazon Connect Customer instance gives healthcare organizations the flexibility, security, and native feature access that multi-tenant environments can’t match, including the following:

  • Multi-language, specialty-based routing (English, Spanish, Russian, Mandarin) directly improved patient access to the right care team.
  • Migrating from manual workflows to automated IaC-driven continuous integration and continuous delivery (CI/CD) deployment removed third-party management fees and reduced operational costs.
  • HIPAA compliance was maintained throughout using AWS KMS encryption, role-based access controls, and AWS Secrets Manager for credential storage.
  • A 54 percent improvement in patient enrollment shows that modernizing contact center operations delivers measurable clinical and business outcomes.

Results

Within the 13-week implementation window, NYCBS achieved a 54 percent improvement in patient enrollment, which reflects not only operational efficiency, but also improved access to care. The organization removed third-party management fees through direct AWS consumption, reducing operational costs. NYCBS gained real-time visibility into call quality through Operata and the conversational analytics capabilities of Amazon Connect Customer. In addition, the team established a rapid CI/CD pipeline that deploys new features safely and quickly without disrupting patient services.

Conclusion

If your healthcare organization manages complex, high-volume contact center operations, you can apply what NYCBS achieved. A dedicated Amazon Connect Customer instance provides the flexibility, security, and native feature access that healthcare-specific workflows demand. We recommend the following next steps to get started:

Learn more

AWS Partner spotlight

Pronetx, now part of Caylent, an AWS Premier Tier Services Partner, specializes in Amazon Connect. Pronetx helps organizations design, migrate, and operate customer engagement systems on AWS, with a focus on resilience and AI-driven customer experience.

Contact Pronetx | Partner Overview | AWS Marketplace

Multi-Region event-driven failover architecture with Amazon EventBridge and Route 53

Post Syndicated from Napoleone Capasso original https://aws.amazon.com/blogs/compute/multi-region-event-driven-failover-architecture-with-amazon-eventbridge-and-route-53/

Multi-Region Event-Driven Failover Architecture with Amazon EventBridge and Route 53

Event-driven architectures enable applications to respond to events in real-time, providing scalability and loose coupling between components. However, ensuring high availability across multiple AWS regions requires careful design of failover mechanisms. This post demonstrates how to build a resilient multi-region event-driven architecture using Amazon EventBridge, Amazon API Gateway, and Amazon Route 53 health-based failover.

Overview

Organizations building event-driven applications need to achieve high availability and disaster recovery capabilities. This architecture provides automatic failover between AWS regions while maintaining regional independence for event processing. The solution uses Amazon Route 53 health checks to monitor regional Amazon API Gateway endpoints and automatically routes traffic to healthy regions without manual intervention.

The architecture delivers several key benefits. Regional independence reduces latency by processing events in the same region where they originate. Amazon DynamoDB global tables provide automatic data replication across regions, ensuring data availability during regional failures. The solution provides robust failover capabilities while maintaining architectural simplicity.

Organizations with strict availability requirements can find this solution particularly valuable. All event processing remains within AWS regions, and failover occurs automatically based on health check results. The architecture supports both planned maintenance windows and unplanned regional outages, providing flexibility for operational needs.

Solution overview

The solution implements an active-passive multi-region architecture where events flow through Amazon API Gateway to regional Amazon EventBridge buses. Amazon Route 53 health checks monitor the primary region and automatically route traffic to the secondary region during failures. Each region processes events independently, while Amazon DynamoDB Global Tables replicate data across regions.

The following diagram provides an overview of the solution:

The above diagram depicts the multi-region architecture running across two AWS regions. The Route 53 DNS service serves as the main entry point for the application, with health checks monitoring both regions. Each region contains an identical stack with Amazon API Gateway, Amazon EventBridge, Amazon SQS, and AWS Lambda. The Amazon DynamoDB Global Table replicates data between regions automatically.

Solution deployment

To deploy this solution, follow the instructions in the GitHub repository and clone the repository. The solution deploys in two AWS regions. Ensure valid SSL certificates exist in AWS Certificate Manager (ACM) in both regions for the custom domain.

Prerequisites

For this walkthrough, the following resources are needed:

  • AWS Account: An AWS account with permissions to create and manage Amazon API Gateway, Amazon EventBridge, Amazon SQS, AWS Lambda, Amazon DynamoDB, Amazon Route 53, AWS IAM, and AWS CloudFormation resources
  • AWS Serverless Application Model (SAM): The AWS SAM CLI installed, as the templates use the SAM transform for Lambda and API Gateway resource definitions
  • Domain Name: A registered domain with a Route 53 hosted zone- SSL Certificates: ACM certificates for the custom domain in both deployment regions
  • AWS CLI: The AWS CLI installed and configured with credentials for the target AWS account
  • Region Selection: Two AWS regions for deployment

Walkthrough

The AWS CloudFormation templates from the sample GitHub repository create a secure, multi-region architecture that provides automatic failover for event-driven applications. The templates provision regional API Gateway endpoints, EventBridge buses, SQS queues, Lambda functions, and an Amazon DynamoDB Global Table. The solution establishes health monitoring through Route 53 health checks and configures DNS failover routing. The templates use AWS Serverless Application Model (SAM) transform to simplify Lambda and API Gateway resource definitions.

Step 1: Deploy the primary stack

The primary stack creates the foundational resources in the primary region. This includes the Amazon EventBridge bus, Amazon API Gateway with custom domain, health check, AWS Lambda function, Amazon SQS queue, and Amazon DynamoDB Global Table. The stack creates an EventBridge bus that receives events from API Gateway:

EventBus: 
Type: AWS::Events::EventBus 
Properties: 
Name: !Ref EventBusName

The API Gateway uses AWS service integration to forward events directly to EventBridge:

x-amazon-apigateway-integration: 
type: "aws" 
uri: !Sub "arn:aws:apigateway:${AWS::Region}:events:path//" 
credentials: !GetAtt ApiGatewayEventBridgeRole.Arn 
httpMethod: "POST"

The health check monitors the API Gateway endpoint to determine regional availability:

DomainHealthCheck: 
Type: AWS::Route53::HealthCheck 
Properties: 
HealthCheckConfig: 
Type: HTTPS 
ResourcePath: /Prod/health FullyQualified
DomainName: !Sub ${Api}.execute-api.${AWS::Region}.amazonaws.com 
Port: 443 
RequestInterval: 30 
FailureThreshold: 3

The Route 53 DNS record configures failover routing with the PRIMARY designation:

ApiDnsRecord:
Type: AWS::Route53::RecordSet
Properties:
HostedZoneId: !Ref HostedZoneId
Name: !Ref CustomDomainName
Type: A
SetIdentifier: primary-region
Failover: PRIMARY
HealthCheckId: !Ref DomainHealthCheck

The DynamoDB Global Table creates replicas in both regions:

DataTable: 
Type: AWS::DynamoDB::GlobalTable 
Properties: 
BillingMode: PAY_PER_REQUEST 
Replicas: 
- Region: !Ref AWS::Region 
- Region: !Ref SecondaryRegion

Note the `DataTableName` output value for use in the secondary stack deployment. The `CustomDomainURL` output provides the endpoint to invoke the solution.

Step 2: Deploy the secondary stack

The secondary stack creates identical resources in the secondary region , except for the Amazon DynamoDB table which references the existing Global Table. The secondary stack creates its own Amazon EventBridge bus, Amazon API Gateway, health check, AWS Lambda function, and Amazon SQS queue. The Route 53 DNS record uses the SECONDARY designation

Step 3: Event processing flow

Events flow through the processing pipeline in each region. API Gateway receives events and forwards them to EventBridge using the PutEvents API. EventBridge evaluates event rules and routes matching events to SQS queues. Lambda functions poll the SQS queues and process events in batches. AWS Lambda writes processed data to the DynamoDB Global Table, which replicates across regions.

The Lambda function processes events from the queue and writes to DynamoDB:

def handler(event, context): 
for record in event.get('Records', []): 
body = json.loads(record['body']) 
detail = body.get('detail', {}) 
event_id = body.get('id', '') 
item = { 'id': event_id, 'detail': detail, 'timestamp': datetime.utcnow().isoformat() } 
table.put_item(Item=item)

Testing

Fetch the custom domain URL and test it by sending an event:

curl -X POST https://api.example.com \-H "Content-Type: application/json" \ -d '{ "Detail": { "IsHelloWorldExample": "true" }, "DetailType": "POSTED", "Source": "demo.event" }' -v

The response includes an `X-Region` header indicating which region processed the request. Under normal conditions, this shows the primary region.

To test failover:

  1. Remove the base path mapping for the primary region:
aws apigateway delete-base-path-mapping \ --domain-name api.example.com \ --base-path '(none)' \ --region {primary-region}
  1. Delete the primary API Gateway stage:

aws apigateway delete-stage \ --rest-api-id <primary-api-id> \ --stage-name Prod \ --region {primary-region}

  1. Wait 2-3 minutes for the health check to fail. The Route 53 health check performs checks every 30 seconds with a failure threshold of 3, requiring 90 seconds to detect the failure.
  2. Send another request to the API endpoint:
curl -X POST https://api.example.com \-H "Content-Type: application/json" \ -d '{ "Detail": { "IsHelloWorldExample": "true" }, "DetailType": "POSTED", "Source": "demo.event" }' -v
  1. Verify the failover: The `X-Region` header now shows the secondary region, confirming successful failover.

Verify event processing in the secondary region:

  1. Check the Lambda logs for successful processing:

aws logs tail /aws/lambda/<secondary-lambda-name> --region {secondary region}

You should see log entries similar to:

Processing message: 
{"version":"0",
"id":"abc12345-...",
"source":"demo.event",
"detail-type":"POSTED",...} 
Event Source: demo.event
Detail Type: POSTED
Successfully wrote item to DynamoDB: abc12345-... 
Successfully read item from DynamoDB: 
{'id': 'abc12345-...', 
'source': 'demo.event', 
'detailType': 'POSTED', 
'detail': 
{'data': {'IsHelloWorldExample': 'true'}, 
...}, 
'timestamp': '2025-01-15T18:30:00.000000', 
'processed': True}
  1. Verify the data in Amazon DynamoDB:

aws dynamodb scan \ --table-name <table-name> \ --region {secondary region}```

The scan results should include items with the event details:

{ "Items": 
[ { "id": {"S": "abc12345-..."}, 
"source": {"S": "demo.event"}, 
"detailType": {"S": "POSTED"},
"detail": 
{"M": {"data": 
{"M": 
{"IsHelloWorldExample": 
{"S": "true"}}}}}, 
"timestamp": {"S": "2025-01-15T18:30:00.000000"},
"processed": {"BOOL": true} } ], 
"Count": 1 }
  1. Restore the primary region – recreate the stage:

aws apigateway create-stage \ --rest-api-id <primary-api-id> \ --stage-name Prod \ --deployment-id <deployment-id> \ --region {primary region}

  1. Restore the primary region – recreate the base path mapping:

aws apigateway create-base-path-mapping \ --domain-name api.example.com \ --rest-api-id <primary-api-id> \ --stage Prod \ --region {primary region}

You can find the “deployment-id” by running: aws apigateway get-deployments \ --rest-api-id <primary-api-id> \ --region {primary region}

After 2-3 minutes, the health check passes and Route 53 routes traffic back to the primary region.

Cleanup

To remove the solution and avoid ongoing charges, delete the CloudFormation stacks in the correct order. Delete the secondary stack first, then the primary stack. This order is important because the Amazon DynamoDB Global Table is owned by the primary stack. Warning: Deleting these stacks permanently removes all resources including the Amazon DynamoDB global table and any event data stored in it. Back up any data you need before proceeding. This action cannot be undone. The following resources incur costs while deployed:

  • Amazon API Gateway (REST API)
  • Amazon Route 53 health checks and DNS records
  • Amazon DynamoDB global table (with cross-region replication)
  • AWS Lambda function invocations and duration
  • Amazon SQS queue operations
  • Amazon CloudWatch Logs storage

Delete the secondary stack:

aws cloudformation delete-stack --stack-name secondary-stack --region {secondary region}

Wait for the secondary stack deletion to complete:

aws cloudformation wait stack-delete-complete --stack-name secondary-stack --region {secondary region}

Delete the primary stack:

aws cloudformation delete-stack --stack-name primary-stack --region {primary region}

Wait for the primary stack deletion to complete:

aws cloudformation wait stack-delete-complete --stack-name primary-stack --region {primary region}

This removes all resources including the Amazon EventBridge buses, Amazon API Gateways, AWS Lambda functions, Amazon SQS queues, Amazon DynamoDB Global Table, Amazon Route 53 health checks, DNS records and IAM roles.

Conclusion

This post demonstrates how to establish a resilient multi-region architecture for event-driven applications using Amazon EventBridge, Amazon API Gateway, and Amazon Route 53. The solution uses Route 53 health-based failover, a powerful capability that automatically routes traffic to healthy regions based on health check results. This architecture significantly enhances application availability by providing automatic failover during regional outages while maintaining regional independence for event processing.

How Buildkite Operates Test Analytics at Massive Scale with Amazon MSK and Amazon Managed Service for Apache Flink

Post Syndicated from James Hill original https://aws.amazon.com/blogs/big-data/how-buildkite-operates-test-analytics-at-massive-scale-with-amazon-msk-and-amazon-managed-service-for-apache-flink/

When engineering teams at Slack, Reddit, Canva, Airbnb, Shopify, and Uber need to ship code with confidence, they rely on Buildkite. As a CI/CD platform, Buildkite orchestrates complex build, test, and deployment pipelines for some of the most demanding engineering organizations in the world. It handles everything from routine code commits to artificial intelligence (AI) model-training workloads, processing over 50 billion requests per month.

At the heart of Buildkite’s test orchestration portfolio is Test Engine, a specialized analytics product designed to help engineering teams understand and optimize their test suites at scale. Test Engine aggregates results across thousands of builds, flags flaky tests, runs parallel test execution across machine fleets, and delivers interactive analytics on test execution data. It supports arbitrary metadata tagging for dimensions like instance type, architecture, language version, cloud provider, and feature flags.

The challenge? Delivering all of this in real time, across multiple enterprise tenants, at a volume that would stress even the most robust data infrastructure. In this post, we explore how Buildkite uses Amazon Managed Streaming for Apache Kafka (Amazon MSK) and Amazon Managed Service for Apache Flink to power Test Engine’s streaming-first analytics architecture at scale.

The problem: When scale breaks traditional architectures

Buildkite’s Test Engine must ingest and serve analytics on test telemetry from thousands of distributed pipelines simultaneously, for multiple enterprise customers. The scale is unforgiving: 50 billion test executions per month, 500K events per second at peak ingestion, and webhook payloads reaching 21 MB.

The architectural evolution and its limits

The original Rails and PostgreSQL stack couldn’t sustain this growth. In 2024, the team re-architected around a distributed streaming layer, a stateful stream processor for pre-aggregations, and multiple specialized stores: a key-value store for fast lookups, a relational database for pre-computed aggregates, and an open table format (Iceberg) with a distributed query engine (Trino) for flexible querying.

Yet the core tension remained unsolved. Enterprise customers demanded interactive, arbitrary slicing of billions of records across high-cardinality dimensions, not canned reports. The stream processor couldn’t handle ad hoc aggregations at query time. The key-value store was blind to analytical queries. The distributed query engine offered flexibility but was too slow for interactive use.

The result was a system that was expensive and operationally complex. It included nine relational database clusters, sprawling ETL pipelines, and 24/7 pre-aggregation jobs running regardless of demand. It still couldn’t deliver the one thing customers needed most: fast, flexible, interactive analytics at scale.

Architecture and implementation: MSK and Amazon Managed Service for Apache Flink as the streaming backbone

The solution Buildkite arrived at centers on Amazon Managed Streaming for Apache Kafka (Amazon MSK) and Amazon Managed Service for Apache Flink as the real-time data streaming and processing layers, decoupling high-throughput ingestion from downstream analytics.

The data pipeline

The following diagram shows the end-to-end data flow from CI/CD agents through Amazon MSK and Amazon Managed Service for Apache Flink to the analytics layer.

Architecture diagram showing the Buildkite data pipeline from CI/CD agents through Amazon MSK and Amazon Managed Service for Apache Flink to ClickHouse analytics

Amazon MSK sits at the critical junction between data producers (the distributed CI/CD agents and test collectors running across customer infrastructure) and the downstream processing and analytics layers. Amazon Managed Service for Apache Flink then transforms those raw event streams into enriched, queryable data before it reaches the analytics store.

High-throughput ingestion from CI/CD pipelines

Amazon MSK’s role begins at ingestion. Test collectors embedded in CI/CD pipelines publish test execution events directly to Kafka topics. The existing Amazon MSK cluster handles between 5 MB/sec and 100 MB/sec of inbound data under normal operating conditions. The architecture is designed to absorb the significant variance inherent in CI/CD workloads, where pipeline activity is bursty and correlated with engineering team working hours across global time zones.

When the Buildkite project was initiated, MSK Express Brokers were not yet available, leading the team to adopt MSK Tiered Storage as the primary mechanism for scaling and recovery. With MSK Express Brokers now generally available, the team is evaluating a migration of its most critical log ingestion workload, which sustains up to 1 GB/s at peak ingestion. MSK Express Brokers bring automatic storage scaling with zero storage management overhead, up to 20x faster scaling and 90% faster broker recovery, 3x higher per-broker throughput, 5x more partitions per broker, and built-in Intelligent Rebalancing.

Real-time stream processing with Amazon Managed Service for Apache Flink

Sitting between Amazon MSK and the analytics layer, Amazon Managed Service for Apache Flink acts as the stateful stream processing engine that transforms raw event streams before they reach downstream systems. Buildkite selected Flink for its exactly-once processing, mature stateful computation model, and deep Kafka integration. Handling sustained peaks of over 25,000 events per second, Amazon Managed Service for Apache Flink eliminates the operational overhead of cluster provisioning, version upgrades, checkpointing, and job recovery. This frees engineering teams to focus on application logic.

Amazon Managed Service for Apache Flink powers key stateful processing tasks, including flaky test detection through time-windowed pattern matching, enriching execution events with pipeline and customer metadata, and routing processed data to downstream systems such as ClickHouse for analytics, PostgreSQL for operational workloads, and Amazon Simple Storage Service (Amazon S3) for long-term archival.

Reliability and fault tolerance

Amazon MSK’s three-replica configuration ensures that no single broker failure can cause data loss or ingestion interruption. Combined with flexible data retention, the architecture provides a meaningful replay window. If a downstream consumer (Amazon Managed Service for Apache Flink, ClickHouse, or another service) experiences an outage, it can resume processing from its last committed offset without data loss.

During the migration to the current architecture, Buildkite employed a dual-write strategy: simultaneously writing to both the existing PostgreSQL pipeline and the new Amazon MSK/ClickHouse path. This approach allowed the team to validate data consistency and gradually shift traffic without risking customer-facing disruption. This pattern speaks to the operational maturity Amazon MSK provides.

Operational efficiency gains

The shift to a streaming-first architecture, combined with the downstream simplification of the analytics engine, produced significant operational improvements:

  • Flink workloads reduced by 60%+: Eliminating pre-aggregation jobs that ran continuously regardless of demand.
  • Key/value store completely retired: Amazon MSK’s buffering capability, combined with ClickHouse’s query performance, eliminated the need for a separate fast-lookup store.
  • PostgreSQL capacity cut in half: Nine separate database clusters consolidated and right-sized.
  • Thousands of lines of application code deleted: Simpler architecture means less ETL code, fewer failure modes, and faster onboarding for new engineers.

Platform performance at a glance

Metric Value
Monthly test executions (for test engine platform) 50 billion (4x growth from 3B)
Sustained peak ingestion 500K events/second
Total records in analytics store 200 billion
Log ingestion requests 70,000+ per second
Peak webhook throughput 1.7 GB/second
MSK inbound throughput range 5 MB/sec – 100 MB/sec

Business and developer impact

The technical architecture ultimately exists to serve one purpose: helping developers ship better software faster. The streaming-first architecture built on Amazon MSK and Amazon Managed Service for Apache Flink delivers on that promise across four dimensions.

On-demand analytics replaced pre-computed reports. Customers can now interactively slice and dice 70 billion records across arbitrary metadata dimensions. They get answers to queries like “Show me P50 test durations by instance type and architecture for the last 30 days” in seconds, not hours. Real-time log streaming through the “live tail” feature means developers no longer wait for a build to complete before diagnosing failures. At 25,000 events per second, this experience scales across thousands of concurrent enterprise pipelines without degradation.

Smarter test intelligence comes from Amazon Managed Service for Apache Flink’s stateful flaky test detection: when a test begins exhibiting intermittent failure patterns, Amazon Managed Service for Apache Flink identifies it as it happens, not after the fact. This is what separates a proactive analytics platform from a reactive one. It requires publishing data to Kafka, processing with Flink, and letting ClickHouse handle the complex read requests.

Conclusion: Streaming as a strategic foundation

Buildkite’s journey from a Rails/Postgres monolith to a streaming-first analytics platform reflects a pattern increasingly common among enterprise SaaS companies: a reliable, high-throughput streaming and processing layer is not an optimization. It is a prerequisite for operating at scale.

Amazon MSK and Amazon Managed Service for Apache Flink form the backbone that helps Buildkite ingest 50 billion test executions per month, serve real-time interactive analytics to enterprise customers, and do so at lower cost than the more complex architecture it replaced. Amazon MSK handles durable, elastic event buffering. Amazon Managed Service for Apache Flink transforms raw streams into enriched, queryable data. Together they absorb the operational complexity that would otherwise consume engineering capacity.

For platform engineers evaluating streaming infrastructure for multi-tenant SaaS workloads, the signal is clear: invest in the streaming backbone early, and let managed services handle the operational complexity.

To learn more about Amazon MSK and Amazon Managed Service for Apache Flink, visit aws.amazon.com/msk and aws.amazon.com/managed-service-apache-flink.


About the authors

James Hill

James Hill

James has been building and scaling software systems for more than 25 years, from early web applications to platforms that now process millions of builds every day. Starting his career as a software engineer, James has led teams across Australia, the UK, and globally, solving problems in performance, reliability, and delivery speed at massive scale. Today, he works with some of the world’s largest engineering organizations to help them ship faster and with greater confidence, drawing on deep, hands-on experience in both engineering and product leadership. James is passionate about turning testing from a bottleneck into a feedback engine that accelerates learning across an organization.

Mitch James

Mitch James

Mitch is a Brand and Marketing Strategist with deep expertise crafting end-to-end brand experiences and fostering engaged communities around technical tooling. He brings 15+ years of Brand, Design, and Marketing leadership across devtools, consumer product, and B2B enterprise. Previously, Mitch has built and led creative teams at Adobe, IBM, Salesforce, George P Johnson, Wunderman Thompson, and VML. Today, he leads global marketing and design for Buildkite, working with engineering teams who set the pace at the frontier of software delivery.

Masudur Rahaman Sayem

Masudur Rahaman Sayem

Masudur is a Streaming Data Architect at AWS with over 25 years of experience in the IT industry. He collaborates with AWS customers worldwide to architect and implement data streaming solutions that address complex business challenges. As an expert in distributed computing, Sayem specializes in designing large-scale distributed systems architecture for maximum performance and scalability. He has a keen interest and passion for distributed architecture, which he applies to designing production-ready solutions at internet scale.

Miranda Li

Miranda Li

Miranda is a Senior Solutions Architect at AWS, specializing in Independent Software Vendor (ISV) and cloud-native architectures. With four years dedicated to helping software partners innovate and scale on AWS, she focuses on helping ISVs build and optimize their solutions for the cloud. She brings deep technical expertise in cloud infrastructure and data analytics, with a strong focus on supporting technical customers in areas such as Infrastructure as a Service (IaaS), network architecture, and security. Outside of work, she is an avid badminton player and enjoys staying active through jogging and outdoor adventures.

How Zynga scaled multi-warehouse data governance with Amazon Redshift federated permissions

Post Syndicated from Johan Eklund, Matthew Wongkee, Noelia Tardón original https://aws.amazon.com/blogs/big-data/how-zynga-scaled-multi-warehouse-data-governance-with-amazon-redshift-federated-permissions/

Zynga, a global leader in interactive entertainment operates a portfolio of mobile game studios including Socialpoint, the creators of Dragon City and Monster Legends. Zynga’s analytics platform processes telemetry and revenue data across studios using Amazon Redshift as its central data warehouse.

As Zynga expanded its analytics architecture to include individual studios with their own compute environments, the team faced a challenge: how to maintain centralized data governance while granting studios independent query capacity. Their existing approach to permission management introduced lag and required custom infrastructure if scaled to multiple warehouses.

In this post, we walk through how Zynga adopted Amazon Redshift federated permissions and AWS IAM Identity Center to enforce consistent, tiered data access across provisioned and serverless Amazon Redshift environments without building custom synchronization pipelines.

The challenge

Zynga needed to onboard Socialpoint’s current Amazon Redshift workloads and make Zynga’s central data available to them. Zynga’s existing production cluster would house the Socialpoint raw data, but the compute would come from another warehouse set up as a consumer. At the same time, Zynga’s data access control policies would need to be enforced across all warehouses. Zynga uses a tiered access control policy which would need to be synced across all consumers with no permission lag or manual grant synchronization.

During the migration, Socialpoint’s specific extract, transform, and load (ETL) processes would be included in Zynga’s central ETLs and their data ingestion pipeline would be replaced by Zynga’s latest generation of data ingestion infrastructure. Because the migration process happens in stages, Amazon Redshift sizing would also gradually need to increase.

The team evaluated two alternatives before arriving at a solution:

  • AWS Lake Formation couldn’t manage local and cross-cluster permissions using the same interface, and required AWS Access and Identity Management (IAM) or IAM Identity Center authentication for all users including service accounts.
  • Manual grants on consumer clusters introduced a delay between when permissions were updated on the producer and when they took effect on the consumer. This approach would also require an external job that synced permissions and would be unlikely to scale well beyond 2–3 consumers.

Solution overview

Zynga implemented a solution using three AWS services working together:

  1. Amazon Redshift federated permissions enabled cross-cluster queries without explicit data shares. Permissions granted on the producer cluster propagate immediately to consumer workgroups through AWS Glue Data Catalog registration.
  2. AWS IAM Identity Center provides unified authentication through federation with Okta. When users sign in, their Okta group memberships are provisioned through a System for Cross-domain Identity Management (SCIM) and automatically map to Amazon Redshift roles, removing the need for external synchronization jobs.
  3. Amazon Redshift Serverless provides the compute layer for Socialpoint, scaling to zero when idle and avoiding the need to pre-size a provisioned cluster during the migration period.

The architecture uses a dual-grant approach where every permission is granted to both an IAM Identity Center group (for users) and a federated IAM role (for service accounts). This gives both authentication paths the same access.

How it works

Authentication with IAM Identity Center

Zynga’s existing Okta directory syncs to IAM Identity Center, which is connected to the Amazon Redshift Serverless workgroup. When a user authenticates, Amazon Redshift automatically creates a user mapped to their email address and assigns them to roles based on their Okta group membership.

For example, an analyst in the Gamma Tier group signs in and is automatically assigned the AWSIDC:role.sso.gamma role in Amazon Redshift. No manual role assignment or synchronization job is required.

Service accounts, used for programmatic access, authenticate differently. Either using their IAM role and calling the get-credentials API, or by using the new federated permissions feature. Each service account assumes a federated IAM role, which creates a corresponding federated user in Amazon Redshift (for example, IAMR:role_iam_gamma).

The identity layer shows two authentication paths. Interactive users authenticate through Okta, which syncs group memberships to AWS IAM Identity Center. On login, IAM Identity Center automatically maps users to tiered Amazon Redshift roles (AWSIDC:role.sso.gamma, beta, alpha). Service accounts take a separate path, assuming a cluster IAM role that creates a corresponding federated user (IAMR:role_iam_gamma) in Amazon Redshift.

Figure 1 – Identity layer

The dual-grant approach

To ensure that both users and service accounts can access the same data, every read permission is granted to both the IAM Identity Center group and the federated IAM role in a single statement:

GRANT SELECT ON schema.table TO
	'IAMR:role_iam_gamma',
	ROLE 'AWSIDC:role.sso.gamma';

Transitioning the producer cluster

The shared provisioned cluster already had active users with local grants. To avoid disruption, Zynga implemented a tri-grant approach on the producer during the transition period. Existing stored procedures were modified to grant permissions to three targets: the legacy local role, the IAM Identity Center group, and the federated IAM role.

GRANT SELECT ON schema.table TO
	ROLE role_rs_gamma,
	ROLE 'AWSIDC:role.sso.gamma','IAMR:role_iam_gamma';

This approach maintains backward compatibility for existing users on the producer while enabling immediate access from the new serverless workgroup. The long-term plan includes migration of all producer users to IAM Identity Center and retire the legacy local grants.

Stored procedures for consistent governance

Rather than requiring users to construct dual-grant statements manually, Zynga created stored procedures that encapsulate the grant logic:

  • grant_read accepts a table name and access tier, then issues the appropriate dual-grant for both the IAM Identity Center group and the federated IAM role.
  • grant_write grants data definition language (DDL) and data manipulation language (DML) permissions to the appropriate team-based role.

This provides a consistent interface for permission management regardless of which cluster or workgroup that the user is on.

Architecture diagram in the AWS Cloud showing a federated Amazon Redshift setup. On the left, a Zynga provisioned Amazon Redshift cluster (producer) holds Zynga central data — game telemetry and revenue data — and stored procedures grant_read(table, tier) and grant_write(table, team). On the right, a Socialpoint Amazon Redshift Serverless workgroup (consumer) holds Socialpoint ETL data and the same two stored procedures. Bidirectional arrows between the two environments label the top connection as "Federated queries (cross-cluster reads)" between the data stores, and the bottom connection as "Federated permissions" between the stored procedures.

Figure 2 – Data and compute layer

Results

The migration delivered measurable improvements:

  • Immediate permission propagation – Grants on the producer cluster took effect on the consumer workgroup instantly, replacing a process that previously required manual intervention and introduced lag.
  • Zero additional infrastructure cost – Federated permissions, federated queries, and IAM Identity Center added no incremental costs to the architecture.
  • Removed custom synchronization – The team removed the need for Lake Formation configurations, external AWS Lambda functions, and Airflow workflows for permission management.
  • Scalable pattern – The same architecture can be extended to additional studio workgroups without duplicating permission management logic.

Lessons learned

Zynga adopted federated permissions shortly after the feature launched in US West Oregon (us-west-2) in January 2026. The team shared several observations from their early adoption:

  • Run a proof of concept first. The team validated the full permission model in a test environment before deploying to production, including testing that existing data shares to other clusters were not disrupted.
  • Plan for the dual-grant requirement. Because IAM Identity Center users and federated IAM roles are distinct identity types, every read permission requires two grants. Encapsulating this in stored procedures prevents errors and reduces cognitive overhead.
  • Start with serverless for new workloads. With Amazon Redshift Serverless, the team can avoid sizing decisions during the migration period. If usage patterns later justify it, they can migrate to a provisioned cluster from a serverless snapshot with minimal downtime.
  • Engage with AWS. As an early adopter of a new feature, Zynga maintained regular contact with the Amazon Redshift team through their AWS Technical Account Manager to report issues and request enhancements.

Conclusion

Zynga’s adoption of Amazon Redshift federated permissions demonstrates how organizations with multi-cluster Amazon Redshift architectures can enforce centralized data governance without building custom synchronization infrastructure. By combining federated permissions with IAM Identity Center and Amazon Redshift Serverless, the team established a pattern that scales to additional studios while maintaining consistent access controls and reducing operational overhead.

To learn more about the services used in this post, see the following resources:


About the authors

Johan Eklund

Johan Eklund is a Principal Software Engineer on the Data Engineering team at Zynga, focused on building and operating large-scale data platforms on AWS. Day-to-day, his work spans Redshift architecture, ETL orchestration, platform operations, and security and access control. Outside of work, Johan enjoys exploring art and music through creative coding and DIY hardware synthesizers.

Matthew Wongkee

Matthew Wongkee is a Principal Software Engineer at Zynga, where he builds scalable and high-performance data platforms and infrastructure, including Amazon Redshift. He focuses on enabling teams to leverage their data through self-service capabilities, including data discovery, query optimization, curated datasets, and data quality. In his spare time, he practices short track speed skating.

Noelia Tardón

Noelia Tardón is a Principal Software Engineer at Zynga. Based in Barcelona, she works on large-scale cloud data platforms with a focus on performance optimization and cost efficiency. She designs and builds data lake and data warehouse solutions while ensuring compliance with data protection regulations. Outside of work, she enjoys spending time with her family and running.

Sandeep Adwankar

Sandeep is a Senior Technical Product Manager at AWS. Based in the California Bay Area, he works with customers around the globe to translate business and technical requirements into products that enable customers to improve how they manage, secure, and access data.

Satesh Sonti

Satesh is a Principal Analytics Specialist Solutions Architect based out of Atlanta, specializing in building enterprise data platforms, data warehousing, and analytics solutions. He has over 19 years of experience in building data assets and leading complex data platform programs for banking and insurance clients across the globe.

Photo of AUthor - 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, data lakes, data ingestion pipelines, and cloud distributed architectures.

How Amazon is moving to integrate catalogs to improve data discovery with Amazon SageMaker

Post Syndicated from Pradeep Misra original https://aws.amazon.com/blogs/big-data/how-amazon-is-moving-to-integrate-catalogs-to-improve-data-discovery-with-amazon-sagemaker/

Enterprises face challenges when teams create data assets outside of central data catalogs. It adds overhead for discovery, and limits collaboration. Amazon’s Business Data Technologies (BDT) team has built an enterprise data catalog (Andes) for sharing datasets under well-defined policies. However, teams created catalog of local datasets and other non-tabular assets such as dashboards and metrics, outside Andes. This made it difficult to discover all assets in a consolidated way.

In this post, we share how Amazon.com is working to integrate catalogs by extending enterprise data catalog Andes with Amazon SageMaker.

Need for expanding catalog and governance from datasets to data assets

Without a single solution, users had to search multiple catalogs depending upon the asset type. Teams spent considerable time indexing the different catalogs and identifying the right one for their task. This slowed them down and took time away from solving the business problems.

To address these challenges, BDT team identified four critical capabilities needed:

  1. Multimodal catalog – Data consumers required the ability to blend enterprise data with local datasets and use them together for specific use cases. Teams sought to discover not only datasets, but also assets such as metrics, dashboards, and business files, to obtain a complete view of available resources. This necessitated a catalog that consolidates datasets and data assets in one location.
  2. Uniform governance and enforcement – To maintain best data protection practices and support business goals, teams need consistent enterprise-wide data governance where they request access once and the system enforces that access uniformly across all compute engines, alleviating fragmented or redundant access management. For internal systems, there was need for trusted identity propagation so user identity is preserved and used across AWS and internal systems for consistent enforcing.
  3. Multi-approval workflows – The solution supports multiple approval workflows within a single system, using Andes for dataset approvals and a custom workflow for dashboard approvals to maintain total governance and visibility across data assets.
  4. Delegated ownership – While enterprise teams retain overarching governance responsibility, business-specific data stewards required the ability to modify select attributes and apply appropriate tags to assets produced by their respective producers and consumers.

Solution: Unify datasets and data assets with Amazon SageMaker

Amazon chose to extend Andes with Amazon SageMaker to enhance the discovery experience. SageMaker offers native support for multimodal catalogs, and integrated with enterprise identity management, making it the ideal foundation for extending Andes’ governance model.

Rather than broadcasting assets across multiple domains, a single enterprise-wide domain standardizes and synchronizes data assets in one place. This domain is associated with AWS IAM Identity Center, which is connected to Amazon’s corporate identity system to maintain best data protection practices by limiting direct permissions and using corporate identity and group-based permissions.

Architecture diagram showing how Amazon SageMaker integrates with enterprise data catalog Andes and AWS IAM Identity Center

This integrated architecture directly addresses the identified challenges:

  • Single-pane asset discovery – Datasets and data assets are accessible through a single, consolidated view, avoiding the need to navigate across disparate systems or domains. This simplifies discovery and reduces the time to insight for teams across the organization.
  • Extended governance – Governance of both enterprise-wide and local datasets is orchestrated through a single system.
  • Extended observability – Trusted Identity Propagation (TIP) through AWS IAM Identity Center allows human users to access data interactively using their corporate identities. This provides audit-trail visibility into who is accessing what data for audits and organization’s observability requirements.
  • Amazon tool integration – Integration with Git and other internal systems automates management of accounts, permissions, and approvals. This reduces manual overhead and helps maintain that access controls remain tightly aligned with existing business workflows.

Design overview

This section describes the key features and design of the Amazon SageMaker integration. The technical implementation consists of three core components:

1) Catalog connectors

Amazon built connectors and ingestion paths to bring data assets into Amazon SageMaker while maintaining business continuity and preserving existing governance:

  • Andes integration: SageMaker provides APIs to synchronize assets from external catalogs. BDT extended this to bring Andes datasets (with their sophisticated metadata, business context) into the integrated experience. The integration preserves Andes’ permission model and governance workflows, to maintain existing security standards and best practices intact.
  • Account onboarding: Teams self-serve onboard their AWS accounts through an AWS Lambda-based integration. When creating projects, SageMaker queries this service to determine which accounts a user’s identity can access.

2) Delegated ownership

When data systems scale across business units, centralized governance teams need to delegate permissions for catalog enrichment, policy enforcement, and metadata management.

  • Catalog enhancement allows business teams to define and publish their own business glossaries, curated vocabularies of domain-specific terms, definitions, and relationships, directly within the catalog. Allowing business owners to author and maintain these glossaries increased accuracy and discoverability of catalog assets. Data consumers across the enterprise benefit from clearer, more consistent terminology.

3) Integration with consumption and access tooling

Teams discover data in SageMaker Unified Studio and consume it through both SageMaker Unified Studio and internal tooling:

  • Data discovery: SageMaker Unified Studio integrates with Amazon-wide Identity Center allowing almost all Amazon users to authenticate and search for cataloged assets. This integration addresses the data discovery problem by providing enterprise-wide visibility into available data resources.
  • Integrated development environment: SageMaker Unified Studio provides built-in tooling out of the box including a Query Editor for SQL analytics and Amazon SageMaker AI for machine learning (ML), which helps teams access data, build models, and collaborate across organizational boundaries.
  • Code repository integration: Manage code with full Git operations supported from SageMaker Unified Studio. Query code and notebook code persist to GitFarm (Amazon’s internal Git system), allowing teams to view and manage their work through Amazon’s standard version control system.
  • Native analytics integration: Projects directly connect to AWS analytics engines including Amazon Athena for SQL, AWS Glue and Amazon EMR for Apache Spark, and Amazon Redshift for data warehousing. User-authored jobs use Andes governance and permissions across engines for consistent access control.

SageMaker implementation results

SageMaker catalog now encompasses various types of data assets from across the organization, representing an expansion from datasets alone to a complete inventory of data, dashboards, metrics, models, and other data assets, all while maintaining best practices and appropriate access and use guardrails.

“SageMaker provides a unified catalog that makes discovery and sharing of data assets, metrics and dashboards across teams straightforward, with direct integration to Andes datasets. SageMaker delivers deep integration through Git repository connections and enterprise identity management that aligns with existing Amazon workflows.”

– Gerry Moses, Sr. Principal TPM, Amazon

  • Faster data discovery – Data consumers can go to one place to locate trusted, high-quality assets with significantly less friction, which reduces the time from question to insight. By surfacing well-documented, governed assets through an enriched catalog, teams can confidently identify the right data for their use cases without navigating sprawling, inconsistent inventories or relying on tribal knowledge.
  • Improved collaboration – Breaks down data silos by making curated assets discoverable and reusable across Amazon. When teams can build on shared, authoritative datasets rather than creating redundant copies, data proliferation is reduced.

Conclusion

By integrating their existing governance tooling with Amazon SageMaker to build a centralized data catalog, BDT is creating a foundation for faster, more efficient data discovery across teams. Amazon SageMaker helped unify diverse data types with their existing catalog and enabled collaboration across teams to help them find the right data. By integrating with existing governance frameworks, BDT demonstrates how organizations can expand their catalog capabilities while preserving existing enterprise investments.

To learn more and get started with Amazon SageMaker Unified Studio, visit aws.amazon.com/sagemaker/unified-studio or the AWS console.


About the authors

Matt David

Matt David

Matt is a Sr PMM, specializing in helping data teams with AI-powered analytics. His areas of interest include self-service analytics, data democratization, and preparing organizations for the age of AI agents. He brings extensive experience from his roles at Atlassian, Hex, and DataCamp.

Gerry Moses

Gerry Moses

Gerry is a Senior Principal Technical Program Manager in Business Data Technologies where he leads joint Amazon/AWS programs. His work improved data governance for Amazon’s Andes data lake, enabled broader AWS technology adoption by data lake users, and influenced product improvements that benefited all AWS customers.

Ramesh Singh

Ramesh Singh

Ramesh is a Senior Product Manager Technical at AWS in Seattle, Washington, currently with the Amazon SageMaker team. He is passionate about building high-performance ML/AI and analytics products that help enterprise customers achieve their critical goals using cutting-edge technology.

Pradeep Misra

Pradeep Misra

Pradeep is a Principal Analytics and Applied AI leader at AWS. He is passionate about solving customer challenges using data, analytics, and AI/ML. Outside of work, he likes exploring new places, trying new cuisines, and playing badminton with his family. He also likes doing science experiments, building LEGOs, and watching movies with his daughters.

Eunji Kang

Eunji Kang

Eunji is a Principal Product Manager Technical focusing on democratizing data across Amazon teams for fast data-driven business decisions without compromising security and compliance.

Trevor Gasdaska

Trevor Gasdaska

Trevor is a Principal Engineer focusing on data compliance and agentic AI workflows for Big Data Technologies at Amazon. He builds tools that help teams govern and use data at scale.

Brad Porter

Brad Porter

Brad is a Principal Business Development Manager at Amazon Web Services. He works with Amazon.com and enterprise customers to define and accelerate go-to-market strategies across Data Analytics, AI/ML, and Generative AI. He has over 20 years of experience in cloud strategy, enterprise infrastructure, and technology leadership.

How ALS GeoAnalytics LITHOLENS ™ revolutionizes core logging through machine learning with Amazon EKS

Post Syndicated from Saransh Burman original https://aws.amazon.com/blogs/architecture/how-als-geoanalytics-litholens-revolutionizes-core-logging-through-machine-learning-with-amazon-eks/

In the mining industry, accurate geological analysis is required for improving mine design and development. Traditionally, this involved labor-intensive and time-consuming on-site inspections of drill core samples, often conducted in remote and challenging environments. ALS GeoAnalytics has streamlined this process through its LITHOLENS ™ platform, a machine learning (ML)-powered system that uses deep learning and machine vision to automate core logging. LITHOLENS ™ significantly enhances data consistency, operational efficiency, and scalability while significantly reducing logging-related costs and lowering greenhouse gas emissions to support sustainable mineral extraction.

This post explores how ALS GeoAnalytics successfully deployed LITHOLENS ™ with Amazon Elastic Kubernetes Service (Amazon EKS) to scale model training and inference while minimizing cost.

The challenge

Development of a new mine involves the creation of a 3D map of the ore body, known as a geological or resource model. This model drives all future design decisions and creating it requires drilling thousands of holes throughout the ore body to examine the structure and composition of the samples extracted. This process is subject to numerous challenges that affect both active and historical drilling campaigns. Challenges such as:

  • Remote site access requiring geologists to travel long distances to visually inspect physical core boxes
  • Subjective interpretations led to inconsistencies, with different experts often producing varying geological logs
  • Underutilized historical imagery from past campaigns lacked standardized tools for meaningful analysis
  • Lost or degraded physical samples made it difficult to revisit legacy data or validate past interpretations
  • Limited transparency in logging and decision-making processes hindered collaboration and accountability
  • Scheduling bottlenecks arose from reliance on a small pool of qualified experts
  • Non-standardized data collection methods prevented effective scaling and cross-project comparison

These limitations not only delayed project timelines but also restricted the ability to generate reliable, high-resolution geological insights—ultimately impeding the speed and effectiveness of exploration strategies.

Machine learning at geological scale

ALS GeoAnalytics developed a comprehensive suite of machine learning and computer vision models to automate geological and geotechnical logging, transforming raw core imagery and data into actionable insights.

A machine learning pipeline formed the foundation for high-resolution visual analysis. It begins with the Color Extraction module, which scans each image to identify unique pixel colors and store the results in Amazon Simple Storage Service (Amazon S3). This data is fed into the Color Clustering module, where users can specify clustering parameters and choose from algorithms such as K-Means, which assigns pixels to clusters based on proximity to centroids, or the Gaussian Mixture Model (GMM), which uses probabilistic distributions to capture more complex variance structures within the color data. These methods effectively reduced image complexity and helped highlight mineralogical variation.

To quantify color composition along the core, the Percentage Report module was introduced. It segmented each image into user-defined sections (for example, 20 cm intervals) and calculated the proportional distribution of each color cluster, enabling spatial analysis of lithological patterns.

On the deep learning front, the team developed and deployed an advanced suite of models tailored for geological and geotechnical analysis. A highlight of this work was the development of RoQE Net, a state-of-the-art neural network designed for geotechnical parameter extraction. RoQE Net demonstrated exceptional accuracy in computing Rock Quality Designation (RQD) and extracting alpha angles, key metrics for assessing core integrity and rock mass quality. In parallel, VeinNet and CobbleNet were engineered to identify and map complex geological features such as veins, cobbles, and lithological structures with high precision. These models were benchmarked against industry standards and consistently outperformed traditional methods in terms of accuracy, reliability, and scalability.Together, these machine learning and deep learning components form the backbone of the LITHOLENS ™ platform—delivering automated, scalable, and highly accurate geological intelligence that accelerates decision-making and enhances the efficiency of exploration and resource modeling workflows.

Solution architecture

ALS GeoAnalytics built LITHOLENS ™ on AWS using a hybrid architecture that combines containerized workloads with serverless components. The system uses Amazon EKS for compute-intensive machine learning tasks, AWS Lambda for API operations, Amazon S3 for data storage, and Amazon Relational Database Service (Amazon RDS) for structured data management.

Figure 1: Architecture Diagram

LITHOLENS ™ uses a unified API model to drive next-generation rock and mineral data analysis. This unified API created is a unified application programming interface that combines multiple services, data streams, and analytic capabilities into a single, powerful access point. Unlike traditional APIs—which might deliver basic, one-dimensional data—you can use the unified API to connect, analyze, and automate complex workflows across departments, vendors, and a wide variety of data sources all at once. With the unified REST API, users can submit geological analysis jobs, monitor progress, and retrieve results through a single interface. This API combines multiple services and data streams into one access point, so users can automate complex workflows across departments and data sources.

Architecture flow:

  1. Request Intake – Jobs are submitted through Amazon API Gateway with a payload specifying job parameters and EKS configuration.
  2. Job Orchestration – The API backend, running on AWS Lambda, provisions EKS containers with the appropriate configuration. User data scripts bootstrap each instance with required setup and execution commands.
  3. Execution and Data Flow
    • Input data is retrieved from Amazon S3.
    • Computation is performed on EKS pods using G6 instances.
    • Logs and intermediate results are continuously tracked.
    • Results are stored in S3 or persisted into RDS through dedicated API calls.
  4. Resource Management – Upon job completion, EKS containers instances automatically shut down, reducing costs.

Architecting for scale and efficiency

To handle variable workloads efficiently, ALS GeoAnalytics implemented a hybrid architecture that’s designed for both performance and cost. The system uses Amazon EKS for compute-intensive ML tasks while using AWS Lambda for lightweight API operations and job orchestration.

Key architectural decisions:

  • Amazon EKS for ML Workloads – Deep learning model training and inference require sustained compute power with GPU acceleration. EKS provides the container orchestration needed to manage these workloads across G6 instances, with automatic scaling based on job queue depth.
  • Lambda for API Gateway – Job submission, status checking, and result retrieval are handled through serverless functions. This removes the overhead of maintaining always-on API servers for sporadic client requests, reducing costs during low-usage periods.
  • Pre-configured AMIs – Custom Amazon Machine Images contain all required dependencies and model artifacts, reducing container startup time from several minutes to under 30 seconds. This approach improves job throughput and reduces compute costs by minimizing idle time.
  • Automated Resource Management -–EKS clusters scale down to zero when no jobs are queued, so compute resources are only consumed during active processing. Combined with S3 for data persistence and RDS for metadata, this creates a cost-effective architecture that scales with actual usage.

This design addresses the geological industry’s unpredictable workload patterns while maintaining the performance needed for complex computer vision tasks.

Business impact and results

LITHOLENS ™ has seen success with 10 different mining companies on over 40 active projects, with substantially accelerated project completion and a standard analysis process used across all projects. This new approach has made mineral detection and classification more accurate while reducing the need for experts to visit sites. Teams can now trace how analysis decisions are made, grade minerals more consistently, and plan projects and assign resources more effectively. Real-time monitoring and reporting give managers up-to-the-minute information on how projects are progressing.

Conclusion

The massive scalability of Amazon EKS has allowed ALS GeoAnalytics to fundamentally transform how core logging and analysis is conducted. AWS suite of services enables LITHOLENS ™ to efficiently implement computer vision and machine learning, bringing new operational capabilities to our customers and opening business opportunities throughout the mining industry. The success of LITHOLENS ™ demonstrates how cloud computing and AI can help modernize a long-standing industry like mining, creating value through improved operational efficiency, accuracy, and scalability. ALS GeoAnalytics continues to evolve its platform on AWS, using cloud computing to push the boundaries of what’s possible, and looking to grow LITHOLENS ™ in to promising applications in oil and gas, civil engineering, and even space exploration.


About the authors

How Synthesia optimizes generative AI video inference on Amazon EC2 G7e instances

Post Syndicated from Moises Hernandez original https://aws.amazon.com/blogs/architecture/how-synthesia-optimizes-generative-ai-video-inference-on-amazon-ec2-g7e-instances/

Synthesia, an enterprise-focused AI video platform, has transformed content creation, helping everyone to create video content without cameras or microphones. To achieve this, Synthesia allows its users to create video avatars that synthesize the likeness and voice of real people. Synthesia achieves this through a series of in-house developed models based on various architectures, including latent diffusion video generation models.
Customers like Synthesia often choose to host their models on Amazon Elastic Compute Cloud (Amazon EC2) instances because of the flexibility and control over the underlying hardware that the service provides, and among them they find the G7e instance family to be a cost-efficient option to serve GPU-memory intensive generative AI video models, giving customers access to NVIDIA RTX PRO 6000 Blackwell GPUs, with 96GB of GPU memory.

When generating videos using AI models with a Variational Auto Encoder (VAE) Decoder in the architecture, customers often find the GPU utilization is bottlenecked by the saving rate of the video frames to a file held on storage. This causes GPU stalls and reduces average GPU kernel utilization, which is the percentage of time the GPU is actively executing compute kernels rather than idle.

This post introduces a video decoding optimization technique that we have ideated in collaboration with Synthesia Research Engineering team, which we call Asynchronous Frame Generation Pipeline. Adopting this technique allows you to overlap GPU compute, device-to-host (D2H) data transfer, and host-side post-processing. In this post, we apply this technique to the VAE decoder of a Wan video generation model as an example, where our benchmarks on G7e show increased GPU kernel utilization from 82% to 99.9%, in turn leading to an 8.2% decrease in latency (and increase in throughput) for video decoding. We expect this technique to benefit any customer with a chunked video generation pipeline that transfers frames to host memory.

You can find an end-to-end example implementation of the Asynchronous Generation Pipeline applied to the Hugging Face Diffusers format of the Wan 2.2 14B Model in the associated GitHub repository.

In the following section, you will see how latent diffusion models generate video and why the traditional sequential decoding approach can lead to inefficient GPU utilization.

Understanding the sequential decoding bottleneck

Latent diffusion video generation models have emerged as powerful tools for generating temporally coherent video sequences. To reduce compute and memory requirements, these models perform the diffusion process in a compressed latent space of a variational auto-encoder (VAE), as depicted in the below figure, which has generally a much lower dimensionality than the originating video pixel space.

Fig. 1 High-level architecture of a VAE model. The depicted frames are taken from the Wan 2.2 repository sample video used in the example implementation in the next sections.

At inference time, a latent representation is initialized with noise and iteratively denoised via a Diffusion Process. To make sure that the output follows the text prompt, every denoising step is conditioned on the text input if present. After the final denoising step, the Diffusion Process yields a generated video that is still represented in the latent space of the VAE. The last step therefore consists in decoding the latent video back into a human-readable pixel video using the decoder part of the VAE.

Processing the whole latent or pixel video at once is usually too resource-intensive, even on large GPUs. It is therefore common to split the video along the temporal dimension and to decode video frames one latent frame at a time, resulting in a chunk of, for example, 4 consecutive pixel frames, as shown in Fig 2.

Fig.2 Decoding one latent results in a chunk of 4 time-consecutive pixel frames which are transferred from the GPU to the host.

Once a chunk has been decoded and processed on the GPU, the corresponding pixel frames must be transferred back to host (CPU) memory with a D2H transfer, so they can be written to a file or further processed. If you wait to transfer the video frames once the entire video has been decoded, the full decoded video must fit in GPU memory before being transferred, making this strategy hard to scale for arbitrarily long videos. A more memory-efficient alternative is to transfer the decoded frames every time a chunk is decoded, so that the GPU memory footprint of the decoder scales with the size of the chunk instead of the full video.

Traditionally, a newly generated set of frames in a chunk N is passed from GPU memory to CPU RAM synchronously, and it is committed to storage before the CPU can launch the CUDA kernels (referred to as kernels from now on) that process the N+1 chunk. This leads to systematic GPU stalls between chunks, as the copy from device to host prevents the GPU from immediately starting work on the next chunk, reducing overall hardware utilization, and in turn increasing processing time. We will refer to this synchronous process as Sequential Frame Generation Pipeline, which is depicted in Fig. 3.


Fig.3 Schematic representation of the Sequential Frame Generation Pipeline. Launching the kernels processing Chunk N+1 needs to wait for the full copy and storage of the frames in Chunk N.

In the following section, you will learn how to overcome this bottleneck using an asynchronous pipeline that overlaps GPU computation with data transfers and host-side processing.

Asynchronous Frame Generation Pipeline

To minimize GPU stalling, and in turn increase GPU utilization, you need to modify the Sequential Frame Generation Pipeline introduced in the previous section, so that all host-side CPU work (such as appending decoded frames to a file) runs in parallel with a stream of uninterrupted device-side kernels. This section shows you how to achieve this using as an example the implementation of an Asynchronous Wan VAE Decoder, which is implemented with PyTorch and you can find in the associated GitHub repository.

By default, PyTorch schedules work on a single (default) CUDA stream per device which executes the operations in issue order unless additional streams are explicitly created. To decouple compute and D2H copies, the implementation uses two CUDA streams so that the GPU is allowed to overlap compute kernels, which are enqueued on the default stream, and D2H copies on a dedicated copy stream. Compute kernels are enqueued on the default stream, which from now on can be referred to as Compute Stream, and D2H copies on a dedicated copy stream, namely the Copy Stream. In Fig. 4 you can find the full setup on a GPU accelerated instance (G7e in our example) depicted.


Fig. 4 High level diagram of the key components in our implementation of the Asynchronous Frame Generation Pipeline.

On the host side, kernel submissions are interleaved with D2H transfers and post-transfer processing and, to avoid host-side blocking calls and maximize GPU utilization the Asynchronous Frame Generation Pipeline introduces two mechanics:

  • A dedicated Worker CPU thread responsible for reading chunks from Host Memory (RAM), and writing them to file, leaving the main Python thread to focus on launching kernels and scheduling D2H transfers.
  • Two in-memory Buffers on the GPU Memory (VRAM), and on the Host Memory (RAM), and page-lock the required Host Memory buffers to make sure D2H copies are performed fully asynchronously.

Using a double-buffer strategy makes sure that for adjacent chunks the compute, D2H transfer, and host processing can overlap safely as they operate on distinct memory buffers.


Fig. 5 Schematic representation of the interplay between Events, Streams, Buffers, and Worker component in the Asynchronous Frame Generation Pipeline implementation. Events are depicted as left and right conditions, expressing what the process WAITs for, and ISSUEs when it has completed. The size of the boxes representing operations has been scaled to fit the text, and therefore not representative of the actual processing time.

Since buffers can be accessed concurrently by different components, the implementation introduces a synchronization mechanism to avoid different kernels and threads corrupting data held in the buffer. This can be achieved using CUDA Events which are used as a barrier that clears if it can answer closed questions such as: Has decoding of chunk N completed? You can find the interplay between Streams, Threads and Events depicted in Fig. 5.

G7e benchmark results

To quantify the impact of the Asynchronous Frame Generation pipeline, you can benchmark the decoding of a single 41 latent-frames test video for both the Synchronous and Asynchronous pipeline on a g7e.2xlarge EC2 instance using this sample notebook. To have a common baseline, the benchmarks are based on the unoptimized Hugging Face Diffusers Wan 2.2 14B model, and, in both cases, the benchmarking loop performs an initial full decoding cycle to let CUDA and PyTorch initialize and allocate memory pools and cache. After warmup, the benchmarking session carries out 10 consecutive full video decoding cycles. We have run an end-to-end run as an example and present the results in Table 1.

Metric Synchronous (time s /video) Asynchronous (time s / video)
min 21.98 20.16
mean 21.99 20.17
P99 22.01 20.20

Table. 1 Benchmark results for 10 consecutive decoding runs for the Synchronous and Asynchronous pipelines.

The example results show a speed gain of up to 8.2% on this benchmark, decreasing the Real Time Factor of the decoder from 3.21 to 2.95. Considering the g7e.2xlarge pricing as of writing this post ($3.36 per GPU on-demand in the Ohio region), this leads to an average saving of $896 for decoding 1,000 hours of video on a single GPU. This is a theoretical saving, calculated assuming that the underlying model operates at full computational efficiency without bottlenecks.

To have a qualitative understanding of how this performance boost is achieved, you can profile both the Synchronous and Asynchronous pipelines using this sample notebook. The results for an example run are reported in Fig. 6 and Fig. 7, showing the absence of GPU stalls in the Asynchronous pipeline as opposed to the Synchronous pipeline.


Fig. 6 Profile of the Synchronous pipeline single thread and stream. As the main CPU thread is writing Chunk N frames to disk, the GPU stream stalls, waiting for the CPU to launch the kernels needed to process Chunk N+1


Fig.7 In the Asynchronous pipeline, the Compute Stream is not interrupted by the copying and writing of the frames to disk. The Main Thread is free to launch the processing kernels. This figure is displayed with a different zoom level than Fig. 6 to highlight the qualitative differences.

By focusing on the temporal axis for two consecutive chunks in the steady state, you can quantify the GPU kernel utilization increase, which, in our example run was 82% for the Synchronous case, increasing to 99.9% for the Asynchronous case.

Conclusion

In this post, we demonstrated how the Asynchronous Frame Generation Pipeline can improve GPU utilization when decoding latent videos on Amazon EC2 G7e instances. By decoupling GPU compute from device-to-host transfers and host-side I/O using dual CUDA streams, pinned memory buffers, and a dedicated worker thread, we eliminated the GPU stalls inherent in traditional synchronous decoding pipelines.

The sample benchmarks on the Wan 2.2 14B VAE decoder show an 8.2% reduction in decoding latency, translating to approximately $896 in savings per 1,000 hours of decoded video on a single GPU when considering the g7e.2xlarge pricing as of writing this post ($3.36 per GPU on-demand in the Ohio region). These gains come without any changes to model weights or inference quality, purely from better hardware utilization. We expect the kernel utilization gain to be even more impactful on optimized and compiled models, which make more efficient use of the GPU.

The techniques presented here are not specific to the Wan architecture, nor to the specific GPU utilized. Any chunked video generation pipeline that transfers frames to host memory can benefit from this approach. We encourage you to explore the associated sample implementation to experiment with how the Asynchronous Frame Generation Pipeline could be integrated into your own video generation workloads.

To get started with G7e instances for your generative AI video workloads, visit the Amazon EC2 G7e instance page.


About the authors

How Smartsheet built Real-time Dynamic Filtering on Apache Flink reducing $40K/month in messaging costs

Post Syndicated from Emre Kartoglu original https://aws.amazon.com/blogs/big-data/how-smartsheet-built-real-time-dynamic-filtering-on-apache-flink-reducing-40k-month-in-messaging-costs/

Processing hundreds of thousands of events per second while maintaining sub-second latency is a challenge many organizations face when building real-time data-driven applications. When filter policy changes propagate in up to 15 minutes, dynamic event routing becomes impractical, forcing teams to over-consume events and discard over 90% after costly per-event lookups. Smartsheet, a work management solution serving millions of users and processing hundreds of thousands of events per second to power features like live collaboration, workflows, and real-time notifications, faced exactly this problem.

In this post, you learn how Smartsheet built a Real-time Dynamic Filtering (RDF) system on Amazon Managed Service for Apache Flink, cutting messaging costs by over $40,000 per month and improving live collaboration latency by 1.8x.

The challenge: Static filter policies in a dynamic world

The Smartsheet event-driven architecture publishes hundreds of thousands of events per second to an Amazon Simple Notification Service (Amazon SNS) topic. Internal teams subscribe to this topic, typically by creating an Amazon Simple Queue Service (Amazon SQS) queue with an associated SNS filter policy defined through infrastructure as code (IaC). These filter policies are typically static and specify the types of events a consumer wants to receive, such as “sheet row created,” “sheet row updated,” or “sheet row deleted.”

Although SNS supports programmatic changes to filter policies, the SNS documentation notes that changes can take up to 15 minutes to take effect. This eventual consistency window created a significant problem for Smartsheet live collaboration feature.

Live collaboration requires knowing, in real time, which sheets have active collaborators. When a user opens a sheet, the system needs to immediately start receiving events for that sheet. When they close it, the system should stop. With a 15-minute propagation delay on filter policy changes, dynamic per-sheet filtering through SNS was impractical.

The workaround was brute force: subscribe to all events (hundreds of thousands per second), pull them into an SQS queue, and use compute to check each event against Amazon DynamoDB to determine whether the sheet had active collaborators. Over 90% of events were discarded after this lookup.

Architecture diagram showing events flowing from SNS to SQS with per-event DynamoDB lookups before RDF

Figure 1: Before RDF — all events flow through SNS to SQS, with per-event DynamoDB lookups to filter. Over 90% of events are discarded after processing.

  1. Every event published to the SNS topic is delivered to the SQS queue, regardless of whether any consumer needs it.
  2. The consumer AWS Lambda reads every message from the SQS queue and must evaluate each one individually.
  3. For each event, the consumer queries DynamoDB to check whether the sheet has active collaborators. This per-event lookup adds latency and DynamoDB read costs on the hot path.
  4. After the DynamoDB lookup, over 90% of events are found to have no active collaborators and are discarded.

This approach had three compounding cost and performance problems:

  • SNS-to-SQS data transfer costs: approximately $10,000 per month to deliver all events to the queue
  • SQS costs: approximately $30,000 per month to receive, process, and delete the full event volume
  • DynamoDB costs and latency: per-event lookups to check collaborator status added load to DynamoDB and increased end-to-end data delivery latency

The solution: Real-time Dynamic Filtering with Apache Flink

To solve this, Smartsheet built a system called Real-time Dynamic Filtering (RDF) on Amazon Managed Service for Apache Flink. The core insight was to move the filtering logic into the stream processing layer itself, using Flink’s KeyedCoProcessFunction, a feature that joins and processes multiple streams by a shared key, to maintain dynamic filter policies in Flink state (RocksDB).

How it works

The RDF Flink application reads from two streams:

  1. Filter policy stream, sourced from Amazon DynamoDB Streams. When a team calls the RDF client to change their filter policy (for example, “start receiving events for sheet X”), the change is written to a DynamoDB table and propagated through DynamoDB Streams to the Flink application.
  2. Data stream, the stream of sheet events (creates, updates, deletes) that were previously delivered through SNS.

One challenge remained: some consumers need every event, regardless of sheet. When a consumer subscribes to all events, the system needs every parallel Flink task to know about it. The team solved this using Flink’s broadcast state, which replicates a small set of “subscribe to everything” policies across all tasks. Because only a handful of consumers use this mode, the memory overhead stays negligible.

Architecture diagram showing the RDF system with DynamoDB Streams feeding filter policies to the Flink application

Figure 2: After RDF — consumer teams update filter policies via client libraries. DynamoDB Streams propagates changes to the Flink application, which filters the data stream in real time using keyed state (RocksDB) for specific sheet subscriptions and broadcast state for “all sheets” subscriptions.

  1. When a consumer team wants to start or stop receiving events for a specific sheet, it calls the RDF client, a thin wrapper over the DynamoDB SDK. The filter policy change is written to that consumer’s dedicated DynamoDB table. Each consumer has its own table, providing isolated permissions and preventing noisy neighbor issues.
  2. DynamoDB Streams captures every filter policy change as a change data capture (CDC) record and streams it to the Flink application in real time.
  3. Filter policy records
    1. Filter policy records for specific sheets are routed to the KeyedCoProcessFunction, keyed by SheetID. This makes sure that filter state and event data for the same sheet are co-located in the same Flink parallel task. State is stored in the RocksDB backend, which uses memory when available and spills to disk when necessary, so the system to scale without JVM heap constraints.
    2. Filter policy records where a consumer has called listenToAllEvents() are broadcast to all parallel Flink tasks via Flink’s broadcast state. Because broadcast state lives in JVM heap, it is used exclusively for these “all sheets” records (of which there are very few), keeping the heap footprint small.
  4. The full stream of CDC events flows into the KeyedCoProcessFunction, partitioned by SheetID. Each parallel task receives only the events for the sheets it is responsible for and applies the corresponding filter state to decide whether to forward or drop each event.
  5. The broadcast state (containing “all sheets” subscriptions) is made available to all parallel instances of the KeyedCoProcessFunction, so that consumers subscribed to all events are never filtered out regardless of which task processes their events.
  6. Only events that match an active filter policy are forwarded to the consumer’s SQS queue. The result: sub-second filter policy propagation (p95 ≤1s), elimination of per-event DynamoDB lookups, and over $40,000/month in cost savings.

Critically, because the filter policy state is persisted in Flink’s RocksDB state backend, the application does not need to perform a DynamoDB lookup for every event. Within 1 second of a filter policy change, the Flink application reads the change from the DynamoDB Streams source, updates its internal state, and begins filtering the data stream accordingly.

Results

The impact of RDF was immediate and measurable across multiple dimensions:

Cost reduction

Cost category Before RDF After RDF Monthly savings
SNS → SQS Data Transfer ~$10K/month Eliminated ~$10K
SQS Event Ingestion ~$30K/month ~$2K ~$28K
DynamoDB Collaborator Lookups Significant load Eliminated (state in Flink) Included in total
AWS Lambda ~$12K/month ~$5K/month ~$7K
Total ~$45K/month

Latency improvement

  • 1.8x improvement in live collaboration data delivery latency. Users see changes from collaborators faster than before.
  • Filter policy propagation reduced from up to 15 minutes to a p95 of under 1 second

If your architecture follows a similar fan-out pattern where consumers discard a large percentage of events after per-event lookups, you could achieve comparable cost reductions by moving filtering into the stream processing layer. The savings scale with your event volume and the percentage of events currently discarded.

Key design decisions

Several architectural choices were critical to the success of this solution:

  1. Keyed state with selective broadcast: Specific sheet subscriptions are stored in keyed state using the RocksDB state backend. The system scales to a large number of filter policies without JVM heap constraints. Flink’s broadcast state is used only for the small number of “all sheets” subscriptions, where every parallel task needs visibility. Because broadcast state is stored in JVM heap, limiting its use to these few records keeps the heap footprint manageable.
  2. DynamoDB Streams as the filter policy source: Rather than building a custom control plane, the team used DynamoDB Streams to propagate filter policy changes. DynamoDB Streams gave the team durability, ordering guarantees, and a native Flink source connector integration.
  3. RocksDB state backend: Persisting filter state in RocksDB eliminated the need for external lookups on the hot path, keeping per-event processing latency low even as the number of active filter policies grows.
  4. Client library abstraction: Publishing internal Golang and Java clients lowered the adoption barrier. The client is a thin abstraction on top of the DynamoDB SDK. Each consumer has its own dedicated DynamoDB table and corresponding filter stream, which provides two benefits: it allows fine-grained AWS Identity and Access Management (AWS IAM) permissions per client, and it mitigates the noisy neighbor problem by isolating each consumer’s filter policy traffic. Teams don’t need to understand Flink internals. They interact with a simple API to manage their subscriptions.

Next steps

The live collaboration team was the first adopter of RDF, but the architecture was designed as a shared platform. Smartsheet is now expanding RDF to additional internal teams, including workflow automation and notification routing, where similar fan-out patterns exist. The team is also exploring automatic scaling policies to optimize Flink cluster costs during off-peak hours.

Conclusion

Smartsheet Real-time Dynamic Filtering system demonstrates how Amazon Managed Service for Apache Flink can solve problems that go beyond stream processing. By combining Flink’s broadcast state pattern with CoProcessFunction, Smartsheet replaced a costly and latency-bound SNS/SQS fan-out architecture with a sub-second dynamic filtering platform. The result: over $40,000 per month in savings, 1.8x improvement in live collaboration latency, and a reusable platform that multiple teams are now adopting.

If you process high-volume event streams and need to dynamically control which events reach specific consumers, this pattern can help you reduce costs and latency, whether for live collaboration, workflow automation, notification routing, or multi-tenant event delivery.

To learn more about the services used in this post, visit:


About the authors

Emre Kartoglu

Emre is a Principal Engineer at Smartsheet, where he led the adoption of Apache Flink to power features such as sheet history, live collaboration, and automations. Previously a software engineer at AWS helping build Amazon Managed Service for Apache Flink, he is an active open-source contributor to the Apache Flink project, including FLIP-418 for data skew calculation and visualization and the AWS connectors for Apache Flink. He is based in London, UK, and is passionate about operational excellence, building products, establishing mechanisms, and using data to drive positive culture change.

Rony Blum

Rony Blum

Rony Blum is a Senior Solutions Architect at AWS based in Seattle, working with ISV customers to design and implement advanced cloud architectures, specializing in SaaS solutions, multi-tenant systems, and Generative AI applications. Outside of work, Rony enjoys exploring the Pacific Northwest trails on foot and hitting the slopes during ski season.

Francisco Morillo

Francisco Morillo

Francisco Morillo is a Sr. Streaming Solutions Architect at AWS, specializing in real-time analytics architectures. With over five years in the streaming data space, Francisco has worked as a data analyst for startups and as a big data engineer for consultancies, building streaming data pipelines. He has deep expertise in Amazon Managed Streaming for Apache Kafka (Amazon MSK) and Amazon Managed Service for Apache Flink.

Modernizing Excel VBA to Python at Scale with AWS Transform custom

Post Syndicated from Somnath Chatterjee original https://aws.amazon.com/blogs/devops/modernizing-excel-vba-to-python-at-scale-with-aws-transform-custom/

Learn how AWS Transform custom can help migrate Excel VBA applications to modern Python code while overcoming context window limitations, preserving functional equivalence, and enabling cloud-native deployment—turning weeks of manual rewriting into hours of AI-guided transformation.

Introduction

Many organizations maintain dozens of Excel VBA applications built over decades, containing business-critical logic trapped in workbooks—budget planning tools, demand planning, inventory management, financial modeling, and engineering calculations. Manual migration typically costs thousands of dollars per workbook and takes weeks, while traditional AI tools fail on large codebases that exceed context windows.

This post demonstrates how you can migrate VBA to Python using AWS Transform custom, addressing three key challenges: processing large codebases through intelligent chunking, converting legacy code to maintainable Python while preserving functionality, and validating equivalence through automated testing. You can reuse the transformation across similar projects or apply it to entire portfolios.

With AWS Transform custom, you can accelerate migration timelines, eliminate transcription errors, and scale from single applications to enterprise portfolios.

Solution Overview

The following diagram illustrates how AWS Transform custom migrates VBA source code to Python output through a four-step process powered by an AI agentic system.

Architecture diagram showing how AWS Transform custom migrates VBA source code to Python output through a four-step process powered by an AI agentic system

Figure1:AWS Transform custom VBA to Python migration architecture

AWS Transform custom provides an interactive workflow where you describe your migration requirements. The system interprets your intent and iteratively refines the transformation definition until it meets your specifications. As the system processes your code, it improves the quality of each subsequent run. Once you finalize a transformation, you can publish it to a registry so your team can reuse it across multiple projects without starting from scratch.

Migration Approach

The migration follows a three-phase process that takes you from defining your transformation through execution and validation and finally scaling across your portfolio.

In the first phase, transformation definition creation, you start an interactive session with AWS Transform custom to describe your migration requirements. You can reference your VBA code, API documentation, and target framework guides as context. The system uses these inputs to automatically generate transformation rules and patterns, which you can iteratively refine through build validation until the definition accurately captures your migration logic.

In the second phase, execution and validation, AWS Transform custom applies the transformation to your codebase. It intelligently chunks large codebases into logical modules and processes them in dependency-aware order, so cross-module references remain intact. Throughout execution, the system continuously validates builds and tests, automatically detecting and correcting errors as they arise.

In the third phase, scale and reuse, you publish your finalized transformation to the registry, making it available for your team to apply across similar projects. You can run campaign-based bulk executions across multiple repositories, extract knowledge items for continuous improvement, and integrate the transformation into your CI/CD pipelines.

Key capabilities

AWS Transform custom addresses the core challenges you face when migrating large VBA codebases to Python.

For context window management, the system automatically segments your codebase into logical modules, tracks cross-module dependencies, preserves interface contracts across chunks, and maintains state throughout the transformation. This means you can process codebases that far exceed standard AI context window limits without losing coherence between modules.

For intelligent code restructuring, AWS Transform custom recognizes VBA idioms and maps them to Python equivalents. It refactors procedural code into object-oriented designs, replaces Windows-specific APIs with cross-platform libraries, and applies proper encapsulation and separation of concerns. The result is a clean, maintainable Python that follows modern coding standards.

For functional equivalence preservation, the system generates automated tests based on the original VBA behavior and runs regression testing after each transformation step. It benchmarks performance metrics such as timing and resource usage and validates edge cases to confirm that your transformed Python code produces the same results as the original VBA application.

Prerequisites

Before you begin, you need an AWS account with the appropriate permissions to use AWS Transform custom. Start by configuring authentication and setting up the AWS CLI for your environment.

To create an IAM user for AWS Transform, follow the step-by-step instructions for creating an IAM user and managing IAM policies in the IAM User Guide.

Setup using AWS CLI:

# Create policy
cat > transform-policy.json << 'EOF'
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": ["transform-custom:*"],
      "Resource": "*"
    }
  ]
}
EOF

aws iam create-policy \
  --policy-name AWSTransformCustomPolicy \
  --policy-document file://transform-policy.json

# Attach to your IAM user (replace with your username and account ID)
aws iam attach-user-policy \
  --user-name YOUR_USERNAME \
  --policy-arn arn:aws:iam::YOUR_ACCOUNT_ID:policy/AWSTransformCustomPolicy

Local Environment Setup

  • Operating System: Linux, macOS, or WSL (Windows Subsystem for Linux)
  • Node.js: Version 20 or higher (required for AWS Transform CLI installation via npm)
  • Git: Required for all target repositories
  • Internet Access: Required for AWS Transform service communication

AWS Transform CLI Installation

Follow the official AWS Transform Custom Getting Started Guide for complete installation and setup instructions, including:

  • Platform requirements (Linux, macOS, or WSL)
  • Installation script usage
  • Authentication configuration
  • Network requirements and firewall rules

Source Code Repository

  • VBA application source code (Excel workbook with macros or exported .bas files)
  • Git repository initialized in source directory
  • Build/test commands defined (if applicable)

Target Environment Setup (for Python migration)

  • Python 3.8 or higher installed
  • Virtual environment tool (venv or virtualenv)
  • Target framework dependencies (e.g., pygame for game applications, tkinter/PyQt6 for GUI, pandas for data processing, openpyxl for spreadsheet operations)

Walkthrough: VBA to Python Migration

Animated demonstration of the VBA to Python migration workflow using AWS Transform custom

Figure2:AWS Transform custom CLI executing a VBA to Python transformation

Step 1: Prepare Your VBA Application

Initialize Git Repository

AWS Transform Custom requires your code to be in a Git repository. If your code isn’t already in Git:

cd your-vba-project
git init
git add .
git commit -m "Initial VBA code"

Step 2: Create Transformation Definition

Start AWS Transform Interactive Session

Provide Migration Context

Screenshot showing the AWS Transform custom interactive session interface for providing migration context

Figure3:AWS Transform custom interactive session with migration context

When you start the interactive session, provide a natural language description of your migration goal, such as “Migrate VBA application to Python.” Specify the target framework you want to use, for example, pygame for GUI applications or pandas for data processing. You should also include document references to give the system the context it needs — this includes your VBA code files, Python framework documentation, and any API migration guides relevant to your project.

Example conversation:

User: I want to migrate a VBA application to Python.
AWS Transform: I’ll help you create a transformation definition. Let me analyze the VBA patterns and generate Python equivalents…

Define Scope and Entry Criteria

Before creating your transformation, review your existing codebase to understand its structure, patterns, and dependencies. Identify the code type and technology stack you are working with, such as an Excel VBA game that uses Windows API calls. Check the transformation registry for existing similar transformations to avoid duplicating work. From there, define a clear objective and scope for your transformation, and document the entry criteria that determine what code qualifies. Outline detailed implementation steps with specific technical mappings, establish validation and exit criteria for measuring success, and name your transformation appropriately so your team can discover and reuse it.

Iterative Refinement

After you provide your migration context, AWS Transform custom generates an initial transformation definition.You can review this definition at: ~/.aws/atx/custom/<session-id>/artifacts/tp-staging/transformation_definition.md.

Examine the generated rules and provide feedback on patterns, edge cases, or missing scenarios. AWS Transform custom incorporates your feedback and regenerates the definition, allowing you to iterate until the transformation accurately captures your migration logic.

Key Patterns to Address

During the transformation definition process, you should account for the common patterns that differ between VBA and Python. AWS Transform custom maps VBA ColorIndex values to standard RGB tuples and converts Range objects to Python data structures such as lists and arrays. Do While loops in VBA translate to event-driven loops in Python. The transformation replaces Windows API calls with cross-platform libraries that provide portable alternatives. VBA UserForms map to Python GUI frameworks like tkinter or PyQt6, where form controls become Python widgets. VBA Property Get/Let constructs convert to Python @property and @setter decorators. Finally, VBA’s 1-based array indexing must be adjusted to Python’s 0-based indexing throughout your codebase.

Step 3: Execute Transformation

Apply Transformation to Codebase

# Execute transformation interactively
atx custom def exec \
  --transformation-name "VBA-to-Python-Migration" \
  --code-repository-path "./your-vba-project" \
  --build-command "python3 -m py_compile **/*.py"

# Execute transformation non-interactively
atx custom def exec \
  --transformation-name "VBA-to-Python-Migration" \
  --code-repository-path "./your-vba-project" \
  --build-command "python3 -m py_compile **/*.py" \
  --trust-all-tools \
  --non-interactive

What Happens During Execution

When you run the transformation, AWS Transform custom begins with agent planning. It analyzes your codebase structure, identifies all VBA components and their dependencies, and generates a step-by-step transformation plan with logical ordering — for example, data models first, then business logic, then UI. Each step in the plan has a clear scope and validation criteria.

Next, the system performs automatic code chunking. It analyzes your codebase size and complexity, then segments the code into logical modules such as constants, game logic, rendering, and controller. These chunks are processed in dependency order based on the plan, and the system manages the context window by focusing on one module at a time.

Throughout execution, AWS Transform custom tracks dependencies by mapping relationships between VBA subroutines, verifying that dependent code references remain valid, and maintaining interface contracts across modules.

The transformation proceeds incrementally, converting one module at a time. After each module is transformed, the system validates the build. If a failure occurs, it automatically rolls back and retries the transformation for that module before moving on.

Sample Output:

Analyzing codebase structure...
Identified 4 logical modules: constants, core_logic, rendering, main_controller
Transforming module 1/4: constants
 - Converting VBA Enums to Python IntEnum
 - Mapping ColorIndex values to RGB tuples
 - Generating color constants
 ✓ Build validation passed
Transforming module 2/4: core_logic
 - Converting collision detection subroutines
 - Refactoring global variables to class attributes
 - Translating VBA arrays to Python lists
 ✓ Build validation passed
Transforming module 3/4: rendering
 - Replacing Range object manipulation with pygame rendering
 - Converting cell-based drawing to pixel-based graphics
 - Implementing screen update logic
 ✓ Build validation passed
Transforming module 4/4: main_controller
 - Converting Do While loop to pygame event loop
 - Replacing GetAsyncKeyState with pygame.event.get()
 - Implementing game state management
 ✓ Build validation passed
Transformation complete! Generated 5 Python modules.

Review Transformed Code

After the transformation completes, check the generated Python files to verify the output. You can review the transformation logs at ~/.aws/atx/custom/<conversation-id>/logs/ to understand the decisions the system made during each step. Examine the build validation results to confirm that the transformed code compiles and passes all checks.

Step 4: Validate Functional Equivalence

Automated Test Generation

AWS Transform generates validation tests based on original VBA behavior:

test_validation.py – Verifies core logic:

# Example generated test (conceptual)
def test_collision_detection():
 """Verify collision logic matches VBA behavior"""
 # Test cases extracted from VBA code analysis

def test_scoring_calculation():
 """Ensure scoring algorithm is preserved"""

def test_state_transitions():
 """Validate game state changes"""

test_performance.py – Benchmarks non-functional requirements:

def test_frame_rate():
 """Verify rendering meets 60 FPS target"""

def test_response_time():
 """Ensure input handling latency matches VBA"""

Run Validation Suite

# Execute all tests
python3 -m pytest tests/

# Run with coverage
python3 -m pytest --cov=. tests/

Manual Validation Checklist

  • Application launches without errors
  • UI renders correctly (layout, colors, sizing)
  • User interactions work as expected (keyboard, mouse)
  • Core functionality produces correct results
  • Performance meets requirements (no lag, smooth rendering)
  • Edge cases handled properly (boundary conditions, invalid inputs)

Step 5: Refine and Iterate

As you refine your transformation, you may encounter a few common issues. Here is how you can address them and provide feedback to AWS Transform custom.

Timing and performance differences can occur when the Python application runs faster or slower than the VBA original. To fix this, adjust timing constants and frame rate limiters. For example, if you notice the game runs too fast because VBA used Sleep(500) for piece drops, you can provide that feedback. AWS Transform custom corrects this by replacing time.sleep() with pygame.time.Clock.tick(60).

Color rendering mismatches happen when colors in the Python output don’t match the VBA version. This is typically caused by incorrect RGB mappings for VBA ColorIndex values. If you notice that ColorIndex 3 should be pure red (255,0,0) but is showing as dark red, provide that feedback. AWS Transform custom updates the COLOR_MAP dictionary with accurate RGB values.

Collision detection bugs may appear when pieces move through walls or other pieces. This requires refining boundary checking and collision logic. For instance, if rotation near walls allows pieces to go out of bounds, you can report this issue. AWS Transform custom adds boundary validation before rotation commits to resolve it.

Continual learning in action

Each time you provide a correction, AWS Transform custom captures it as a knowledge item. Future transformations automatically incorporate these fixes, so the same issues don’t recur. With each execution, the system improves quality for similar migrations across your portfolio.

Step 6: Publish and Scale

Save Transformation as Draft (for testing)

atx custom def save-draft \
  --name "VBA-to-Python-Migration" \
  --source-directory "~/.aws/atx/custom/<session-id>/artifacts/tp-staging"

Publish to Registry (for team-wide use)

atx custom def publish \
  --name "VBA-to-Python-Migration" \
  --description "Migrate Excel VBA applications to Python with pygame rendering" \
  --source-directory "~/.aws/atx/custom/<session-id>/artifacts/tp-staging"

Apply to Multiple Projects

# Apply to each project individually
atx custom def exec -n "VBA-to-Python-Migration" -p ./project1 -c "python3 -m py_compile **/*.py" --trust-all-tools --non-interactive
atx custom def exec -n "VBA-to-Python-Migration" -p ./project2 -c "python3 -m py_compile **/*.py" --trust-all-tools --non-interactive

CI/CD Integration

# Example GitHub Actions workflow
name: VBA Modernization
on:
  push:
    branches: [main]
jobs:
  transform:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Run VBA to Python transformation
        run: |
          atx custom def exec \
            --transformation-name "VBA-to-Python-Migration" \
            --code-repository-path "." \
            --build-command "python3 -m py_compile **/*.py" \
            --non-interactive \
            --trust-all-tools

Benefits

By using AWS Transform custom, you can reduce tech debt and accelerate enterprise modernization at scale. The service supports diverse transformation use cases beyond VBA to Python, adapting to the specific needs of your codebase and target platform. With continual learning and improvement, each transformation builds on the corrections and knowledge items from previous runs, delivering higher quality results over time. Once you define a transformation, you can apply it everywhere — across similar projects, teams, and entire application portfolios — without recreating the migration logic from scratch.

Architecture Evolution

Before migration, your legacy VBA application is constrained to a Windows-only execution environment, tightly coupled to the Excel runtime. The code follows a procedural style with a global state, and testing and deployment are manual processes.

After migration with AWS Transform custom, your application follows a modular architecture with clean separation of concerns across constants, logic, rendering, and controller layers. The code uses object-oriented design with proper encapsulation and runs cross-platform on Windows, macOS, and Linux. The transformed application is cloud-ready, with a structure that supports containerization through Dockerfile generation, serverless adaptation with decoupled logic and rendering, and standard Python packaging through requirements.txt and setup.py. It also integrates with modern DevOps workflows, including automated testing with pytest, CI/CD pipeline compatibility, and version control through Git.

You can deploy the transformed application in several ways: containerized with Docker and Kubernetes for scalable web applications, serverless with AWS Lambda for event-driven processing, as a standalone desktop executable with PyInstaller, or as a web application with FastAPI or Flask wrappers for browser access.

The following screenshot shows the transformed Tetris application running as a Python pygame application.

Screenshot of the transformed Tetris application running as a Python pygame application

Figure4:Transformed Tetris game running as a Python pygame application

Transformation definition creation

When creating your transformation definition, provide comprehensive context by including your VBA code, target framework documentation, and migration guides. Focus on documenting application behavior by describing what the application does functionally, not just its code structure. Identify platform-specific dependencies such as Windows APIs, Excel features, and ActiveX controls that require special handling during migration. Define clear success criteria including performance targets, functional requirements, and test coverage expectations. Be explicit about construct mappings in your transformation definition — the more specific you are, the more consistent the output.

Execution strategy

Start with a pilot project by testing your transformation on a smaller, representative application before applying it broadly. Validate incrementally by reviewing each module transformation before proceeding to the next. Capture corrections as feedback by documenting issues and fixes so the system can incorporate them through continual learning. Iterate on edge cases to refine the transformation for corner cases and error conditions that may not surface during initial runs.

Quality assurance

Generate comprehensive automated test suites from the original VBA behavior to verify functional equivalence. Run performance benchmarking to confirm that non-functional requirements match the original application. Conduct manual validation through user acceptance testing to verify UI and UX consistency. After any refinements, run the full test suite for regression prevention to ensure that fixes in one area haven’t introduced issues elsewhere.

Scaling across your portfolio

When you are ready to scale, categorize your applications by similarity — for example, data processing, UI-heavy, or calculation-focused workbooks. Create transformation variants with customized definitions for each category. Use campaigns for bulk execution to process multiple applications in parallel. Monitor and aggregate results across your portfolio to track success rates, identify common issues, and measure time savings.

Cleanup

Archive Conversation Logs

# Conversation logs are in ~/.aws/atx/custom/<conversation-id>/
# Archive for future reference before cleanup
tar -czf vba-migration-logs.tar.gz ~/.aws/atx/custom/<conversation-id>/

Remove Temporary Files

# Remove conversation data and transformation session artifacts (kept for 30 days automatically)
rm -rf ~/.aws/atx/custom/<conversation-id>

# Remove draft transformations (if not needed)
atx custom def delete --name "VBA-to-Python-Migration-Draft"

Manage Published Transformations

# List your transformations
atx custom def list

# Delete transformations no longer needed
atx custom def delete --name "VBA-to-Python-Migration-Old-Version"

# Update transformation tags for organization
atx custom def tag --transformation-name "VBA-to-Python-Migration" \
  --tags "team:platform-engineering,language:python,status:production"

Knowledge Items Management

# List knowledge items for transformation
atx custom def list-ki --transformation-name "VBA-to-Python-Migration"

# Review and approve valuable knowledge items
atx custom def get-ki --transformation-name "VBA-to-Python-Migration" --id <ki-id>

# Enable approved knowledge items
atx custom def update-ki-status \
  --transformation-name "VBA-to-Python-Migration" \
  --id <ki-id> \
  --status ENABLED

# Delete low-quality knowledge items
atx custom def delete-ki --transformation-name "VBA-to-Python-Migration" --id <ki-id>

Conclusion

In this post, you learned how AWS Transform custom can help you migrate Excel VBA applications to Python at scale. With AWS Transform custom, you can reduce weeks of manual migration work to hours. Intelligent chunking handles large codebases that exceed standard AI context window limits, while continuous validation and automated testing preserve functional equivalence throughout the process. Because transformations are reusable, and the system learns from each execution, you gain compound efficiency with every subsequent migration. The transformed applications are cloud-ready, supporting containerization, serverless deployment, and modern DevOps workflows.

AWS Transform custom is a strong fit when you are modernizing a portfolio of dozens or hundreds of similar legacy applications, when functional equivalence must be guaranteed for quality-critical migrations, or when you have repeatable migration patterns across multiple projects.

Next Steps

The transformation demonstrated in this post — VBA to Python migration with context window management, automated restructuring, and functional validation — is a template you can apply to other modernization challenges, such as mainframe COBOL to Java, Progress ABL to Spring Boot, or .NET Framework to .NET Core. The principles remain consistent: intelligent code analysis, dependency-aware processing, continuous validation, and transformation reuse.

Call to Action

Get Started with AWS Transform Custom

Documentation and Resources:

Explore AWS-Managed Transformations: Access pre-built transformations for common migration patterns:

  • AWS SDK Java v1 to v2: AWS/java-aws-sdk-v1-to-v2
  • Python Version Upgrades: AWS/python-version-upgrade
  • Node.js Version Upgrades: AWS/nodejs-version-upgrade
  • Java Version Upgrades: AWS/java-version-upgrade

Estimate Your Migration: Use the AWS Pricing Calculator to estimate costs for your specific modernization project.


Ankit Srivastava

is a Strategic Technical Account Manager at Amazon Web Services (AWS), where he serves as a trusted advisor to global enterprise customers. With over 15 years of experience in cloud architecture, DevOps and distributed systems, Ankit helps organizations navigate cloud transformation, architecture modernization, and harness the power of Generative AI on AWS.

Somnath Chatterjee

is an accomplished Senior Technical Account Manager at Amazon Web Services (AWS), Somnath is dedicated to guiding customers in crafting and implementing their cloud solutions on AWS. He collaborates strategically with customers to help them run cost-optimized and resilient workloads in the cloud. Beyond his primary role, Somnath holds specialization in the compute, SAP and Developer Experience technical field community. With over 14 years of experience in the information technology industry, he excels in cloud architecture and helps customers achieve their desired outcomes on AWS.

Detect and resolve HBase inconsistencies faster with AI on Amazon EMR

Post Syndicated from Yu-Ting Su original https://aws.amazon.com/blogs/big-data/detect-and-resolve-hbase-inconsistencies-faster-with-ai-on-amazon-emr/

HBase operations teams spend hours manually correlating logs, metadata, and consistency reports to identify root causes. Traditional approaches require deep expertise and extensive investigation across scattered data sources, directly impacting MTTR and operational efficiency. As HBase deployments scale and expertise becomes increasingly scarce, organizations face mounting pressure to maintain service reliability while managing growing operational complexity. The manual nature of troubleshooting creates bottlenecks that delay incident resolution, increase operational costs, and risk service degradation during critical business periods.

In this post, we show you how to build an AI-powered troubleshooting solution using Amazon OpenSearch Service vector search and intelligent analysis. This solution reduces HBase inconsistency resolution from hours to minutes and root cause identification from days to hours through natural language queries over operational data. This democratizes HBase troubleshooting capabilities across teams and reducing dependency on specialized expertise.

Solution overview

The solution addresses HBase troubleshooting challenges through data processing, vector search, and AI-powered analysis. It processes operational data from Amazon EMR clusters, generates semantic vector embeddings, and enables natural language queries for intelligent troubleshooting.
Key components include:

  • Amazon EMR HBase: Runs HBase workloads with Amazon S3 as the HBase rootdir for durable, scalable storage
  • Data Processing: Extracts and processes HBase logs, HBCK reports, and metadata with vector embeddings
  • Amazon OpenSearch Service: Provides vector search capabilities with k-NN algorithms for semantic analysis
  • AI Analysis Interface: Enables natural language queries with context-aware recommendations
  • Custom Knowledge Base: Supports organization-specific runbooks and troubleshooting procedures by ingesting Git repositories via Kiro CLI‘s /knowledge add command, enabling the AI assistant to reference custom operational guides alongside HBase source code and operational tools

AWS cloud architecture diagram showing an HBase log analysis system with EMR cluster, VPC networking, IAM roles, Lambda functions, OpenSearch domain, and supporting services for scalable log processing and analytics.

The preceding diagram illustrates how the HBase log analysis system troubleshoots inconsistencies through automated workflows across AWS services.

When an operations team needs to investigate HBase issues, the engineer connects over SSH to the Amazon EMR primary node and runs the error collection script, which gathers logs from HBase master and RegionServer nodes and uploads them to Amazon S3. Next, the engineer connects to the Analytics Amazon Elastic Compute Cloud (Amazon EC2) instance and executes the automated processing script, which downloads logs from Amazon S3, generates semantic vector embeddings, and injects them into Amazon OpenSearch Service for k-NN-based semantic search. The engineer then queries the Kiro CLI AI Assistant using natural language to investigate. Kiro searches Amazon OpenSearch Service for relevant log entries and uses Amazon Bedrock to analyze patterns, correlate errors across components, and provide actionable recommendations. This reduces troubleshooting time from hours to minutes. The system operates within an Amazon Virtual Private Cloud (Amazon VPC) with private subnets for Amazon EMR and Analytics Amazon EC2, AWS Identity and Access Management (AWS IAM) roles for access control, Parameter Store for configuration, and Amazon CloudWatch for monitoring.

Prerequisites

For this walkthrough, you need the following prerequisites:

AWS account setup

  • An AWS account with administrative access for initial deployment
  • AWS Command Line Interface (AWS CLI) configured with administrative credentials

Required AWS IAM permissions

For infrastructure deployment

Your deployment user or role needs the following permissions:

  • Your deployment user or role requires sufficient access to AWS CloudFormation, Amazon S3, AWS IAM, and AWS System Manager.
  • The user or role must have the ability to create AWS CloudFormation stacks.

Infrastructure deployment:

  • For infrastructure deployment, you need AWS CloudFormation stack management permissions.
  • You also require sufficient access to create and manage the following resources:
    • Amazon OpenSearch Service domains
    • Amazon EC2 instances, Amazon VPCs, security groups, and networking components
    • AWS IAM roles and policies
    • AWS Systems Manager Parameter Store entries
    • Amazon CloudWatch Logs groups
    • Amazon S3 bucket for access logs and session logs

Runtime service roles

The AWS CloudFormation stack automatically creates two specialized AWS IAM roles designed with least-privilege access principles.

The first role is the Amazon OpenSearch Service Role, which manages Amazon VPC networking and Amazon CloudWatch logging for the Amazon OpenSearch Service domain.

The second role is the Application Role, which provides minimal Amazon OpenSearch Service and Amazon S3 access specifically for log processing applications and secure log ingestion operations.

Network requirements

  • Amazon VPC with private subnets for secure Amazon OpenSearch Service deployment
  • NAT Gateway for outbound internet access from private subnets
  • Security groups configured for HTTPS-only communication

Running Kiro CLI on Amazon EC2

Kiro platform requirements:

Kiro subscription

  • Active Kiro License: Valid subscription to Kiro platform
  • User Account: Registered Kiro user account with appropriate permissions
  • API Access: Kiro API keys or authentication tokens for CLI access

AWS Identity Center integration

  • AWS IAM Identity Center Setup: AWS IAM Identity Center enabled in your AWS organization
  • Permission Sets: Configured permission sets for Kiro users with appropriate AWS access
  • User Assignment: Users assigned to relevant AWS accounts and permission sets
  • SAML/OIDC Configuration: Identity provider integration if using external identity systems

Additional prerequisites

  • Python 3.7+ and Node.js installed locally
  • Python 3.11+ for AWS Lambda runtime environment (required for OpenSearch MCP server compatibility)
  • Sufficient service quotas for Amazon OpenSearch Service instances and Amazon EC2 resources
  • Recommended access to the analysis instance via AWS Systems Manager Session Manager (recommended). Amazon EMR clusters running HBase workloads
  • EMR_EC2_Default_Role of Amazon EMR EC2 instance profile can execute describe-stacks on AWS CloudFormation stacks in us-east-1
  • Basic familiarity with HBase operations

The deployment follows AWS security best practices with resource-specific permissions, regional restrictions, and encrypted data storage. All AWS IAM policies implement least-privilege access patterns to help secure operation of the log analysis pipeline.

Walkthrough

This walkthrough demonstrates deploying and configuring the AI-powered HBase troubleshooting solution in five key steps:

  1. Deploy AWS infrastructure using AWS CloudFormation
  2. Configure Amazon EMR analysis log collection
  3. Process and index HBase data
  4. Enable AI-powered analysis
  5. Add custom knowledge base (optional)

The complete solution is available in our GitHub repository.

Step 1: Deploy the infrastructure

Deploy the required AWS infrastructure including Amazon OpenSearch Service domain, Amazon EC2 instances, and AWS IAM roles.

To deploy the infrastructure

  1. Deploy AWS CloudFormation stack. Please update [email protected] to an email address for security alerts and Advanced Intrusion Detection Environment (AIDE) reports:
# Deploy to development environment
aws cloudformation create-stack \
  --stack-name dev-hbase-log-analysis \
  --template-body file://cloudformation/hbase-log-analysis-simple.yaml \
  --parameters \
    ParameterKey=EnvironmentName,ParameterValue=dev \
    ParameterKey=EC2InstanceType,ParameterValue=m7g.xlarge \
    ParameterKey=SecurityAlertEmail,[email protected] \
  --capabilities CAPABILITY_IAM \
  --region us-east-1
# Wait for deployment to complete (~15-20 minutes)
aws cloudformation wait stack-create-complete \
  --stack-name dev-hbase-log-analysis \
  --region us-east-1
  1. Note the deployment outputs including Amazon OpenSearch Service endpoint and Amazon EC2 instance details in the AWS CloudFormation console.

AWS CloudFormation stack outputs table displaying infrastructure resource identifiers including IAM roles, EC2 instances, security groups, S3 buckets, OpenSearch domain configuration, and VPC details for an HBase log analysis application in the development environment.

The deployment creates:

  • Amazon OpenSearch Service domain with vector search capabilities
  • Amazon EC2 instance for data processing and AI analysis
  • AWS IAM roles with appropriate permissions
  • Security groups and Amazon VPC configuration

Step 2: Connect to Amazon EC2 instance and set up system

Connect to the Amazon EC2 instance using AWS Systems Manager (SSM) and set up the required components.

To connect and set up the system

  1. Run the following commands to get the instance ID from AWS CloudFormation outputs and connect via AWS Systems Manager (SSM):
# Get instance ID
INSTANCE_ID=$(aws cloudformation describe-stacks \
  --stack-name dev-hbase-log-analysis \
  --query 'Stacks[0].Outputs[?OutputKey==`EC2InstanceId`].OutputValue' \
  --output text \
  --region us-east-1)
# Connect via SSM
aws ssm start-session --target $INSTANCE_ID --region us-east-1

Terminal screenshot showing AWS CLI commands to retrieve an EC2 instance ID from CloudFormation stack outputs and establish an AWS Systems Manager Session Manager connection to the instance in the us-east-1 region.

  1. Clone the repository and run automated setup:
# On EC2 instance
sudo su - ec2-user

# Re-install aws cli
sudo dnf remove awscli -y

# For ARM64 (Graviton instances - default)
curl "https://awscli.amazonaws.com/awscli-exe-linux-aarch64.zip" -o "awscliv2.zip"

# For x86_64 (if using non-Graviton instances)
# curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"

unzip awscliv2.zip
sudo ./aws/install

# update $PATH in ~/.bashrc
echo 'export PATH=$PATH:/usr/local/bin/' >> ~/.bashrc

# Reload ~/.bashrc
source ~/.bashrc

# Fork and clone the source code repository on GitHub: sample-emr-hbase-inconsistencies-detection-recovery-mcp-kiro
git clone https://github.com/YOUR_USERNAME/sample-emr-hbase-inconsistencies-detection-recovery-mcp-kiro.git hbase-analysis
cd hbase-analysis

# Run automated setup
chmod +x ./scripts/setup/automated-system-setup.sh
./scripts/setup/automated-system-setup.sh \
  --emr-version emr-7.12.0 \
  --stack-name dev-hbase-log-analysis \
  --region us-east-1

The automated setup script installs:

  • System dependencies (awscli, git, unzip)
  • uv package manager and OpenSearch MCP Server
  • Kiro CLI and configuration with AWS IAM Identity Center authentication. The script will automatically add Apache HBase open source repo and Apache HBase open source operational tools to knowledge bases
  • HBase source repositories for your Amazon EMR version
  • Python dependencies and MCP server configuration
  1. Add your own knowledge base to Kiro CLI

To enhance Kiro CLI’s analysis capabilities with Apache HBase open-source repositories, your organization’s HBase runbooks and troubleshooting guides, you can add your own knowledge base repositories. Here are the commands. Please periodically validate and maintain your runbook contents so that they remain accurate and up-to-date, reflecting any changes in your HBase environment, configurations, or operational procedures.:

# Navigate to the HBase repositories directory
cd /opt/hbase-repositories
# Clone your organization's HBase runbook repository
git clone <runbook-repository-url> <your-own-runbook-repo>
# Example:
# git clone https://github.com/your-org/hbase-runbooks.git hbase-runbooks
# git clone https://gitlab.company.com/ops/hbase-troubleshooting.git hbase-troubleshooting
# Add your custom repositories to Kiro CLI knowledge base manually (run these commands inside kiro-cli):
echo "/knowledge add --name \"Your custom HBase knowledge base\" --path /opt/hbase-repositories/<your-own-runbook-repo>" | kiro-cli
# Example:
# echo "/knowledge add --name \"Company HBase runbooks\" --path /opt/hbase-repositories/hbase-runbooks" | kiro-cli
# echo "/knowledge add --name \"HBase troubleshooting guides\" --path /opt/hbase-repositories/hbase-troubleshooting" | kiro-cli

Step 3: Configure Amazon EMR log analysis collection

Set up data collection from your Amazon EMR clusters to gather HBase logs, metadata, and consistency reports using the recommended direct collection method.
To configure Amazon EMR log analysis collection

  1. On your Amazon EMR cluster primary node, run the following commands to download the collection scripts:
# On EMR primary node
sudo su - hadoop

# Fork and clone the source code repository on GitHub: sample-emr-hbase-inconsistencies-detection-recovery-mcp-kiro
git clone https://github.com/YOUR_USERNAME/sample-emr-hbase-inconsistencies-detection-recovery-mcp-kiro.git hbase-analysis
cd hbase-analysis
  1. Run the interactive collection wizard:
# Run collection wizard
python3 scripts/utilities/emr_log_collection/emr_cluster_wizard_v2.py

Input the parameters like the EMR cluster’s jobflow ID, the log analysis Amazon S3 bucket name, and the lookback hours. The default value of the lookback hours is 4 hours.

Terminal screenshot of EMR Cluster Log Collection Wizard V2 showing an interactive command-line interface for configuring HBase diagnostic log collection from Amazon EMR clusters, with step indicators, input fields for job flow ID and S3 bucket, validation confirmations, and lookback hour configuration.

  1. The collection wizard performs these actions:
  • Collects HBase logs from local filesystem. Please reference to prerequisites for the access permission.
  • Runs sudo -u hbase hbase hbck -details (or hbck2 for HBase 2.x)
  • Runs hdfs dfs -ls -R /hbase or aws s3 ls <hbase-root-dir> –recursive
  • Runs hbase shell <<< 'scan "hbase:meta"'
  • Creates properly named files matching analysis system requirements
  • Uploads to Amazon S3 with correct naming conventions

Here’s the data collection summary:

Terminal screenshot showing EMR Cluster Log Collection Wizard V2 completion summary with job flow ID, S3 bucket location, 4-hour lookback period, green success confirmation message, S3 file path, and detailed listing of seven collected diagnostic files including HBCK reports, HBase meta table scans, root directory paths, process information, log collection summary, node logs from all servers, and collection metadata in JSON format.

You can check the uploaded contents through AWS CLI.

aws s3 ls s3://<log-path> --recursive

Here’s a screenshot of the outputs.

Terminal screenshot showing AWS CLI command output listing HBase diagnostic files and logs collected from an EMR cluster and stored in Amazon S3, displaying timestamps, file sizes, and complete S3 object paths including diagnostics directory with HBCK reports, meta table scans, root directory listings, process information, and logs directory with compressed application logs from HBase master and regionserver nodes.

  1. On the Analysis Amazon EC2 instance, download collected files to the Analysis Amazon EC2 instance.
# On analytics EC2 instance
sudo su - ec2-user

# Download logs from S3
mkdir -p /tmp/hbase-log-analysis
cd /tmp/hbase-log-analysis
aws s3 sync s3://<S3-BUCKET-NAME>/emr-logs/<EMR-JOBFLOW-ID>/ .

You can get your jobflow ID from Amazon EMR console:

Amazon EMR clusters management dashboard displaying a table with clusters, showing one cluster entry named "test" in waiting status with green indicator, creation time, elapsed time, normalized instances, along with filter controls, search functionality, pagination showing page 1, and action buttons for View details, Terminate, Clone, and Create cluster operations.

The generated files (hbase-hbase-master-ip-xxx-xxx-xxx-xxx.ec2.internal.log.gz, hbase-hbase-regionserver-ip-xxx-xxx-xxx-xxx.ec2.internal.log.gz, hbck_report.txt, hbase_rootdir_paths.txt, hbase_meta.txt, hbase_processes.txt, log_copy_summary.txt) should be aligned with the automated processing script requirements as following.

Terminal screenshot showing recursive ls -lRt command output listing HBase diagnostic files and logs in /tmp/hbase-log-analysis/ directory, displaying file permissions, ownership by ec2-user, file sizes, timestamps, and complete directory structure including diagnostics directory with text files (manifest.json, HBCK report, meta table scan, process information, root directory paths, log copy summary), logs directory with nested nodes subdirectory containing redacted instance IDs, and applications/hbase subdirectories with compressed RegionServer and Master log files.

Step 4: Process and index data

Process the collected HBase data and create vector embeddings for intelligent search capabilities.To process and index the data, please navigate to the project directory on the Analysis EC2 instance, and run automated-log-processing.sh:

sudo su – ec2-user
cd ~/hbase-analysis
chmod +x ./scripts/processing/automated-log-processing.sh
./scripts/processing/automated-log-processing.sh \
  --job-flow-id j-YOUR-JOB-FLOW-ID \
  --stack-name dev-hbase-log-analysis

The processing scripts extract and parse HBase logs and generate dimensional vector embeddings from HBase log messages using sentence transformer models to enable semantic search beyond keyword matching. The system uses the all-MiniLM-L6-v2 model by default (producing 384-dimensional embeddings), but supports configurable models with different embedding dimensions, automatically adapting the OpenSearch vector index to match the chosen model’s output. The system processes comprehensive HBase operational data including region operations, compaction activities, Write-Ahead Log events, memstore operations, and cluster management information from HMaster and RegionServer logs. Vector embeddings capture error messages, exception stack traces, performance warnings, and multi-line log entries through intelligent text preprocessing. This semantic representation enables advanced troubleshooting where users can query conceptually for “region server performance issues” or “memory pressure” and receive contextually relevant results across different log files and time periods. The vector search capabilities support error correlation by grouping similar exceptions, performance analysis by identifying related bottlenecks, and operational pattern recognition. Each log entry is stored in Amazon OpenSearch Service with original metadata (timestamp, log level, source file, job flow ID) alongside the embedding vector, enabling both structured queries and AI-powered semantic analysis. This approach transforms raw HBase logs into a searchable knowledge base supporting anomaly detection, trend analysis, and predictive insights for proactive cluster management and troubleshooting.

All scripts use AWS IAM authentication automatically. Here’s a screenshot of the data processing outputs.

Terminal screenshot showing successful completion of HBase log analysis processing, green checkmark, confirmation message "Successfully processed 4 file(s)", and next steps section displaying three numbered instructions with redacted URLs for accessing OpenSearch Dashboards, starting Kiro CLI for AI-powered analysis, and querying data using job flow ID, followed by troubleshooting documentation references for HBase inconsistency analysis and log analysis guides.

Step 5: Enable AI-powered analysis

Configure the AI analysis interface to enable natural language queries against your HBase operational data.

To set up AI-powered analysis

  1. Launch Kiro CLI (already configured by automated setup):

kiro-cliCheck mcp and knowledge bases. /mcp list

Terminal screenshot showing MCP list command output displaying one configured MCP server named "opensearch-mcp-server" with command "uvx" in green and white text on dark background with pink shell prompt, featuring a purple "Configured MCP Servers" header with checkbox icon and green horizontal separator line.

/knowledge show

Terminal screenshot showing "/knowledge show" command output displaying Agent kiro_default's knowledge base with repositories: Apache HBase source code, and HBase operational tools

If you cannot see these 2 knowledge bases, you can manually add them through the following commands:

# Note: Large repositories (~500MB) may take a while to index. Check progress with: /knowledge show
/knowledge add --name "HBase operational tools" --path /opt/hbase-repositories/hbase-operator-tools"
/knowledge add --name "Apache HBase source code" --path /opt/hbase-repositories/hbase"
  1. Use natural language queries to analyze your HBase data. The AI analysis uses both the OpenSearch MCP Server for querying indexed data and the Filesystem knowledge bases for accessing HBase source code. You can add your custom runbooks for Kiro’s reference as well.

For HBase inconsistency analysis:

# HBase Inconsistency Detection and Remediation Guidelines
## Search Strategy
- Use fuzzy search for case variations/typos, term query for exact region IDs, match_phrase for paths, query_string for logs
- Always use .keyword subfields for exact text matching
- Cross-reference filesystem (wildcard: {"wildcard": {"path": "*<region_id>*"}}) with hbase:meta (match: {"match": {"row_key": "<region_id>"}})
- The total region count in hbase meta must match the total matched document count of wildcard path like "*/.regioninfo" in hbase rootdir path.  
- All terms of region_name.keyword for a region encoded name must match a wildcard path like "*/.regioninfo"
- All terms of table_name.keyword for a table must match a wildcard path like "*/.tabledesc*"
- 1595e783b53d99cd5eef43b6debb2682 is the master store region that will locate in <hbase-root-dir>/MasterData/data/master/store/1595e783b53d99cd5eef43b6debb2682/
- May cross check with the raw logs in /tmp/hbase-log-analysis/
## Issue Types
Orphan regions, missing .regioninfo, missing/extra regions in hbase:meta, rowkey holes, stuck RIT, master initialization failures
## Analysis Steps
### 1. Cross-Reference Meta vs Filesystem
- Filesystem regions NOT in hbase:meta → ORPHAN REGION
- Meta regions NOT in filesystem → MISSING REGION
### 2. Validate Region Chain Continuity
- Sort regions by STARTKEY, verify region[i].ENDKEY == region[i+1].STARTKEY
- First STARTKEY must be '', last ENDKEY must be ''
- Gaps → ROWKEY HOLE
### 3. Check Region States
- state != 'OPEN' → Check RIT
- Missing server assignment → UNASSIGNED
- Multiple servers → SPLIT BRAIN
- "deployed_servers" field must have only one region server address like "ip-xxx-xxx-xxx-xxx.ec2.internal,16020,1770781485397" . The value should not be null or have multiple values. 
### 4. Validate .regioninfo Files
- Missing .regioninfo in region directory → CORRUPT REGION
### 5. Cross-Check HBCK Report
- Compare orphan counts, RIT regions, filesystem vs meta region counts
### 6. Analyze Logs
- Search: "updating hbase:meta row=<region>", "STUCK", "RIT", "Failed" + "<region>", "Split"/"Merge" + "<region>"
## Remediation
- Reference knowledge bases: "Apache HBase source code", "HBase operational tools"
- Use hbck2: /usr/lib/hbase-operator-tools/hbase-hbck2.jar
- Prefix commands with sudo -u hbase
- Use aws s3 for S3-based rootdir
- Wait 300s after creating holes before hbck fixMeta (catalog janitor cycle)
- Use unassign instead of deprecated close_region
- If the region does not have .regioninfo in  <hbase-root-dir>/data/<namespace>/<table-name>/<region-encoded-name>/ but hbase:meta has that region's information and that region has been deployed on a healthy region server, you can use hbase shell to unassign and assign the region to re-generate .regioninfo
- Always add "sudo -u hbase hbase" before "hbase shell" and "hbase hbck" commands
## Job flow
Target: <your-job-flow-id>
Inconsistency to detect: All kinds of inconsistencies

You can trust or input “y” or “t” to grant Kiro to search through mcp and knowledge bases.

Terminal screenshot showing MCP tool execution authorization prompt.

You may get some outputs like this: Kiro checked for any HBase issue.

Terminal screenshot showing HBase database query results for user table entries with server configuration details and an HBase Inconsistency Detection Framework analysis report

Kiro summarized the examination results.

Terminal screenshot displaying HBase inconsistency detection analysis results for job flow, showing one critical missing .regioninfo file issue for HBase region in a HBase table, with cluster health metrics, risk assessment, recommended fixes, and generated diagnostic reports.

Kiro provided mitigation commands after Kiro summarized the issue.

Terminal screenshot displaying a structured HBase quick fix guide with three sections: recommended fix procedure with sequential steps for region reassignment, verification steps using AWS S3 and HBCK2 tools, and impact assessment showing 30-60 second downtime, zero data loss risk, and isolated region scope for fixing missing .regioninfo file in HBase region.

Cleaning up

To avoid incurring future charges, delete the resources created during this walkthrough.

To clean up the resources

  1. Delete the AWS CloudFormation stack from AWS Management Console:

AWS CloudFormation Stacks management console displaying a list view with stacks, showing the "dev-hbase-log-analysis" stack with CREATE_COMPLETE status, along with action buttons for Delete, Update stack, Stack actions, and Create stack.

  1. Clean up Amazon EMR cluster resources (if created only for this walkthrough):
AWS EMR Clusters management console showing page clusters with a cluster in "Waiting" status
  1. Verify resource cleanup in the AWS Console to verify that all resources are deleted and review your AWS bill to confirm no unexpected charges.

Important considerations:

  • Amazon OpenSearch Service domains take several minutes to fully delete
  • Amazon S3 buckets with versioning retain object versions
  • Use smaller instance types for development to optimize costs
  • Monitor usage with AWS Cost Explorer

Conclusion

In this post, we showed you how to build an AI-powered HBase troubleshooting solution that transforms manual log analysis into an automated workflow. By combining Amazon OpenSearch Service vector search with Amazon Bedrock-powered analysis through the Kiro CLI, operations teams can resolve complex HBase inconsistencies faster and gain deeper operational insights. The solution demonstrates how AI augments human expertise to improve operational efficiency, reducing HBase inconsistency resolution from hours to minutes and root cause identification from days to hours. Ready to transform your HBase operations? Get started with the GitHub repository and explore the Amazon OpenSearch Service documentation for additional guidance on vector search capabilities.

Acknowledgments

The author would like to thank Xi Yang, Anirudh Chawla, and Sasidhar Puthambakkam for their contributions to developing the technical solution. Xi Yang is a Senior Hadoop System Engineer and Amazon EMR subject matter expert at AWS. Anirudh Chawla is an AWS Analytics Specialist Solution Architect who helps organizations empower businesses to harness their data effectively through AWS’s analytics platform. Sasidhar Puthambakkam is a Senior Hadoop Systems Engineer and Amazon EMR Subject Matter Expert who provides architectural guidance for complex BigData workloads.


About the authors

Yu-Ting Su

Yu-ting Su, Sr. Hadoop System Engineer, AWS Support Engineering. Yu-Ting is a Sr. Hadoop Systems Engineer at Amazon Web Services (AWS). Her expertise is in Amazon EMR and Amazon OpenSearch Service. She’s passionate about distributing computation and helping people to bring their ideas to life.

Agentic application modernization at scale with Strands and Amazon Transform custom

Post Syndicated from Kanishk Mahajan original https://aws.amazon.com/blogs/devops/use-generative-ai-agents-for-application-modernization-at-scale-with-strands-amazon-transform-custom-and-amazon-bedrock-agentcore/

Introduction

Modernizing applications by upgrading language runtimes, migrating SDKs, and refactoring frameworks is important for cloud adoption but can be labor-intensive at scale. Each repository requires analysis of dependencies and transformation needs; custom transformation logic must be built and validated, and changes are often executed sequentially across codebases. If you have hundreds of applications, this stretches timelines from months to years, while introducing inconsistency across your teams.

To address this, Amazon Web Services (AWS) provides a composable set of building blocks. AWS Transform custom enables reusable, CLI-driven code transformations for upgrading runtimes, SDKs, and frameworks consistently across large portfolios. Strands Agents provides a framework for building multi-agent systems that coordinate complex transformation workflows. Amazon Bedrock AgentCore delivers the managed runtime, memory, and observability to operate these agents reliably in production. Together, they replace manual, sequential modernization with an intelligent, automated approach that scales.

In this post, we show you how to combine these services to build a generative AI–powered, agentic modernization system that can automatically analyze application repositories, determine required changes, create missing transformations, and execute them in parallel at scale.

Solution overview

The solution uses an agentic architecture that separates intelligent decision-making from deterministic execution, enabling automation at scale while maintaining consistency and control. In this post, you will build an AI-driven application modernization system that demonstrates how multi-agent workflows can be applied to large-scale code transformation scenarios. You interact with the system through a React-based frontend or API interface, submitting individual repositories or batch workloads via CSV inputs. Requests are processed asynchronously through an API layer that invokes an orchestrator agent running on Amazon Bedrock AgentCore, which coordinates specialized agents to analyze codebases, identify transformation requirements, and manage execution workflows. Results are stored and surfaced through the interface, allowing users to track progress and review outputs in real time.The workflow begins with repository analysis, where the system inspects application codebases to identify languages, dependencies, and required upgrades such as runtime version changes or SDK migrations. Based on this analysis, the system maps each application to an existing transformation when available. If no suitable transformation exists, a creation agent dynamically generates one using natural language instructions and publishes it to a centralized registry for reuse, creating a continuously improving system where transformation coverage expands over time.

Once transformations are identified or created, an execution agent runs them at scale by invoking AWS Batch jobs that execute the AWS Transform custom CLI, enabling parallel processing across multiple repositories. The orchestrator coordinates all agents, maintains workflow state using Amazon Bedrock AgentCore Memory, and ensures reliable execution through structured task decomposition, tool invocation, and error handling. While the example focuses on application re-platforming, the same architectural pattern can be applied to other large-scale code analysis and automation workflows.

The following architecture diagram (Figure 1) illustrates the various components of our solution as outlined in this section:architecture diagram describing the multi agent strands and agentcore deployment

Figure 1: AWS Transform custom Agentic Orchestration Architecture using Strands agents and Amazon Bedrock AgentCore

Prerequisites

Complete the following prerequisites:

  1. Install the AWS Command Line Interface (AWS CLI).
  2. Install the AWS SAM CLI v1.100.0+
  3. Install Docker v20.x+.
  4. Install Node.js v18.x+
  5. Install Python v3.11+
  6. Install the AWS CDK CLI
  7. Enable access to a Bedrock model for the orchestrator in your deployment region. The default model can be configured through the Amazon Bedrock model access console. To use a different model, set `BEDROCK_MODEL_ID` in `deployment/config.env` before Step 3 and enable access to that model instead. Model access approval can take a few minutes in some accounts, so complete this step before deploying.

Dependencies

The Strands Agents implementation has the following dependencies that are packaged in the DockerFile:

  1. Strands multi-agent framework: strands-agents
  2. Strands agent tools and utilities: strands-agents-tools
  3. HTTP library for API calls: requests
  4. Amazon Bedrock AgentCore SDK: bedrock-agentcore
  5. AWS SDK for Python: boto3

Deploy the solution

The solution is available for download on the GitHub repo. This post walks through the CDK + SAM deployment path (Option A in the repository README). The repository also includes a CDK-only option (Option B); see the repository README for details.

Step 1: Clone the repository

git clone https://github.com/aws-samples/aws-transform-custom-samples.git

cd aws-transform-custom-samples/agentic-atx-platform

Step 2: Configure AWS Credentials

# Configure AWS CLI

aws configure

# Verify credentials

aws sts get-caller-identity

Step 3: Deploy ATX CLI Container image and frontend using AWS CDK

# Copy configuration template (defaults work for most setups; edit only to change region or Bedrock model)

cd deployment

cp config.env.template config.env

# Authenticate with Amazon ECR Public (required for the Docker base image pull)

aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws

# Build the UI placeholder so CDK’s UI stack has ui/dist/ to deploy

cd ../ui && npm install && npx vite build

# Install CDK dependencies and bootstrap (run once per account/region)

cd ../cdk

npm install cdk bootstrap

# Build TypeScript and deploy the three stacks

npx tsc

CDK_DEFAULT_ACCOUNT=$(aws sts get-caller-identity --query Account --output text)

cdk deploy AtxContainerStack AtxInfrastructureStack AtxUiStack --require-approval never

# Note for accounts without a default VPC , pass the VPC context flags to `cdk deploy`:

cdk deploy AtxContainerStack AtxInfrastructureStack AtxUiStack --require-approval never

cdk deploy AtxContainerStack AtxInfrastructureStack AtxUiStack --require-approval never -c existingVpcId=vpc-xxx -c existingSubnetIds=subnet-aaa,subnet-bbb -c existingSecurityGroupId=sg-ccc

# Subnets must be public (auto-assign public IP enabled) or private with a NAT gateway so Fargate tasks can reach Amazon ECR, Amazon S3, and Git repositories.

Step 4: Deploy Strands Agents to AgentCore runtime using AWS SAM

cd ../sam./deploy.sh

# Invoke the deploy Lambda to create the AgentCore Runtime via the bedrock-agentcore-control SDK (takes 2-5 minutes)

aws lambda invoke --function-name atx-deploy-agentcore \ --region us-east-1 \ --cli-binary-format raw-in-base64-out \ --payload '{"action":"deploy"}' \ --cli-read-timeout 900 /tmp/deploy-output.jsoncat /tmp/deploy-output.json

Step 5: Wire the AgentCore runtime ARN into the async invoke Lambda

ACCOUNT_ID=$(aws sts get-caller-identity --query Account --output text)

RUNTIME_ARN=$(python3 -c "import json; print(json.loads(json.load(open('/tmp/deploy-output.json'))['body'])['runtime_arn'])")aws lambda update-function-configuration \ --function-name atx-async-invoke-agent \ --region us-east-1 \ --environment "Variables={AGENT_RUNTIME_ARN=${RUNTIME_ARN},RESULT_BUCKET=atx-custom-output-${ACCOUNT_ID},JOBS_TABLE=atx-transform-jobs}"

Step 6: Rebuild and deploy the frontend with AgentCore API endpoint

# Update the React application with the deployed API endpoint and redeploy it.

API_URL=$(aws cloudformation describe-stacks \ --stack-name AtxAgentCoreSAM \ --region us-east-1 \ --query 'Stacks[0].Outputs[?OutputKey==`ApiEndpoint`].OutputValue' \ --output text)

cd ../ui

VITE_API_ENDPOINT=$API_URL npx vite build./deploy-aws.sh

# This rebuilds the React application with the correct API endpoint, uploads it to Amazon S3, and invalidates the Amazon CloudFront distribution.

Step 7: Access the application

# After deployment completes, retrieve the CloudFront distribution URL from the AWS CloudFormation outputs and open it in your browser to access the application UI.

aws cloudformation describe-stacks \ --stack-name AtxUiStack \ --region us-east-1 \ --query 'Stacks[0].Outputs[?OutputKey==`WebsiteUrl`].OutputValue' \ --output text

Using the application

The UI exposes five tabs covering the complete modernization workflow: browsing available transformations, executing a transformation on a single repository, creating a new custom transformation with natural language, batch-processing a CSV of repositories, and tracking job status. This section walks through two of the most common flows.

Create a custom transformation from natural language

Open the Create Custom tab, describe the transformation in plain English (for example, “Upgrade Spring Boot 2 applications to Spring Boot 3”), and optionally provide a reference repository URL. The creation agent analyzes the source, generates a transformation definition, and publishes it to the ATX registry for reuse across the portfolio.

Describing a custom transformation in plain English. The form accepts a name, description, optional reference repository, and natural-language requirements.

Figure 2: Describing a custom transformation in plain English. The form accepts a name, description, optional reference repository, and natural-language requirements.After submitting, the orchestrator clones the reference repository, analyzes the source, and generates a transformation definition tailored to the actual code patterns found in the codebase. This takes 1–5 minutes depending on repository size. The generated definition is then shown for review in the Jobs tab, where it can be edited before publishing to the ATX registry.

Figure 3: The AI-generated transformation definition shown for review in the Jobs tab. The agent analyzed the Flask codebase and produced a detailed definition covering routes, request handling, response patterns, and Blueprint architecture. The user can edit the definition in-place and click Publish to Registry when ready.Once published, the new transformation appears in the Transformations tab alongside AWS-managed transformations and can be executed the same way on any repository.

Run a batch of repositories from a CSV

Open the CSV Batch tab and upload a CSV listing repository URLs and target transformations. A sample `sample-batch.csv` is included in the repository at `agentic-atx-platform/ui/sample-batch.csv`. The preview shows the parsed rows before submission. On Submit All, each row becomes a separate AWS Batch job running in parallel, and the Jobs tab shows live status as repositories complete.

Uploading a batch of repositories for parallel processing. The CSV lists a source repository URL, target transformation, optional validation commands, and additional plan context per row.

Figure 4: Uploading a batch of repositories for parallel processing. The CSV lists a source repository URL, target transformation, optional validation commands, and additional plan context per row. Each row becomes an independent AWS Batch job on submission.

Clean up

To avoid recurring charges, remove the resources after trying the solution.

Step 1: Delete the SAM Stack

sam delete --stack-name AtxAgentCoreSAM --region us-east-1 --no-prompts

Step 2: Delete the CDK Stacks

Remove the three CDK stacks in reverse order. The S3 buckets are configured with `autoDeleteObjects: true`, so CDK will empty them before deletion.

cd cdk

npx cdk destroy AtxUiStack AtxInfrastructureStack AtxContainerStack --force

Conclusion

In this post, you learned how to build a generative AI–powered, agentic system for application modernization that can analyze application repositories, determine required code changes, create missing transformations, and execute those transformations at scale. By combining AWS Transform Custom for transformation execution with Amazon Bedrock AgentCore for orchestration, and Strands Agents for multi-agent coordination and AWS Transform container solution for parallel processing, this approach demonstrates how intelligent automation can be applied to large-scale code transformation workflows.

This solution directly addresses the challenges of traditional modernization approaches. It reduces manual effort by automating repository analysis and transformation mapping, eliminates gaps in transformation coverage by dynamically generating reusable transformations, and significantly improves scalability through parallel execution using AWS Batch.

By introducing a centralized, agent-driven workflow with built-in observability and state management, organizations can achieve faster, more consistent, and governed modernization across large application portfolios. To get started, deploy the solution in your AWS environment, test it with a sample repository or batch workload, and extend it by creating custom transformations tailored to your applications. You can further integrate this approach into your CI/CD pipelines to enable continuous modernization and accelerate your cloud migration initiatives.


About the authors

Kanishk Mahajan is Principal – AI/ML with AWS Professional Services. In this role, he leads GenAI and agentic transformations for some of AWS largest customers in Telco and Media & Entertaintment.

Sandeep Batchu is a Senior Security Architect at Amazon Web Services, with extensive experience in software engineering, solutions architecture, and cybersecurity. Passionate about bridging business outcomes with technological innovation, Sandeep guides customers through their cloud and generative AI journey, helping them design and implement secure, scalable, and resilient architectures in the era of AI-driven transformation.

Venugopalan Vasudeven (Venu) is a Principal Specialist Solutions Architect at AWS, where he leads Agentic AI initiatives focused on AWS Transform. He helps customers adopt and scale AI-powered developer and modernization solutions to accelerate innovation and business outcomes.

Enable real-time mainframe analytics with Precisely Connect and Amazon S3

Post Syndicated from Supreet Padhi, Rochelle Grubbs original https://aws.amazon.com/blogs/big-data/enable-real-time-mainframe-analytics-with-precisely-connect-and-amazon-s3/

This is a guest post by Supreet Padhi, Technology Architect, Strategic Technologies, and Rochelle Grubbs, Senior Director, Solution Architect at Precisely in partnership with AWS.

Business leaders face a critical challenge to enable real-time analytics. Their most valuable data sits in mainframe systems that reliably process billions of transactions daily, but extracting value for modern analytics and AI remains complex and costly. Traditional mainframe-to-cloud integration approaches require multi-step replication with intermediary systems, creating operational overhead, latency, and data integrity risks. This complexity delays insights, increases infrastructure costs, limits agility, and blocks organizations from using AI and machine learning on their mainframe data.

Precisely, a global leader in data integrity with over 12,000 customers including 95 of the Fortune 100, has announced an expansion of its collaboration with AWS through new enhancements to Precisely Connect. Precisely is an AWS Data and Analytics ISV Competency and AWS Migration and Modernization ISV Competency partner. Precisely has service specializations in Amazon Redshift and Amazon Relational Database Service (Amazon RDS).

In Stream mainframe data to AWS in near-real time with Precisely and Amazon MSK, we showed you how to set up mainframe CDC and the AWS Mainframe Modernization – Data Replication for IBM z/OS Amazon Machine Image (AMI) available in AWS Marketplace. In this post, we discuss how you can use Precisely Connect to enable real-time, direct replication of mainframe data to Amazon Simple Storage Service (Amazon S3), and how your organization can extend this foundation using Amazon S3 Tables for advanced analytics.

Real-time mainframe data access

Organizations that can connect their mainframe environments with modern cloud platforms can gain advantages through improved agility, reduced operational costs, and enhanced analytics capabilities.For example, moving appropriate analytics and reporting workloads to the cloud can significantly reduce mainframe operational costs while maintaining performance and reliability. Real-time data access makes insights available within seconds rather than waiting for batch processing cycles, enabling faster responses to market changes and customer needs. Eliminating bulk data extracts and intermediary systems also reduces infrastructure and maintenance expenses. This frees IT resources to focus on higher-value initiatives.

However, implementing mainframe-to-cloud integrations presents unique technical challenges that require specialized solutions. These include converting mainframe character encoding (EBCDIC) to standard ASCII format and handling mainframe-specific data types such as packed decimal (COMP) fields. You also need to manage the complexity of VSAM (Virtual Storage Access Method) files that can store multiple record types in a single file, and maintain real-time synchronization without impacting mainframe performance.

Change Data Capture (CDC) technology addresses these challenges through incremental data movement that eliminates disruptive bulk extracts by streaming only changed data to cloud targets, minimizing system impact and ensuring data currency. Real-time synchronization keeps cloud applications in sync with mainframe systems, enabling immediate insights and responsive operations.

Precisely Connect: Real-time data replication to Amazon S3

With Precisely Connect, you can replicate data directly from mainframes to Amazon S3 in real time, eliminating the need for intermediaries and simplifying modernization.Data flows directly from mainframe sources, including Db2 z/OS, IMS, and VSAM, to Amazon S3, eliminating intermediary steps and reducing both latency and operational complexity. You can move mainframe data directly to Amazon S3 data lakes and analytics platforms without managing complex, multi-step replication processes.

The simplicity of this approach reduces maintenance overhead and integration complexity by removing the need for staging servers, middleware, or batch processing systems. After data lands in Amazon S3, it becomes immediately available for downstream AWS workloads. You can use Amazon Athena for SQL queries, AWS Glue for ETL and data cataloging, Amazon EMR for big data processing, Amazon SageMaker AI for machine learning, and Amazon Quick Sight for business intelligence dashboards.

Solution overview

Here we present a solution architecture for streaming mainframe data changes from Db2z through AWS Mainframe Modernization – Data Replication for IBM z/OS AMI directly to Amazon S3 and then using Amazon S3 Tables for advanced analytics capabilities.

By introducing direct S3 replication and streamlining deployment through the pre-configured AWS Marketplace AMI, you can deploy in minutes rather than weeks. This creates new possibilities for data distribution, transformation, and consumption. This architecture offers several key benefits:

  1. Simplified deployment – Accelerate implementation using the preconfigured AWS Marketplace AMI
  2. Direct replication – Eliminate intermediary systems by streaming data directly to Amazon S3, reducing latency and operational overhead
  3. Real-time synchronization – Capture changes as they occur on the mainframe, ensuring downstream applications operate on current data
  4. Flexible analytics options – Use S3 Tables for Iceberg-compatible tabular data storage
  5. Comprehensive AWS integration – Gain immediate access to Amazon EMR, Amazon Athena, AWS Glue, Amazon SageMaker AI, and Amazon Quick Sight
  6. Natural language data access – Through the MCP Server for Amazon S3 Tables, AI assistants can interact with structured data using conversational interfaces without needing to write SQL queries.

Prerequisites

To complete the solution, you need the following prerequisites:

Precisely components

  1. AWS Mainframe Modernization – Data Replication for IBM z/OS – Deploy this Precisely Connect AMI from AWS Marketplace. This pre-configured image contains the Apply Engine and Controller Daemon components required for replicating mainframe data changes to Amazon S3.
  2. Precisely Connect CDC Capture/Publisher – Deploy the Precisely Connect CDC Capture/Publisher on your mainframe environment. This component captures changes from Db2z logs and streams them to the Apply Engine over TCP/IP.

For detailed setup and configuration steps for Precisely components, refer to our previous post Stream mainframe data to AWS in near-real time with Precisely and Amazon MSK.

Connectivity requirements

  1. Have network connectivity established between your mainframe environment and AWS using your organization’s approved connectivity method (such as AWS Direct Connect or VPN).
  2. Verify that firewall rules allow TCP/IP communication between the mainframe Capture/Publisher and the Apply Engine.

AWS analytics components (optional extension)

After mainframe data lands in Amazon S3, your organization can extend its analytics capabilities using AWS services. One approach is to use Amazon EMR streaming jobs to process and write data to Amazon S3 Tables. After the data is stored in S3 Tables, the data can be queried directly using Amazon Athena for ad-hoc SQL analysis. This extension is optional and represents one of several ways to consume and analyze mainframe data after it reaches Amazon S3.

The following diagram illustrates the solution architecture.

image-BDB-5540-1-architecture

  1. Capture/Publisher – Connect CDC Capture/Publisher captures Db2 changes from Db2 logs using IFI 306 Read and communicates captured data changes to a target engine through TCP/IP.
  2. Controller Daemon – The Controller Daemon authenticates all connection requests, managing secure communication between the source and target environments.
  3. Apply Engine – The Apply Engine receives the changes from the Publisher agent and applies the changed data to the target Amazon S3.
  4. Amazon S3 – Serves as the scalable data lake foundation where replicated mainframe data lands.
  5. Amazon EMR streaming job – As data arrives, an instance of the Amazon EMR streaming job writes the data to target tables in Amazon S3 Tables.
  6. Amazon Athena – Queries data stored in Amazon S3 Tables using standard SQL.

This architecture provides a clean separation between the data capture process and the data consumption process, allowing each to scale independently. When CDC data arrives in Amazon S3, you can use Amazon S3 Tables to store Db2 z/OS, VSAM, and IMS data in an open table format (Apache Iceberg) that is ready for analytics, providing a flexible path to mainframe modernization.

Quantifiable business value

Organizations implementing this solution typically see significant reductions in mainframe operational costs by offloading analytics and reporting workloads to the cloud. The elimination of intermediary infrastructure reduces both capital and operational expenses. The reduced maintenance burden frees IT resources to focus on strategic initiatives rather than managing complex replication systems. Speed and agility improvements are equally significant. Near real-time data availability, measured in seconds to minutes rather than hours to days, enables organizations to respond rapidly to market changes and operational events. The rapid deployment of new analytics use cases without requiring mainframe changes accelerates innovation. Organizations gain access to the full breadth of AWS services that can be used immediately after data lands in Amazon S3.

From an analytics and AI perspective, the solution creates a unified data platform that brings together mainframe, cloud-native, and third-party data sources. This unified view enables advanced machine learning on historical and current data, delivering predictive insights that drive proactive decision-making across the organization.

Customer story

A leading global payments provider put this into practice. The payments provider was struggling to generate timely analytics and insights from Point of Sale (POS) transaction data. As one of the world’s largest payment providers, they process hundreds of thousands of transactions per second. Users expect to swipe their card and have their transaction approved in seconds. New architecture was needed to keep up with customer demands and volume. By streaming mission-critical mainframe data directly to AWS in real time using Precisely Connect and landing it in Amazon S3 Tables, the company used storage built on the Apache Iceberg open standard. This approach enables high-performance analytics directly on mainframe data alongside cloud-native sources.

Conclusion

In this post, we demonstrated how Precisely Connect enables real-time, direct data replication from mainframes to Amazon S3, eliminating intermediaries and simplifying mainframe modernization.

Your organization can further extend this foundation with Amazon S3 Tables, purpose-built storage for Apache Iceberg tables in S3, enabling analytical applications to query the most current mainframe data using tools such as Amazon Athena, Amazon EMR, and Amazon Redshift.

Get started by deploying AWS Mainframe Modernization – Data Replication for IBM z/OS from AWS Marketplace and use Amazon S3 as a target for your mainframe use cases. Learn more about Precisely’s mainframe data integration capabilities at precisely.com. Contact AWS and Precisely experts to discuss your specific modernization challenges and design a proof-of-concept that demonstrates business value quickly.


About the authors

image-BDB-5540-2

Supreet Padhi

Supreet is a Technology Architect at Precisely. He has been with Precisely for more than 14 years, with specialty in streaming data use cases and technology, with emphasis on data warehouse architecture. He is responsible for research and development in areas such as Change Data Capture (CDC), streaming ETL, metadata management, and VectorDBs.

image-BDB-5540-3

Rochelle Grubbs

Rochelle is a Senior Director and Solution Architect for Precisely’s Data Integration solutions and has been with Precisely for over 11 years. She has spent the last several years focusing on databases, analytics, data trends, data integration, and GenAI. Rochelle is an expert on Precisely’s OEM AWS Mainframe Migration offering and is driven to help customers successfully migrate their applications and workloads to the cloud.

image-BDB-5540-4

Tamara Astakhova

Tamara is a Sr. Partner Solutions Architect in Data and Analytics at AWS with over two decades of expertise in architecting and developing large-scale data analytics systems. In her current role, she collaborates with strategic partners to design and implement sophisticated AWS-optimized architectures. Her deep technical knowledge and experience make her an invaluable resource in helping organizations transform their data infrastructure and analytics capabilities.

Deloitte optimizes EKS environment provisioning and achieves 89% faster testing environments using Amazon EKS and vCluster

Post Syndicated from Samuel Lefki original https://aws.amazon.com/blogs/architecture/deloitte-optimizes-eks-environment-provisioning-and-achieves-89-faster-testing-environments-using-amazon-eks-and-vcluster/

Managing multiple Amazon Elastic Kubernetes Service (Amazon EKS) clusters for development and testing environments can present significant operational and cost challenges for enterprises. Deloitte, a global professional services organization, faced these challenges while provisioning dedicated Amazon EKS clusters for their quality assurance (QA) testing environments. In this post, we explore how Deloitte used Amazon EKS and vCluster to transform their testing infrastructure.

Business challenges

Before implementing vCluster, Deloitte provisioned dedicated Amazon EKS clusters on AWS for each ephemeral testing need. This approach could take up to 45 minutes per cluster. QA engineers required isolated environments to test specific combinations of application components, but they relied heavily on the platform team to provision and manage those clusters. Each environment also carried the overhead of its own ingress controllers, DNS setup, and monitoring agents, creating significant infrastructure duplication and operational load.

Key challenges included:

  • Slow provisioning times of 30-45 minutes for each new environment, including a dedicated Amazon EKS cluster, Application Load Balancers (ALB), Amazon Route 53 records
  • High AWS infrastructure costs from running multiple dedicated Amazon EKS clusters
  • Significant platform team overhead managing multiple environments
  • Resource duplication across clusters, such as load balancers, Route 53 entries, and monitoring agents
  • Complex access management across multiple AWS Identity and Access Management (AWS IAM) roles and Kubernetes Role-based access control (RBAC) configurations

These operational inefficiencies not only slowed down QA team development cycles but also increased costs and created bottlenecks that prevented teams from working independently.

Solution overview

To address these challenges, Deloitte implemented a solution combining Amazon EKS with vCluster. The Amazon EKS host cluster serves as the foundation, providing the underlying compute and networking resources. On top of this infrastructure, vCluster enables the creation of lightweight, fully functional virtual clusters that act like independent Kubernetes environments. This gives QA teams dedicated spaces for their work without the overhead of managing dozens of separate Amazon EKS clusters.

Essential platform services such as Kubernetes controllers and monitoring agents are deployed once on the host cluster and shared across all virtual clusters. This approach reduces resource duplication and streamlines management. With Amazon EKS Auto Mode, the solution also brings dynamic autoscaling, ensuring that compute resources are allocated just in time to meet demand, further optimizing costs.

Architecture overview

Figure 1: Architecture diagram illustrating users accessing applications hosted across multiple virtual clusters.

  1. Users access the applications over the public internet via HTTPS requests.
  2. Their connections are secured via HTTPS, which is terminated at the Application Load Balancer (ALB).
  3. The Application Load Balancer (ALB) directs users to the appropriate application based on predefined rules.
  4. Each application that users deploy runs in its own virtual cluster with dedicated Amazon Elastic Block Store (Amazon EBS) storage.

Key components:

  • Amazon EKS host cluster with Auto Mode enabled: The foundation of the solution, providing the underlying Kubernetes infrastructure
  • Virtual clusters (vCluster): Multiple isolated Kubernetes clusters running within the host cluster. Each virtual cluster represents an isolated testing environment for QA validation and application testing.
  • Shared controllers: These controllers run on the host cluster and are shared across all virtual clusters:
    • Load Balancer Controller: Manages the creation and configuration of load balancers for applications running in the virtual clusters
    • Storage Controller: Manages the creation of Amazon EBS volumes or Amazon Elastic File System (Amazon EFS) mount points, providing persistent storage for applications
  • Application Load Balancer (ALB): Fronts the host cluster nodes, distributing traffic and ensuring high availability
  • AWS Certificate Manager (ACM): An ACM certificate is attached to the ALB to terminate HTTPS connections and provide secure communication

Outcomes

Deloitte’s implementation of Amazon EKS with vCluster delivered measurable results. Environment provisioning time dropped from 45 minutes to under 5 minutes, representing an 89% reduction that translates to immediate productivity gains. The QA team has reclaimed around 500 hours annually, shifting focus from repetitive setup tasks to higher-value testing work. Infrastructure efficiency improved significantly through resource consolidation. By deploying workloads to a shared host cluster and enabling virtual clusters to share those resources, Deloitte saves over 50 vCPUs and more than 200 GB of memory at peak usage.

On the AWS front, consolidating to fewer Amazon EKS control planes reduced management overhead and costs. Cost optimization improved further, with up to 70% savings by running workloads on Amazon Elastic Compute Cloud (Amazon EC2) Spot Instances, with Amazon EKS Auto Mode providing efficient, automated autoscaling and provisioning. The architecture was further streamlined by implementing a single load balancer capable of serving traffic to applications across multiple virtual clusters. This reduced complexity and simplified monitoring and troubleshooting.

The vCluster itself proved transformative. Deloitte now runs more than 50 virtual clusters efficiently on a single shared Amazon EKS host cluster. Teams can now provision their own testing environments in under 5 minutes without platform team involvement, compared to submitting requests and waiting 30-45 minutes previously. Both QA and application teams now have faster access to the environments they need. Tooling complexity decreased significantly. Instead of managing more than ten separate tool deployments (reverse proxy, monitoring agents, controllers, etc.), teams now rely on a single shared stack that’s easier to maintain and operate. These improvements collectively position Deloitte with a more scalable, cost-effective, and manageable AWS environment that’s ready to grow with evolving business needs.

Walkthrough

This section provides a simplified overview of the solution, preserving the core elements implemented at Deloitte. It covers the deployment of two applications on separate virtual clusters, the ability to access the vCluster platform, and the configuration of both applications to operate under the same domain and load balancer.

Prerequisites

Before beginning the deployment, verify that the following resources are in place:

  • Amazon Virtual Private Cloud (Amazon VPC) and subnets: An Amazon VPC and the necessary subnets must be created
  • Amazon EKS Cluster: An Amazon EKS cluster should be set up within the designated Amazon VPC and subnets, with Auto Mode enabled
  • Service IPv4 range: The Amazon EKS service IPv4 range must be set to 10.96.0.0/12 (the service Classless Inter-Domain Routing (CIDR) range used by vCluster)
  • IAM roles: The following IAM roles must be created
  • Domain name: A domain name, along with the necessary certificate and DNS configuration, must be obtained
  • kubectl and Helm: Command-line tools for Kubernetes management

Deployment steps

The following walkthrough guides you through deploying the solution. You’ll start by creating and validating the required certificate, then deploy vCluster with Amazon EKS Auto Mode and configure the ALB. Next, you’ll access the vCluster console to create two virtual clusters. Finally, you’ll deploy an application in both virtual clusters and expose it through an Application Load Balancer using path-based routing.

Step 1: Create and validate certificate

export DOMAIN_NAME=<sub domain name to create>
export ZONE_ID=<domain zone ID>
export CERTIFICATE_ARN=$(aws acm request-certificate \
  --domain-name $DOMAIN_NAME \
  --validation-method DNS \
  --output text)

# Get certificate validation records
CERT_NAME=$(aws acm describe-certificate \
  --certificate-arn $CERTIFICATE_ARN \
  --query 'Certificate.DomainValidationOptions[0].ResourceRecord.Name' \
  --output text)

CERT_VALUE=$(aws acm describe-certificate \
  --certificate-arn $CERTIFICATE_ARN \
  --query 'Certificate.DomainValidationOptions[0].ResourceRecord.Value' \
  --output text)

# Create DNS validation record
aws route53 change-resource-record-sets \
  --hosted-zone-id $ZONE_ID \
  --change-batch '{
    "Changes": [{
      "Action": "CREATE",
      "ResourceRecordSet": {
        "Name": "'"$CERT_NAME"'",
        "Type": "CNAME",
        "TTL": 300,
        "ResourceRecords": [{"Value": "'"$CERT_VALUE"'"}]
      }
    }]
  }'

The expected outcome of the preceding commands is the creation and validation of a certificate in AWS Certificate Manager (ACM) within the specified region.

Step 2: Deploy vCluster with Amazon EKS Auto Mode and Application Load Balancer

The following command configures the ingress class for ALB provisioning and the storage class for Amazon EBS persistent volumes.

kubectl apply -f - <<EOF
apiVersion: eks.amazonaws.com/v1
kind: IngressClassParams
metadata:
  name: alb
spec:
  scheme: internet-facing
  group:
    name: vcluster
---
apiVersion: networking.k8s.io/v1
kind: IngressClass
metadata:
  name: alb
  annotations:
    ingressclass.kubernetes.io/is-default-class: "true"
spec:
  controller: eks.amazonaws.com/alb
  parameters:
    apiGroup: eks.amazonaws.com
    kind: IngressClassParams
    name: alb
---
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: auto-ebs-sc
  annotations:
    storageclass.kubernetes.io/is-default-class: "true"
provisioner: ebs.csi.eks.amazonaws.com
volumeBindingMode: WaitForFirstConsumer
parameters:
  type: gp3
  encrypted: "true"
EOF

Next, deploy the vCluster application:

helm repo add vcluster https://charts.loft.sh
helm upgrade --install vcluster-pro vcluster/vcluster-platform -n vcluster-poc --create-namespace --version 4.0.1 --values <(cat <<EOF
resources:
  limits:
    memory: 4Gi
  requests:
    cpu: "1"
    memory: 4Gi
replicaCount: 1
config:
  projectNamespacePrefix: loft-p-
  audit:
    enabled: true
  loftHost: <domain>
admin:
  create: true
  username: admin 
  password: password
ingress:
  enabled: true
  name: loft-ingress
  annotations:
    alb.ingress.kubernetes.io/subnets: <public subnets> # 2 public subnets minimum
    alb.ingress.kubernetes.io/certificate-arn: <ACM cert ARN>
    alb.ingress.kubernetes.io/scheme: internet-facing
    alb.ingress.kubernetes.io/load-balancer-name: vcluster-alb
  host: <domain>
  ingressClass: alb
  path: /*
  tls:
    enabled: false
    secret: loft-tls
affinity:
  nodeAffinity:
    preferredDuringSchedulingIgnoredDuringExecution:
      - weight: 1
        preference:
          matchExpressions:
            - key: eks.amazonaws.com/compute-type
              operator: In
              values:
                - auto
EOF
)

Note: Replace admin and password in the preceding Helm chart installation with custom username and password as appropriate.

After you have provisioned the load balancer, create an alias for the Application Load Balancer:

export ALB_NAME=vcluster-alb

# Get ALB details
ALB_HOSTED_ZONE=$(aws elbv2 describe-load-balancers \
  --names $ALB_NAME \
  --query 'LoadBalancers[0].CanonicalHostedZoneId' \
  --output text)

ALB_DNS_NAME=$(aws elbv2 describe-load-balancers \
  --names $ALB_NAME \
  --query 'LoadBalancers[0].DNSName' \
  --output text)

# Create Route 53 alias record
aws route53 change-resource-record-sets \
  --hosted-zone-id $ZONE_ID \
  --change-batch '{
    "Changes": [{
      "Action": "CREATE",
      "ResourceRecordSet": {
        "Name": "'"$DOMAIN_NAME"'",
        "Type": "A",
        "AliasTarget": {
          "HostedZoneId": "'"$ALB_HOSTED_ZONE"'",
          "DNSName": "'"$ALB_DNS_NAME"'",
          "EvaluateTargetHealth": false
        }
      }
    }]
  }'

The expected outcome is the successful deployment of the vCluster platform on the Amazon EKS cluster, accessible via the load balancer and domain provisioned in the previous steps.

Figure 2: vCluster environment login page.

Step 3: Access the vCluster console and create virtual clusters

  1. Open a web browser and navigate to the vCluster domain set up earlier
  2. Log in with the following credentials:
    • Username: admin
    • Password: password
  3. On first login, complete the setup questions to start the 13-day vCluster platform trial by providing:
    • Name
    • Email address
    • Company name

Step 4: Create two virtual clusters through the console

Using the vCluster UI, create a new virtual cluster and select the “Deploy with vCluster platform (default)” option. Replace the content of the vcluster.yaml file with the following configuration:

sync:
  fromHost:
    ingressClasses:
      enabled: true
    storageClasses:
      enabled: true
  toHost:
    ingresses:
      enabled: true
controlPlane:
  coredns:
    enabled: true
    embedded: true

This configuration enables synchronization between the host cluster and the virtual cluster for both ingress classes and storage classes, making host cluster resources available within the virtual cluster. It also synchronizes ingress resources from the virtual cluster back to the host cluster. The coredns section configures the virtual cluster control plane to deploy a DNS management pod, supporting DNS resolution for applications within the virtual cluster. After adding the configuration, create the cluster. After you have created the cluster and it is healthy, repeat the process to create the second cluster.

Figure 3: vCluster environment with two newly created virtual clusters.

Step 5: Deploy applications

After both clusters are up and running:

  1. Select and connect to the virtual cluster as shown in the following figure.

Figure 4: vCluster main page and connect option to a single virtual cluster.

  1. Download the kubeconfig file and use it to connect to the cluster.

Figure 5: Connect to virtual cluster menu.

  1. After you have established access to the newly created virtual cluster via kubectl, run the following command to deploy the application to the cluster.
kubectl apply -f - <<EOF
apiVersion: v1
kind: Namespace
metadata:
  name: echoserver
---
apiVersion: v1
kind: Service
metadata:
  name: echoserver
  namespace: echoserver
spec:
  ports:
    - port: 80
      targetPort: 8080
      protocol: TCP
  type: NodePort
  selector:
    app: echoserver
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: echoserver
  namespace: echoserver
spec:
  selector:
    matchLabels:
      app: echoserver
  replicas: 1
  template:
    metadata:
      labels:
        app: echoserver
    spec:
      containers:
      - image: registry.k8s.io/e2e-test-images/echoserver:2.5
        imagePullPolicy: Always
        name: echoserver
        ports:
        - containerPort: 8080
        volumeMounts:
        - name: ebs-volume
          mountPath: /mnt/data
      volumes:
      - name: ebs-volume
        persistentVolumeClaim:
          claimName: ebs-claim
---
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: echoserver
  namespace: echoserver
  annotations:
    alb.ingress.kubernetes.io/load-balancer-name: vcluster-alb
    alb.ingress.kubernetes.io/subnets: <subnet ids>
    alb.ingress.kubernetes.io/certificate-arn: <certificate arn>
    alb.ingress.kubernetes.io/scheme: internet-facing
    alb.ingress.kubernetes.io/group.order: "-999"
spec:
  ingressClassName: alb
  rules:
    - host: <domain name>
      http:
        paths:
          - path: /<app name>
            pathType: ImplementationSpecific
            backend:
              service:
                name: echoserver
                port:
                  number: 80
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: ebs-claim
  namespace: echoserver
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 1Gi
  storageClassName: auto-ebs-sc
EOF

This manifest creates the following:

  • Namespace: Creates a new namespace called echoserver
  • Service: Creates a service named echoserver within the echoserver namespace
  • Deployment: Creates a deployment named echoserver in the echoserver namespace
  • Ingress: Creates an ingress resource that routes traffic to the echoserver service on port 80 via internet-facing load balancer (for testing purposes)
  • Persistent Volume Claim (PVC): Requests storage resources for the application

Note: Replace placeholders in the YAML file with the appropriate values.

Repeat the preceding steps on the second virtual cluster. Upon resource deployment, Amazon EKS adds new path-based rules to the ALB that match the deployed Ingress objects, exposing the applications.

Figure 6: Application Load Balancer rules illustrating rules from different applications through the ingress object.

Step 6: Validate the implementation

To verify that the virtual cluster setup is working correctly, perform the following validation checks:

  • Cluster deployment verification: Verify the successful deployment of both App1 and App2 in their respective virtual clusters. Validate pod status and service functionality
  • Application accessibility testing: Test that both applications are properly exposed through the single Application Load Balancer:
    • App1: https://<domain-name>/app1
    • App2: https://<domain-name>/app2

Clean up

To avoid incurring unnecessary charges, remove the deployed resources when they’re no longer needed. Start by uninstalling the vCluster Helm release, then delete the Amazon Route 53 DNS records and AWS Certificate Manager certificate:

# Uninstall vCluster
helm uninstall vcluster-pro

# Set these variables if running in a new terminal session
export DOMAIN_NAME=<your domain name>
export ZONE_ID=<your zone ID>
export ALB_NAME=vcluster-alb

# Look up certificate ARN by domain name
export CERTIFICATE_ARN=$(aws acm list-certificates \
  --query "CertificateSummaryList[?DomainName=='$DOMAIN_NAME'].CertificateArn" \
  --output text)

# Get ALB details
ALB_HOSTED_ZONE=$(aws elbv2 describe-load-balancers \
  --names $ALB_NAME \
  --query 'LoadBalancers[0].CanonicalHostedZoneId' \
  --output text)

ALB_DNS_NAME=$(aws elbv2 describe-load-balancers \
  --names $ALB_NAME \
  --query 'LoadBalancers[0].DNSName' \
  --output text)

# Get certificate validation records
CERT_NAME=$(aws acm describe-certificate \
  --certificate-arn $CERTIFICATE_ARN \
  --query 'Certificate.DomainValidationOptions[0].ResourceRecord.Name' \
  --output text)

CERT_VALUE=$(aws acm describe-certificate \
  --certificate-arn $CERTIFICATE_ARN \
  --query 'Certificate.DomainValidationOptions[0].ResourceRecord.Value' \
  --output text)

# Delete Route 53 alias record
aws route53 change-resource-record-sets \
  --hosted-zone-id $ZONE_ID \
  --change-batch '{
    "Changes": [{
      "Action": "DELETE",
      "ResourceRecordSet": {
        "Name": "'"$DOMAIN_NAME"'",
        "Type": "A",
        "AliasTarget": {
          "HostedZoneId": "'"$ALB_HOSTED_ZONE"'",
          "DNSName": "'"$ALB_DNS_NAME"'",
          "EvaluateTargetHealth": false
        }
      }
    }]
  }'

# Delete Route 53 validation record
aws route53 change-resource-record-sets \
  --hosted-zone-id $ZONE_ID \
  --change-batch '{
    "Changes": [{
      "Action": "DELETE",
      "ResourceRecordSet": {
        "Name": "'"$CERT_NAME"'",
        "Type": "CNAME",
        "TTL": 300,
        "ResourceRecords": [{"Value": "'"$CERT_VALUE"'"}]
      }
    }]
  }'

# Delete ACM certificate
aws acm delete-certificate --certificate-arn $CERTIFICATE_ARN

Conclusion

In this post, we showed how Deloitte used Amazon EKS with vCluster to reduce environment provisioning time by 89%, reclaim 500 hours annually, and cut infrastructure costs through resource consolidation. Ready to transform your development and testing infrastructure? Start by evaluating your current environment provisioning process and identifying opportunities to consolidate workloads using Amazon EKS with vCluster. Whether you’re looking to reduce setup times from hours to minutes, empower your teams with self-service capabilities, or optimize AWS costs through resource consolidation, this solution provides a proven path forward.

Visit the Amazon EKS documentation to learn more about Auto Mode and explore how virtual clusters can help your organization achieve similar gains in speed, efficiency, and operational agility. If you have questions or feedback about this post, leave a comment in the comments section.


About the authors

AWS Transform custom: Enterprise Code Modernization with the Learn-Scale-Improve Flywheel

Post Syndicated from Venugopalan Vasudevan original https://aws.amazon.com/blogs/devops/aws-transform-custom-enterprise-code-modernization-with-the-learn-scale-improve-flywheel/

Enterprise modernization has reached an inflection point. You can transform one repository easily. Existing tools, including AWS Transform custom, work well for individual repositories, and the process is understood. But what about 50 repositories? 100? 200? When you need to modernize at enterprise scale, transforming code is only part of the challenge. Coordinating people, capturing knowledge, and maintaining quality across your entire portfolio are also important.

In this post, we explore how AWS Transform custom’s bulk automation capabilities address the enterprise coordination problem through intelligent learning and scaled execution. You will see how one customer reduced end-to-end modernization timelines from 7-12 weeks to 2.5 weeks, delivering a 3-5x reduction in delivery time and 10-20x reduction in total effort hours. Most importantly, you will learn how to start your own transformation journey immediately.

The Coordination Problem at Enterprise Scale

Ask any enterprise architect about their last major modernization initiative, and you will hear familiar stories. As an example, an enterprise software company needed to migrate a large legacy codebase to a modern platform. Their projection: 12 weeks of intensive work coordinating across multiple teams.

The code transformation itself took days. The remaining weeks were consumed by the end-to-end activities surrounding it: orchestrating teams across time zones, ensuring consistent patterns across codebases with different histories, and managing dependencies so upstream changes did not break downstream systems. Teams tracked status through meetings and spreadsheets and captured tribal knowledge that existed only in senior developer heads.

This is the enterprise coordination problem. When you scale from one repository to hundreds, coordination overhead explodes. Each additional repository adds not just its own complexity, but new integration points, edge cases, and unanticipated coordination requirements.

The Hidden 70% Gap

In enterprise engagements, we have observed that code transformation represents approximately 30% of the modernization effort. The remaining 70% include things like test generation, validation, comprehensive documentation, business analysis, and organizational coordination across hundreds of moving pieces.

This gap explains why productivity gains from transformation tools rarely materialize. The tools handle code changes, but organizations still struggle with coordination, validation, and knowledge capture. The transformation is completed quickly, but the project takes months.

Here is what we see: traditional approaches fail at enterprise scale because they treat each repository as an independent challenge. Teams repeat work across codebases, make inconsistent decisions, and lose learnings when developers move to different projects. Organizational knowledge remains trapped in individual heads rather than becoming reusable assets.

A New Approach to Enterprise Modernization

AWS Transform custom takes a different approach to enterprise modernization. Rather than repeating the same operation hundreds of times, the service learns from every execution and applies that knowledge to improve future transformations.

The Learn-Scale-Improve Flywheel

The workflow follows a deliberate progression designed to maximize learning while minimizing risk. It begins with a focused learn pilot, scales through bulk automation, and improves through deliberate review, creating a flywheel where each cycle produces better results than the last (Figure 1).

Iterative transformation workflow with three stages: LEARN (interactive pilot, refine TD), SCALE (bulk execution, overnight processing), and IMPROVE (review and approve knowledge items). Arrows show the cycle: org knowledge captured flows from Learn to Scale, edge cases observed flow from Scale to Improve, and TD improves flows from Improve back to Learn.

Figure 1: Learn Scale and Improve Flywheel for AWS Transform custom transformation

Learn — You start with two to three representative repositories and execute transformations in interactive mode. You work directly with the AI agent, providing feedback on decisions and validating quality at each step. When the agent encounters ambiguity, it asks questions. You provide guidance, and the system captures that context. At the end of the pilot, you review the feedback and modify the transformation definition. The result is a transformation definition enhanced with your organizational knowledge, ready to scale.

Scale — You shift to non-interactive mode for bulk execution. The system processes dozens or hundreds of repositories overnight without manual intervention, applying patterns learned during the pilot. It validates transformations using your build and test commands and tracks progress across your portfolio in real time. What previously required weeks of team coordination happens overnight. During execution, the system captures observations: new edge cases, unexpected patterns, and optimization opportunities the pilot did not encounter.

Improve — After each round of bulk execution, you review the knowledge items the system captured during processing. These observations surface patterns and edge cases specific to repositories the pilot did not cover. You approve the valuable learnings, and your transformation definition improves for the next iteration. This review step ensures quality control. The system does not self-modify. Transformation owners decide which learnings get incorporated.

The Scale-Improve cycle repeats. Each round of bulk execution generates insights that make the next round more effective. Transformation success rates increase, manual intervention decreases, and edge case handling improves with every iteration.

This flywheel transforms how enterprises capture and share institutional knowledge. Transformation definitions are not automation scripts. They are organizational assets that encode how your company approaches specific modernization scenarios. When an architect defines a transformation strategy, that strategy becomes a reusable definition stored in your registry. When your team identifies best practices, those practices become embedded within the transformation definition and automatically apply across all repositories. Previously, when a senior developer left your team, that knowledge disappears with them. With AWS Transform custom, their expertise is captured in transformation definitions and knowledge items available to the entire organization. Individual expertise becomes an organizational capability.

An Enterprise Customer Modernization Case Study

These productivity gains are production outcomes, not theoretical projections. An enterprise software company needed to migrate a large volume of production-grade Control-M workflows to Apache Airflow, a modernization requiring both technical precision and consistency across a complex, interdependent codebase. Their estimate was 12 weeks of intensive coordination across multiple teams, with risk of inconsistency and integration failures.

Using AWS Transform custom, the company executed an iterative learn-scale-improve workflow. During the pilot phase, they ran interactive transformations on representative repositories, reviewed results, and refined transformation definitions. With each iteration, transformation definitions improved in edge case handling and accuracy. They then shifted to non-interactive bulk execution across their portfolio and completed the full migration in 2.5 weeks.

The validation achieved a 100% success rate across all workflows in scope. Edge case handling improved by 60% compared to the customer’s existing approach, and the transformed code demonstrated a 19% runtime performance improvement while meeting industry expert code quality standards. This proves that organizations can achieve both migration speed and production readiness, with 3-5x faster delivery timelines and 10-20x reduction in total effort hours compared to traditional approaches.

Get Started: Transform Your Repository Portfolio

AWS Transform custom bulk automation capabilities are available as a solution in this Github repo. Follow the learn-scale-improve workflow to begin your transformation journey.

Prerequisites

Before beginning, ensure you have:

  • An AWS account with AWS Transform custom access enabled
  • AWS CLI configured with appropriate credentials
  • Git installed on your local machine or CI/CD environment
  • IAM permissions for AWS Transform custom operations

Your Implementation Path

AWS Transform custom supports Java upgrades (e.g., 8 to 17, 17 to 21), Python migrations (e.g., 3.7 to 3.11), Node.js updates (e.g., 14 to 20), AWS SDK migrations (e.g., boto2 to boto3, SDK v1 to v2), and other transformations. Beyond these AWS-managed transformations, you can create custom transformation definitions for organization-specific standards, proprietary framework migrations, and architectural patterns unique to your environment.

AWS Transform custom integrates naturally into your existing development processes. The CLI connects with CI/CD pipelines like Jenkins, GitLab CI, or GitHub Actions. Transformations create code in local Git branches that flow through your standard code review and merge processes. The web interface provides centralized visibility for tracking progress across teams. Validation commands execute automatically during transformation, ensuring code builds successfully and tests pass before changes are considered complete. At the end of the transformation, if validation criteria fail, the transformation is marked as failed.

To accelerate your path to scaled execution, AWS provides an open-source sample repository that gives you a production-ready starting point for running transformations across multiple repositories and transformation definitions simultaneously. The aws-transform-custom-samples scaled execution repository includes scripts that orchestrate bulk execution, manage repository queuing, and handle status tracking across your portfolio. Rather than building orchestration from scratch, you clone the sample, configure it with your repository list and transformation definitions, and begin executing scaled transformations immediately.

Conclusion

Enterprise modernization at scale requires more than code transformation tools. The real challenges are coordination across teams, learning from execution, and capturing knowledge as organizational assets. AWS Transform custom learn-scale-improve workflow addresses these challenges through continual learning that improves quality with every execution, organizational knowledge capture that transforms tribal expertise into reusable assets, and bulk automation that scales consistently across hundreds of repositories. When the next critical security vulnerability requires framework updates across your repositories, or a new runtime version unlocks performance improvements, you respond in days rather than months — using transformation definitions you have already proven.

Real customers have reduced delivery timelines by 3-5x and total effort hours by 10-20x, compressing modernization from months to weeks. These are not aspirational goals. They are production results from organizations using AWS Transform custom today.

Begin Your Transformation Today

Follow the learn-scale-improve workflow on two to three representative repositories, refine your transformation definitions, then scale across your portfolio.
To dive deeper into AWS Transform custom bulk automation capabilities, explore these resources:

      • AWS Transform custom Documentation — Technical documentation covering all capabilities, API references, and integration guides: AWS Transform custom
      • Scaled Execution Sample Repository — Open-source scripts for running transformations across multiple repositories and transformation definitions: aws-transform-custom-samples
      • Transformation Registry — Discover AWS-managed transformations and create custom definitions: aws-transform-custom-samples

Contact your AWS account team or visit the AWS Transform custom documentation to begin your journey.

 


 

About the authors

meghan-author

Meghan Kothari

Meghan Kothari is a Senior Technical Product Manager with the Customer Experience and Business Trends team, where he partners with AWS leadership on strategic deep dives to discover evolving trends in agentic AI-driven application development and modernization. His background as a solutions architect and full-stack developer gives him a unique hands-on perspective to help shape the developer experience. 

Venu-author

Venugopalan Vasudevan

Venugopalan Vasudevan (Venu) is a Principal Specialist Solutions Architect at AWS, where he leads Agentic AI initiatives focused on AWS Transform. He helps customers adopt and scale AI-powered developer and modernization solutions to accelerate innovation and business outcomes.

grilli-author

Rodney Grilli

Rodney Grilli is a Principal Technologist at AWS, specializing in product and code modernization using agentic AI services. He builds solutions that help customers modernize their product portfolios and accelerate their transformations into AI-Native Enterprises.

Real-time analytics: Oldcastle integrates Infor with Amazon Aurora and Amazon Quick Sight

Post Syndicated from Avdhesh Paliwal original https://aws.amazon.com/blogs/architecture/real-time-analytics-oldcastle-integrates-infor-with-amazon-aurora-and-amazon-quick-sight/

This post is cowritten with Avdhesh Paliwal from Oldcastle.

Oldcastle APG is one of the largest suppliers of construction materials in North America, including asphalt and concrete. The company also provides construction and paving services across more than 150 facilities. As the company migrated from on-premises systems to Infor Cloud ERP hosted on Amazon Web Services (AWS), they faced a critical challenge: maintaining the real-time operational reporting capabilities that hundreds of users across customer service, finance, logistics, and manufacturing depended on daily.

This post explores how Oldcastle used AWS services to transform their analytics and AI capabilities by integrating Infor ERP with Amazon Aurora and Amazon Quick Sight. We discuss how they overcame the limitations of traditional cloud ERP reporting to deploy real-time dashboards and build a scalable analytics system. This practical, enterprise-grade approach offers a blueprint that organizations can adapt when extending ERP capabilities with cloud-native analytics and AI.

Challenges with cloud ERP reporting

The primary challenge that we faced was finding a solution that could accomplish the following:

  • Maintain real-time data access – Our on-premises environment supported hundreds of complex real-time reports, but Infor ERP Cloud’s configuration-based reporting covered minimal reports of our operational needs.
  • Support complex reporting requirements – Users needed multi-dimensional analysis across customer service, finance, logistics, and manufacturing functions.
  • Provide seamless user experience – Business users demanded integrated reporting within the ERP network without switching between multiple systems.
  • Enable advanced analytics –We needed capabilities for demand forecasting, machine learning (ML) capabilities, and intelligent search across real-time data.
  • Scale efficiently – The solution needed to support over 100 concurrent users and process millions of transactions while maintaining performance.
  • Expose Data using API – The solution needed to expose data through APIs, allowing both external and internal applications to access and consume the data securely and efficiently.

Our existing batch reporting process created significant operational challenges across our organization. We had to wait for batch reports, which consumed valuable time and led to delays in critical decision-making across many of our teams. This lag prevented us from capitalizing on real-time business insights and responding quickly to operational issues or economic changes. Without immediate data visibility, our managers couldn’t make timely, data-driven decisions, resulting in missed opportunities for improvement and competitive advantage. Our IT team also struggled with constant report requests but lacked a scalable system to deliver them efficiently, further compounding the productivity loss across our organization.

Solution overview

AWS Solutions Architects worked closely with our application team to build a comprehensive analytics and AI solution to address these challenges. The architecture uses Infor Data Fabric Stream Pipelines to deliver real-time data to AWS. It powers operational dashboards, artificial intelligence and machine learning (AI/ML) models, and intelligent search capabilities. This approach aligns with Infor’s broader strategy of integrating ERP data, data lake information, machine learning (ML) predictions, and documentation to provide comprehensive end-to-end business solutions.

Real-time data streaming architecture

The foundation of our solution is Infor’s Data Fabric Stream Pipelines, an add-on feature that provides real-time streaming data processing. When data events are ingested into Data Fabric, Stream Pipelines processes them immediately and continuously without waiting for storage in the data lake. This approach minimizes the data journey and accelerates operations, helping us extract insights from our data in real time.

The end-to-end workflow consists of the following components:

Data ingestion – Infor Data Fabric tables stream changes in real-time. We enabled Stream Pipelines as an add-on feature within our Infor Cloud ERP environment. We Configure the specific ERP table that we want to stream (such as sales orders, inventory, financial transactions) to publish change events immediately upon data modification. Stream Pipelines captures insert, update, and delete operations with metadata about the operation type and timestamp.

Load distribution – Because Infor can’t reach our private VPC directly, we use Elastic Load Balancing (ELB) to distribute traffic and provide secure database access. We implemented a Network Load Balancer (NLB) with static Elastic IP addresses in public subnets, giving us stable, allowlisted IP addresses for Infor’s outbound connections. We configured an Amazon Relational Database Service (Amazon RDS) router with Amazon Elastic Compute Cloud (Amazon EC2) instances as NLB targets. These routers forward traffic from the NLB to our Amazon Aurora database in the private subnet using iptables NAT rules. This makes sure that even if the IP of Aurora changes during failover, our static Elastic IPs remain constant. We configured security groups to accept HTTPS traffic (port 443) only from Infor’s IP ranges on the NLB and allow traffic only from the NLB to the RDS routers.

Connection management – We use Amazon RDS Proxy to manage database connections and provide automatic failover. We deployed RDS Proxy in the private subnet between our RDS router instances and Aurora cluster to pool and reuse connections. This is critical for handling our high-frequency streaming data. We configured the proxy with IAM authentication for secure credentials and set connection pool parameters based on our expected concurrent stream volume to handle burst traffic without overwhelming the database. With automatic failover enabled, if our primary Aurora instance fails, RDS Proxy automatically redirects traffic to the promoted replica, maintaining continuous data flow.

Data storage – We store our operational data in Amazon Aurora PostgreSQL- Compatible Edition with multi-Availability Zone deployment for high availability. We provisioned an Aurora PostgreSQL cluster with one writer instance and multiple reader instances across different Availability Zones. We designed our database schema to handle the incoming streaming data, storing it in JSONB columns for flexible querying while using the native JSON functions of Aurora PostgreSQL when we need to parse and normalize specific fields. We created indexes on frequently queried fields to maintain query performance as our data volume grows. We also configured automated backups with point-in-time recovery and set up the automatic storage of Aurora scaling to accommodate our data growth.

Analytics and visualization – Amazon Quick Sight delivers the interactive dashboards and pixel-perfect reports our teams need. We created a Quick Sight account and established a connection to our Aurora PostgreSQL database using VPC connectivity with credentials stored in AWS Secrets Manager. We identified which datasets benefit from SPICE (Super-fast, Parallel, In-memory Calculation Engine) caching—typically aggregated or frequently accessed data—and configured incremental refresh schedules to keep them current. We built our dashboards using the visual interface of Quick Sight, using calculated fields for business logic, parameters for user interactivity, and row-level security rules to make sure that users only see data that they’re authorized to access. For pixel-perfect reports, we use the pixel-perfect report feature of Quick Sight to create formatted documents suitable for printing or regulatory compliance.

Embedded integration – We securely embedded Amazon Quick Sight dashboards within Infor OS through Amazon API Gateway, which generates dynamic URLs for seamless user access. We enabled Quick Sight embedding in our AWS account and registered our Infor domain. We created an API Gateway REST API with Lambda functions that authenticate users, validate Infor session tokens, and call QuickSight’s GenerateEmbedUrlForRegisteredUser API to produce time-limited, signed URLs with row-level security. Our Lambda function maps Infor user roles to Quick Sight permissions and applies dashboard filters based on the user’s organizational context. We configured CORS settings in API Gateway to allow requests from our Infor domain and implemented rate limiting. On the Infor side, we embedded the Quick Sight dashboards using iframe elements that call our API Gateway endpoint, providing a seamless experience where our users access analytics without leaving the ERP interface.

The following diagram illustrates the real-time analytics architecture:

Architecture diagram showing Infor ERP data flowing via Postgres streaming through Amazon Route 53, a Network Load Balancer, and RDS Proxy to Amazon Aurora PostgreSQL inside a VPC, with Amazon QuickSight for visualization and Amazon API Gateway plus Lambda generating embedded dashboard URLs for a reporting application. Amazon CloudWatch and IAM provide monitoring and access control.

This embedded experience aligns with Infor’s broader system strategy of integrating insights seamlessly into workflows.

Results and business impact

The implementation using this architecture on AWS brought substantial benefits, directly addressing the critical challenges that we faced and demonstrating measurable value in employee productivity and core business process optimization.

Business process improvement

The solution successfully addressed Oldcastle’s key operational challenges:

Challenge: Limited visibility into real-time operations

– Solution delivered: Deployed over 50 complex dashboards and reports in eight months, providing immediate visibility across customer service, finance, logistics, and manufacturing.- Technical achievement: Used Infor Data Fabric Stream Pipelines to process data events immediately upon ingestion, alleviating delays from traditional batch processing.- Impact: Real-time streaming architecture using the NDJSON format makes sure decision-makers have access to current operational data when they need it most.

Challenge: Fragmented user experience requiring multiple systems

– Solution delivered: Dashboards embedded directly into the Infor environment through Amazon API Gateway.- Technical achievement: Generated dynamic URLs for secure embedding with single sign-on capabilities.- Impact: Users access insights without leaving their familiar interface, with personalized views based on roles and permissions that maintain context across the application.

Screenshot of an Amazon QuickSight dashboard embedded in Infor M3, showing a Cash Desk Reconciliation report with a transaction table on the left, pie charts breaking down payment types (Visa, Amex, Mastercard, Cash) by cash desk location, and a detailed transaction grid below with customer order numbers, invoice dates, and payment amounts.

Challenge: Inflexible reporting limiting business agility

– Solution delivered: Both interactive dashboards and pixel-perfect reports are available to meet diverse business needs.- Technical achievement: Quick Sight SPICE caching enables subsecond response times on complex analytics across large datasets.- Impact: On-demand access for immediate insights, scheduled distribution, custom formatting aligned with corporate standards, and multiple export formats (PDF, CSV, Excel).

Screenshot of a JIT Report embedded in an ERP application showing two sections: Demand By Day and Projected On Hand by Day. Each section displays a grid of inventory items with daily quantity columns spanning multiple weeks, with backorder and on-hand counts highlighted in blue for items requiring attention.

Challenge: Delayed decision-making due to outdated data

– Solution delivered: Real-time visibility into operations enabled faster, data-driven decisions.- Technical achievement: The Multi-AZ deployment of Amazon Aurora PostgreSQL maintains high availability and continuous data access.- Impact: Decision-makers can respond immediately to operational issues and economic changes with current, reliable data.

Scalability and performance

The architecture delivered exceptional scale and performance, addressing concerns about future growth: – High concurrency: Supports over 100 concurrent users without performance degradation- Data volume handling: Processes millions of transactions daily in real-time- Elastic scaling: Aurora read replicas automatically scale based on demand- Future-ready: Architecture designed to expand to additional regions and use cases- Cost efficiency: AWS services avoided complex third-party integrations, with infrastructure costs scaling efficiently with business growth- API capabilities: Ability to expose data using AWS technologies enables integration with third-party and internal applications

Conclusion

Our journey proves that cloud ERP migrations don’t require sacrificing real-time operational reporting capabilities. By combining Infor Data Fabric Stream Pipelines with AWS analytics and AI services, we’ve maintained real-time data access, accelerated innovation, improved user experience, and built a system that scales efficiently as our business needs evolve. The combination of Infor’s enterprise-grade ERP system with the comprehensive analytics capabilities of AWS has given us the best of both worlds. As we expand our AWS analytics and AI capabilities, we’re not just maintaining parity with on-premises systems, we’re unlocking new sources of business value that weren’t possible before.

Further reading

For more information on the services mentioned in the post, see the following resources:

AWS services:

  • Amazon Aurora PostgreSQL Features – Learn more about the high-availability database that powers Oldcastle’s real-time data storage and Multi-AZ deployment strategy
  • Amazon Quick Sight Embedded Analytics – Explore how to embed interactive dashboards and pixel-perfect reports directly into your enterprise applications, as demonstrated in Oldcastle’s Infor OS integration
  • Amazon Bedrock for Generative AI – Discover opportunities to enhance your analytics system with AI-powered insights and intelligent search capabilities
  • Elastic Load Balancing – Understand how to distribute traffic and secure database connections when integrating cloud ERP systems with AWS services
  • Amazon API Gateway – Learn how to create secure, dynamic URLs for embedding analytics and exposing data through APIs to internal and external applications

Infor Resources:


About the authors

Enhancing Identity Intelligence with Babel Street Match and Amazon OpenSearch

Post Syndicated from Kunal Sharma original https://aws.amazon.com/blogs/big-data/enhancing-identity-intelligence-with-babel-street-match-and-amazon-opensearch/

This post is co-authored with Gil Irizarry, Mae Wells-Kress and Craig Harmon from Babel Street. 

Can your system tell “John Smith” apart from “John Smith”?

Organizations requiring identity intelligence increasingly face challenges due to complexity of matching names and entities across vast, multilingual, and constantly evolving datasets. Whether helping border security, combating financial crimes, or maintaining regulatory compliance, the accuracy of identity and entity resolution directly determines whether threats are detected, investigations succeed, and regulatory requirements are met. Yet, linguistic diversity, transliterations, inconsistent data formats, and legacy system limitations continue to create friction, leading to false positives, missed matches, and costly manual reviews. As customers ingest and analyze petabytes of unstructured and structured data in Amazon OpenSearch Service, the need for intelligent, scalable, and multilingual matching becomes increasingly important. This is where the integration of Babel Street (an AWS Partner) with OpenSearch Service provides a solution that helps organizations enhance precision, reduce noise, and accelerate insights from their high-volume data environments.

This post explores how combining Babel Street Match with OpenSearch Service provides a solution that helps your organization to handle large-scale, multilingual data.

The growing complexity of identity and entity resolution

As organizations ingest and analyze massive volumes of multilingual and inconsistently formatted data, accurately matching names and entities becomes increasingly difficult. Variations in spelling, transliterations, semantic differences, cultural naming conventions, and incomplete or noisy records can contribute to mismatches. These challenges are compounded by legacy systems, fragmented data pipelines, operational inefficiencies, and evolving regulatory requirements—especially in sectors where precision is a requirement.

Evaluating and enhancing identity in high-volume enterprise environments

Amazon OpenSearch Service is a fully managed, scalable search and analytics service that enables organizations to ingest, search, visualize, and analyze massive volumes of data in near real time. Built to handle structured and unstructured information from diverse sources, it powers use cases ranging from security analytics and log monitoring to enterprise search and advanced analytical applications.

Babel Street delivers risk intelligence trusted by organizations across government, defense, and the private sector. The offering combines access to vast volumes of multilingual data with advanced analytics to uncover hidden identities, secure vendor networks, and identify emerging risks with precision, speed, and scale. From national security to regulatory compliance and enterprise resilience, Babel Street provides the strategic advantage needed to stay ahead of risk, safeguard operations, and protect missions.

Babel Street Match, an offering from Babel Street incorporates advanced identity risk intelligence capabilities, which enhance the precision and reliability of screening processes. This advanced solution uses sophisticated matching techniques to verify identities and identify variations in personal data—including aliases, alternate spellings, and differences in biographical details, helping organizations separate legitimate individuals from potential threats. The ability to screen names, addresses, dates, and other identifiers across different scripts and languages helps reduce false positives and negatives, helps accurately detect critical risks with transparent scoring to meet compliance and audit requirements. Further, Babel Street Match streamlines screening workflows, reduces the burden of manual reviews, and elevates the accuracy of threat detection.

The following diagram shows the details of OpenSearch Service and Babel Street Match Plugin integration.

Architecture diagram showing Babel Street Match Plugin integration with AWS services, including AWS Marketplace, Amazon S3, and Amazon OpenSearch Service across two AWS accounts for secure entity matching.

Babel Street Match integrates directly with the OpenSearch Service domain through a lightweight plugin that runs inside your own AWS account where you have full control of your data. The Match plugin sends encrypted match requests to Babel Street’s fully managed Match engine, where the core matching engine performs the entity-resolution logic. The results return to you in real time, enhancing your existing OpenSearch Service workflows with advanced name- and entity-matching capabilities. Meanwhile, Babel Street’s control plane handles licensing, monitoring, and AWS Marketplace integration behind the scenes, provides continuous validation, automated updates, and a seamless operational experience.

Example use cases

The solution combines enterprise-scale search and analytics with AI-powered, multilingual identity intelligence. This section showcases example use cases where integration has enhanced organizations’ capabilities.

  • Border Screening: Help agencies identify high-risk travelers, cargo, and networks to strengthen point-of-entry security with faster, automated risk assessment.
  • Financial Services Compliance: Help Financial institutions and the FinTechs that serve them by offering AI-driven solutions for name screening, adverse media monitoring, and know your customer (KYC)/know your vendor (KYV) due diligence.
  • Identity and Organization Screening: Help businesses needing identity and organization screening by providing AI, analytics, and advanced matching technologies to assist in addressing complex screening challenges.
  • Customer and Vendor Onboarding: Help governments and financial institutions by providing research, analytics, and advanced matching technologies needed to quickly and confidently onboard customers and vendors at scale.

Customer Success Stories

Here’s how leading organizations are leveraging Babel Street Match and Amazon OpenSearch Service to solve real-world identity challenges:

  • A European online brokerage faced AML (anti-money laundering) compliance challenges with its outdated name-matching system, which produced excessive false positives and couldn’t process longer multilingual names. After implementing Babel Street Match on OpenSearch Service, the firm achieved up to 70% better accuracy across 25 languages—significantly reducing manual work and speeding customer payments.
    Babel Street Match Improves FI’s Name-Matching Accuracy by Up to 70% on OpenSearch
  • A major border agency struggled with an outdated screening system that flagged 15% of travelers as potential watchlist matches—overwhelming agents and creating long queues. After implementing Babel Street Match, false positives dropped dramatically (from 80,000 to just 100 in one test), hardware needs fell by 70%, and travelers with common names can now pass through faster. As one stakeholder put it: “Name matching is not our biggest problem anymore.”
    Enabling Stronger, Safer Borders with AI-powered Screening by Babel Street Match

Getting Started with Babel Street Match for Amazon OpenSearch Service

Amazon OpenSearch Service supports third-party plugins like Babel Street Match for OpenSearch. This plugin is supported on OpenSearch version 2.15 or higher and licenses can be obtained through AWS Marketplace.

Installing Babel Street Match for Amazon OpenSearch Service

Prerequisites: Obtain the license file from Babel Street and upload it to an S3 bucket in the same AWS Region as your OpenSearch domain.

Installation Steps:

  1. Create packages – In the OpenSearch Service console, create a package for your license file and select the Babel Street Match plugin from the available options
  2. Associate packages – Link both the license and plugin packages to your OpenSearch domain
  3. Verify – Monitor the domain update and confirm the plugin is active

For details, refer to AWS documentation “Installing third-party plugins in Amazon OpenSearch Service” and Babel Street installation guide which provides detailed guidance on pre-requisites, installation and using the plugin.

Conclusion

Together, Babel Street Match and OpenSearch Service help organizations cut through false positives and catch true matches faster. The result? Greater precision, efficiency, and speed—whether protecting entities, maintaining compliance, or securing supply chains. That’s business-critical identity intelligence in action.

Explore how Babel Street Match on Amazon OpenSearch Service can elevate your organization’s identity intelligence capabilities and transform the screening operations through an interactive or customized demo on Babel Street’s website.

Portions of this content describing Babel Street products and services are provided by Babel Street. AWS is not responsible for the accuracy of third-party product information.


About the Authors

Kunal Sharma

Kunal Sharma is a Sr. Solutions Architect at AWS. He works with AWS Worldwide Public Sector (WWPS) partners to build and scale cloud-native solutions. As an SA, he thrives on turning complex customer challenges into elegant, well-architected solutions — one whiteboard session at a time.

Gil Irizarry

Gil is the Chief Innovation Officer at Babel Street. He specializes in applying natural language processing and AI to identity resolution use cases. Gil’s work combines computational linguistics, machine learning and AI to produce state-of-the-art entity extraction and resolution applications. Gil’s focus on innovation led to his winning of Babel Street’s internal hackathon two years in a row.

Mae Wells-Kress

Mae Wells-Kress is the Vice President of Strategic Marketing at Babel Street. She has extensive experience across strategic and creative marketing roles, she implements process-driven lead generation efforts and develops strategic campaigns, events, and messaging that connect with audiences and helps organizations advance their missions in high stakes environments.

Craig Harmon

Craig is the Director of Partner Management at Babel Street. He leads the company’s strategic alliance with Amazon Web Services (AWS). A former Senior Partner Account Manager at AWS, Craig brings a hyperscaler‑native perspective to building and scaling partnerships that drive revenue growth and deepen technical collaboration. He is passionate about operational excellence and the design of high‑performance partner models that translate cloud innovation into measurable outcomes for customers and partners.

Building Memory-Intensive Apps with AWS Lambda Managed Instances

Post Syndicated from Guy Haddad original https://aws.amazon.com/blogs/compute/building-memory-intensive-apps-with-aws-lambda-managed-instances/

Building memory-intensive applications with AWS Lambda just got easier. AWS Lambda Managed Instances gives you up to 32 GB of memory—3x more than standard AWS Lambda—while maintaining the serverless experience you know. Modern applications increasingly require substantial memory resources to process large datasets, perform complex analytics, and deliver real-time insights for use cases such as in-memory analytics, Machine Learning (ML) model inference, and real-time semantic search. AWS Lambda Managed Instances gives you a familiar serverless programming model and experience combined with the flexibility of being able to choose the underlying Amazon EC2 instance types and providing developers with access to large memory configurations.

In this post, you will see how AWS Lambda Managed Instances enables memory-intensive workloads that were previously challenging to run in serverless environments, using an AI-powered customer analytics application as a practical example. You’ll see cost savings of up to 33% compared to standard Lambda for predictable workloads, while eliminating the operational overhead of managing EC2 instances.

Understanding AWS Lambda Managed Instances

AWS Lambda Managed Instances runs your AWS Lambda functions on the Amazon EC2 instance types of your choice in your account, including Graviton4 and memory-optimized instance types. AWS handles underlying infrastructure lifecycle including provisioning, scaling, patching, and routing, while you benefit from Amazon EC2 pricing advantages like Savings Plans and Reserved Instances.

Key benefits include:

  • Flexible instance selection: Choose from compute-optimized (C), general-purpose (M), and memory-optimized (R) instance families
  • Configurable memory-CPU ratios: Optimize resource allocation for your workload
  • Multi-concurrent invocations: One execution environment handles multiple invocations simultaneously, improving utilization for I/O-heavy applications
  • Dynamic scaling: Instances scale based on CPU utilization without cold starts

AWS Lambda Managed Instances is best suited for high-volume, predictable workloads that benefit from sustained compute capacity and larger memory configurations.

Memory-Intensive Workloads Work Best with AWS Lambda Managed Instances

This blog focuses on one of AWS Lambda Managed Instances’ most powerful capabilities: running memory-intensive workloads that require more than the standard AWS Lambda’s 10 GB memory and 250MB ZIP limits. Here are the use cases where AWS Lambda Managed Instances helps:

  • In-Memory Analytics — Load gigabytes of structured data into memory at initialization and serve sub-millisecond analytical queries across thousands of invocations
  • ML Model Inference — Keep large model weights resident in memory across invocations for consistent, low-latency inference without a dedicated endpoint.
  • Real-Time Semantic Search — Build vector similarity search over large embedding indexes held entirely in memory, enabling natural language queries over millions of records without an external vector database.
  • Graph Processing — Hold large graph structures in memory for traversal algorithms that require the full graph to be accessible at once.
  • Scientific & Numerical Computing — Run simulations, Monte Carlo methods, and large matrix operations that require substantial working memory and benefit from memory-optimized Amazon EC2 instance families.
  • Large-Scale Report Generation — Aggregate and transform multi-gigabyte datasets in memory to generate complex reports or dashboards on demand, without staging data through intermediate storage.

Use Case: AI-Powered Customer Analytics with AWS Lambda Managed Instances

To demonstrate the power of AWS Lambda Managed Instances for memory-intensive applications, we built an AI-Powered Customer Analytics application that combines in-memory data processing with ML-based semantic search. The application loads in memory 1 million customer behavioral records (sessions, purchases, browsing patterns) from a Parquet file in S3 into a Pandas DataFrame and an embeddings cache consuming 200MB, then responds for analytics queries:

  1. Customer Analysis — Deep-dive into individual customer behavior: engagement scores, conversion rates, purchase patterns, and AI-generated customer segments
  2. Semantic Search — Natural language queries powered by FastEmbed (sentence-transformers/all-MiniLM-L6-v2) that find similar customers using vector similarity
  3. Cohort Analysis — Real-time segmentation by device, country, age group with aggregated metrics

Architecture Overview

Our AI-powered customer analytics application demonstrates this in practice: 1 million records in memory (200MB), a compact sentence transformer model for semantic search, sub-second query performance, and zero infrastructure to manage. The solution uses a simple, serverless architecture:

  • Customer transaction data (Parquet format) is stored in Amazon S3
  • Amazon Cognito User Pool authenticates users and issues JWT tokens for API access
  • Amazon API Gateway routes requests with Cognito authorizer validation, rate limiting (5 requests/second, burst 10), X-Ray tracing, and access logging
  • AWS Lambda function with AWS Lambda Managed Instances loads the entire dataset (200MB) and all-MiniLM-L6-v2 model (900MB) into memory during initialization while also performing a threaded embeddings cache generation. This step can consume about 14GB of the allocated memory, exceeding standard AWS Lambda’s 10 GB limit
  • Analytics queries execute against the in-memory data using the model
  • Results are returned in milliseconds for interactive analysis

Architecture diagram

Deploy the Application

The below steps walk you through deploying the application to AWS using the AWS Serverless Application Model (SAM). The deployment process packages your Lambda function code, uploads artifacts to Amazon S3, and provisions all required AWS resources including Lambda functions, IAM roles, and any configured VPC networking via AWS CloudFormation.

Prerequisites

Make sure you have the following tools installed locally:

  • AWS CLI configured with credentials
  • SAM CLI installed
  • Python 3.13+ installed locally
  • Docker or Finch (required for container builds)
  • AWS account with appropriate permissions
  • A VPC with at least 2 subnets (across different Availability Zones) and a security group — required for the Lambda Managed Instances capacity provider
  • Supported regions: Check AWS Capabilities by Region for supported regions

Getting Started

The complete source code for this application is available in our GitHub repository. To deploy it yourself follow the below steps and refer to the full deployment instructions hosted on GitHub.

1. Clone the repository

git clone https://github.com/aws-samples/sample-lambda-managed-instances-analytics.git

2. Navigate to the project folder

cd sample-lambda-managed-instances-analytics

chmod +x setup-data.sh deploy-lambda.sh

3. Generate sample data and upload to S3

./setup-data.sh

This script will create an S3 bucket (if needed), generate 1M rows of sample data, and upload the data to S3.

4. Build and deploy the Lambda function

./deploy-lambda.sh

This script will build the container image with FastEmbed, push it to ECR, and deploy the Lambda function along with Capacity Provider, API Gateway, and Cognito User Pool. After deployment, it automatically generates the UI authentication configuration and prompts you to create a test user.

SAM template

Capacity provider configuration

Run the Application

1. Start the UI

The application includes a simple HTML-based UI through which you can test the AWS Lambda function using Amazon API Gateway:

cd ui && python3 -m http.server 8000

2. Open your browser at http://localhost:8000 and click ‘Sign In’ to authenticate via Cognito using the username/password that you created during deployment

Starting the UI

3. Enter your API endpoint URL. Test connection and click system Info.

Testing the connection

Test the Application

a. Customer Analysis — Enter one or more User IDs to get more information on the customer behavior: engagement scores, conversion rates, purchase patterns, and AI-generated customer segments

Running customer analysis

b. Semantic Search – Enter natural language queries like “list high value customers from USA” in the Semantic Search and verify the results. Note that the response is very fast as the analytics data and FastEmbed models are loaded into memory during init stage

Running semantic search

c. Cohort Analysis — Enter the query data to get Real-time segmentation by device, country, age group with aggregated metrics

Running cohort analysis

Observability

AWS Lambda Managed Instances automatically publishes metrics to Amazon CloudWatch, giving you visibility into function performance and capacity utilization. Monitor InitDuration to track dataset and model load time at startup, MaxMemoryUsed to confirm your data fits within configured memory, and ProvisionedConcurrencySpilloverInvocations to detect when AWS Lambda Managed Instances capacity is exhausted.

Enable AWS Lambda Insights for enhanced per-invocation metrics including CPU time and memory utilization over time. Use Amazon CloudWatch Log Insights to query INIT_START, INIT_END, and REPORT log entries for initialization and memory details per invocation.

AWS Lambda Insights

What Makes This Better with AWS Lambda Managed Instances

Without AWS Lambda Managed Instances, building this same application would require one of these alternatives:

  • Option A: EC2 with auto-scaling — Full control, full responsibility: patching, scaling policies, load balancing, and deployment pipelines — all on you.
  • Option B: Redesign for standard Lambda — Swap in-memory data for an external database and replace the ML model with Amazon SageMaker endpoint. More latency, more cost, more complexity.

With AWS Lambda Managed Instances, you write a single AWS Lambda function, define a Capacity Provider, and deploy with SAM. AWS Lambda handles the Amazon EC2 instances, scaling, and lifecycle, giving you the memory you need with the operational simplicity you want. The in-memory approach eliminates network latency and disk I/O, delivering consistent sub-200ms response times for complex analytics.

Cost Considerations

AWS Lambda Managed Instances uses Amazon EC2-based pricing with a management fee. For predictable workloads, you can leverage Amazon EC2 Savings Plans or Reserved Instances to reduce costs significantly.

Example cost comparison (us-east-1, 32 GB memory, 1M invocations/month):

  • AWS Lambda (standard): ~$267/month (on-demand pricing)
  • AWS Lambda Managed Instances: ~$180/month (with 1-year Compute Savings Plan)
  • Savings: 33% reduction

The cost benefits increase with higher memory configurations and sustained workloads that can take advantage of Amazon EC2 pricing discounts.

Best Practices

Based on experience building this solution, here are key recommendations:

  • Memory sizing: Start with your dataset size plus 50% overhead for processing. Monitor Amazon CloudWatch metrics to optimize.
  • Initialization strategy: Load large datasets during the init phase to amortize the cost across multiple invocations.
  • Concurrency configuration: Set PerExecutionEnvironmentMaxConcurrency based on your workload’s I/O characteristics. Higher values work well for I/O-bound analytics.
  • Data format: Use columnar formats like Parquet for efficient memory usage and fast loading.
  • Monitoring: Track initialization duration, memory utilization, and invocation latency in Amazon CloudWatch to identify optimization opportunities.

Cleanup

When you’re done exploring the solution, it’s good practice to remove all provisioned resources to avoid ongoing charges. For the full cleanup commands and exact steps, refer to the project’s README.md in GitHub repository.

Conclusion

AWS Lambda Managed Instances opens up a new class of serverless applications that support larger AWS Lambda layer packages and more memory. Memory-intensive workloads — in-memory analytics, ML inference, graph processing, scientific computing — can now run with the simplicity of AWS Lambda and the resources of Amazon EC2. The customer analytics example demonstrates how in-memory processing with AWS Lambda Managed Instances delivers performance improvements over traditional database queries while maintaining serverless benefits like automatic scaling and pay-per-use pricing.

Ready to get started? Explore the AWS Lambda Managed Instances documentation and try building your own memory-intensive serverless application. You can find the complete code for this example on GitHub.

How Aigen transformed agricultural robotics for sustainable farming with Amazon SageMaker AI

Post Syndicated from Purna Sanyal original https://aws.amazon.com/blogs/architecture/how-aigen-transformed-agricultural-robotics-for-sustainable-farming-with-amazon-sagemaker-ai/

This post is cowritten with Yuri Brigance, and Usman M. Khan from Aigen.

Aigen builds autonomous robots designed to help farmers remove herbicide-resistant weeds and improve crop yield through AI-driven technology. These robots operate without chemicals, using renewable energy, and provide real-time, field-level data to enhance decision-making. Using advanced computer vision AI, Aigen’s robots autonomously identify and remove weeds without harming crops, giving farmers an eco-friendly, cost-effective solution to traditional weed management and efficient farming. As its robotic fleet expanded, Aigen’s on-premises infrastructure became a bottleneck in scaling its model-building pipeline.

In this post, you will learn how Aigen modernized its machine learning (ML) pipeline with Amazon SageMaker AI to overcome industry-wide agricultural robotics challenges and scale sustainable farming. This post focuses on the strategies and architecture patterns that enabled Aigen to modernize its pipeline across hundreds of distributed edge solar robots and showcase the significant business outcomes unlocked through this transformation. By adopting automated data labeling and human-in-the-loop validation, Aigen increased image labeling throughput by 20x while reducing image labeling costs by 22.5x.

Key challenges of scaling field agricultural robots

Aigen’s initial ML pipeline was designed to build task-specific edge models for its field robots. Robot data was uploaded to Amazon Simple Storage Service (Amazon S3) for manual labeling. The annotated datasets were then used to train new task-specific edge models on Aigen’s on-premises infrastructure. However, this ML pipeline introduced several limitations:

  • Connectivity Constraints: Inconsistent internet in rural areas hampered communication between robots and cloud.
  • High Data Labeling Cost: Manual data labeling of thousands of new samples data per day proved prohibitively expensive and time-consuming.
  • Limited Computational Power: Training specialized edge models and fine-tuning foundation models (FMs) for specific tasks using on-premises hardware was a bottleneck due to limited parallelism and GPU compute power with on-premises RTX 3090 machines.
  • Scalability Issues: Model Training and data labeling batch Inference had to compete for the same RTX 3090 machines, causing delays either for the data science team for model training or the data labeling team for batch inference.

Solution

Aigen addresses these challenges by adopting an AWS AI-driven, cloud-native approach that enables scalable and automated operations:

  • Edge Computing: Robots use AWS IoT Core and cloud utilities to safely offload data to Amazon S3, even in low-connectivity regimes.
  • Automated Data Pipeline: Data collected by the robots flows through an Extract, Transform, and Load (ETL) pipeline for preprocessing. Data labeling is accelerated using an ensemble of vision foundation models (Grounding DINO, Owl-ViT, SAM2, CLIPSeg) along with custom expert vision models to automatically annotate large volumes of field imagery. Through active learning, the pipeline selects and down-samples the most informative samples, which are then reviewed and refined by human annotators before being passed downstream into the model training workflow.
  • Cloud native ML Pipeline: Aigen accelerates model training on Amazon SageMaker AI, using Distributed Data Parallel (DDP) across multi-GPU clusters to achieve faster iteration cycles and efficient hyperparameter tuning. By scaling training in the cloud, Aigen removes resource contention between model training and data labeling batch inference. This results in improved throughput, reduced wait times, and a more predictable ML workflow for data science and labeling teams.

Let’s take a closer look at how Aigen’s solution architecture is designed to meet diverse machine learning needs, from data labeling to real-time inference on autonomous field robots, starting with its model architecture.

Model architecture

Aigen’s models are classified in four hierarchical categories that form a progression from broad, general-purpose models to highly specialized models tailored for edge computing. Foundation Models (L1) are the starting point, with each subsequent category building on the previous model, adding specificity or performance enhancements.

Figure 1: Aigen Model Architecture

Figure 1: Aigen Model Architecture

  1. Foundation models use a combination of Aigen’s proprietary and open source foundation vision models to support plant detection, wheel detection, general object recognition, and segmentation. SAM2 is the primary model for generating segmentation masks, while Grounding DINO provides prompt-based annotation for objects like cars and people. Aigen employs a leading image generation model with ControlNet + Depth to create synthetic data with an option to fine-tune LoRA adapters to produce samples like field data. Aigen’s large vision models, trained on extensive field datasets, serve as robust foundations for crop identification and as high-quality starting points for building specialized pre-labeling models.
  2. Expert models are distilled from FMs and trained on annotated field images to perform precise, task-specific vision workloads. They generate high-quality pre-labels, bounding boxes, segmentation masks, and keypoint detections, which are then validated and refined by human annotators. Segmentation combined with key points allows the system to identify fine-grained plant anatomy, such as stems and other structural features. These models use both Vision Transformer and CNN-based architectures, and contain 10s of millions of parameters.
  3. Student models are compact, full-precision (FP32) models designed for ultra-low latency and minimal memory usage and are continuously fine-tuned on the latest data. Distilled from expert models, they remain extremely small, typically under 1.5M parameters, and are further improved through quantization-aware training (QAT), pruning, and other compression techniques. These optimizations enable efficient edge deployment, requiring as little as 2 Tera Operations Per Second (TOPS) while achieving real-time, double-digit frames per second (FPS) within the robot’s perception stack. Each student model is task-specific, tailored to individual crops (for example, tomato, cotton, sugar beets, soybeans) and various view angles such as top-down or intra-row.
  4. Edge models are built by further improving the full-precision student models for inference on the robot’s Neural Processing Unit (NPU). It undergoes QAT, followed by conversion to TFLite and INT8 quantization to reduce model size, lower power consumption, and increase inference throughput on the robot’s NPU. Purpose built for ultra-efficient edge inference, these models run on a 2.3-TOPS NPU using roughly 1.5W of power while sustaining real-time, double-digit FPS performance. These models contain 1M–1.2M parameters and occupy about 2 MB of memory.

This hybrid multi model ecosystem approach works well to balance model accuracy with edge computing constraints.

Modernized cloud native architecture for continuous model improvement

The modernized architecture forms a closed loop of nearly continuous model improvement, connecting field data collection from the robot to iterative training and rapid redeployment of updated models back onto the robot. This end-to-end cycle enables faster refinement, higher accuracy, and ongoing adaptation to real-world conditions.

Figure 2: Aigen modernized architecture

Figure 2: Aigen modernized architecture

The following sections describe the end-to-end process illustrated in the architecture diagram, from field data ingestion into AWS to continuous model delivery back to the robotic fleet. The workflow is organized into three key stages:

  1. Data Collection and Data Ingestion: Field Robots connect to AWS services using AWS IoT Core. Raw data, including navigation and crop-camera video (RGB + Depth), robot telemetry (odometry, frame timestamps), camera intrinsics/extrinsic, and job metadata, is continuously transmitted from the robots to Amazon S3 buckets. These data provide centralized storage for field, crop, and task specific downstream processing.
  2. Data Processing and Data Labeling: Aigen ETL unpacks the raw data, catalogs it, and stores it in Amazon S3. SageMaker AI processing jobs perform batch inference on this data and label the images using an ensemble of expert models running on the G5/G6 family of GPU instances. Aigen’s active learning process down-selects pre-labeled images and sends them for human review, where annotators validate and correct identified errors. Active learning analyzes images, embeddings, predictions, and other signals to identify the most informative samples for training. This approach removes the need to annotate every data point, often millions per field per season, by prioritizing images where the model struggles or those that add diversity. With multiple selection criteria, active learning helps keep dataset size manageable, control labeling effort, and verify only the most relevant samples are used to improve model performance.
  3. Model Training: The final annotated data is stored back in Amazon S3. SageMaker AI Training jobs pull this data from Amazon S3 and use multi-GPU instances to train expert, student and edge models. Edge-optimized models are deployed to the robots, while the newly finetuned expert models are used for the next cycle of data labeling.

Built on a cloud-native architecture, the workflow uses AWS services to deliver reliability, and robust performance, while effectively addressing the key challenges of scaling Aigen’s robotic fleet. The automated process collects data from field robot and use that in model training in the cloud, minimizing manual intervention while maintaining efficiency. Human-in-the-loop validation ensures high-quality training data by having annotators review and correct AI-generated pre-labels. Finally, active learning creates a positive feedback loop that continuously improves models by prioritizing the most relevant training data, enhancing robotic performance in real-world conditions.

Business benefits

This AI-powered solution delivered the following benefits:

  • Cost Efficiency: Reduced labeling costs from ~$2.00 to $0.089 per image, achieving a 22.5× cost reduction
  • Faster Annotation Pipeline: Reduced average annotation time from 14 minutes 57 seconds with manual labeling to just 41 seconds with SageMaker batch inference. This acceleration shortens model delivery for new crops from months to weeks, enabling quicker deployment and unlocking new business opportunities.
  • Rapid Scaling Gains: Experiment capacity increased from five per week on on-premises infrastructure to hundreds per week using Amazon SageMaker AI, achieving a 20× increase in throughput over previous hardware.
  • Innovation
    • The powerful GPU instances of Amazon SageMaker AI enabled the training and fine-tuning of advanced Vision Transformers models, which were not feasible on limited on-premises hardware. This access to state-of-the-art (SOTA) GPUs accelerates model innovation.
    • Scalable training infrastructure removes GPU bottlenecks by enabling parallel experimentation. This allows faster testing of new architectures and hyperparameters tuning, significantly speeding up model innovation compared to the slow, sequential workflow imposed by limited on-premises GPU capacity.

Key learnings

Amazon SageMaker AI has been instrumental in Aigen’s robotics system transformation, delivering significant benefits across the machine learning pipeline:

  • Self-Managed AI Infrastructure: SageMaker AI removes the need for Aigen to build and maintain auto scaling GPU compute infrastructure. This reduction in development costs allows Aigen to focus more on model development rather than infrastructure management, accelerating the production of deployment-ready models.
  • Streamlined ML Workflow: SageMaker AI streamlines the entire ML lifecycle, from data preparation to model deployment. Its flexibility supports the use of various built-in features and custom processes such as pre-labeling that cut down the time required to produce high-quality training data.
  • Efficient Resource Utilization: The managed infrastructure of SageMaker AI lowers operational overhead, supports continuous model updates, such as daily fine-tuning as plants grow, without resource bottlenecks. For example, when moving to a new customer’s cotton field with different soil, lighting, or crop varieties, the base cotton model may underperform. With SageMaker AI, Aigen can rapidly ingest new data and fine-tune models on this new condition to improve performance. Over multiple seasons and fields, this process builds a diverse, high-quality dataset that steadily strengthens the model family.

To achieve similar results in your organization, start by evaluating your current data labeling costs and consider implementing active learning techniques to reduce manual annotation overhead.

Conclusion

By using AWS services, particularly SageMaker AI, Aigen moved beyond the limitations of its on-premises infrastructure and established a foundation for continued growth and innovation. The new architecture delivers the scalability, efficiency, and intelligence needed to expand its fleet of eco-friendly agricultural robots, bringing sustainable farming practices to more fields worldwide. Aigen’s journey illustrates how generative AI can modernize machine learning pipelines for robotics, enabling more productive and environmentally sustainable agriculture. You can implement a similar architecture pattern to improve the machine learning pipeline.

Get started with model training and model inference by visiting Amazon SageMaker AI Studio. Creating your first Serverless ML flow pipeline is also supported in SageMaker AI Studio for additional workflow flexibility.


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