Tag Archives: DSQL

AWS Weekly Roundup: AWS AI/ML Scholars program, Agent Plugin for AWS Serverless, and more (March 30, 2026)

Post Syndicated from Prasad Rao original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-aws-ai-ml-scholars-program-agent-plugin-for-aws-serverless-and-more-march-30-2026/

Last week, what excited me most was the launch of the 2026 AWS AI & ML Scholars program by Swami Sivasubramanian, VP of AWS Agentic AI, to provide free AI education to up to 100,000 learners worldwide. The program has two phases: a Challenge phase where you’ll learn foundational generative AI skills, followed by a fully funded three-month Udacity Nanodegree for the top 4,500 performers. Anyone 18 or older can apply, with no prior AI or ML experience required. Applications close on June 24, 2026. Visit the AWS AI & ML Scholars webpage to learn more and apply.

The AWS AI & ML Scholars Program is back

I’m also excited about the start of AWS Summit season, kicking off with AWS Summit Paris on April 1, followed by London on April 22. AWS Summits are free in-person events where builders and innovators can learn about Cloud and AI, think big, and make new connections. Explore the AWS Summits near you and join us in person.

Now, let’s dive into this week’s AWS news…

Last week’s launches
Here are last week’s launches that caught my attention:

  • Announcing Amazon Aurora PostgreSQL serverless database creation in seconds — Amazon Aurora PostgreSQL now offers express configuration, a streamlined setup with preconfigured defaults that supports creating and connecting to a database in seconds. With just two clicks, you can launch an Aurora PostgreSQL serverless database. You can modify certain settings during or after creation.
  • Amazon Aurora PostgreSQL now available with the AWS Free Tier — Amazon Aurora PostgreSQL is now available on the AWS Free Tier. If you’re new to AWS, you receive $100 in AWS credits upon sign-up and can earn an additional $100 in credits by using services like Amazon Relational Database Service (Amazon RDS).
  • Announcing Agent Plugin for AWS Serverless — With the new Agent Plugin for AWS Serverless, you can easily build, deploy, troubleshoot, and manage serverless applications using AI coding assistants like Kiro, Claude Code, and Cursor. This plugin extends AI assistants with structured capabilities by packaging skills, sub-agents, and Model Context Protocol (MCP) servers into one modular unit. It automatically loads the guidance and expertise you need throughout development to build production-ready serverless applications on AWS.
  • Amazon SageMaker Studio now supports Kiro and Cursor IDEs as remote IDEs — You can now remotely connect from Kiro and Cursor IDEs to Amazon SageMaker Studio. This lets you use your existing Kiro and Cursor setup, including spec-driven development, conversational coding, and automated feature generation, while accessing the scalable compute resources of Amazon SageMaker Studio.
  • Introducing visual customization capability in AWS Management Console — You can now customize your AWS Management Console with visual settings like account color and control which regions and services you see. Hiding unused regions and services helps you focus better and work faster by reducing cognitive load and unnecessary scrolling.
  • Announcing Aurora DSQL connector to simplify building Ruby applications — You can now use the Aurora DSQL Connector for Ruby (pg gem) to easily build Ruby applications on Aurora DSQL. The Ruby Connector simplifies authentication and improves security by automatically generating tokens for each connection, eliminating the risks of traditional passwords while maintaining full compatibility with existing pg gem features.
  • AWS Lambda increases the file descriptor limit for functions running on Lambda Managed Instances — AWS Lambda increases the file descriptor limit from 1,024 to 4,096, a 4x increase, for functions running on Lambda Managed Instances (LMI). You can now run I/O intensive workloads such as high-concurrency web services and file-heavy data processing pipelines without running into file descriptor limits.
  • AWS Lambda now supports up to 32 GB of memory and 16 vCPUs for Lambda Managed Instances — AWS Lambda functions on Lambda Managed Instances now support up to 32 GB of memory and 16 vCPUs. You can run compute-intensive workloads like data processing, media transcoding, and scientific simulations without managing infrastructure. Plus, you can adjust the memory-to-vCPU ratio (2:1, 4:1, or 8:1) to fit your workload.
  • Announcing Bidirectional Streaming API for Amazon Polly — Traditional text-to-speech APIs use a request-response pattern. The new Bidirectional Streaming API for Amazon Polly is designed for conversational AI applications that generate text or audio incrementally, like large language model (LLM) responses. This lets you start synthesizing audio before the full text is available.

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

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

  • AWS Summits — As I mentioned earlier, join AWS Summits in 2026 for free in-person events where you can explore emerging cloud and AI technologies, learn best practices, and network with industry peers and experts. Upcoming Summits include Paris (April 1), London (April 22), Bengaluru (April 23–24), Singapore (May 6), Tel Aviv (May 6), and Stockholm (May 7).
  • AWS Community Days — Community-led conferences where content is planned, sourced, and delivered by community leaders, featuring technical discussions, workshops, and hands-on labs. Upcoming events include San Francisco (April 10) and Romania (April 23–24).

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse the AWS Events and Webinars for upcoming AWS-led in-person and virtual events and developer-focused events.

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

— Prasad

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

AWS Weekly Roundup: Amazon Aurora DSQL, MCP Servers, Amazon FSx, AI on EKS, and more (June 2, 2025)

Post Syndicated from Prasad Rao original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-aurora-dsql-mcp-servers-amazon-fsx-ai-on-eks-and-more-june-2-2025/

It’s AWS Summit Season! AWS Summits are free in-person events that take place across the globe in major cities, bringing cloud expertise to local communities. Each AWS Summit features keynote presentations highlighting the latest innovations, technical sessions, live demos, and interactive workshops led by Amazon Web Services (AWS) experts. Last week, events took place at AWS Summit Tel Aviv and AWS Summit Singapore.

The following photo shows the packed keynote at AWS Summit Tel Aviv.

AWS Summit Tel Aviv Keynote

Find an AWS Summit near you and join thousands of AWS customers and cloud professionals taking the next step in their cloud journey.

Last week, the announcement that piqued my interest most was the general availability of Amazon Aurora DSQL, which was introduced in preview at re:Invent 2024. Aurora DSQL is the fastest serverless distributed SQL database that enables you to build always available applications with virtually unlimited scalability, the highest availability, and zero infrastructure management.

Aurora DSQL active-active distributed architecture is designed for 99.99% single-Region and 99.999% multi-Region availability with no single point of failure and automated failure recovery. This means your applications can continue to read and write with strong consistency, even in the rare case an application is unable to connect to a Region cluster endpoint.

Single and multi region deployment of Amazon Aurora DSQL

What’s more fascinating is the journey behind building Aurora DSQL, a story that goes beyond the technology in the pursuit of engineering efficiency. Read the full story in Dr. Werner Vogels’ blog post, Just make it scale: An Aurora DSQL story.

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

  • Announcing new Model Context Protocol (MCP) servers for AWS Serverless and Containers – MCP servers are now available for AWS Lambda, Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Kubernetes Service (Amazon EKS), and Finch. With MCP servers, you can get from idea to production faster by giving your AI assistants access to an up-to-date framework on how to correctly interact with your AWS service of choice. To download and try out the open source MCP servers, visit the aws-labs GitHub repository.
  • Announcing the general availability of Amazon FSx for Lustre Intelligent-Tiering – FSx for Lustre Intelligent-Tiering, a new storage class, automatically optimizes costs by tiering cold data to the applicable lower-cost storage tier based on access patterns and includes an optional SSD read cache to improve performance for your most latency-sensitive workloads.
  • Amazon FSx for NetApp ONTAP now supports write-back mode for ONTAP FlexCache volumes – Write-back mode is a new ONTAP capability that helps you achieve faster performance for your write-intensive workloads that are distributed across multiple AWS Regions and on-premises file systems.
  • AWS Network Firewall Adds Support for Multiple VPC Endpoints – AWS Network Firewall now supports configuring up to 50 Amazon Virtual Private Cloud (Amazon VPC) endpoints per Availability Zone for a single firewall. This new capability gives you more options to scale your Network Firewall deployment across multiple VPCs, using a centralized security policy.
  • Cost Optimization Hub now supports Savings Plans and reservations preferences – You can now use Cost Optimization Hub, a feature within the Billing and Cost Management Console, to configure preferred Savings Plans and reservation term and payment options preferences, so you can see your resulting recommendations and savings potential based on your preferred commitments.
  • AWS Neuron introduces NxD Inference GA, new features, and improved tools – With the release of Neuron 2.23, the NxD Inference library (NxDI) moves from beta to general availability and is now recommended for all multi-chip inference use cases. Neuron 2.23 also introduces new training capabilities, including context parallelism and Odds Ratio Preference Optimization (ORPO), and adds support for PyTorch 2.6 and JAX 0.5.3.
  • AWS Pricing Calculator, now generally available, supports discounts and purchase commitment – We announced the general availability of the AWS Pricing Calculator in the AWS console. You can now create more accurate and comprehensive cost estimates by providing two types of cost estimates: cost estimation for a workload, and estimation of a full AWS bill. You can also import your historical usage or create net new usage when creating a cost estimate. Additionally, with the new rate configuration inclusive of both pricing discounts and purchase commitments, you can gain a clearer picture of potential savings and cost optimizations for your cost scenarios.
  • AWS CDK Toolkit Library is now generally available – AWS CDK Toolkit Library provides programmatic access to core AWS CDK functionalities such as synthesis, deployment, and destruction of stacks. You can use this library to integrate CDK operations directly into your applications, custom CLIs, and automation workflows, offering greater flexibility and control over infrastructure management.
  • Announcing Red Hat Enterprise Linux for AWS – Red Hat Enterprise Linux (RHEL) for AWS, starting with RHEL 10, is now generally available, combining Red Hat’s enterprise-grade Linux software with native AWS integration. RHEL for AWS is built to achieve optimum performance of RHEL running on AWS.

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

Additional updates
Here are some additional projects, blog posts, and news items that you might find interesting:

  • Introducing AI on EKS: powering scalable AI workloads with Amazon EKS – AI on EKS is a new open source initiative from AWS designed to help you deploy, scale, and optimize AI/ML workloads on Amazon EKS. AI on EKS repository includes deployment-ready blueprints for distributed training, LLM inference, generative AI pipelines, multi-model serving, agentic AI, GPU and Neuron-specific benchmarks, and MLOps best practices.
  • Revolutionizing earth observation with geospatial foundation models on AWS – Emerging transformer-based vision models for geospatial data—also called geospatial foundation models (GeoFMs)—offer a new and powerful technology for mapping the earth’s surface at a continental scale. This post explores how Clay Foundation’s Clay foundation model can be deployed for large-scale inference and fine-tuning on Amazon SageMaker. You can use the ready-to-deploy code samples to get started quickly with deploying GeoFMs in your own applications on AWS.

High level solution flow for inference and fine tuning using Geospatial Foundation Models

  • Going beyond AI assistants: Examples from Amazon.com reinventing industries with generative AI – Non-conversational applications offer unique advantages, such as higher latency tolerance, batch processing, and caching, but their autonomous nature requires stronger guardrails and exhaustive quality assurance compared to conversational applications, which benefit from real-time user feedback and supervision. This post examines four diverse Amazon.com examples of non-conversational generative AI applications.

Upcoming AWS events
Check your calendars and sign up for these upcoming AWS events:

  • AWS Summits – Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Stockholm (June 4), Sydney (June 4–5), Hamburg (June 5), Washington (June 10–11), Madrid (June 11), Milan (June 18), Shanghai (June 19–20), and Mumbai (June 19).
  • AWS re:Inforce – Mark your calendars for AWS re:Inforce (June 16–18) in Philadelphia, PA. AWS re:Inforce is a learning conference focused on AWS security solutions, cloud security, compliance, and identity.
  • AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Milwaukee, USA (June 5), Mexico (June 14), Nairobi, Kenya (June 14), and Colombia (June 28).

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

– Prasad

Amazon Aurora DSQL is now generally available

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/amazon-aurora-dsql-is-now-generally-available/

Today, we’re announcing the general availability of Amazon Aurora DSQL, the fastest serverless distributed SQL database with virtually unlimited scale, the highest availability, and zero infrastructure management for always available applications. You can remove the operational burden of patching, upgrades, and maintenance downtime and count on an easy-to-use developer experience to create a new database in a few quick steps.

When we introduced the preview of Aurora DSQL at AWS re:Invent 2024, our customers were excited by this innovative solution to simplify complex relational database challenges. In his keynote, Dr. Werner Vogels, CTO of Amazon.com, talked about managing complexity upfront in the design of Aurora DSQL. Unlike most traditional databases, Aurora DSQL is disaggregated into multiple independent components such as a query processor, adjudicator, journal, and crossbar.

These components have high cohesion, communicate through well-specified APIs, and scale independently based on your workloads. This architecture enables multi-Region strong consistency with low latency and globally synchronized time. To learn more about how Aurora DSQL works behind the scenes, watch Dr. Werner Vogels’ keynote and read about an Aurora DSQL story.

The architecture of Amazon Aurora DSQL
Your application can use the fastest distributed SQL reads and writes and scale to meet any workload demand without any database sharding or instance upgrades. With Aurora DSQL, its active-active distributed architecture is designed for 99.99 percent availability in a single Region and 99.999 percent availability across multiple Regions. This means your applications can continue to read and write with strong consistency, even in the rare case an application is unable to connect to a Region cluster endpoint.

In a single-Region configuration, Aurora DSQL commits all write transactions to a distributed transaction log and synchronously replicates all committed log data to user storage replicas in three Availability Zones. Cluster storage replicas are distributed across a storage fleet and automatically scale to ensure optimal read performance.

Multi-Region clusters provide the same resilience and connectivity as single-Region clusters while improving availability through two Regional endpoints, one for each peered cluster Region. Both endpoints of a peered cluster present a single logical database and support concurrent read and write operations with strong data consistency. A third Region acts as a log-only witness which means there is is no cluster resource or endpoint. This means you can balance applications and connections for geographic locations, performance, or resiliency purposes, making sure readers consistently see the same data.

Aurora DSQL is an ideal choice to support applications using microservices and event-driven architectures, and you can design highly scalable solutions for industries such as banking, ecommerce, travel, and retail. It’s also ideal for multi-tenant software as a service (SaaS) applications and data-driven services like payment processing, gaming platforms, and social media applications that require multi-Region scalability and resilience.

Getting started with Amazon Aurora DSQL
Aurora DSQL provides a easy-to-use experience, starting with a simple console experience. You can use familiar SQL clients to leverage existing skillsets, and integration with other AWS services to improve managing databases.

To create an Aurora DSQL cluster, go to the Aurora DSQL console and choose Create cluster. You can choose either Single-Region or Multi-Region configuration options to help you establish the right database infrastructure for your needs.

1. Create a single-Region cluster

To create a single-Region cluster, you only choose Create cluster. That’s all.

In a few minutes, you’ll see your Aurora DSQL cluster created. To connect your cluster, you can use your favorite SQL client such as PostgreSQL interactive terminal,  DBeaver, JetBrains DataGrip, or you can take various programmable approaches with a database endpoint and authentication token as a password. You can integrate with AWS Secrets Manager for automated token generation and rotation to secure and simplify managing credentials across your infrastructure.

To get the authentication token, choose Connect and Get Token in your cluster detail page. Copy the endpoint from Endpoint (Host) and the generated authentication token after Connect as admin is chosen in the Authentication token (Password) section.

Then, choose Open in CloudShell, and with a few clicks, you can seamlessly connect to your cluster.

After you connect the Aurora DSQL cluster, test your cluster by running sample SQL statements. You can also query SQL statements for your applications using your favorite programming languages: Python, Java, JavaScript, C++, Ruby, .NET, Rust, and Golang. You can build sample applications using a Django, Ruby on Rails, and AWS Lambda application to interact with Amazon Aurora DSQL.

2. Create a multi-Region cluster

To create a multi-Region cluster, you need to add the other cluster’s Amazon Resource Name (ARN) to peer the clusters.

To create the first cluster, choose Multi-Region in the console. You will also be required to choose the Witness Region, which receives data written to any peered Region but doesn’t have an endpoint. Choose Create cluster. If you already have a remote Region cluster, you can optionally enter its ARN.

Next, add an existing remote cluster or create your second cluster in another Region by choosing Create cluster.

Now, you can create the second cluster with your peer cluster ARN as the first cluster.

When the second cluster is created, you must peer the cluster in us-east-1 in order to complete the multi-Region creation.

Go to the first cluster page and choose Peer to confirm cluster peering for both clusters.

Now, your multi-Region cluster is created successfully. You can see details about the peers that are in other Regions in the Peers tab.

To get hands-on experience with Aurora DSQL, you can use this step-by-step workshop. It walks through the architecture, key considerations, and best practices as you build a sample retail rewards point application with active-active resiliency.

You can use the AWS SDKs, AWS Comand Line Interface (AWS CLI), and Aurora DSQL APIs to create and manage Aurora DSQL programmatically. To learn more, visit Setting up Aurora DSQL clusters in the Amazon Aurora DSQL User Guide.

What did we add after the preview?
We used your feedback and suggestions during the preview period to add new capabilities. We’ve highlighted a few of the new features and capabilities:

  • Console experience –We improved your cluster management experience to create and peer multi-Region clusters as well as easily connect using AWS CloudShell.
  • PostgreSQL features – We added support for views, unique secondary indexes for tables with existing data and launched Auto-Analyze which removes the need to manually maintain accurate table statistics. Learn about Aurora DSQL PostgreSQL-compatible features.
  • Integration with AWS services –We integrated various AWS services such as AWS Backup for a full snapshot backup and Aurora DSQL cluster restore, AWS PrivateLink for private network connectivity, AWS CloudFormation for managing Aurora DSQL resources, and AWS CloudTrail for logging Aurora DSQL operations.

Aurora DSQL now provides a Model Context Protocol (MCP) server to improve developer productivity by making it easy for your generative AI models and database to interact through natural language. For example, install Amazon Q Developer CLI and configure Aurora DSQL MCP server. Amazon Q Developer CLI now has access to an Aurora DSQL cluster. You can easily explore the schema of your database, understand the structure of the tables, and even execute complex SQL queries, all without having to write any additional integration code.

Now available
Amazon Aurora DSQL is available today in the AWS US East (N. Virginia), US East (Ohio), US West (Oregon) Regions for single- and multi-Region clusters (two peers and one witness Region), Asia Pacific (Osaka) and Asia Pacific (Tokyo) for single-Region clusters, and Europe (Ireland), Europe (London), and Europe (Paris) for single-Region clusters.

You’re billed on a monthly basis using a single normalized billing unit called Distributed Processing Unit (DPU) for all request-based activity such as read/write. Storage is based on the total size of your database and measured in GB-months. You are only charged for one logical copy of your data per single-Region cluster or multi-Region peered cluster. As a part of the AWS Free Tier, your first 100,000 DPUs and 1 GB-month of storage each month is free. To learn more, visit Amazon Aurora DSQL Pricing.

Give Aurora DSQL a try for free in the Aurora DSQL console. For more information, visit the Aurora DSQL User Guide and send feedback to AWS re:Post for Aurora DSQL or through your usual AWS support contacts.

— Channy