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The AWS Heroes program recognizes a vibrant, worldwide group of AWS experts whose enthusiasm for knowledge-sharing has a real impact within the community. Heroes go above and beyond to share knowledge in a variety of ways in developer community. We introduce our newest AWS Heroes in the second quarter of 2025.

To find and connect with more AWS Heroes near you, visit the categories in which they specialize Community Heroes, Container Heroes, Data Heroes, DevTools Heroes, Machine Learning Heroes, Security Heroes, and Serverless Heroes.

Last week’s launches
In addition to the inspiring celebrations, here are some AWS launches that caught my attention.

• Agentic capabilities for Amazon Q Developer Chat in the AWS Management Console and chat applications – In the AWS Management Console, Microsoft Teams, and Slack, Amazon Q Developer can now answer more complex queries by automatically identifying appropriate tools across more than 200 AWS services, breaking queries into executable steps, and showing its reasoning process as it works.
• Claude Sonnet 4 model on Amazon Q Developer CLI – Amazon Q Developer CLI now supports Anthropic’s Claude Sonnet 4 and allows developers to switch between premium models (Sonnet 4, 3.7, and 3.5) using simple commands like /model or q chat --model.
• Amazon API Gateway now supports dynamic request routing for REST APIs – Amazon API Gateway now supports routing rules for REST APIs using custom domain names, allowing traffic distribution based on HTTP headers, URL base paths, or both.
• Amazon Athena announces managed query results to streamline analysis workflows – Managed query results is a new feature that automatically stores, encrypts, and manages the lifecycle of query results for customers at no additional cost.
• A new monitoring dashboard in the AWS Network Firewall console – The new dashboard provides enhanced visibility into network traffic patterns, including top flows, TLS Server Name Indication (SNI), HTTP Host headers, long-lived TCP connections, and failed TCP handshakes.

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

Other AWS news
Here are some additional projects, blog posts that you might find interesting:

• Up to 45 percent price reduction for Amazon EC2 NVIDIA GPU-accelerated instances – AWS is reducing the price of NVIDIA GPU-accelerated Amazon EC2 instances (P4d, P4de, P5, and P5en) by up to 45 percent for On-Demand and Savings Plan usage. We are also making the very new P6-B200 instances available through Savings Plans to support large-scale deployments.
• Introducing public AWS API models – AWS now provides daily updates of Smithy API models on GitHub, enabling developers to build custom SDK clients, understand AWS API behaviors, and create developer tools for better AWS service integration.
• The AWS Asia Pacific (Taipei) Region is now open – The new Region provides customers with data residency requirements to securely store data in Taiwan while providing even lower latency. Customers across industries can benefit from the secure, scalable, and reliable cloud infrastructure to drive digital transformation and innovation.
• Amazon EC2 has simplified the AMI cleanup workflow – Amazon EC2 now supports automatically deleting underlying Amazon Elastic Block Store (Amazon EBS) snapshots when deregistering Amazon Machine Images (AMIs).
• The Lab where AWS designs custom chips – Visit Annapurna Labs in Austin, Texas—a combination of offices, workshops, and even a mini data center—where Amazon Web Services (AWS) engineers are designing the future of computing.

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

• Join re:Inforce from anywhere – If you aren’t able to make it to Philadelphia (June 16–18), tune in remotely. Get free access to the re:Inforce keynote and innovation talks live as they happen.
• 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: Shanghai (June 19 – 20), Milano (June 18), Mumbai (June 19) and Japan (June 25 – 26).
• AWS re:Invent – Mark your calendars for AWS re:Invent (December 1 – 5) in Las Vegas. Registration is now open
• 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: 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!

– Betty

This post was originally published on this site

Today, Amazon Web Services (AWS) announced that AWS Asia Pacific (Taipei) Region is generally available with three Availability Zones and Region code ap-east-2. The new Region brings AWS infrastructure and services closer to customers in Taiwan.

As the first infrastructure Region in Taipei and the fifteenth Region in Asia Pacific, the new Region expands the AWS global footprint to 117 Availability Zones across 37 geographic Regions worldwide. The new AWS Region will help developers, startups, and enterprises, as well as education, entertainment, financial services, healthcare, manufacturing, and nonprofit organizations run their applications and serve end users while maintaining data residency in Taiwan.

AWS in Taiwan

AWS has maintained a presence in Taiwan for more than a decade, starting with the opening of the AWS Taipei office in 2014. Since then, AWS has introduced many infrastructure offerings in Taiwan including:

In 2014, AWS launched the first Amazon CloudFront edge location and added another in 2018, offering customers a secure and efficient content delivery network for accelerating data, video, application, and API delivery worldwide.

In 2018, AWS established two AWS Direct Connect locations in Taiwan to enhance connectivity options. With the launch of the AWS Asia Pacific (Taipei) Region, we’ve added a new Direct Connect location in Taiwan to provide customers with higher speed and bandwidth.

In 2020, AWS launched AWS Outposts in Taiwan, helping customers seamlessly extend AWS infrastructure and services to their on-premises or edge locations for a consistent hybrid experience.

In 2022, AWS launched AWS Local Zone in Taipei to support low-latency applications requiring single-digit millisecond responsiveness.

Today, with the launch of the AWS Asia Pacific (Taipei) Region, we further strengthen our commitment to support innovation in Taiwan. Organizations in regulated industries will be able to store data locally while maintaining complete control over data location and movement. From high-tech manufacturing to semiconductor companies and small and medium enterprises (SMEs), businesses will gain access to the scalable infrastructure needed for growth and innovation.

AWS customers in Taiwan

Organizations across Taiwan are already using AWS to innovate and deliver differentiated experiences to their customers, for example:

Cathay Financial Holdings (CFH) is a leader in financial technology in Taiwan. It continuously introduces the latest technology to create a full-scenario financial service ecosystem. Since 2021, CFH has built a cloud environment on AWS that strengthens its security control and meets compliance requirements.

“Cathay Financial Holdings will continue to accelerate digital transformation in the industry, also improve the stability, security, timeliness, and scalability of our financial services,” said Marcus Yao, senior executive vice president of CFH. “With the new AWS Region in Taiwan, CFH is expected to provide customers with even more diverse and convenient financial services.”

Gamania Group is revolutionizing the entertainment landscape by integrating AI with celebrity IP through their innovative Vyin AI platform. Gamania utilized the robust and scalable infrastructure of AWS to develop secure, responsive AI interactions.

Benjamin Chen, chief strategy officer and head of Innovation Lab, said: “The core goal of Vyin AI is to create a digital identity that is fully interactive, lifelike, and safe to use. This demands technologies that are stable, responsive, and secure. To that end, we rely on the robust and resilient cloud infrastructure of AWS, and look forward to the low-latency advantages offered by the AWS Region in Taiwan. AWS provides a highly stable and secure environment for Vyin AI to provide users with secure and AI hallucination free interactions. AWS Cloud services allow us to focus more on core AI technology innovation and the enhancement of the ‘hyper-personalized interactive’ user experience, thereby accelerating product iteration and optimization.”

Chunghwa Telecom is a leader in cloud network services in Taiwan with the broadest mainstream 5G bandwidth, exceptional network speed, and globally recognized mobile internet capabilities. Chunghwa Telecom utilizes generative AI platforms such as Amazon Bedrock to build innovative services and create intelligent applications for various industries.

Dr. Rong-Shy Lin, president of CHT, stated: “With the launch of the AWS Region in Taiwan, CHT’s partnership with AWS has entered a new phase. We will deepen the integration of key advantages of the AWS Region, such as low latency and local data storage, combining them with CHT’s extensive backbone network, rich cloud experience, and professional team that has obtained multiple AWS Competency certifications. This will allow CHT to provide solutions that meet strict security and compliance requirements for government, financial, critical infrastructure, and highly regulated industries. At the same time, we are utilizing AWS technologies such as Amazon Bedrock to develop innovative applications and accelerate digital transformation and AI adoption. We will continue to provide optimized cloud and network services in Taiwan while supporting customers’ global expansion.”

AWS Partners in Taiwan

The AWS Partner Network in Taiwan plays a crucial role in helping customers adopt cloud technologies and maximize value from the new AWS Asia Pacific (Taipei) Region. These specialized partners combine deep technical expertise with local market knowledge to accelerate digital transformation across industries.

eCloudvalley Digital Technology Group is an AWS Premier Tier Services Partner with a team of cloud experts with more than 600 certifications.

“eCloudvalley Group has always embraced our mission of being a cloud evangelist, driving the adoption of cloud technology across Taiwan’s industries,” said MP Tsai, chairman of eCloudvalley Group. “With over a decade of close collaboration with AWS, we are honored to help more and more customers and industries move to the cloud while being part of customers’ digital transformation journey on AWS. We believe that the launch of the AWS Asia Pacific (Taipei) Region will further support Taiwan companies’ digital transformation and innovation in Taiwan with its world-leading cloud technology, while industries with higher local data residency requirements, such as finance and healthcare, will be able to further advance their cloud transformation journey.”

Nextlink Technology Inc. is an AWS Premier Consulting Partner, certified Managed Service Provider (MSP) and has AWS Level 1 Managed Security Service Provider (MSSP) and Government Consulting Competency.

“The investment of AWS in local infrastructure will help drive the digital transformation of Taiwan companies, boosting the development of various industries spanning from traditional industries to emerging digital sectors,” said Shasta Ho, the CEO of Nextlink Technology Inc. “We look forward to continuing working with AWS to help enterprises across industries deeply utilize the new AWS Asia Pacific (Taipei) Region. This local advantage will address customer needs in data localization, low latency, compliance, and high performance computing workloads. We also look forward to using AWS world-leading cloud technologies to power customers’ digital transformation journeys while contributing to the diversification of Taiwan’s economy.”

SAP has been a strategic partner of AWS for more than a decade, with thousands of enterprise customers worldwide running their SAP workloads on AWS.

“SAP is thrilled to see AWS establish new data centers in Taiwan,” said George Chen, SAP global vice president and managing director for Taiwan, Hong Kong, and Macau. “This investment provides Taiwan enterprises with greater choice, lower service latency, and enhanced operational flexibility. As a long-term strategic partner, SAP is committed to accelerating cloud transformation for these businesses. Through RISE with SAP, we can help customers seamlessly migrate to the cloud, enjoying greater flexibility, scalability, and reduced operational costs. By combining SAP’s enterprise solutions with the robust cloud platform of AWS, we’ll jointly empower Taiwan’s enterprises to unlock innovative AI applications and run their core businesses securely and reliably locally, driving Taiwan enterprise cloud transformation together.”

Supporting sustainable innovation in Taiwan

As Taiwan progresses toward its goal of net-zero emissions by 2050, AWS Cloud solutions are empowering organizations to enhance operational efficiency while reducing environmental impact. The new AWS Asia Pacific (Taipei) Region incorporates the AWS commitment to sustainability, helping organizations meet both technical and environmental objectives.

Ace Energy is a pioneer in Taiwan’s energy management sector. Since 2013, Ace Energy has been using AWS services such as Amazon Simple Storage Service (Amazon S3), Amazon Elastic Compute Cloud (Amazon EC2), and AWS IoT Core to provide innovative energy solutions through their Energy Saving Performance Contract model. Ace Energy has deployed energy management solutions across 1,000 locations, helped a semiconductor manufacturer reduce steam consumption by 65 percent, achieved 22 million new Taiwan dollars in annual energy savings, and decreased carbon emissions by 8,000 tons through their waste heat recovery technology.

Taiwan Power Company (Taipower) is Taiwan’s state power utility and has revolutionized its operations through AWS since 2018. By implementing smart grid technologies with drones, robotics, and virtual reality for smart patrol, Taipower has enhanced customer experience through the “Taiwan Power” application. The company has improved operational efficiency through data-driven decision-making and earned six consecutive Platinum Awards in the Corporate Sustainability category at the Taiwan Corporate Sustainability Awards.

Building cloud skills together

Since 2014, AWS has built comprehensive programs for cloud education and skills development in Taiwan. For example, educational programs such as AWS Academy, AWS Educate, and AWS Skill Builder have helped train more than 200,000 people in Taiwan on cloud skills. These programs will expand alongside our infrastructure investments to build a foundation for Taiwan’s digital future.

Taiwan boasts a vibrant AWS community that welcomes your involvement. Take part in knowledge-sharing and networking at local AWS User Groups in Taipei, engage with the four celebrated AWS Heroes in Taiwan, or consider becoming part of the growing community of AWS enthusiasts by joining the ranks of the 17 AWS Community Builders already contributing to Taiwan’s cloud ecosystem. All these community connections provide valuable opportunities to accelerate your cloud journey through local expertise and collaborative learning.

Stay tuned
The AWS Asia Pacific (Taipei) Region is ready to support your business. You can find a detailed list of the services available in this Region on the AWS Services by Region page. For news about AWS Region openings, check out the Regional news of the AWS News Blog.

Start building on the Asia Pacific (Taipei) Region now.

–Betty

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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.

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.

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.

• 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

This post was originally published on this site

Seismic imaging is a geophysical technique used to create detailed pictures of the Earth’s subsurface structure. It works by generating seismic waves that travel into the ground, reflect off various rock layers and structures, and return to the surface where they’re detected by sensitive instruments known as geophones or hydrophones. The huge volumes of acquired data often reach petabytes for a single survey and this presents significant storage, processing, and management challenges for researchers and energy companies.

Customers who run these seismic imaging workloads or other high performance computing (HPC) workloads, such as weather forecasting, advanced driver-assistance system (ADAS) training, or genomics analysis, already store the huge volumes of data on either hard disk drive (HDD)-based or a combination of HDD and solid state drive (SSD) file storage on premises. However, as these on premises datasets and workloads scale, customers find it increasingly challenging and expensive due to the need to make upfront capital investments to keep up with performance needs of their workloads and avoid running out of storage capacity.

Today, we’re announcing the general availability of the Amazon FSx for Lustre Intelligent-Tiering, a new storage class that delivers virtually unlimited scalability, the only fully elastic Lustre file storage, and the lowest cost Lustre file storage in the cloud. With a starting price of less than $0.005 per GB-month, FSx for Lustre Intelligent-Tiering offers the lowest cost high-performance file storage in the cloud, reducing storage costs for infrequently accessed data by up to 96 percent compared to other managed Lustre options. Elasticity means you no longer need to provision storage capacity upfront because your file system will grow and shrink as you add or delete data, and you pay only for the amount of data you store.

FSx for Lustre Intelligent-Tiering 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. Intelligent-Tiering delivers high performance whether you’re starting with gigabytes of experimental data or working with large petabyte-scale datasets for your most demanding artificial intelligence/machine learning (AI/ML) and HPC workloads. With the flexibility to adjust your file system’s performance independent of storage, Intelligent-Tiering delivers up to 34 percent better price performance than on premises HDD file systems. The Intelligent-Tiering storage class is optimized for HDD-based or mixed HDD/SSD workloads that have a combination of hot and cold data. You can migrate and run such workloads to FSx for Lustre Intelligent-Tiering without application changes, eliminating storage capacity planning and management, while paying only for the resources that you use.

Prior to this launch, customers used the FSx for Lustre SSD storage class to accelerate ML and HPC workloads that need all-SSD performance and consistent low-latency access to all data. However, many workloads have a combination of hot and cold data and they don’t need all-SSD storage for colder portions of the data. FSx for Lustre is increasingly used in AI/ML workloads to increase graphics processing unit (GPU) utilization, and now it’s even more cost optimized to be one of the options for these workloads.

FSx for Lustre Intelligent-Tiering
Your data moves between three storage tiers (Frequent Access, Infrequent Access, and Archive) with no effort on your part, so you get automatic cost savings with no upfront costs or commitments. The tiering works as follows:

Frequent Access – Data that has been accessed within the last 30 days is stored in this tier.

Infrequent Access – Data that hasn’t been accessed for 30 – 90 days is stored in this tier, at a 44 percent cost reduction from Frequent Access.

Archive – Data that hasn’t been accessed for 90 or more days is stored in this tier, at a 65 percent cost reduction compared to Infrequent Access.

Regardless of the storage tier, your data is stored across multiple AWS Availability Zones for redundancy and availability, compared to typical on-premises implementations, which are usually confined within a single physical location. Additionally, your data can be retrieved instantly in milliseconds.

Creating a file system
I can create a file system using the AWS Management Console, AWS Command Line Interface (AWS CLI), API, or AWS CloudFormation. On the console, I choose Create file system to get started.

I select Amazon FSx for Lustre and choose Next.

Now, it’s time to enter the rest of the information to create the file system. I enter a name (veliswa_fsxINT_1) for my file system, and for deployment and storage class, I select Persistent, Intelligent-Tiering. I choose the desired Throughput capacity and the Metadata IOPS. The SSD read cache will be automatically configured by FSx for Lustre based on the specified throughput capacity. I leave the rest as the default, choose Next, and review my choices to create my file system.

With Amazon FSx for Lustre Intelligent-Tiering, you have the flexibility to provision the necessary performance for your workloads without having to provision any underlying storage capacity upfront.

I wanted to know which values were editable after creation, so I paid closer attention before finalizing the creation of the file system. I noted that Throughput capacity, Metadata IOPS, Security groups, SSD read cache, and a few others were editable later. After I start running the ML jobs, it might be necessary to increase the throughput capacity based on the volumes of data I’ll be processing, so this information is important to me.

The file system is now available. Considering that I’ll be running HPC workloads, I anticipate that I’ll be processing high volumes of data later, so I’ll increase the throughput capacity to 24 GB/s. After all, I only pay for the resources I use.

The SSD read cache is scaled automatically as your performance needs increase. You can adjust the cache size any time independently in user-provisioned mode or disable the read cache if you don’t need low-latency access.

Good to know

• FSx for Lustre Intelligent-Tiering is designed to deliver up to multiple terabytes per second of total throughput.
• FSx for Lustre with Elastic Fabric Adapter (EFA)/GPU Direct Storage (GDS) support provides up to 12x (up to 1200 Gbps) higher per-client throughput compared to the previous FSx for Lustre systems.
• It can deliver up to tens of millions of IOPS for writes and cached reads. Data in the SSD read cache has submillisecond time-to-first-byte latencies, and all other data has time-to-first-byte latencies in the range of tens of milliseconds.

Now available
Here are a couple of things to keep in mind:

FSx Intelligent-Tiering storage class is available in the new FSx for Lustre file systems in the US East (N. Virginia, Ohio), US West (N. California, Oregon), Canada (Central), Europe (Frankfurt, Ireland, London, Stockholm), and Asia Pacific (Hong Kong, Mumbai, Seoul, Singapore, Sydney, Tokyo) AWS Regions.

You pay for data and metadata you store on your file system (GB/months). When you write data or when you read data that is not in the SSD read cache, you pay per operation. You pay for the total throughput capacity (in MBps/month), metadata IOPS (IOPS/month), and SSD read cache size for data and metadata (GB/month) you provision on your file system. To learn more, visit the Amazon FSx for Lustre Pricing page. To learn more about Amazon FSx for Lustre including this feature, visit the Amazon FSx for Lustre page.

Give Amazon FSx for Lustre Intelligent-Tiering a try in the Amazon FSx console today and send feedback to AWS re:Post for Amazon FSx for Lustre or through your usual AWS Support contacts.

– Veliswa.

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Today, we’re introducing specialized Model Context Protocol (MCP) servers for Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Kubernetes Service (Amazon EKS), and AWS Serverless, now available in the AWS Labs GitHub repository. These open source solutions extend AI development assistants capabilities with real-time, contextual responses that go beyond their pre-trained knowledge. While Large Language Models (LLM) within AI assistants rely on public documentation, MCP servers deliver current context and service-specific guidance to help you prevent common deployment errors and provide more accurate service interactions.

You can use these open source solutions to develop applications faster, using up-to-date knowledge of Amazon Web Services (AWS) capabilities and configurations during the build and deployment process. Whether you’re writing code in your integrated development environment (IDE), or debugging production issues, these MCP servers support AI code assistants with deep understanding of Amazon ECS, Amazon EKS, and AWS Serverless capabilities, accelerating the journey from code to production. They work with popular AI-enabled IDEs, including Amazon Q Developer on the command line (CLI), to help you build and deploy applications using natural language commands.

• The Amazon ECS MCP Server containerizes and deploys applications to Amazon ECS within minutes by configuring all relevant AWS resources, including load balancers, networking, auto-scaling, monitoring, Amazon ECS task definitions, and services. Using natural language instructions, you can manage cluster operations, implement auto-scaling strategies, and use real-time troubleshooting capabilities to identify and resolve deployment issues quickly.
• For Kubernetes environments, the Amazon EKS MCP Server provides AI assistants with up-to-date, contextual information about your specific EKS environment. It offers access to the latest EKS features, knowledge base, and cluster state information. This gives AI code assistants more accurate, tailored guidance throughout the application lifecycle, from initial setup to production deployment.
• The AWS Serverless MCP Server enhances the serverless development experience by providing AI coding assistants with comprehensive knowledge of serverless patterns, best practices, and AWS services. Using AWS Serverless Application Model Command Line Interface (AWS SAM CLI) integration, you can handle events and deploy infrastructure while implementing proven architectural patterns. This integration streamlines function lifecycles, service integrations, and operational requirements throughout your application development process. The server also provides contextual guidance for infrastructure as code decisions, AWS Lambda specific best practices, and event schemas for AWS Lambda event source mappings.

Let’s see it in action
If this is your first time using AWS MCP servers, visit the Installation and Setup guide in the AWS Labs GitHub repository to installation instructions. Once installed, add the following MCP server configuration to your local setup:

Install Amazon Q for command line and add the configuration to ~/.aws/amazonq/mcp.json. If you’re already an Amazon Q CLI user, add only the configuration.

{
  "mcpServers": {
    "awslabs.aws-serverless-mcp":  {
      "command": "uvx",
      "timeout": 60,
      "args": ["awslabs.aws_serverless_mcp_server@latest"],
    },
    "awslabs.ecs-mcp-server": {
      "disabled": false,
      "command": "uv",
      "timeout": 60,
      "args": ["awslabs.ecs-mcp-server@latest"],
    },
    "awslabs.eks-mcp-server": {
      "disabled": false,
      "timeout": 60,
      "command": "uv",
      "args": ["awslabs.eks-mcp-server@latest"],
    }
  }
}

For this demo I’m going to use the Amazon Q CLI to create an application that understands video using 02_using_converse_api.ipynb from Amazon Nova model cookbook repository as sample code. To do this, I send the following prompt:

I want to create a backend application that automatically extracts metadata and understands the content of images and videos uploaded to an S3 bucket and stores that information in a database. I'd like to use a serverless system for processing. Could you generate everything I need, including the code and commands or steps to set up the necessary infrastructure, for it to work from start to finish? - Use 02_using_converse_api.ipynb as example code for the image and video understanding.

Amazon Q CLI identifies the necessary tools, including the MCP serverawslabs.aws-serverless-mcp-server. Through a single interaction, the AWS Serverless MCP server determines all requirements and best practices for building a robust architecture.

I ask to Amazon Q CLI that build and test the application, but encountered an error. Amazon Q CLI quickly resolved the issue using available tools. I verified success by checking the record created in the Amazon DynamoDB table and testing the application with the dog2.jpeg file.

To enhance video processing capabilities, I decided to migrate my media analysis application to a containerized architecture. I used this prompt:

I'd like you to create a simple application like the media analysis one, but instead of being serverless, it should be containerized. Please help me build it in a new CDK stack.

Amazon Q Developer begins building the application. I took advantage of this time to grab a coffee. When I returned to my desk, coffee in hand, I was pleasantly surprised to find the application ready. To ensure everything was up to current standards, I simply asked:

please review the code and all app using the awslabsecs_mcp_server tools 

Amazon Q Developer CLI gives me a summary with all the improvements and a conclusion.

I ask it to make all the necessary changes, once ready I ask Amazon Q developer CLI to deploy it in my account, all using natural language.

After a few minutes, I review that I have a complete containerized application from the S3 bucket to all the necessary networking.

I ask Amazon Q developer CLI to test the app send it the-sea.mp4 video file and received a timed out error, so Amazon Q CLI decides to use the fetch_task_logs from awslabsecs_mcp_server tool to review the logs, identify the error and then fix it.

After a new deployment, I try it again, and the application successfully processed the video file

I can see the records in my Amazon DynamoDB table.

To test the Amazon EKS MCP server, I have code for a web app in the auction-website-main folder and I want to build a web robust app, for that I asked Amazon Q CLI to help me with this prompt:

Create a web application using the existing code in the auction-website-main folder. This application will grow, so I would like to create it in a new EKS cluster

Once the Docker file is created, Amazon Q CLI identifies generate_app_manifests from awslabseks_mcp_server as a reliable tool to create a Kubernetes manifests for the application.

Then create a new EKS cluster using the manage_eks_staks tool.

Once the app is ready, the Amazon Q CLI deploys it and gives me a summary of what it created.

I can see the cluster status in the console.

After a few minutes and resolving a couple of issues using the search_eks_troubleshoot_guide tool the application is ready to use.

Now I have a Kitties marketplace web app, deployed on Amazon EKS using only natural language commands through Amazon Q CLI.

Get started today
Visit the AWS Labs GitHub repository to start using these AWS MCP servers and enhance your AI-powered developmen there. The repository includes implementation guides, example configurations, and additional specialized servers to run AWS Lambda function, which transforms your existing AWS Lambda functions into AI-accessible tools without code modifications, and Amazon Bedrock Knowledge Bases Retrieval MCP server, which provides seamless access to your Amazon Bedrock knowledge bases. Other AWS specialized servers in the repository include documentation, example configurations, and implementation guides to begin building applications with greater speed and reliability.

To learn more about MCP Servers for AWS Serverless and Containers and how they can transform your AI-assisted application development, visit the Introducing AWS Serverless MCP Server: AI-powered development for modern applications, Automating AI-assisted container deployments with the Amazon ECS MCP Server, and Accelerating application development with the Amazon EKS MCP server deep-dive blogs.

— Eli

This post was originally published on this site

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

This post was originally published on this site

Today, we are announcing EKS Dashboard, a centralized display that enables cloud architects and cluster administrators to maintain organization-wide visibility across their Kubernetes clusters. With EKS Dashboard, customers can now monitor clusters deployed across different AWS Regions and accounts through a unified view, making it easier to track cluster inventory, assess compliance, and plan operational activities like version upgrades.

As organizations scale their Kubernetes deployments, they often run multiple clusters across different environments to enhance availability, ensure business continuity, or maintain data sovereignty. However, this distributed approach can make it challenging to maintain visibility and control, especially in decentralized setups spanning multiple Regions and accounts. Today, many customers resort to third-party tools for centralized cluster visibility, which adds complexity through identity and access setup, licensing costs, and maintenance overhead.

EKS Dashboard simplifies this experience by providing native dashboard capabilities within the AWS Console. The Dashboard provides insights into 3 different resources including clusters, managed node groups, and EKS add-ons, offering aggregated insights into cluster distribution by Region, account, version, support status, forecasted extended support EKS control plane costs, and cluster health metrics. Customers can drill down into specific data points with automatic filtering, enabling them to quickly identify and focus on clusters requiring attention.

Setting up EKS Dashboard

Customers can access the Dashboard in EKS console through AWS Organizations’ management and delegated administrator accounts. The setup process is straightforward and includes simply enabling trusted access as a one-time setup in the Amazon EKS console’s organizations settings page. Trusted access is available from the Dashboard settings page. Enabling trusted access will allow the management account to view the Dashboard. For more information on setup and configuration, see the official AWS Documentation.

A quick tour of EKS Dashboard

The dashboard provides both graphical, tabular, and map views of your Kubernetes clusters, with advanced filtering, and search capabilities. You can also export data for further analysis or custom reporting.

Beyond EKS clusters

EKS Dashboard isn’t limited to just Amazon EKS clusters; it can also provide visibility into connected Kubernetes clusters running on-premises or on other cloud providers. While connected clusters may have limited data fidelity compared to native Amazon EKS clusters, this capability enables truly unified visibility for organizations running hybrid or multi-cloud environments.

Available now

EKS Dashboard is available today in the US East (N. Virginia) Region and is able to aggregate data from all commercial AWS Regions. There is no additional charge for using the EKS Dashboard. To learn more, visit the Amazon EKS documentation.

This new capability demonstrates our continued commitment to simplifying Kubernetes operations for our customers, enabling them to focus on building and scaling their applications rather than managing infrastructure. We’re excited to see how customers use EKS Dashboard to enhance their Kubernetes operations.

— Micah;