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As I was preparing for this week’s roundup, I couldn’t help but reflect on how database technology has evolved over the past decade. It’s fascinating to see how architectural decisions made years ago continue to shape the way we build modern applications. This week brings a special milestone that perfectly captures this evolution in cloud database innovation as Amazon Aurora celebrated 10 years of database innovation.

Amazon Web Services (AWS) Vice President Swami Sivasubramanian reflected on LinkedIn about his journey with Amazon Aurora, calling it “one of the most interesting products” he’s worked on. When Aurora launched in 2015, it shifted the database landscape by separating compute and storage. Now trusted by hundreds of thousands of customers across industries, Aurora has grown from a MySQL-compatible database to a comprehensive platform featuring innovations such as Aurora DSQL, serverless capabilities, I/O-Optimized pricing, zero-ETL integrations, and generative AI support. Last week’s celebration on August 21 highlighted this decade-long transformation that continues to simplify database scaling for customers.

Last week’s launches

In addition to the inspiring celebrations, here are some AWS launches that caught my attention:

• AWS Billing and Cost Management introduces customizable Dashboards — This new feature consolidates cost data into visual dashboards with multiple widget types and visualization options, combining information from Cost Explorer, Savings Plans, and Reserved Instance reports to help organizations track spending patterns and share standardized cost reporting across accounts.
• Amazon Bedrock simplifies access to OpenAI open weight models — AWS has streamlined access to OpenAI’s open weight models (gpt-oss-120b and gpt-oss-20b), making them automatically available to all users without manual activation while maintaining administrator control through IAM policies and service control policies.
• Amazon Bedrock adds batch inference support for Claude Sonnet 4 and GPT-OSS models —This feature provides asynchronous processing of multiple inference requests with 50 percent lower pricing compared to on-demand inference, optimizing high-volume AI tasks such as document analysis, content generation, and data extraction with Amazon CloudWatch metrics for tracking batch workload progress
• AWS launching Amazon EC2 R8i and R8i-flex memory-optimized instances — Powered by custom Intel Xeon 6 processors, these new instances deliver up to 20 percent better performance and 2.5 times higher memory throughput than R7i instances, making them ideal for memory-intensive workloads like databases and big data analytics, with R8i-flex offering additional cost savings for applications that don’t fully utilize compute resources.
• Amazon S3 introduces batch data verification feature — A new capability in S3 Batch Operations that offers efficient verification of billions of objects using multiple checksum algorithms without downloading or restoring data, generating detailed integrity reports for compliance and audit purposes regardless of storage class or object size.

Other AWS news

Here are some additional projects and blog posts that you might find interesting:

• Amazon introduces DeepFleet foundation models for multirobot coordination — Trained on millions of hours of data from Amazon fulfillment and sortation centers, these pioneering models predict future traffic patterns for robot fleets, representing the first foundation models specifically designed for coordinating multiple robots in complex environments.
• Building Strands Agents with a few lines of code — A new blog demonstrates how to build multi-agent AI systems with a few lines of code, enabling specialized agents to collaborate seamlessly, handle complex workflows, and share information through standardized protocols for creating distributed AI systems beyond individual agent capabilities.
• AWS Security Incident Response introduces ITSM integrations — New integrations with Jira and ServiceNow provide bidirectional synchronization of security incidents, comments, and attachments, streamlining response while maintaining existing processes, with open source code available on GitHub for customization and extension to additional IT service management (ITSM) platforms.
• Finding root-causes using a network digital twin graph and agentic AI — A detailed blog post shows how AWS collaborated with NTT DOCOMO to build a network digital twin using graph databases and autonomous AI agents, helping telecom operators to move beyond correlation to identify true root causes of complex network issues, predict future problems, and improve overall service reliability.

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: Toronto (September 4), Los Angeles (September 17), and Bogotá (October 9).
• AWS re:Invent 2025 — This flagship annual conference is coming to Las Vegas from December 1–5. The event catalog is now available. Mark your calendars for this not to be missed gathering of the AWS community.
• 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: Adria (September 5), Baltic (September 10), Aotearoa (September 18), South Africa (September 20), Bolivia (September 20), Portugal (September 27).

Join the AWS Builder Center to learn, build, and connect with builders in the AWS community. Browse here for upcoming in-person and virtual developer-focused events.

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

– Betty

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Let me start this week’s update with something I’m especially excited about – the upcoming BeSA (Become a Solutions Architect) cohort. BeSA is a free mentoring program that I host along with a few other AWS employees on a volunteer basis to help people excel in their cloud careers. Last week, the instructors’ lineup was finalized for the 6-week cohort starting September 6. The cohort will focus on migration and modernization on AWS. Visit the BeSA website to learn more.

Another highlight for me last week was the announcement of six new AWS Heroes for their technical leadership and exceptional contributions to the AWS community. Read the full announcement to learn more about these community leaders.

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

• Amazon EC2 Single GPU P5 instances are now generally available — You can right-size your machine learning (ML) and high performance computing (HPC) resources cost-effectively with the new Amazon Elastic Compute Cloud (Amazon EC2) P5 instance size with one NVIDIA H100 GPU.
• AWS Advanced Go Driver is generally available — You can now use the AWS Advanced Go Driver with Amazon Relational Database Service (Amazon RDS) and Amazon Aurora PostgreSQL-Compatible and MySQL-Compatible database clusters for faster switchover and failover times, Federated Authentication, and authentication with AWS Secrets Manager or AWS Identity and Access Management (IAM). You can install the PostgreSQL and MySQL packages for Windows, Mac, or Linux, by following the installation guides in GitHub.
• Expanded support for Cilium with Amazon EKS Hybrid Nodes — Cilium is a Cloud Native Computing Foundation (CNCF) graduated project that provides core networking capabilities for Kubernetes workloads. Now, you can receive support from AWS for a broader set of Cilium features when using Cilium with Amazon EKS Hybrid Nodes including application ingress, in-cluster load balancing, Kubernetes network policies, and kube-proxy replacement mode.
• Amazon SageMaker AI now supports P6e-GB200 UltraServers — You can accelerate training and deployment of foundational models (FMs) at trillion-parameter scale by using up to 72 NVIDIA Blackwell GPUs under one NVLink domain with the new P6e-GB200 UltraServer support in Amazon SageMaker HyperPod and Model Training.
• Amazon SageMaker HyperPod now supports fine-grained quota allocation of compute resources, topology-aware-scheduling of LLM tasks and custom Amazon Machine Images (AMIs) — You can allocate fine-grained compute quota for GPU, Trainium accelerator, vCPU, and vCPU memory within an instance to optimize compute resource distribution. With topology-aware scheduling, you can schedule your large language model (LLM) tasks on an optimal network topology to minimize network communication and enhance training efficiency. Using custom AMIs, you can deploy clusters with pre-configured, security-hardened environments that meet your specific organizational requirements.

Additional updates
Here are some additional news items and blog posts that I found interesting:

• Celebrating 10 years of Amazon Aurora innovation — Join the livestream event on August 21, 2025, to celebrate a decade of Aurora database innovation.
• AWS named as a Leader in 2025 Gartner Magic Quadrant for Strategic Cloud Platform Services — For the fifteenth consecutive year, Gartner has named AWS a Leader in the Gartner Magic Quadrant for Strategic Cloud Platform Services (SCPS), making AWS the longest-running Magic Quadrant Leader.
• Introducing AWS Cloud Control API (CCAPI) MCP Server — You can now use natural language to managing cloud infrastructure using CCAPI MCP Server. You can create, read, update, delete, and list resources using natural language.
• Introducing Amazon Bedrock AgentCore Identity — AgentCore Identity provides a centralized capability to manage agent identities, securing credentials, and supporting seamless integration with AWS and third-party services through Sigv4, standardized OAuth 2.0 flows, and API key.
• Introducing Amazon Bedrock AgentCore Gateway — A fully managed service to connect AI agents with tools and services. It serves as a centralized tool server, providing a unified interface where agents can discover, access, and invoke tools.

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

• AWS re:Invent 2025 (December 1-5, 2025, Las Vegas) — The AWS flagship annual conference offering collaborative innovation through peer-to-peer learning, expert-led discussions, and invaluable networking opportunities.
• AWS Summits — Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Coming up soon are summits in Johannesburg (August 20) and Toronto (September 4).
• 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: Adria (September 5), Baltic (September 10), Aotearoa (September 18), and South Africa (September 20).

Join the AWS Builder Center to learn, build, and connect with builders in the AWS community. Browse here for upcoming in-person and virtual developer-focused events.

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

– Prasad

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On August 4, 2025, Gartner published its Gartner Magic Quadrant for Strategic Cloud Platform Services (SCPS). Amazon Web Services (AWS) is the longest-running Magic Quadrant Leader, with Gartner naming AWS a Leader for the fifteenth consecutive year.

In the report, Gartner once again placed AWS highest on the “Ability to Execute” axis. We believe this reflects our ongoing commitment to giving customers the broadest and deepest set of capabilities to accelerate innovation as well as unparalleled security, reliability, and performance they can trust for their most critical applications.

Here is the graphical representation of the 2025 Magic Quadrant for Strategic Cloud Platform Services.

Gartner recognized AWS strengths as:

• Largest cloud community – AWS has built a strong global community of cloud professionals, providing significant opportunities for learning and engagement.
• Cloud-inspired silicon – AWS has used its cloud computing experience to develop custom silicon designs, including AWS Graviton, AWS Inferentia, and AWS Trainium, which enable tighter integration between hardware and software, improved power efficiency, and greater control over supply chains.
• Global scale and operational execution – AWS’s significant share of global cloud market revenue has enabled it to build a larger and more robust network of integration partners than some other providers in this analysis, which in turn helps organizations successfully adopt cloud.

The most common feedback I hear from customers is that AWS has the largest and most dynamic cloud community, making it easy to ask questions and learn from millions of active customers and tens of thousands of partners globally. We recently launched our community hub, AWS Builder Center to connect directly with AWS Heroes and AWS Community Builders. You can also explore and join AWS User Groups and AWS Cloud Clubs in a city near you.

We have also focused on facilitating the digital transformation of enterprise customers through a number of enterprise programs, such as the AWS Migration Acceleration Program. Using generative AI on migration and modernization, we introduced AWS Transform, the first agentic AI service developed to accelerate enterprise modernization of mission-critical business workloads such as .NET, mainframe, and VMware.

Access the complete full Gartner report to learn more. It outlines the methodology and evaluation criteria used to develop their assessments of each cloud service provider included in the report. This report can serve as a guide when choosing a cloud provider that helps you innovate on behalf of your customers.

— Channy

Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner and Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.

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Ten years ago, we announced the general availability of Amazon Aurora, a database that combined the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases.

As Jeff described it in its launch blog post: “With storage replicated both within and across three Availability Zones, along with an update model driven by quorum writes, Amazon Aurora is designed to deliver high performance and 99.99% availability while easily and efficiently scaling to up to 64 TiB of storage.”

When we started developing Aurora over a decade ago, we made a fundamental architectural decision that would change the database landscape forever: we decoupled storage from compute. This novel approach enabled Aurora to deliver the performance and availability of commercial databases at one-tenth the cost.

This is one of the reasons why hundreds of thousands of AWS customers choose Aurora as their relational database.

Today, I’m excited to invite you to join us for a livestream event on August 21, 2025, to celebrate a decade of Aurora database innovation.

A brief look back at the past
Throughout the evolution of Aurora, we’ve focused on four core innovation themes: security as our top priority, scalability to meet growing workloads, predictable pricing for better cost management, and multi-Region capabilities for global applications. Let me walk you through some key milestones in the Aurora journey.

We previewed Aurora at re:Invent 2014, and made it generally available in July 2015. At launch, we presented Aurora as “a new cost-effective MySQL-compatible database engine.”

In June 2016, we introduced reader endpoints and cross-Region read replicas, followed by AWS Lambda integration and the ability to load tables directly from Amazon S3 in October. We added database cloning and export to Amazon S3 capabilities in June 2017 and full compatibility with PostgreSQL in October that year.

The journey continued with the serverless preview in November 2017, which became generally available in August 2018. Global Database launched in November 2018 for cross-Region disaster recovery. We introduced blue/green deployments to simplify database updates, and optimized read instances to improve query performance.

In 2023, we added vector capabilities with pgvector for similarity search for Aurora PostgreSQL, and Aurora I/O-Optimized to provide predictable pricing with up to 40 percent cost savings for I/O-intensive applications. We launched Aurora zero-ETL integration with Amazon Redshift which enables near real-time analytics and ML using Amazon Redshift on petabytes of transactional data from Aurora by removing the need for you to build and maintain complex data pipelines that perform extract, transform, and load (ETL) operations. This year we added Aurora MySQL zero-ETL integration with Amazon Sagemaker, enabling near real-time access of your data in the lakehouse architecture of SageMaker to run a broad range of analytics.

In 2024, we made it as effortless as just one click to select Aurora PostgreSQL as a vector store for Amazon Bedrock Knowledge Bases and launched Aurora PostgreSQL Limitless Database, a serverless horizontal scaling (sharding) capability.

To simplify scaling for customers, we also increased the maximum storage to 128 TiB in September 2020, allowing many applications to operate within a single instance. Last month, we’ve further simplified scaling by doubling the maximum storage to 256 TiB, with no upfront provisioning required and pay-as-you-go pricing based on actual storage used. This enables even more customers to run their growing workloads without the complexity of managing multiple instances while maintaining cost efficiency.

Most recently, at re:Invent 2024, we announced Amazon Aurora DSQL, which became generally available in May 2025. Aurora DSQL represents our latest innovation in distributed SQL databases, offering active-active high availability and multi-Region strong consistency. It’s the fastest serverless distributed SQL database for always available applications, effortlessly scaling to meet any workload demand with zero infrastructure management.

Aurora DSQL builds on our original architectural principles of separation of storage and compute, taking them further with independent scaling of reads, writes, compute, and storage. It provides 99.99% single-Region and 99.999% multi-Region availability, with strong consistency across all Regional endpoints.

And in June, we launched Model Context Protocol (MCP) servers for Aurora, so you can integrate your AI agents with your data sources and services.

Let’s celebrate 10 years of innovation
By attending the August 21 livestream event, you’ll hear from Aurora technical leaders and founders, including Swami Sivasubramanian, Ganapathy (G2) Krishnamoorthy, Yan Leshinsky, Grant McAlister, and Raman Mittal. You’ll learn directly from the architects who pioneered the separation of compute and storage in cloud databases, with technical insights into Aurora architecture and scaling capabilities. You’ll also get a glimpse into the future of database technology as Aurora engineers share their vision and discuss the complex challenges they’re working to solve on behalf of customers.

The event also offers practical demonstrations that show you how to implement key features. You’ll see how to build AI-powered applications using pgvector, understand cost optimization with the new Aurora DSQL pricing model, and learn how to achieve multi-Region strong consistency for global applications.

The interactive format includes Q&A opportunities with Aurora experts, so you’ll be able to get your specific technical questions answered. You can also receive AWS credits to test new Aurora capabilities.

If you’re interested in agentic AI, you’ll particularly benefit from the sessions on MCP servers, Strands Agents, and how to integrate Strands Agents with Aurora DSQL, which demonstrate how to safely integrate AI capabilities with your Aurora databases while maintaining control over database access.

Whether you’re running mission-critical workloads or building new applications, these sessions will help you understand how to use the latest Aurora features.

Register today to secure your spot and be part of this celebration of database innovation.

To the next decade of Aurora innovation!

— seb

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We are excited to announce the latest cohort of AWS Heroes, recognized for their exceptional contributions and technical leadership. These passionate individuals represent diverse regions and technical specialties, demonstrating notable expertise and dedication to knowledge sharing within the AWS community. From AI and machine learning to serverless architectures and security, our new Heroes showcase the breadth of cloud innovation while fostering inclusive and engaging technical communities. Join us in welcoming these community leaders who are helping to shape the future of cloud computing and inspiring the next generation of AWS builders.

Kristine Armiyants – Masis, Armenia

Community Hero Kristine Armiyants is a software engineer and cloud support engineer who transitioned into technology from a background in finance, having earned an MBA before becoming self-taught in software development. As the founder and leader of AWS User Group Armenia for over 2.5 years, she has transformed the local tech landscape by organizing Armenia’s first AWS Community Day, scaling it from 320 to 440+ attendees, and leading a team that brings international-scale events to her country. Through her technical articles in Armenian, hands-on workshops, and “no-filter” blog series, she makes cloud knowledge more accessible while mentoring new user group organizers and early-career engineers. Her dedication to community building has resulted in five new AWS Community Builders from Armenia, demonstrating her commitment to creating inclusive spaces for learning and growth in the AWS community.

Nadia Reyhani – Perth, Australia

Machine Learning Hero Nadia Reyhani is an AI Product Engineer who integrates DevOps best practices with machine learning systems. She is a former AWS Community Builder and regularly presents at AWS events on building scalable AI solutions using Amazon SageMaker and Bedrock. As a Women in Digital Ambassador, she combines technical expertise with advocacy, creating inclusive spaces for underrepresented groups in cloud and AI technologies.

Raphael Manke – Karlsruhe, Germany

DevTools Hero Raphael Manke is a Senior Product Engineer at Dash0 and the creator of the unofficial AWS re:Invent planner, which is used to help build a schedule for the event. With a decade of AWS experience, he specializes in serverless technologies and DevTools that streamline cloud development. As the organizer of the AWS User Group in Karlsruhe and a former AWS Community Builder, he actively contributes to product enhancement through public speaking and direct collaboration with AWS service teams. His commitment to the AWS community spans from local user group leadership to providing valuable feedback to service teams.

Rowan Udell – Brisbane, Australia

Security Hero Rowan Udell is an independent AWS security consultant specializing in AWS Identity and Access Management (IAM). He has been sharing AWS security expertise for over a decade through books, blog posts, meet-ups, workshops, and conference presentations. Rowan has taken part in many AWS community programs, was an AWS Community Builder for four years, and is part of the AWS Community Day Australia Organizing Committee. A frequent speaker at AWS events including Sydney Summit and other community meetups, Rowan is known for transforming complex security concepts into simple, practical, and workable solutions for businesses securing their AWS environments.

Sangwoon (Chris) Park – Seoul, Korea

Serverless Hero Sangwoon (Chris) Park leads development at RECON Labs, an AI startup specializing in AI-driven 3D content generation. He is a former AWS Community Builder and the creator of “AWS Classroom” YouTube channel, and he shares practical serverless architecture knowledge with the AWS community. Chris hosts monthly AWS Classroom Meetups and the AWS KRUG Serverless Small Group, actively promoting serverless technologies through community events and educational content.

Toshal Khawale – Pune, India

Community Hero Toshal Khawale is an experienced technology leader with over 22 years of expertise in engineering and AWS cloud technology, holding 12 AWS certifications that demonstrate his cloud knowledge. As a Managing Director at PwC, Toshal guides organizations through cloud transformation, digital innovation, and application modernization initiatives, having led numerous large-scale AWS migrations and generative AI implementations. He was an AWS Community Builder for six years and continues to serve as the AWS User Group Pune Leader, actively fostering community engagement and knowledge sharing. Through his roles as a mentor, frequent speaker, and advocate, Toshal helps organizations maximize their AWS investments while staying at the forefront of cloud technology trends.

Learn More

Visit the AWS Heroes webpage if you’d like to learn more about the AWS Heroes program, or to connect with a Hero near you.

— Taylor

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Today, we’re announcing the general availability of Amazon Elastic VMware Service (Amazon EVS), a new AWS service that lets you run VMware Cloud Foundation (VCF) environments directly within your Amazon Virtual Private Cloud (Amazon VPC). With Amazon EVS, you can deploy fully functional VCF environments in just hours using a guided workflow, while running your VMware workloads on qualified Amazon Elastic Compute Cloud (Amazon EC2) bare metal instances and seamlessly integrating with AWS services such as Amazon FSx for NetApp ONTAP.

Many organizations running VMware workloads on premises want to move to the cloud to benefit from improved scalability, reliability, and access to cloud services, but migrating these workloads often requires substantial changes to applications and infrastructure configurations. Amazon EVS lets customers continue using their existing VMware expertise and tools without having to re-architect applications or change established practices, thereby simplifying the migration process while providing access to AWS’s scale, reliability, and broad set of services.

With Amazon EVS, you can run VMware workloads directly in your Amazon VPC. This gives you full control over your environments while being on AWS infrastructure. You can extend your on-premises networks and migrate workloads without changing IP addresses or operational runbooks, reducing complexity and risk.

Key capabilities and features

Amazon EVS delivers a comprehensive set of capabilities designed to streamline your VMware workload migration and management experience. The service enables seamless workload migration without the need for replatforming or changing hypervisors, which means you can maintain your existing infrastructure investments while moving to AWS. Through an intuitive, guided workflow on the AWS Management Console, you can efficiently provision and configure your EVS environments, significantly reducing the complexity to migrate your workloads to AWS.

With Amazon EVS, you can deploy a fully functional VCF environment running on AWS in a few hours. This process eliminates many of the manual steps and potential configuration errors that often occur during traditional deployments. Furthermore, with Amazon EVS you can optimize your virtualization stack on AWS. Given the VCF environment runs inside your VPC, you have full full administrative access to the environment and the associated management appliances. You also have the ability to integrate third-party solutions, from external storage such as Amazon FSx for NetApp ONTAP or Pure Cloud Block Store or backup solutions such as Veeam Backup and Replication.

The service also gives you the ability to self-manage or work with AWS Partners to build, manage, and operate your environments. This provides you with flexibility to match your approach with your overall goals.

Setting up a new VCF environment

Organizations can streamline their setup process by ensuring they have all the necessary pre-requisites in place ahead of creating a new VCF environment. These prerequisites include having an active AWS account, configuring the appropriate AWS Identity and Access Management (IAM) permissions, and setting up a Amazon VPC with sufficient CIDR space and two Route Server endpoints, with each endpoint having its own peer. Additionally, customers will need to have their VMware Cloud Foundation license keys ready, secure Amazon EC2 capacity reservations specifically for i4i.metal instances, and prepare their VLAN subnet information planning.

To help ensure a smooth deployment process, we’ve provided a Getting started hub, which you can access from the EVS homepage as well as a comprehensive guide in our documentation. By following these preparation steps, you can avoid potential setup delays and ensure a successful environment creation.

Let’s walk through the process of setting up a new VCF environment using Amazon EVS.

You will need to provide your Site ID, which is allocated by Broadcom when purchasing VCF licenses, along with your license keys. To ensure a successful initial deployment, you should verify you have sufficient licensing coverage for a minimum of 256 cores. This translates to at least four i4i.metal instances, with each instance providing 64 physical cores.

This licensing requirement helps you maintain optimal performance and ensures your environment meets the necessary infrastructure specifications. By confirming these requirements upfront, you can avoid potential deployment delays and ensure a smooth setup process.

Once you have provided all the required details, you will be prompted to specify your host details. These are the underlying Amazon EC2 instances that your VCF environment will get deployed in.

Once you have filled out details for each of your host instances, you will need to configure your networking and management appliance DNS details. For further information on how to create a new VCF environment on Amazon EVS, follow the documentation here.

After you have created your VCF environment, you will be able to look over all of the host and configuration details through the AWS Console.

Additional things to know

Amazon EVS currently supports VCF version 5.2.1 and runs on i4i.metal instances. Future releases will expand VCF versions, licensing options, and more instance type support to provide even more flexibility for your deployments.

Amazon EVS provides flexible storage options. Your Amazon EVS local Instance storage is powered by VMware’s vSAN solution, which pools local disks across multiple ESXi hosts into a single distributed datastore. To scale your storage, you can leverage external Network File System (NFS) or iSCSI-based storage solutions. For example, Amazon FSx for NetApp ONTAP is particularly well-suited for use as an NFS datastore or shared block storage over iSCSI.

Additionally, Amazon EVS makes connecting your on-premises environments to AWS simple. You can connect from on-premises vSphere environment into Amazon EVS using a Direct Connect connection or a VPN that terminates into a transit gateway. Amazon EVS also manages the underlying connectivity from your VLAN subnets into your VMs.

AWS provides comprehensive support for all AWS services deployed by Amazon EVS, handling direct customer support while engaging with Broadcom for advanced support needs. Customers must maintain AWS Business Support on accounts running the service.

Availability and pricing

Amazon EVS is now generally available in US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Frankfurt), Europe (Ireland), and Asia Pacific (Tokyo) AWS Regions, with additional Regions coming soon. Pricing is based on the Amazon EC2 instances and AWS resources you use, with no minimum fees or upfront commitments.

To learn more, visit the Amazon EVS product page.

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As a developer advocate at AWS, I’ve worked with many enterprise organizations who operate critical applications across multiple AWS Regions. A key concern they often share is the lack of confidence in their Region failover strategy—whether it will work when needed, whether all dependencies have been identified, and whether their teams have practiced the procedures enough. Traditional approaches often leave them uncertain about their readiness for Regional switch.

Today, I’m excited to announce Amazon Application Recovery Controller (ARC) Region switch, a fully managed, highly available capability that enables organizations to plan, practice, and orchestrate Region switches with confidence, eliminating the uncertainty around cross-Region recovery operations. Region switch helps you orchestrate recovery for your multi-Region applications on AWS. It gives you a centralized solution to coordinate and automate recovery tasks across AWS services and accounts when you need to switch your application’s operations from one AWS Region to another.

Many customers deploy business-critical applications across multiple AWS Regions to meet their availability requirements. When an operational event impacts an application in one Region, switching operations to another Region involves coordinating multiple steps across different AWS services, such as compute, databases, and DNS. This coordination typically requires building and maintaining complex scripts that need regular testing and updates as applications evolve. Additionally, orchestrating and tracking the progress of Region switches across multiple applications and providing evidence of successful recovery for compliance purposes often involves manual data gathering.

Region switch is built on a Regional data plane architecture, where Region switch plans are executed from the Region being activated. This design eliminates dependencies on the impacted Region during the switch, providing a more resilient recovery process since the execution is independent of the Region you’re switching from.

Building a recovery plan with ARC Region switch
With ARC Region switch, you can create recovery plans that define the specific steps needed to switch your application between Regions. Each plan contains execution blocks that represent actions on AWS resources. At launch, Region switch supports nine types of execution blocks:

• ARC Region switch plan execution block–let you orchestrate the order in which multiple applications switch to the Region you want to activate by referencing other Region switch plans.
• Amazon EC2 Auto Scaling execution block–Scales Amazon EC2 compute resources in your target Region by matching a specified percentage of your source Region’s capacity.
• ARC routing controls execution block–Changes routing control states to redirect traffic using DNS health checks.
• Amazon Aurora global database execution block–Performs database failover with potential data loss or switchover with zero data loss for Aurora Global Database.
• Manual approval execution block–Adds approval checkpoints in your recovery workflow where team members can review and approve before proceeding.
• Custom Action AWS Lambda execution block–Adds custom recovery steps by executing Lambda functions in either the activating or deactivating Region.
• Amazon Route 53 health check execution block–Let you to specify which Regions your application’s traffic will be redirected to during failover. When executing your Region switch plan, the Amazon Route 53 health check state is updated and traffic is redirected based on your DNS configuration.
• Amazon Elastic Kubernetes Service (Amazon EKS) resource scaling execution block–Scales Kubernetes pods in your target Region during recovery by matching a specified percentage of your source Region’s capacity.
• Amazon Elastic Container Service (Amazon ECS) resource scaling execution block–Scales ECS tasks in your target Region by matching a specified percentage of your source Region’s capacity.

Region switch continually validates your plans by checking resource configurations and AWS Identity and Access Management (IAM) permissions every 30 minutes. During execution, Region switch monitors the progress of each step and provides detailed logs. You can view execution status through the Region switch dashboard and at the bottom of the execution details page.

To help you balance cost and reliability, Region switch offers flexibility in how you prepare your standby resources. You can configure the desired percentage of compute capacity to target in your destination Region during recovery using Region switch scaling execution blocks. For critical applications expecting surge traffic during recovery, you might choose to scale beyond 100 percent capacity, and setting a lower percentage can help achieve faster overall execution times. However, it’s important to note that using one of the scaling execution blocks does not guarantee capacity, and actual resource availability depends on the capacity in the destination Region at the time of recovery. To facilitate the best possible outcomes, we recommend regularly testing your recovery plans and maintaining appropriate Service Quotas in your standby Regions.

ARC Region switch includes a global dashboard you can use to monitor the status of Region switch plans across your enterprise and Regions. Additionally, there’s a Regional executions dashboard that only displays executions within the current console Region. This dashboard is designed to be highly available across each Region so it can be used during operational events.

Region switch allows resources to be hosted in an account that is separate from the account that contains the Region switch plan. If the plan uses resources from an account that is different from the account that hosts the plan, then Region switch uses the executionRole to assume the crossAccountRole to access those resources. Additionally, Region switch plans can be centralized and shared across multiple accounts using AWS Resource Access Manager (AWS RAM), enabling efficient management of recovery plans across your organization.

Let’s see how it works
Let me show you how to create and execute a Region switch plan. There are three parts in this demo. First, I create a Region switch plan. Then, I define a workflow. Finally, I configure the triggers.

Step 1: Create a plan

I navigate to the Application Recovery Controller section of the AWS Management Console. I choose Region switch in the left navigation menu. Then, I choose Create Region switch plan.

After I give a name to my plan, I specify a Multi-Region recovery approach (active/passive or active/active). In Active/Passive mode, two application replicas are deployed into two Regions, with traffic routed into the active Region only. The replica in the passive Region can be activated by executing the Region switch plan.

Then, I select the Primary Region and Standby Region. Optionally, I can enter a Desired recovery time objective (RTO). The service will use this value to provide insight into how long Region switch plan executions take in relation to my desired RTO.

I enter the Plan execution IAM role. This is the role that allows Region switch to call AWS services during execution. I make sure the role I choose has permissions to be invoked by the service and contains the minimum set of permissions allowing ARC to operate. Refer to the IAM permissions section of the documentation for the details.

Step 2: Create a workflow

When the two Plan evaluation status notifications are green, I create a workflow. I choose Build workflows to get started.

Plans enable you to build specific workflows that will recover your applications using Region switch execution blocks. You can build workflows with execution blocks that run sequentially or in parallel to orchestrate the order in which multiple applications or resources recover into the activating Region. A plan is made up of these workflows that allow you to activate or deactivate a specific Region.

For this demo, I use the graphical editor to create the workflow. But you can also define the workflow in JSON. This format is better suited for automation or when you want to store your workflow definition in a source code management system (SCMS) and your infrastructure as code (IaC) tools, such as AWS CloudFormation.

I can alternate between the Design and the Code views by selecting the corresponding tab next to the Workflow builder title. The JSON view is read-only. I designed the workflow with the graphical editor and I copied the JSON equivalent to store it alongside my IaC project files.

Region switch launches an evaluation to validate your recovery strategy every 30 minutes. It regularly checks that all actions defined in your workflows will succeed when executed. This proactive validation assesses various elements, including IAM permissions and resource states across accounts and Regions. By continually monitoring these dependencies, Region switch helps ensure your recovery plans remain viable and identifies potential issues before they impact your actual switch operations.

However, just as an untested backup is not a reliable backup, an untested recovery plan cannot be considered truly validated. While continuous evaluation provides a strong foundation, we strongly recommend regularly executing your plans in test scenarios to verify their effectiveness, understand actual recovery times, and ensure your teams are familiar with the recovery procedures. This hands-on testing is essential for maintaining confidence in your disaster recovery strategy.

Step 3: Create a trigger

A trigger defines the conditions to activate the workflows just created. It’s expressed as a set of CloudWatch alarms. Alarm-based triggers are optional. You can also use Region switch with manual triggers.

From the Region switch page in the console, I choose the Triggers tab and choose Add triggers.

For each Region defined in my plan, I choose Add trigger to define the triggers that will activate the Region.Finally, I choose the alarms and their state (OK or Alarm) that Region switch will use to trigger the activation of the Region.

I’m now ready to test the execution of the plan to switch Regions using Region switch. It’s important to execute the plan from the Region I’m activating (the target Region of the workflow) and use the data plane in that specific Region.

Here is how to execute a plan using the AWS Command Line Interface (AWS CLI):

aws arc-region-switch start-plan-execution 
--plan-arn arn:aws:arc-region-switch::111122223333:plan/resource-id 
--target-region us-west-2 
--action activate

Pricing and availability
Region switch is available in all commercial AWS Regions at $70 per month per plan. Each plan can include up to 100 execution blocks, or you can create parent plans to orchestrate up to 25 child plans.

Having seen firsthand the engineering effort that goes into building and maintaining multi-Region recovery solutions, I’m thrilled to see how Region switch will help automate this process for our customers. To get started with ARC Region switch, visit the ARC console and create your first Region switch plan. For more information about Region switch, visit the Amazon Application Recovery Controller (ARC) documentation. You can also reach out to your AWS account team with questions about using Region switch for your multi-Region applications.

I look forward to hearing about how you use Region switch to strengthen your multi-Region applications’ resilience.

— seb

This post was originally published on this site

Today, we’re announcing the general availability of Amazon DocumentDB Serverless, a new configuration for Amazon DocumentDB (with MongoDB compatibility) that automatically scales compute and memory based on your application’s demand. Amazon DocumentDB Serverless simplifies database management with no upfront commitments or additional costs, offering up to 90 percent cost savings compared to provisioning for peak capacity.

With Amazon DocumentDB Serverless, you can use the same MongoDB compatible-APIs and capabilities as Amazon DocumentDB, including read replicas, Performance Insights, I/O optimized, and integrations with other Amazon Web Services (AWS) services.

Amazon DocumentDB Serverless introduces a new database configuration measured in a DocumentDB Capacity Unit (DCU), a combination of approximately 2 gibibytes (GiB) of memory, corresponding CPU, and networking. It continually tracks utilization of resources such as CPU, memory, and network coming from database operations performed by your application.

Amazon DocumentDB Serverless automatically scales DCUs up or down to meet demand without disrupting database availability. Switching from provisioned instances to serverless in an existing cluster is as straightforward as adding or changing the instance type. This transition doesn’t require any data migration. To learn more, visit How Amazon DocumentDB Serverless works.

Some key use cases and advantages of Amazon DocumentDB Serverless include:

• Variable workloads – With Amazon DocumentDB Serverless, you can handle sudden traffic spikes such as periodic promotional events, development and testing environments, and new applications where usage might ramp up quickly. You can also build agentic AI applications that benefit from built-in vector search for Amazon DocumentDB and serverless adaptability to handle dynamically invoked agentic AI workflows.
• Multi-tenant workloads – You can use Amazon DocumentDB Serverless to manage individual database capacity across the entire database fleet. You don’t need to manage hundreds or thousands of databases for enterprises applications or multi-tenant environments of a software as a service (SaaS) vendor.
• Mixed-use workloads – You can balance read and write capacity in workloads that periodically experience spikes in query traffic, such as online transaction processing (OLTP) applications. By specifying promotion tiers for Amazon DocumentDB Serverless instances in a cluster, you can configure your cluster so that the reader instances can scale independently of the writer instance to handle the additional load.

For steady workloads, Amazon DocumentDB provisioned instances are more suitable. You can select an instance class that offers a predefined amount of memory, CPU power, and I/O bandwidth. If your workload changes when using provisioned instances, you should manually modify the instance class of your writer and readers. Optionally, you can add serverless instances to an existing provisioned Amazon DocumentDB cluster at any time.

Amazon DocumentDB Serverless in action
To get started with Amazon DocumentDB Serverless, go to the Amazon DocumentDB console. In the left navigation pane, choose Clusters and Create.

On the Create Amazon DocumentDB cluster page, choose Instance-based cluster type and then Serverless instance configuration. You can choose minimum and maximum capacity DCUs. Amazon DocumentDB Serverless is supported starting with Amazon DocumentDB 5.0.0 and higher with a capacity range of 0.5–256 DCUs.

If you use features such as auditing and Performance Insights, consider adding DCUs for each feature. To learn more, visit Amazon DocumentDB Serverless scaling configuration.

To add a serverless instance to an existing provisioned cluster, choose Add instances on the Actions menu when you choose the provisioned cluster. If you use a cluster with an earlier version such as 3.6 or 4.0, you should first upgrade the cluster to the supported engine version (5.0).

On the Add instances page, choose Serverless in the DB instance class section for each new serverless instance you want to create. To add another instance, choose Add instance and continue adding instances until you have reached the desired number of new instances. Choose Create.

You can perform a failover operation to make a DocumentDB Serverless instance the cluster writer. Also, you can convert any remaining provisioned Amazon DocumentDB instances to DocumentDB Serverless instances by changing an instance’s class or removing them from the cluster by deleting an Amazon DocumentDB instance.

Now, you can connect to your Amazon DocumentDB cluster using AWS CloudShell. Choose Connect to cluster, and you can see the AWS CloudShell Run command screen. Enter a unique name in New environment name and choose Create and run.

When prompted, enter the password for the Amazon DocumentDB cluster. You’re successfully connected to your Amazon DocumentDB cluster, and you can run a few queries to get familiar with using a document database.

To learn more, visit Creating a cluster that uses Amazon DocumentDB Serverless and Managing Amazon DocumentDB Serverless in the AWS documentation.

Now available
Amazon DocumentDB Serverless is now available starting with Amazon DocumentDB 5.0 for both new and existing clusters. You only pay a flat rate per second of DCU usage. To learn more about pricing details and Regional availability, visit the Amazon DocumentDB pricing page.

Give these new features a try in the Amazon DocumentDB console and send feedback to AWS re:Post for Amazon DocumentDB or through your usual AWS Support contacts.

— Channy