11/9/2026, 12:00:00 AM ~ 11/10/2026, 12:00:00 AM (UTC)

Recent Announcements

AWS Glue adds 100 new native connectors for popular SaaS applications

AWS Glue now supports 100 new native connectors that simplify access to data in popular SaaS and REST API-based applications. Previously, bringing data from SaaS applications into AWS required writing custom code. Now, teams can create a connection once in the AWS Glue Data Catalog and reuse it across ETL jobs with the same permissions model they already use for AWS Glue.\n The new connectors span generative AI, finance, payments, marketing, product analytics, human resources, engineering, IT operations, and collaboration tools. AI teams can bring usage and cost data from model providers such as Anthropic to track generative AI adoption and spend. Finance teams can consolidate transactions from applications such as Adyen, Square, and Xero for faster reporting. Product and marketing teams can combine billing, usage, and campaign data from tools such as Chargebee, Amplitude, and Klaviyo to understand customer lifetime value. HR teams can unify workforce data from systems such as BambooHR and Gusto for headcount planning. Engineering teams can join incident and deployment data from tools such as PagerDuty, Sentry, Jenkins, and Vercel to track reliability. You can load the integrated data into Amazon S3 or Amazon Redshift for analytics and reporting.

The 100 new AWS Glue connectors are available in all AWS Commercial Regions on Glue versions 5.0 and above.

To learn more, see the complete list of available connectors and learn about connecting to individual connectors in the AWS Glue documentation.

AWS STS now supports cross-partition federation for AWS identities

AWS Security Token Service (AWS STS) now lets your workloads use their AWS Identity and Access Management (IAM) identity in one partition to get temporary credentials for an IAM role in a different partition, without storing long-term credentials, which can help improve your security posture. Partitions are groups of AWS Regions, such as the commercial and AWS GovCloud (US) Regions, and each partition has an independent instance of IAM, creating a hard boundary between them. Previously, organizations that run workloads across partitions commonly relied on long-term credentials, such as IAM user access keys, that don’t expire automatically.\n With this launch, your workload requests a short-lived token from AWS STS in its own partition and exchanges it for temporary AWS credentials in the other partition, using the existing GetWebIdentityToken and AssumeRoleWithWebIdentity APIs. You control which identities can access each role, apply governance across your organization, and audit token use in AWS CloudTrail in both partitions. This launch helps with scenarios such as consolidated security and compliance reporting, and centralized operations tooling. If your organization already operates a public key infrastructure (PKI), IAM Roles Anywhere is another option for cross-partition access.

Cross-partition federation is available at no additional charge in all commercial AWS Regions, the AWS GovCloud (US) Regions, the China Regions, and the AWS European Sovereign Cloud (Germany) Region. To learn more, see Federating AWS identities across AWS partitions in the IAM User Guide and the blog post Simplify cross-partition access with AWS IAM outbound identity federation.

Amazon Quick is now AWS Quick

Amazon Quick, the AI workspace built only for work, is now AWS Quick, a name that reflects the enterprise security and governance built into Quick from the start as an AWS service. The new name now appears across the Quick website, the web, desktop, and mobile apps, and AWS documentation. Existing customers don’t need to take any action, and Quick works the same way it did before.\n Amazon Quick Sight, the business intelligence capability in Quick, is now AWS Quick Sight. Quick Sight customers can keep using their dashboards, datasets, and analyses just as they do today, with no changes to their data, settings, or pricing.

With Quick, teams use AI agents to research, analyze, and act across the data and applications they already use, regardless of vendor. Quick understands your role, your team, and how your work connects, and anyone can build agents, apps, and automations in plain language that become shared assets for the whole team. Quick is HIPAA eligible and holds compliance certifications including FedRAMP, SOC 2, and ISO 27001. Your data stays in your environment and never trains someone else’s models.

AWS Quick is available in many AWS Regions. For more information, see the AWS Region table.

To learn more, see AWS Quick.

Claude Fable 5.2 is now available on AWS GovCloud (US)

Claude Fable 5.2 is now available on AWS GovCloud (US) and brings Anthropic’s most capable generally available model for technical work to regulated customers. Fable 5.2 sets a new standard on the hardest projects in coding, math, and science, and Anthropic recommends it for fields like software engineering, scientific research, and finance, where judgment, iteration, and accuracy are critical. Claude Mythos 5.2, the same underlying model as Claude Fable 5.2, with its full cyber and bio capabilities retained for cybersecurity and biology research, is available through Anthropic’s verification programs.\n Claude Fable 5.2 can work for hours and up to days unattended, driving autonomous work and taking on bigger, more challenging projects. It takes on large, loosely defined engineering problems across multiple repositories and monorepos, tests its own work as it goes, and is more willing to question inherited conclusions, so it finds root causes previous Claude models miss. In Anthropic’s testing, it coordinated hundreds of subagents as a lead agent and flagged results that don’t reproduce. Beyond coding, it develops new analytical methods for scientific research and brings frontier reasoning to quantitative finance.

Anthropic has designated Fable 5.2 and Mythos 5.2 as covered models, which require data retention for safety monitoring. Until Enterprise Frontier Safeguards is available, eligible customers can use Fable 5.2 with zero data retention. Visit the Amazon Bedrock abuse detection page to learn more about details regarding data retention for these models.

To learn more, see the Amazon Bedrock documentation, and regional availability.

Amazon Bedrock now offers Claude Mythos 5.2 to Eligible Customers in AWS GovCloud (US)

Amazon Bedrock, the platform for building generative AI applications and agents at production scale, now offers Claude Mythos 5.2 to eligible customers in AWS GovCloud (US). Claude Mythos 5.2 is Anthropic’s most advanced AI model to date for use cases across cybersecurity, software coding, and complex reasoning tasks. The model can identify sophisticated security vulnerabilities in software and demonstrate exploitability. It can also comprehend large codebases and deliver actionable findings with less manual guidance than previous AI models. This enables security teams to accelerate defensive cybersecurity work, find and fix security vulnerabilities in the world’s most critical software, and address them before threats emerge. Claude Mythos 5.2 is the first of Anthropic’s AI models with powerful cybersecurity capabilities to be available in AWS GovCloud (US).\n Claude Mythos 5.2 is available to eligible customers in AWS GovCloud (US) Regions through Amazon Bedrock. Access is limited to allow-listed organizations; to get started, visit Anthropic’s Cyber Verification Program.

Amazon Connect Customer launches evaluation form templates

Amazon Connect Customer now provides evaluation form templates so managers don’t have to build forms from scratch, reducing the time to deploy automated evaluations of human and AI agent performance. These templates cover typical customer interaction scenarios: customer service, inbound sales, and self-service. Each template assesses human and AI agent behaviors. For example, the customer service template includes questions such as “Did the agent show empathy toward the customer’s issue?” Templates also include questions that assess business outcomes such as customer satisfaction and attrition risk, so managers can see how agent behaviors relate to outcomes. Managers can use the templates as-is or customize them before deploying.\n This feature is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (London), Asia Pacific (Singapore), Asia Pacific (Sydney), and Asia Pacific (Tokyo). To learn more, visit our documentation and our webpage. For Amazon Connect Customer pricing, visit our pricing page.

Introducing an open-source UI for graph analytics over data lakes with Amazon Neptune Analytics

Today, AWS is introducing an open-source solution with a local web UI that helps customers turn data lake records into graphs for analysis with Amazon Neptune Analytics. By projecting selected records and relationships, customers can investigate connections across tables and run graph algorithms without building their own ETL pipelines.\n For example, a customer could project accounts and transactions into a graph to explore how accounts are connected or identify communities that may warrant investigation. Doing this today often means writing queries to prepare node and relationship data, creating a Neptune Analytics graph, and managing the import. Customers may also need to build a selective projection so they do not load an entire dataset for a narrower question.

The new UI brings those steps into single workflow. To run graph analytics over iceberg tables, customers select an Athena catalog and database, define a property graph using SQL/PGQ, and preview rows before import. The solution runs the projection queries through Athena, stages the selected data in Amazon S3, and imports it into Neptune Analytics. The UI helps customers manage the resulting graph. They can open the loaded graph in Graph Explorer to query and visualize relationships. [1]

The UI runs locally and it is available in all regions where Amazon Neptune Analytics is available. To get started, see the setup instructions.

Amazon DocumentDB Serverless now provides up to 35% higher throughput

Amazon DocumentDB Serverless now provides up to 35% higher throughput with the launch of platform version 4. Platform versions represent improvements in performance, scaling capabilities, or features, and are automatically managed at the cluster level.\n All new serverless clusters launch on the latest platform version available in your AWS Region. You can upgrade existing clusters on platform version 3 to platform version 4 by applying the serverless-platform-version-update pending maintenance action. This gives you control over when to apply the update, either immediately or during your next scheduled maintenance window. After you upgrade to a newer platform version, you can’t downgrade to a previous version.

You can view the platform version of a cluster in the AWS Management Console or through the ServerlessV2PlatformVersion field of the DescribeDBClusters API. For more information, see Amazon DocumentDB serverless scaling capacity and Checking and upgrading the platform version of a serverless cluster.

Amazon Athena introduces system tables for observability, auditing, and usage analytics

Amazon Athena now offers system tables which automatically capture your usage data and make it available for observability, usage analysis, and audit use cases. When you enable system tables, usage data is available within minutes of query completion across your workgroups and stored as service-managed, read-only Iceberg tables on Amazon S3 Tables with configurable retention. You can query it directly with Athena, while AWS manages the tables for you, including schema definition, partitioning, compaction, and snapshot maintenance, removing all operational burden.\n Athena provides usage data through APIs like GetQueryExecution, CloudWatch metrics, and CloudTrail logs. With this launch, usage data is also available as iceberg tables that you can search, filter, aggregate, and join directly without building or maintaining custom pipelines. Platform teams can query the query_executions table to build cost allocation dashboards that show data scanned by workgroup, by user, or by table using simple SQL statements, and without custom ETL. Additionally, the AWS Agent Toolkit provides skills for querying system tables to surface usage patterns and optimization recommendations. Because the data is stored in the open Apache Iceberg format, you can also query it with DuckDB, Amazon Redshift, Amazon EMR or any other Iceberg-compatible engine.

Athena system tables are available in all AWS Regions where Athena and Amazon S3 Tables are available. To get started, enable system tables from the Athena console or API. To learn more, visit the Amazon Athena documentation or the  blog.

Claude Fable 5.2 is now available on AWS

Claude Fable 5.2 is now available on AWS and brings Anthropic’s most capable generally available model for technical work to all customers. Fable 5.2 sets a new standard on the hardest projects in coding, math, and science, and Anthropic recommends it for fields like software engineering, scientific research, and finance, where judgment, iteration, and accuracy are critical. Claude Mythos 5.2, the same underlying model as Claude Fable 5.2 with its full cyber and bio capabilities retained for cybersecurity and biology research, is available through Anthropic’s verification programs.\n Claude Fable 5.2 can work for hours and up to days unattended, driving autonomous work and taking on bigger, more challenging projects. It takes on large, loosely defined engineering problems across multiple repositories and monorepos, tests its own work as it goes, and is more willing to question inherited conclusions, so it finds root causes previous Claude models miss. In Anthropic’s testing, it coordinated hundreds of subagents as a lead agent and flagged results that don’t reproduce. Beyond coding, it develops new analytical methods for scientific research and brings frontier reasoning to quantitative finance.

Anthropic has designated Fable 5.2 and Mythos 5.2 as covered models, which require data retention for safety monitoring. Until Enterprise Frontier Safeguards is available, eligible customers can use Fable 5.2 with zero data retention. Visit the Amazon Bedrock abuse detection page to learn more about details regarding data retention for these models.

Customers have two ways to access Claude Fable 5.2: Amazon Bedrock and Claude Platform on AWS. To learn more, see the Amazon Bedrock documentation, regional availability, and Claude Platform on AWS documentation.

AWS Analytics services now support Workday Data Cloud integration

Today, AWS announces a new bi-directional, zero-copy integration between AWS Analytics services and Workday Data Cloud. Instead of building and maintaining ETL pipelines to replicate data between systems, joint customers can now query across their AWS and Workday datasets in place, without moving or copying data, while retaining existing security controls in both environments.\nFrom AWS, you can use Amazon SageMaker Unified Studio, Amazon EMR, AWS Glue, Amazon Redshift, and Amazon Athena to query Workday data through Workday Live Data Query or federate to Apache Iceberg tables in Workday Data Cloud through catalog federation. Configure a connection in SageMaker Unified Studio, AWS Glue, or AWS Lake Formation consoles and begin querying immediately. Access is governed by AWS and Workday permissions. From Workday, you can register your AWS Glue Data Catalog Iceberg REST endpoint as an external catalog in Workday Analytics and query your AWS Iceberg tables directly. Common use cases include joining workforce data with business data for talent analytics, workforce planning, and machine learning. This integration is available in all AWS commercial regions where AWS Glue is available. To learn more, see the AWS Glue User Guide, the Workday Data Cloud integration with AWS on Workday Data Cloud, and the AWS Big Data Blog post.

Introducing AWS Nitro Sandboxes on Amazon EKS, ECS and EC2 for hardware isolated pods, tasks and VMs

AWS Nitro Sandboxes are now available as lightweight VMs managed by the Nitro Hypervisor. You can use Nitro Sandboxes for an Amazon EKS pod, or Amazon ECS task, or lightweight Virtual Machines (VMs), including AI agents running inside them, in their own hardware-isolated virtual machine powered by the AWS Nitro System. Each workload gets the isolation boundary of a Nitro isolated virtual machine. This allows you to safely run untrusted, multi-tenant, or regulated workloads within the resources of a single instance.\n Nitro Sandboxes run on your EC2 instances with near-native EC2 performance and inherits Nitro-hypervisor isolation. You can also validate your workloads through cryptographic attestation, so that secrets are released only to Nitro Sandboxes that have proven their integrity. With Nitro Sandboxes, you can safely execute LLM-generated code in an isolated environment of its own all while improving utilization and lowering compute costs by securely sharing a single EC2 instance across multiple agentic workloads. You can easily enable EKS pods or ECS tasks to run on Nitro Sandboxes and each pod or task gets its own isolated environment. Enabling Nitro Sandboxes in EKS or ECS is a one-line change and each pod or task gets its own VM, with your workloads remaining fully native (Pod Identity, VPC CNI, EBS/EFS/S3 storage, and CloudWatch all work out of the box). If you want full control, run Nitro Sandboxes directly on EC2 with your own AMI, orchestration, and with virtual machine monitors (VMMs). Amazon Linux and Firecracker are natively supported, and patches for other options are available today.

AWS Nitro Sandboxes are available today on most nitro-based ec2 instances, in all commercial regions worldwide, at no additional charge.

To learn more and get started, see the EKS User Guide or ECS User Guide. For implementation details or to fully customize your Nitro Sandbox usage, see the Nitro Sandboxes documentation in the EC2 user guide.

Amazon S3 Access Points for Amazon FSx are now available in additional AWS Regions

You can now attach Amazon S3 Access Points to your Amazon FSx for NetApp ONTAP file systems in 4 additional AWS Regions, and to your Amazon FSx for OpenZFS file systems in 20 additional AWS Regions. With this capability, your file data in Amazon FSx is accessible for use with the broad range of artificial intelligence, machine learning, and analytics services and applications that work with S3, while your data continues to reside on your FSx file system.\n An S3 Access Point for Amazon FSx is an S3 endpoint that gives S3-based applications and services access to the data on your FSx file system through the Amazon S3 API, without copying or moving that data. For example, you can connect generative AI applications and agents to your file data with Amazon Bedrock Knowledge Bases so they deliver relevant, contextual responses. Users can query the same data in place with Amazon Athena or transform it with AWS Glue, and you can share files with partners over SFTP, FTPS, and FTP using AWS Transfer Family. You can also access your files from serverless compute such as Amazon ECS and AWS Fargate, or from your own S3-based applications.

Amazon S3 Access Points for FSx for ONTAP and FSx for OpenZFS are now available in additional AWS Regions across North America, South America, Europe, Asia Pacific, and Africa. For the full list of AWS Regions supported for each file system, see the FSx for ONTAP and FSx for OpenZFS user guides. Get started by creating and attaching S3 Access Points using the Amazon FSx console, the AWS Command Line Interface (AWS CLI), or the AWS Software Development Kit (AWS SDK). To learn more, visit the Amazon FSx for NetApp ONTAP and Amazon FSx for OpenZFS product pages, the Amazon S3 Access Points page, and the FSx for ONTAP and OpenZFS user guides.

Amazon EKS Auto Mode and Karpenter now support Dynamic Resource Allocation

Amazon Elastic Kubernetes Service (Amazon EKS) now supports Dynamic Resource Allocation (DRA) for NVIDIA GPUs and Elastic Fabric Adapter (EFA) network devices when using Amazon EKS Auto Mode and Karpenter. You can now define the device attributes your Kubernetes workloads need with DRA, share GPU devices across Pods, and allocate topology-aligned GPU and EFA devices while Amazon EKS Auto Mode and Karpenter dynamically scale Amazon EC2 instances based on workload demand.\n With DRA, you define ResourceClaim or ResourceClaimTemplate objects that describe what GPU or EFA devices can be allocated to workloads, by model, architecture, or other attributes. When running distributed training or inference, you can define PCIe-aligned GPU and EFA devices for optimal performance. Workloads then request those DRA objects in their configuration, and Karpenter automatically launches the lowest-cost instances that match. When using Amazon EKS Auto Mode, the NVIDIA DRA driver and EFA DRA driver (DRANET) are built-in, and you simply define your DRA objects and use them in your workloads. Amazon EKS Auto Mode automatically prevents workloads using DRA and workloads using device plugins for the same device type from running on the same nodes to mitigate the risk of silent device oversubscription during migrations.

DRA support for Amazon EKS Auto Mode and Karpenter is available in all AWS Regions, except the AWS GovCloud (US) Regions and the China Regions. DRA support is available for all Kubernetes versions in Karpenter, and for Kubernetes versions 1.36 and newer in Amazon EKS Auto Mode. To learn more, see the DRA documentation in the Amazon EKS User Guide and Karpenter documentation.

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