AWS News - 2026-09-24

Today, AWS announces that Amazon EMR on EKS now supports running workloads on IPv6 Amazon Elastic Kubernetes Service (Amazon EKS) clusters. Amazon EMR on EKS enables data platform and analytics teams to run open-source big data frameworks such as Apache Spark and Apache Flink on Amazon EKS. With IPv6 support, teams operating at scale can now run these workloads on IPv6 Amazon EKS clusters, giving them access to the vastly larger address space of IPv6 as their analytics workloads grow.

You can now scale large Spark and Flink workloads using the expanded IPv6 address space, which removes the need for IPv4 conservation workarounds such as secondary CIDR ranges or prefix delegation. High-executor jobs, such as 500-executor Spark jobs, can run concurrently without planning around address limits. To submit workloads, use StartJobRun, Spark Connect Interactive Endpoints, and Amazon SageMaker Unified Studio with no additional configuration, while the Flink, Livy, and Spark Operators are also supported. This capability is available at no additional cost, starting with Amazon EMR releases emr-7.14.0 and emr-spark-8.0.0.

IPv6 cluster support is available in all AWS Regions where Amazon EMR on EKS and IPv6 Amazon EKS clusters are available.

Amazon EMR on EKS IPv6 cluster support includes detailed setup instructions and documentation on supported submission models. Visit the Amazon EMR on EKS product page and the IPv6 documentation to learn more. 

Amazon Connect Customer now provides routing step data in the analytics data lake, making it easier for customers to generate insights from routing decisions. Using the data lake, customers can leverage Amazon Athena and Amazon Quick to analyze trends, such as contacts queued and contacts joined at each routing step, without complex data pipelines. For example, an analyst can now report on how contacts progressed through each routing step, identify where agent matching criteria were relaxed, and measure the impact of routing step configurations on wait times and agent utilization.

Routing step data is available in all AWS regions where Amazon Connect Customer data lake is offered. To learn more about Amazon Connect Customer data lake, see the Amazon Connect Customer Administrator Guide.  To learn more about Amazon Connect Customer, visit the Amazon Connect Customer website. 

AWS announces Salesforce and Zendesk data source connectors for Amazon Bedrock Managed Knowledge Base, a fully managed retrieval-augmented generation (RAG) service. Customers can now sync Salesforce knowledge articles and Zendesk articles and community posts directly into their managed knowledge base.
Previously, bringing content from these platforms into Bedrock Knowledge Bases required building custom ingestion pipelines—now, you provide your instance credentials, and the connectors handle data crawling, metadata extraction, and incremental sync automatically.
These connectors make it easy to build AI agents and assistants grounded in the support and product knowledge your teams already maintain in Salesforce and Zendesk. For example, power a customer-facing support bot with up-to-date Zendesk help center articles and community answers, or build an internal sales enablement assistant that retrieves relevant Salesforce knowledge articles during deal preparation. By keeping your knowledge base in sync with these platforms, your retrieval-augmented generation applications always reflect the latest content without manual intervention.
To learn more, see Salesforce data source connector and Zendesk data source connector in the Amazon Bedrock User Guide. For more information about Amazon Bedrock Managed Knowledge Base, visit the Amazon Bedrock Knowledge Bases product page.

Amazon Kinesis Data Streams now supports service-managed partition keys for On-Demand Standard and On-Demand Advantage streams, automatically distributing records across shards without requiring customers to specify partition keys to publish data. This capability simplifies data ingestion for workloads where record ordering is not required, eliminating hot partition keys and reducing time to production for streaming workloads.

Amazon Kinesis Data Streams is a serverless streaming data service that makes it easy to capture, process, and store data streams at any scale. Many streaming use cases such as log aggregation, metrics collection, and IoT telemetry do not require ordering guarantees and benefit from prewarmed capacity for instant scaling. Previously, customers generated random partition keys (such as UUIDs) to distribute data, but random partitioning can still produce uneven throughput across shards, causing throttling for some partition keys even when the stream has sufficient aggregate capacity. By opting into service-managed partition keys, customers no longer need to specify partition keys when publishing data to streams in on-demand mode. The service automatically distributes records based on available warm capacity, allowing customers to scale to gigabytes per second without maintaining any distribution logic. Customers who want to send records without specifying a partition key can simply upgrade to the latest AWS SDK or Kinesis Producer Library (KPL) version to benefit from this capability.

Service-managed partition keys for Amazon Kinesis Data Streams is available today in all AWS commercial regions at no additional cost. To get started, visit the Amazon Kinesis Data Streams documentation (https://aws.amazon.com/kinesis).

Amazon Relational Database Service (Amazon RDS) for SQL Server now supports Multi-AZ deployments using Always On Availability Groups for SQL Server Developer Edition. Multi-AZ deployments maintain a synchronous standby replica in a different Availability Zone and provide automatic failover during infrastructure failure. You can use this capability to build and test high availability configurations in non-production environments using Developer Edition, which includes all the functionality of Enterprise Edition at no license cost.

You can use this feature to validate your high availability architecture, test failover behavior, and develop against production-equivalent configurations without incurring Enterprise Edition licensing costs. Multi-AZ with Always On Availability Groups is available on SQL Server Developer Edition major versions 2019 and 2022, and on SQL Server 2025 Enterprise Developer Edition. SQL Server 2025 Standard Developer Edition does not support this feature.

For more information about the AWS Regions where this feature is available, see Working with SQL Server Developer Edition on RDS for SQL Server. For pricing details, see Amazon RDS for SQL Server Pricing.

Amazon ElastiCache now supports resource tagging and tag-based access control (TBAC) for Global Datastore. Previously, ElastiCache supported tagging on all resources except Global Datastore, which prevented customers from applying a single, consistent permission and cost-allocation model across their ElastiCache fleet.

With this launch, you can use AddTagsToResource, RemoveTagsFromResource, and ListTagsForResource on a Global Datastore, and reference those tags as conditions in IAM policies and Service Control Policies (SCPs) to grant permissions based on tag attributes rather than enumerating individual resources. Tag changes on a Global Datastore propagate automatically to every Region it spans, so access-control and cost-allocation policies stay consistent without per-Region operations.

There is no additional cost for this feature, and no change to how you create or manage Global Datastores. This feature is available in all AWS Regions where ElastiCache Global Datastore is available. To learn more, see Tagging your ElastiCache resources and Using condition keys with ElastiCache. 

Amazon SageMaker HyperPod Inference Gateway is a Kubernetes-native, GPU-aware routing system that deploys as a single EKS managed add-on on existing SageMaker HyperPod infrastructure with zero application changes. By replacing unintelligent round-robin load balancing with real-time inference-signal-driven routing, it reduces first-token latency by up to 82% and p99 TTFT reductions of 97–98% in mixed-hardware and burst traffic scenarios.

The Gateway is built around 3 core components. The Envoy Endpoint terminates HTTPS traffic and exposes a single private endpoint per cluster. The Body-Based Router reads the model name directly from each incoming request and routes it to the correct GPU pool - enabling one gateway to serve many models from a single endpoint URL with no client-side changes required. The Endpoint Picker continuously scores every model server pod in real time across 6 inference-level signals - KV cache utilization, queue depth, LoRA adapter residency, prefix cache hit rate, predicted latency and running requests - selecting the optimal pod for each individual request.

The gateway works with any OpenAI-compatible model server, including vLLM and SGLang, requiring no application code changes.

Per-cluster routing is available today in all AWS Regions where the SageMaker HyperPod inference add-on is supported. Coming soon - cross-cluster and cross-region routing with a centralized fleet gateway, global rate limiting, and cost-tier-aware traffic shaping.

To learn more, read the launch blog and explore the documentation

Amazon EMR on EKS now supports interactive Apache Spark sessions with Spark Connect. Data engineers and data scientists can develop and debug Apache Spark applications interactively from managed notebooks in Amazon SageMaker Unified Studio and their own IDEs, such as Jupyter and Visual Studio Code, with Spark running on the Amazon EKS clusters they already operate.  

An interactive session provides a persistent Spark context that spans across cells and scripts, letting you blend local Python code execution with remote Spark operations. Spark Connect's client-server architecture decouples your application client from the Spark driver and allows you to maintain your preferred development environment and tooling while Spark runs on your Amazon EKS cluster. This architecture supports workflows including ad hoc data exploration and incremental PySpark job development before deploying to production. Each session runs as pods on a virtual cluster, secured with your AWS Identity and Access Management (IAM) execution role and tagged by project and user.

Spark Connect on Amazon EMR on EKS is available with EMR release 7.14 (Apache Spark 3.5) and emr-spark-8.1.0 (Apache Spark 4.1), in all AWS Commercial Regions. The Amazon SageMaker Unified Studio experience is available in supported AWS Regions.

To get started, visit the Spark Connect on Amazon EMR on EKS documentation or the Amazon SageMaker Unified Studio Getting Started guide.

Amazon GameLift Servers is a fully managed service for deploying, operating, and scaling dedicated game servers for multiplayer games. High latency caused by geographic distance between players and servers remains one of the most common challenges in multiplayer game development. This expansion helps game developers and studios address that challenge by bringing Amazon GameLift Servers to 5 new AWS Regions and 8 AWS Local Zones worldwide.

With this expansion, customers can deploy game server fleets closer to players across Latin America, Southeast Asia, Europe, the Middle East, and South Asia, reducing latency and improving gameplay responsiveness. 

The 5 new AWS Regions are il-central-1 (Tel Aviv), mx-central-1 (Mexico/Queretaro), eu-south-2 (Spain/Zaragoza), ap-south-2 (Hyderabad), and ap-southeast-3 (Jakarta). The 8 new AWS Local Zones are us-east-1-atl-2 (Atlanta), us-east-1-chi-2 (Chicago), us-east-1-iah-2 (Houston), us-east-1-mia-2 (Miami), us-west-2-phx-2 (Phoenix), sa-east-1-bue-1 (Buenos Aires), us-east-1-nyc-2 (NYC) and sa-east-1-lim-1 (Lima).

To learn more about Amazon GameLift Servers and supported locations, or to deploy fleets in these new Regions and Local Zones, visit the Amazon GameLift Servers documentation.

Kiro と ABAP Accelerator を用いて AI-Driven Development Lifecycle（AI-DLC）の手法を適用し、SAP S/4HANA 上にフライト予約アプリケーションを構築する手順を解説します。Inception・Construction・Operations の 3 フェーズを通じて、AI が計画・設計・ABAP コード生成を主導し、ABAP Accelerator が SAP ABAP Test Cockpit（ATC）検証やオブジェクトのアクティベーションを自動化します。ビジネス上の意図から、数週間ではなく数時間で動作するコードへと到達する開発体験を実現します。

グローバルに SAP S/4HANA を運用する企業では、単一リージョンへのアクセスによる TLS ハンドシェイクの遅延と、パスワード頼みの認証によるセキュリティリスクが課題となります。本記事では、Amazon CloudFront の mTLS verify モードでエッジでクライアント証明書を検証し、Application Load Balancer（ALB）のパススルーと SAP ICM の X.509 認証を組み合わせて、パスワードレスなシングルサインオン（SSO）を実現するアーキテクチャを解説します。CloudFront-Viewer-Cert-Pem ヘッダーの転送、S3 トラストストア、AWS Private CA、AWS Shield/AWS WAF による多層防御まで、実装からクリーンアップまでを順を追って説明します。社内テストでは初回ログイン時間を約40〜50%短縮しました。

オランダに住んでいる私は、電車で過ごす時間がかなり多く、たいていその時間を使って、ビルダーコミュニティが発信し […]

今週の週刊生成 AI with AWSでは、AI の「正しさ」をどう測るかという話題が目立ちました。Byron Cook 氏による自動推論の翻訳記事 3 本もあわせて公開されています。国内事例では、第一興商のカラオケ聴感採点 AI、レスターの営業情報プラットフォーム、NTTドコモの AI-DLC 実践（全 2 回）をご紹介します。サービスアップデートでは、新しい AgentCore Runtime が東京リージョンを含む 5 リージョンで利用可能になり、Moonshot AI の Kimi K3 が Amazon Bedrock で一般提供されています。Kiro 関連では、GPT‑5.6 の 1M コンテキスト対応、Claude Fable 5.1 のエンタープライズ向け提供、Kiro Web の自律モードで 2 年以上残っていた issue を人間の作業 5 分未満で解決した事例、Kiro Crew で 1 週間に 1,000 件の PR をマージした話と今週も盛りだくさんです。

本記事は 2026 年 4 月 29 日に公開された、Features and workflows with […]

AWS Direct Connect が専用接続の定額料金を発表、AWS PrivateLink が Tunnel Endpoint を発表、AWS Elastic Beanstalk が Cluster Mode を導入、Moonshot AI の Kimi K3 が Amazon Bedrock で一般提供開始、Amazon Q Console から CloudTrail イベントを自然言語で分析、Amazon Connect Talent が一般提供開始、Amazon Connect Customer がカスタムメトリクスの API による作成・管理・検索に対応、Amazon ECS が Amazon S3 Files のサポートを Amazon EC2 起動タイプに拡張、Amazon Quick が個別シートの生成と画像からの分析構築に対応、AWS Transfer Family が Network Load Balancer (NLB) 配下の SFTP サーバーで送信元 IP 保持をサポート、新しい AgentCore Runtime が Amazon Bedrock AgentCore で利用可能に

Read all about the latest AWS security features, compliance updates, and hands-on resources in our monthly digest posts. You’ll find expert blog posts, new service capabilities, code samples, and workshops. AWS Security Blog posts August brought 20 AWS Security Blog posts organized across seven categories. Identity and access management led the month with five posts […]

Pair OpenCode, an open-source terminal-native AI coding agent, with open weight models on Amazon Bedrock to get a secure, flexible, pay-per-use coding assistant. Learn how to configure multi-model workflows, match the right model to each task, and keep your data in your own AWS account with no infrastructure to manage.

Learn how to build a conversational video intelligence solution on AWS using an agentic architecture. A single Strands Agents SDK agent orchestrates Amazon Bedrock, Amazon Rekognition, and Amazon Transcribe at runtime, deciding which service to call so you can ask natural language questions about your videos and get answers in seconds.

HEMA, a 100-year-old Dutch retailer, turned developer portal-hopping into instant answers by building HAL, an internal AI assistant on Amazon Bedrock AgentCore. Using Model Context Protocol (MCP), HAL delivers governed knowledge inside the tools teams already use, with no AWS credentials on the client and security anchored in Microsoft Entra ID.