AWS News - 2026-07-08

Amazon Redshift RG instances, powered by AWS Graviton processors, are now available in the AWS GovCloud (US-West) and AWS GovCloud (US-East) Regions. RG instances deliver better performance, running data warehouse and data lake workloads up to 2.4x as fast as previous generation RA3 instances, at 30% lower price per vCPU. RG instances include Redshift's custom-built vectorized data lake query engine that processes Apache Iceberg and Parquet data on your cluster nodes, enabling you to run SQL analytics across your data warehouse and data lake using a single engine.

RG instances are available in two instance sizes, rg.xlarge and rg.4xlarge. Customers with existing RA3 clusters can upgrade them to RG using Snapshot & Restore, Elastic Resize, or Classic Resize. RG instances are available with flexible pricing options, including On-Demand, and 1-year and 3-year Reserved Instances with All Upfront, Partial Upfront, and No Upfront payment options. For pricing details, visit the Amazon Redshift pricing page.

To get started, refer to the following resources:

• Amazon Redshift RG Instance Documentation
• RA3 to RG Upgrade Guide
• Amazon Redshift Pricing

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C8ine instances are available in the AWS Europe (Frankfurt) region. C8ine instances are powered by custom sixth generation Intel Xeon Scalable processors, available only on AWS. These instances feature the latest sixth generation AWS Nitro cards, delivering up to 43% higher performance compared to previous generation C6in instances.

C8ine instances offer up to 2.5 times higher packet performance per vCPU versus prior generation network optimized instances, providing up to 2x higher network throughput for traffic going through Internet gateways compared to existing C6in network optimized instances. C8ine instances are designed for security and network virtual appliances, including virtual firewalls, load balancers, and Telco 5G UPF workloads.

Amazon EC2 C8ine instances are available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Frankfurt) regions. C8ine instances are available via Savings Plans and On-Demand instances. For more information, visit the Amazon EC2 C8i instance pages.

Amazon SageMaker Unified Studio now supports OpenLineage compatible data lineage in IAM-based domains, capturing lineage events from Apache Spark jobs run on Amazon EMR, AWS Glue, SageMaker Visual ETL, and notebooks. This capability is already available in IAM Identity Center-based domains. The interactive lineage graph provides an aggregate visual representation of how data moves from source to consumption, with configurable graph depth, event timestamp mode for detailed column-level lineage, and a dataset-only view for simplified visualization. For both IAM-based and IAM Identity Center-based domains, you can programmatically publish, query, and manage data lineage from OpenLineage compatible applications. You can now also remove published events using the DeleteLineageEvent API.

This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. To get started, visit the Amazon SageMaker Unified Studio documentation and API reference.

Amazon EMR Serverless now offers larger worker configurations of 32 vCPUs with up to 244 GB of memory, allowing you to run more compute and memory-intensive workloads. Previously, the largest worker configuration available on EMR Serverless was 16 vCPUs with up to 120 GB of memory. Larger workers can help you improve the runtime performance as well as cost profiles for your workloads.

For shuffle-heavy workloads, larger workers reduce inefficient data transfers between executors. For jobs with data skew, larger workers reduce the chances of out-of-memory failures. For jobs that need to cache data, larger workers allow holding more data in memory, boosting job performance. To take advantage of these benefits, we recommend using larger workers for your compute and memory-intensive Spark and Hive workloads.

To learn more about different worker configurations, please visit EMR Serverless documentation. Larger workers are available in all AWS Regions where EMR Serverless is available.

Amazon S3 Vectors is now available in AWS GovCloud (US-East) and AWS GovCloud (US-West).

Amazon S3 Vectors is purpose-built vector storage for AI agents, inference, Retrieval Augmented Generation (RAG), and semantic search at billion-vector scale. S3 Vectors is designed to provide the same elasticity, durability, and availability as Amazon S3, with a dedicated set of APIs that let you store, access, and query vectors without provisioning any infrastructure.

For a full list of AWS Regions where Amazon S3 Vectors is available, see AWS Regions and endpoints. To learn more, visit the product page, documentation, and the Amazon S3 pricing page.

Amazon GameLift Streams now supports Stream Session Admin Shell, a secure terminal connection to the live runtime environment of a stream session for real-time troubleshooting. You can inspect logs, query running processes, check GPU utilization, and examine application state — all without managing SSH keys, open ports, or infrastructure credentials.

Stream Session Admin Shell provides a terminal connection with the same level of access as your Amazon GameLift Streams applications. To connect, call the new CreateStreamSessionAdminShell API with your stream group and stream session identifiers, then use the returned credentials with the SSM Session Manager plugin for the AWS CLI. The feature supports Linux (Ubuntu 22.04), Proton, and Windows Server 2022 runtimes. The terminal connection is scoped to your application environment and automatically closes when the stream session ends.

Stream Session Admin Shell is available at no additional cost in all AWS Regions where Amazon GameLift Streams is offered. For a full list of supported Regions, see the AWS Region table.

To get started, see the Stream Session Admin Shell developer guide and CreateStreamSessionAdminShell API reference.

Amazon EC2 High Memory U7i instances with 12TB of memory (u7i-12tb.224xlarge) are now available in the AWS Europe (Zurich) region. U7i instances are part of AWS 7th generation and are powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids). U7i-12tb instances offer 12TiB of DDR5 memory, enabling customers to scale transaction processing throughput in a fast-growing data environment.

U7i-12tb instances offer 896 vCPUs, support up to 100Gbps Elastic Block Storage (EBS) for faster data loading and backups, deliver up to 100Gbps of network bandwidth, and support ENA Express. U7i instances are ideal for customers using mission-critical in-memory databases like SAP HANA, Oracle, and SQL Server.

To learn more about U7i instances, visit the High Memory instances page.

Amazon Redshift RA3 クラスターを Graviton ベースの RG インスタンスに移行する方法を解説します。Elastic Resize、Classic Resize、Snapshot/Restore の 3 つの移行戦略を比較し、スムーズな移行のための考慮事項とベストプラクティスを紹介します。

VMware Cloud Foundation (VCF) 9.0 および 9.1 が Amazon Elastic VMware Service (EVS) で利用可能になり、VCF インストールを完全に制御できるようになったことをお知らせいたします。Amazon EVS における私たちの焦点は、お客様が現在お持ちのツール、運用プロセス、スキルを使用して、ニーズに合わせて VCF をデプロイおよび構成できる柔軟性を提供することです。

2022年にスタートし、毎年全国の地域を巡ってきた「AWS デジタル社会実現ツアー」が今年で 5年目 を迎えま […]

オンプレミスの HPC 環境からの移行を進める研究チームは、クラウドへのデプロイの複雑さに悩まされることがよく […]

AWS Summit Japan 2026（2026年6月25日〜26日、幕張メッセ）では、「AWS パートナ […]

With the introduction of models that require data sharing with third-party providers—such as Claude Fable 5—organizations need a way to centrally enforce data retention policies. Amazon Bedrock gives you control over whether your prompts and model outputs are retained after an inference request completes. You might need a way to enforce your retention settings across […]

Bulletin ID: 2026-053-AWS
Scope: AWS
Content Type: Important (requires attention)
Publication Date: 07/07/2026 09:45 AM PDT

Description:

AWS Research and Engineering Studio (RES) is an open-source solution that enables researchers and engineers to create and manage secure virtual desktops and computing resources on AWS.

We identified an improper link resolution before file access issue (CWE-59) in the Auth.GetUserPrivateKey API. An authenticated remote user could read arbitrary files on the cluster-manager EC2 instance by replacing their SSH private key file (~/.ssh/id_rsa) with a symbolic link targeting any file on the host. Because the cluster-manager process runs as root, any file readable by root is exposed, including other users' SSH private keys and application configuration secrets.

Impacted versions: <=2026.03

Please refer to the article below for the most up-to-date and complete information related to this AWS Security Bulletin.

In this post, we explain how S&P Global Market Intelligence implemented an innovative disaster recovery solution for their Capital IQ platform using Amazon FSx for NetApp ONTAP. This solution enables immediate failover to read-only mode in a secondary region within 15 minutes, followed by full read-write recovery when needed. This approach achieves reduction in failover time while maintaining data consistency for global financial operations.

In this post, we show how AWS Finance used chat agents and Flows in Amazin Quick to transform two of their most time-consuming workflows.

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Implementing a data and model monitoring solution is necessary to maintain prediction accuracy and help achieve the best outcome for your machine learning use case. This post shows how you can use open source Evidently together with Amazon SageMaker AI to generate monitoring reports, organize and compare the results in MLflow, scale through pipelines, and trigger drift notifications.

This post walks through building a serverless image editor where users upload a photo, describe an edit in plain English, and receive the result in seconds. The agent runs on AgentCore harness without custom orchestration code. We deploy the full solution, including authentication, encrypted storage, three image editing tools, and a React frontend, with a single deployment command. The infrastructure is defined using AWS Cloud Development Kit (AWS CDK).

In this post, we walk through how multi-dataset Topics work, explain how the chat agent uses defined relationships to generate cross-dataset queries, and demonstrate an end-to-end implementation using a retail analytics scenario in Quick Sight.

This post is for data architects, business intelligence (BI) engineers, and analytics engineers building or optimizing Quick Sight Topics for natural-language Chat-based exploration.

In this post, we shift from concepts to patterns. For each schema, you’ll find a table structure, use cases, implementation steps, and sample SQL queries. We also cover workarounds for advanced scenarios that require extra modeling steps, and close with a summary of current limitations.

Today, we are excited to announce Multi-Dataset Relationships in Amazon Quick Sight. This new capability lets you define logical relationships between Quick Sight datasets and perform runtime joins at query time. Instead of flattening tables ahead of time, you keep each table as its own Quick Sight dataset and declare how those datasets relate to one another inside a Quick Sight Topic.

In this post, we walk through what Dataset Enrichment is, how it differs from legacy Topics, and provide three migration scenarios with step-by-step guidance so you can move your business context into the dataset layer with confidence.