Amazon OpenSearch Service now supports i8g instances, which is the latest generation of storage optimized instances offering the best performance for storage-intensive workloads.

Powered by AWS Graviton4 processors, I8g instances deliver up to 60% better compute performance compared to previous generation I4g instances. I8g instances use the latest third generation AWS Nitro SSDs, local NVMe storage that deliver up to 65% better real-time storage performance per TB while offering up to 50% lower storage I/O latency and up to 60% lower storage I/O latency variability. Built on the AWS Nitro System , these instances offload CPU virtualization, storage, and networking functions to dedicated hardware and software enhancing the performance and security for your workloads.

I8g instances support all OpenSearch versions & Elasticsearch (open source) versions 7.9 and 7.10. To know more about i8g instances, see blog .

Amazon OpenSearch Service supports i8g instances in following AWS Regions : US East (N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (Spain), Asia Pacific (Mumbai) and Asia Pacific (Sydney).

For region specific availability & pricing, visit our pricing page . To learn more about Amazon OpenSearch Service and its capabilities, visit our product page .

Starting today, Amazon EC2 High Memory U7i instances with 12TB of memory (u7i-12tb.224xlarge) are now available in the US Asia Pacific (Seoul) region. U7i-12tb 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 .

AWS HealthOmics introduces support for the Nextflow time directive, which enables customers to set task level timeout controls to limit run duration for specific tasks. With this launch, customers can now set fine-grained controls for their Nextflow workflow tasks to enable automated run cancellation if specific tasks take longer than expected. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs with fully managed biological data stores and workflows.

The Nextflow time directive is now supported in all regions where AWS HealthOmics is available: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Asia Pacific (Singapore), and Israel (Tel Aviv).

To learn more about workflows and the Nextflow time directive support, see the AWS HealthOmics documentation .

The Amazon SageMaker lakehouse architecture now supports tag based access control (TBAC) for managing fine-grained data access across federated catalogs. This capability, previously available only for default AWS Glue Data Catalog resources, is now available across Amazon S3 Tables, Amazon Redshift data warehouses, and federated data sources including Amazon DynamoDB, PostgreSQL, and SQL Server. TBAC enables simplified permission management by logically grouping catalog resources using tags, allows scaling permissions across datasets with a minimal set of permissions, and also facilitates data sharing across different accounts.

TBAC simplifies how administrators manage data access permissions by replacing direct resource-level permissions with tag-based grants. Instead of manually assigning permissions to individual tables or columns, administrators can now efficiently control access through tags that are automatically inherited by resources. This inheritance feature ensures that new tables automatically receive appropriate fine-grained access controls without additional policy modifications.

You can get started with TBAC through the AWS Lake Formation console. Create tags using key-value pairs, associate them with databases, tables, or columns, and grant permissions to principals based on specific tags. Users can then access tagged resources through Amazon Athena, Amazon Redshift, Amazon EMR, or Amazon SageMaker Unified Studio.

This feature is available through the AWS Management Console, AWS CLI, and AWS SDKs in all commercial AWS Regions . To get started, read the blog and visit the Lake Formation Tags documentation . 

Administrators can now control the Model Context Protocol (MCP) servers installed in Amazon Q Developer client applications directly within the AWS console. This provides organizations with the granular control needed to manage external resources safely and effectively.

With this launch an admin has the ability to enable or disable the MCP functionality for all the Q Developer clients in that organization. This means if an administrator disables the functionality, then users will not be allowed to add any MCP servers, nor will any previously defined servers be initialized. Q Developer checks and applies admin settings at the start of each session, and also every 24 hours while the client is running.

MCP admin control support for Amazon Q Developer applies to the Amazon Q Developer CLI, and all Amazon Q Developer plugins: VSCode, JetBrains, Visual Studio, and Eclipse. Check out the documentation to learn more.

Amazon EMR on EC2 announces two significant enhancements for governance: Apache Spark native fine-grained access control (FGAC) via AWS Lake Formation, and support for AWS Glue Data Catalog views. These features allow organizations to improve data security, simplify access management, and enhance data sharing capabilities across their analytics environments.

The Apache Spark native FGAC implementation allows customers to define granular access policies once in AWS Lake Formation and apply them consistently across EMR clusters. This reduces security risks and administrative overhead while providing a unified approach to data governance. Customers can now use familiar Lake Formation grant and revoke statements to manage access controls for their Spark jobs and interactive sessions on EMR on EC2, similar to how this works for other AWS analytics services.

AWS Glue Data Catalog views enables customers to create, manage, and query multi-engine SQL views across AWS regions, accounts, and organizations. This feature allows administrators to create views from Spark jobs that can be queried from multiple engines, while controlling data access through Lake Formation permissions. These permissions include named resource grants, data filters, and tags, with all access requests automatically logged in AWS CloudTrail for comprehensive auditing.

Apache Spark native FGAC and Glue Data Catalog view features are available with Amazon EMR release 7.10 in all AWS Regions where EMR on EC2 is available. To learn more, visit Using AWS Lake Formation with Amazon EMR and Working with AWS Glue Data Catalog Views in the Amazon EMR documentation.

AWS HealthOmics introduces support for third-party container registries, enabled through Amazon Elastic Container Registry (ECR) pull-through cache, along with URI remapping rules for automatic translation of third-party container URIs to ECR URIs. This enhancement enables AWS HealthOmics customers to more easily access containerized tools from popular third-party registries without needing to manually migrate them to private ECR repositories, or make changes to the workflow definition. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs with fully managed biological data stores and workflows.

The ECR pull-through cache capability allows bioinformatics teams to automatically retrieve and cache containers from popular registries including Amazon ECR Public, Docker Hub, Quay, GitHub Container Registry, GitLab Container Registry, Kubernetes container image registry, and Microsoft Azure Container Registry. This helps customers accelerate workflow development and execution by eliminating manual container synchronization tasks. Additionally, the new container URI remapping feature automatically translates third-party registry references in workflow definitions to corresponding private ECR URIs using customer-defined mapping rules, eliminating the need to manually update workflow definitions when migrating workflows.

ECR pull-through cache and container URI remapping features are now supported in all regions where AWS HealthOmics is available: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Asia Pacific (Singapore), and Israel (Tel Aviv).

To learn more about these new features and how to implement them in your workflows, see the AWS HealthOmics documentation .

AWS Identity and Access Management (IAM) now offers three new global condition keys that will make it easier for you to establish a network perimeter. The new condition keys – aws:VpceAccount, aws:VpceOrgPaths, and aws:VpceOrgID – help you ensure that requests to your AWS resources or by your identities are made through your VPC endpoints.

The condition keys provide you with varied levels of granularity, enabling you to implement your network perimeter controls at an account, organization path, and entire organization level. The controls automatically scale with your VPC usage, eliminating the need to enumerate VPC endpoints or update policies as you add or remove them. You can use these condition keys with both new and existing service control policies (SCPs), resource control policies (RCPs), resource-based policies, and identity-based policies.

The condition keys are supported for a select set of AWS services and are available in all commercial AWS Regions where those services support AWS PrivateLink.

To learn more about these new condition keys and supported services, please visit the AWS IAM documentation and AWS blog.

AWS announces Amazon EMR S3A, a new Amazon S3 connector that optimizes performance for Apache Hadoop, Apache Spark, and Apache Hive workloads on Amazon EMR . This new connector enhances the open source S3A architecture with AWS-specific optimizations to help organizations process large-scale data more efficiently. With direct integration support for S3 Express One Zone, S3 Glacier, and AWS Outposts, EMR S3A helps customers leverage different storage options in AWS to optimize both data access speed and storage cost on their EMR workloads.

Additionally, the EMR S3A connector delivers advanced security features and performance capabilities that extend beyond open source S3A. Key improvements include Apache Spark built-in fine-grained access control support, enhanced S3A credentials resolver, MagicCommitter V2 for optimized file writes, and accelerated S3 prefix listing for columnar file formats. These enhancements are available starting with EMR release 7.10 and maintain compatibility with existing applications.

The Amazon EMR S3A connector is available in all AWS Regions where Amazon EMR is available and comes pre-configured with Amazon EMR release version 7.10 and later. To learn more about Amazon EMR S3A, see the Amazon EMR documentation .

Today, Amazon QuickSight is announcing the general availability of a native Google Sheets connector.

Customers can now connect to Google Sheets by logging in with their Google account and importing sheets into a QuickSight SPICE dataset for analysis.

Google Sheets connector for Amazon QuickSight is now available in the following regions: US East (N.Virginia and Ohio), US West (Oregon), Canada (Central), South America (Sao Paulo), Europe (Frankfurt, Stockholm, Ireland, London), Asia Pacific (Singapore, Tokyo, Seoul, Sydney). For more details, read our blog post here .