Category: Announcements

Encryption of both data at rest and in transit is a non-negotiable feature for most organizations. Furthermore, organizations operating in highly regulated and security-sensitive environments—such as those in the financial sector—often require full control over the cryptographic keys used for their workloads. Amazon Managed Service for Apache Flink makes it straightforward to process real-time data […]

We are now announcing support for Lake Formation tag-based access control (LF-TBAC) to federated catalogs of S3 Tables, Redshift data warehouses, and federated data sources such as Amazon DynamoDB, MySQL, PostgreSQL, SQL Server, Oracle, Amazon DocumentDB, Google BigQuery, and Snowflake. In this post, we illustrate how to manage S3 Tables and Redshift tables in the lakehouse using a single fine-grained access control mechanism of LF-TBAC. We also show how to access these lakehouse tables using your choice of analytics services, such as Athena, Redshift, and Apache Spark in Amazon EMR Serverless.

Amazon Redshift Serverless now supports 4 RPU configurations, helping you get started with a lower base capacity that runs scalable analytics workloads beginning at $1.50 per hour. In this post, we examine how this new sizing option makes Redshift Serverless accessible to smaller organizations while providing enterprises with cost-effective environments for development, testing, and variable workloads.

In July 2025, Amazon SageMaker announced support for Amazon Simple Storage Service (Amazon S3) general purpose buckets and prefixes in Amazon SageMaker Catalog that delivers fine-grained access control and permissions through S3 Access Grants. In this post, we explore how this integration addresses key challenges our customers have shared with us, and how data producers, such as administrators and data engineers, can seamlessly share and govern S3 buckets and prefixes using S3 Access Grants, while making it readily discoverable for data consumers.

The Amazon SageMaker lakehouse architecture now automates optimization of Iceberg tables stored in Amazon S3 with catalog-level configuration, optimizing storage in your Iceberg tables and improving query performance. This post demonstrates an end-to-end flow to enable catalog level table optimization setting.

In this blog post, AWS introduces a solution to a common challenge in scientific research – the inefficient management of fragmented scientific data. The post demonstrates how the next generation of Amazon SageMaker, through its Unified Studio and Catalog features, helps scientists streamline their workflow by integrating data management and analytics capabilities.

Today, we’re announcing the open source release of Spark History Server MCP, a specialized Model Context Protocol (MCP) server that transforms this workflow by enabling AI assistants to access and analyze your existing Spark History Server data through natural language interactions. This project, developed collaboratively by AWS open source and Amazon SageMaker Data Processing, turns complex debugging sessions into conversational interactions that deliver faster, more accurate insights without requiring changes to your current Spark infrastructure. You can use this MCP server with your self-managed or AWS managed Spark History Servers to analyze Spark applications running in the cloud or on-premises deployments.

Amazon MQ is a fully managed service for open-source message brokers such as RabbitMQ and Apache ActiveMQ. Today, we are announcing the availability of AWS Graviton3-based Rabbit MQ brokers on Amazon MQ, which runs on Amazon EC2 M7g instances. AWS Graviton processors are custom designed server processors developed by AWS to provide the best price performance for cloud workloads running on Amazon EC2.

Amazon SageMaker has announced an integration with Amazon QuickSight, bringing together data in SageMaker seamlessly with QuickSight capabilities like interactive dashboards, pixel perfect reports and generative business intelligence (BI)—all in a governed and automated manner. In this post, we walk through the complete process of integrating Amazon QuickSight with Amazon SageMaker Unified Studio, demonstrating how teams can move from raw data to published dashboards in a secure and governed environment.

This post demonstrates how AWS Glue R type, G.12X, and G.16X workers help you scale up your AWS Glue for Apache Spark jobs.