AWS Database Blog
Category: Announcements
Introducing Extended Support for Amazon ElastiCache version 4 and version 5 for Redis OSS
Amazon ElastiCache now offers Extended Support so that you can upgrade to a new major version at a pace that meets your business requirements. Extended Support is a paid offering that provides critical security updates, bug fixes, and continued support for ElastiCache versions 4 and 5 for Redis OSS through January 31, 2029. Starting on February 1, 2026, ElastiCache Redis OSS v4 and v5 clusters that haven’t been upgraded will be automatically enrolled in Extended Support to provide continuous availability and security. In this post, we discuss what ElastiCache Extended Support entails, its key benefits, and the upgrade options available.
Year One of Valkey: Open-Source Innovations and ElastiCache version 8.1 for Valkey
In April 2024, AWS announced support for Valkey, a community-driven fork of Redis born out of a shared belief that critical infrastructure software should be vendor neutral and open source. In this post, we share how, just over a year in, we remain fully committed to the Valkey project and announce support for the latest version with Amazon ElastiCache version 8.1 for Valkey. We explore the benefits of Valkey through real-world examples the benefits of the latest innovations, including a new hash table with additional memory efficiencies, support for Bloom filters, observability enhancements, and new functionality.
Implement fast, space-efficient lookups using Bloom filters in Amazon ElastiCache
Amazon ElastiCache now supports Bloom filters: a fast, memory-efficient, probabilistic data structure that lets you quickly insert items and check whether items exist. In this post, we discuss two real-world use cases demonstrating how Bloom filters work in ElastiCache, the best-practices to implement, and how you can save at least 90% in memory and cost compared to alternative implementations. Bloom filters are available in ElastiCache version 8.1 for Valkey in all AWS Regions and at no additional cost.
Announcing Valkey GLIDE 2.0 with support for Go, OpenTelemetry, and batching
AWS recently announced, in partnership with Google Cloud and the Valkey community, the general availability of Valkey General Language Independent Driver for the Enterprise (GLIDE) 2.0, the latest release. Valkey GLIDE is multi-language client library designed for reliability and performance. In this post, we discuss what Valkey GLIDE is and its key benefits, and then dive into its new enhancements.
Supercharging AWS database development with AWS MCP servers
Amazon Aurora, Amazon DynamoDB, and Amazon ElastiCache are popular choices for developers powering critical workloads, including global commerce platforms, financial systems, and real-time analytics applications. To enhance productivity, developers are supplementing everyday tasks with AI-assisted tools that understand context, suggest improvements, and help reason through system configurations. Model Context Protocol (MCP) is at the helm of this revolution, rapidly transforming how developers integrate AI assistants into their development pipelines. In this post, we explore the core concepts behind MCP and demonstrate how new AWS MCP servers can accelerate your database development through natural language prompts.
Amazon DynamoDB zero-ETL integration with Amazon SageMaker Lakehouse – Part 2
Amazon DynamoDB zero-ETL integration with Amazon SageMaker Lakehouse allows you to run analytics workloads on your DynamoDB data without having to set up and manage extract, transform, and load (ETL) pipelines. In this post we cover setting up Amazon SageMaker Unified Studio, followed by running data analysis to showcase its capabilities. We illustrate our solution walkthrough with an example of a credit card company that wants to analyze its customer behavior and spending trends.
Amazon DynamoDB zero-ETL integration with Amazon SageMaker Lakehouse – Part 1
Amazon DynamoDB zero-ETL integration with Amazon SageMaker Lakehouse allows you to run analytics workloads on your DynamoDB data without having to set up and manage extract, transform, and load (ETL) pipelines. In this two-part series, we first walk through the prerequisites and initial setup for the zero-ETL integration. In Part 2, we cover setting up Amazon SageMaker Unified Studio, followed by running data analysis to showcase its capabilities. We illustrate our solution walkthrough with an example of a credit card company that wants to analyze its customer behavior and spending trends.
Amazon Aurora Global Database introduces support for up to 10 secondary Regions
In this post, we dive deep into Amazon Aurora Global Database’s new support for up to 10 secondary Regions and explore use cases it unlocks. An Aurora Global Database consists of one primary Region and up to 10 read-only secondary Regions for low-latency local reads.
Announcing configurable point-in-time recovery periods for Amazon DynamoDB
Amazon DynamoDB enables you to back up your table data continuously by using point-in-time recovery (PITR). When you enable PITR, DynamoDB backs up your table data automatically with per-second granularity. PITR helps protect you against accidental writes and deletes. For example, if a test script accidentally writes to a production DynamoDB table, or someone mistakenly […]
Prevent transaction ID wraparound by using postgres_get_av_diag() for monitoring autovacuum
In this post, we introduce postgres_get_av_diag(), a new function available in RDS for PostgreSQL and Aurora PostgreSQL to monitor aggressive autovacuum blockers.









