Category: Amazon Aurora

Today, we are excited to announce the general availability of Amazon Aurora MySQL 8.4, our latest major version, compatible with community MySQL 8.4.7. This release marks an important milestone for Aurora MySQL customers, introducing a simplified versioning model aligned directly with community MySQL, along with a streamlined patch version experience, and the full set of community MySQL 8.4 enhancements. In this post, we discuss the customer challenges that this release addresses, introduce Aurora MySQL 8.4, walk through the new versioning approach and its benefits for customers, cover the key capabilities delivered in Aurora MySQL 8.4, and show you how to get started.

In this post, we share the version currency timelines for Aurora and RDS open source engines. We also explain why timelines differ across engines and how you can use them to plan your upgrades.

Today, we’re announcing the Remote Query Cache Plugin for the AWS Advanced JDBC Wrapper. The plugin handles query caching automatically. It intercepts JDBC queries, caches results in Amazon ElastiCache for Valkey, and serves subsequent identical queries from cache. Your only application change is prefixing queries with SQL hints. In this post, we show you how to use Amazon CloudWatch Database Insights to identify queries to cache, configure the Remote Query Cache Plugin in your Java applications, and monitor cache effectiveness using Amazon CloudWatch.

In this post, we show how to build an Amazon Aurora DSQL database agent that other AI agents can discover and query through natural language using the A2A protocol. You’ll walk through how to build and deploy this using Amazon Bedrock AgentCore capabilities, including AgentCore Runtime for hosting, AgentCore Gateway for tool access via MCP, and the Strands Agents SDK for agent logic.

In this post, we demonstrate how to configure Aurora DSQL Change Data Capture and stream database changes into Kinesis Data Streams. You will learn how CDC works, how to configure a streaming pipeline, and how to consume change events. By the end of this post, you will have a working CDC pipeline that streams database changes into a durable event stream that downstream applications can process.

In this post, we show you how to migrate from an Amazon RDS for PostgreSQL to Amazon Aurora PostgreSQL-Compatible Edition using seeded logical replication. For live migrations with minimal downtime, AWS provides several approaches, including Aurora read replicas, the snapshot/restore method combined with ongoing replication, and AWS DMS.

Connecting to Amazon Aurora DSQL requires a different approach than traditional PostgreSQL databases. Instead of long-lived passwords, you use short-lived IAM authentication tokens. Instead of static endpoints, you work with distributed cluster endpoints that route connections across Availability Zones. In this post, you learn how to configure connection strings, set up drivers in Python, Java, and Node.js, and implement best practices for authentication, connection pooling, and lifecycle management with Amazon Aurora DSQL.

In this post, you’ll learn how to query Amazon S3 Vectors from Amazon Aurora PostgreSQL-Compatible Edition using standard SQL, and how to combine vector similarity results with relational filters in a single query, for example, finding the most semantically similar products and then filtering by price, stock status, or tenant in one SQL statement.

In this post, we demonstrate how to use AWS Glue to migrate data from an Amazon Aurora database snapshot into an Aurora DSQL cluster.

In this post, we first examine why traditional approaches to distributed consistency fall short for financial workloads. We then walk through how the Amazon Aurora DSQL architecture addresses these challenges, and apply it to three production use cases: core banking, global spend management, and digital currency infrastructure. We close with implementation considerations and how to get started with the Amazon Aurora DSQL Free Tier