Category: Technical How-to

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.

This post is part of a two-part series on upgrading RDS for MySQL 8.0 to 8.4. Here, we cover the end of standard support timeline, extended support costs, upgrade methods, and key best practices. For a step-by-step implementation guide, see Best practices for upgrading RDS for MySQL 8.0 to 8.4 with prechecks, Blue/Green, and rollback.

In this post, you learn how to build a complete upgrade workflow for RDS for MySQL 8.0 to 8.4, covering pre-upgrade compatibility checks, Amazon RDS Blue/Green Deployments for minimal downtime, and a rollback strategy for added protection.

In this post, we show how you can load (import) an Amazon DynamoDB full or incremental table export into a second DynamoDB table with precise control over what gets loaded, at what write rate, and with the ability to observe the progress. This technique helps drive large-scale data migrations and synchronizations where you want maximum control.

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.

New search capabilities are available in ElastiCache version 9.0 for Valkey. In this post, we walk through the new search capabilities, show how they work together, and build a search and recommendation engine from scratch.

Amazon ElastiCache now supports aggregation queries, so you can filter, group, transform, and summarize data directly in your cache with a single query. This post walks through the use cases that aggregations unlock, and shows how they work by building a faceted browsing engine using Amazon ElastiCache for Valkey.