Category: Advanced (300)

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, you learn how HotelTrader reduced inter-availability zone data transfer costs by 95% and improved average latency by 49% by migrating from the Redis Lettuce client to Valkey GLIDE on Amazon ElastiCache. The post walks through how HotelTrader identified hidden cross-AZ data transfer costs in their multi-AZ ElastiCache cluster, implemented Valkey GLIDE’s AZ-affinity read strategy to route requests to local replicas, optimized throughput with request batching, and executed a zero-downtime migration using A/B testing over 15 days.

In this post, we show you how to use the new cdk orphan command to safely migrate a DynamoDB table from the Table construct to TableV2 with zero downtime. Your data stays intact, streams keep flowing, and your application remains available throughout the process.

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.