Category: Technical How-to

In this post, we dive deep into the Aurora backup architecture, how it differs from Amazon RDS backups, and the Amazon CloudWatch metrics available to monitor your backup storage usage. Through detailed scenarios and visualizations, we demonstrate how workload patterns and retention periods impact backup costs. We also explore cross-Region backup options and share recommended practices to optimize your backup storage consumption.

This post walks through migrating data from an Oracle source to Amazon Aurora DSQL, using AWS DMS, Amazon S3, AWS Glue, and AWS Step Functions to create an automated, cost-effective migration pipeline suitable for enterprise-scale deployments.

In this post, we demonstrate how to implement a production-ready CDC solution by using Amazon Aurora for PostgreSQL, Debezium connectors, and Amazon Managed Streaming for Apache Kafka (Amazon MSK). This solution captures database changes in real time and streams them to Kafka topics so that downstream consumers can process the same data for different business purposes.

Today, we announce the Amazon Aurora MySQL power for Kiro. The power connects Kiro’s AI agent to Aurora MySQL and pairs live database access with curated best-practice guidance. You describe what you need in natural language. The agent generates the API calls, SQL, and configuration for you to review and run. In this post, we walk through how the power guides a production migration from Amazon Relational Database Service (Amazon RDS) for MySQL 8.0 to Aurora MySQL through four phases: assessment, replica creation, promotion, and post-cutover validation.

Amazon receives millions of visits every day, and earning each customer’s trust visit after visit is the foundation that the store is built on. A meaningful part of that trust comes down to whether the recommendations we surface feel relevant and whether they reflect what the customer actually cares about in the moment. In this post, we describe an architecture that makes it achievable. Amazon SageMaker hosts a sentence transformer model on a managed endpoint and turns customer query text into dense semantic vectors. Valkey is an open source, in-memory data store with built-in vector search. It’s available on AWS through Amazon ElastiCache and Amazon MemoryDB. In our architecture, we use Amazon-managed Valkey to store the product catalog as a vector index.

In this post, we introduce a modular Terraform template, published in the aws-samples/sample-rds-db2-tools repository, that lets your applications keep their existing custom domain names and ports while preserving end-to-end TLS encryption to Amazon RDS for Db2. The template deploys a Server Name Indication (SNI) based TLS proxy that forwards encrypted traffic without ever decrypting it.

In this post, we show you how to use AWS Fault Injection Service (AWS FIS) to validate that your application handles regional disruptions the way you expect, by running controlled experiments against your DynamoDB global tables. We cover both multi-Region strong consistency (MRSC) and multi-Region eventually consistent (MREC) global tables, because AWS FIS works differently with each.

In this post we cover the two primary failover strategies for DynamoDB global tables, the tradeoffs between them, and the operational considerations that you must be aware of during and after a failover.

This is Part 1 of a series on best practices for DynamoDB global tables. In this post, we focus on preparation: understanding how replication works, what your resilience posture looks like, and the operational groundwork that separates a controlled failover from a scramble.

Customers running IBM Db2 workloads often ask for a repeatable, auditable way to provision Amazon RDS for Db2 that fits their existing infrastructure-as-code practice. In this post, we introduce a modular Terraform template, published in the aws-samples/sample-rds-db2-tools repository. The template takes you from an empty AWS account to a running RDS for Db2 instance tracked in AWS License Manager in under an hour.