AWS Database Blog
Category: Technical How-to
Guide your Amazon Aurora MySQL migration with Kiro powers
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.
Real-time personalized recommendations with Amazon SageMaker and Amazon-managed Valkey
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.
Preserving custom domain names for Amazon RDS for Db2
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.
Best practices for Amazon DynamoDB Global Tables – Part 3: Validating regional resilience with AWS Fault Injection Service
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.
Best practices for Amazon DynamoDB Global Tables – Part 2: Failover strategies
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.
Best practices for Amazon DynamoDB Global Tables – Part 1: Operational readiness
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.
Deploying Amazon RDS for Db2 using Terraform
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.
Automated JDBC query caching with the AWS Advanced JDBC Wrapper
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.
Building an AI-powered grid investigation agent with Aurora DSQL and Amazon Bedrock AgentCore
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.
Improving generative AI accuracy with vector and graph search hybrid queries
In this post, we discuss the differences between vector search and graph search, how to combine the two for hybrid querying, and use cases that benefit from hybrid querying.









