Category: Advanced (300)

In this post, you learn how to use AWS DMS Schema Conversion to migrate Oracle schemas to PostgreSQL. AWS DMS Schema Conversion converts database schemas and code objects to formats compatible with your target database. You also learn how to use AWS DMS to migrate data to Amazon Aurora PostgreSQL-Compatible Edition.

In this post, you will learn how to integrate Okta with AWS IAM Identity Center and implement Amazon Relational Database Service (Amazon RDS) AWS Identity and Access Management (AWS IAM) authentication to create a unified authentication flow. You configure attribute-based access control (ABAC) that automatically maps user identities from your IdP to database permissions, supporting interactive user sessions and helping you avoid shared accounts. By the end, you have a working system where database authentication works exactly like your application authentication.

In this post, you’ll build a working veterinary clinic CLI application that demonstrates production-ready patterns for connecting Drizzle ORM to Aurora DSQL. By the end, you’ll have a running app with one-to-many and many-to-many relationships, and the patterns you learn (UUID primary keys, application-level relationships, and a custom migration runner) work with other TypeScript ORMs on Aurora DSQL too.

In this post, you learn how to build a generative AI–powered migration assistant that helps automate portions of the last mile of code conversion. Using Anthropic’s Claude Sonnet 4.6 on Amazon Bedrock, the Strands Agents framework, and the AWS Knowledge MCP Server, you can automate the conversion and validation of PL/SQL objects against Amazon Aurora PostgreSQL-Compatible Edition. The assistant reads the AWS DMS SC assessment CSV, fetches live PL/SQL source from Oracle, converts each object, deploys the result to Aurora PostgreSQL through AWS Lambda, and runs automated tests, in a single pipeline.

In this post, you’ll build a working veterinary clinic command line interface (CLI) application that demonstrates production-ready patterns for connecting SQLAlchemy to Aurora DSQL. The patterns you implement (UUID primary keys, application-level relationships, and AUTOCOMMIT engine configuration) apply to other Python ORMs on Aurora DSQL.

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.

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.

By implementing modern optimization techniques for Aurora, you can achieve additional cost reduction beyond traditional methods alone. This isn’t only about spending less—it’s about building a more efficient, scalable, and resilient database environment. In this post, we show you a structured approach to optimizing Amazon Aurora database costs. It outlines specific strategies, implementation steps, and best practices across different optimization areas.

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.