AWS Database Blog

In this post, we show how you can build a multi-Region active-active API using Prisma ORM and Amazon Aurora DSQL.

In this post, we demonstrate a serverless approach to continuous forensic artifact collection for Amazon RDS and Amazon Aurora databases. By capturing point-in-time snapshots of database internals on a cadence and storing them in Amazon S3, you create a time-series record that AI tools can analyze in seconds. This turns what was hours of manual investigation into an instant conversation.

Managing payment infrastructure at scale presents unique challenges that impact both performance and operational efficiency. In this post, we share the technical and operational challenges Nubank faced with self-managed PostgreSQL, the evaluation criteria they established for selecting database solutions, and the results from their successful migration to Amazon Aurora PostgreSQL-Compatible Edition. Nubank achieved up to 1,900x query performance improvements in specific cases.

In this post, you learn how to use the sp_invoke_external_rest_endpoint stored procedure on Amazon RDS for SQL Server 2025 to call AWS services and external HTTPS endpoints directly from T-SQL.

In this post, we demonstrate how SQL Server 2025 on Amazon RDS can call Amazon Bedrock foundation models directly from T-SQL using sp_invoke_external_rest_endpoint. This approach removes middleware, reduces latency, and brings AI capabilities directly into database workflows.

In this post, we provide step-by-step instructions for migrating an IBM Db2 database from on-premises AIX or Windows servers to Amazon RDS for Db2 using native Db2 tools (db2look and db2move). We also cover AWS Database Migration Service (AWS DMS) as an alternative approach. It also covers best practices to verify data integrity and minimize cutover time.

In this post, you’ll learn four concrete strategies that help you reduce Aurora DSQL connection overhead, stay within the 100-connections-per-second rate limit, and avoid thundering-herd reconnection storms. By the end, you’ll have a production-ready checklist for configuring connection pools that support reliable performance at scale.

When using Amazon DynamoDB, you sometimes want to perform bulk operations against all the items in a table, which has historically required custom coding. The open source Bulk Executor for DynamoDB simplifies bulk tasks like these. You can use this feature to invoke commands like count, find, delete, or update. No coding is required, even when running at large scale. In this post, we explore the built-in capabilities of Bulk Executor and show you how to install and use it for common bulk operations against your DynamoDB tables.

In this post, we show you how to build a semantic ontology that helps your AI assistants navigate enterprise data efficiently. You’ll learn how to structure a property graph store for data relationships, set up vector indexing for semantic search, and implement an automated fact-learning layer that improves use. This bottom-up approach grounds your ontology in the data that exists, building abstractions from observed patterns rather than theoretical models.

In this post, we show how to rebuild large indexes on Amazon Aurora PostgreSQL by combining Amazon Aurora Blue/Green Deployments with Aurora Optimized Reads. By performing the reindex on the green (staging) environment with a Non-Volatile Memory express (NVMe)-backed instance class, the sort phase uses fast local storage instead of Amazon EBS over the network, and you avoid impacting production workloads.