Category: Amazon Aurora

Learn how to set up the Amazon Aurora DSQL MCP server and use it from your AI coding assistant to run queries, evolve schemas, and check Aurora DSQL compatibility without leaving your IDE. This post walks through installation, the available MCP tools, practical integration patterns, and the security model.

Learn how to use the calling services feature in Amazon CloudWatch Database Insights to identify which applications are calling your databases and view their performance metrics, so you can pinpoint root causes and contact the right team in minutes rather than hours.

In this post, we provide practical guidance for designing applications that scale effectively with the Amazon Aurora DSQL distributed architecture. You will learn how to identify common patterns that limit scalability, apply proven design patterns that distribute workload efficiently, and implement transaction strategies optimized for Aurora DSQL. We cover primary key selection, schema design principles, indexing strategies, and multi-Region optimization, while maintaining full ACID (atomicity, consistency, isolation, and durability) compliance across AWS Regions.

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’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.

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.

In this post, we demonstrate how to use the PostgreSQL 18 logical replication improvements on RDS for PostgreSQL: replicating STORED generated columns with the publish_generated_columns parameter, monitoring conflicts through the new counters in pg_stat_subscription_stats, verifying that parallel streaming is enabled by default, toggling two-phase commit on a running subscription, and configuring idle_replication_slot_timeout for automatic slot cleanup. These features are available on RDS for PostgreSQL 18.0 and later and Aurora PostgreSQL.

In this post, we show you how to deploy an automated pipeline that extracts PostgreSQL audit logs from CloudWatch Logs, converts them into structured comma-separated values (CSV) format, and stores them in Amazon S3 for long-term analysis. The solution processes log entries in near real time after generation.