Category: DSQL

In this post, we show you how to use EXPLAIN plans to diagnose and improve query performance in Amazon Aurora DSQL. We introduce a three-layer filter model as a practical framework for understanding where your predicates are evaluated, and walk through the architecture differences that make Aurora DSQL plans unique, the anatomy of an EXPLAIN output, access method selection, and a step-by-step query improvement workflow.

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 three pagination techniques for Aurora DSQL: OFFSET/LIMIT, cursor-based (keyset), and temporal. You implement keyset pagination in SQL and Python, build it into an API layer, optimize with composite indexes, handle batch processing within the 3,000-row transaction limit, and avoid five common anti-patterns. By the end, you can choose the right pagination method for your workload and implement it with confidence.

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.

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.

In this post, we demonstrate how to configure Aurora DSQL Change Data Capture and stream database changes into Kinesis Data Streams. You will learn how CDC works, how to configure a streaming pipeline, and how to consume change events. By the end of this post, you will have a working CDC pipeline that streams database changes into a durable event stream that downstream applications can process.

Connecting to Amazon Aurora DSQL requires a different approach than traditional PostgreSQL databases. Instead of long-lived passwords, you use short-lived IAM authentication tokens. Instead of static endpoints, you work with distributed cluster endpoints that route connections across Availability Zones. In this post, you learn how to configure connection strings, set up drivers in Python, Java, and Node.js, and implement best practices for authentication, connection pooling, and lifecycle management with Amazon Aurora DSQL.

In this post, we demonstrate how to use AWS Glue to migrate data from an Amazon Aurora database snapshot into an Aurora DSQL cluster.

In this post, we first examine why traditional approaches to distributed consistency fall short for financial workloads. We then walk through how the Amazon Aurora DSQL architecture addresses these challenges, and apply it to three production use cases: core banking, global spend management, and digital currency infrastructure. We close with implementation considerations and how to get started with the Amazon Aurora DSQL Free Tier