Category: DSQL

Node.js teams can run Sequelize ORM on Amazon Aurora DSQL with only a few adaptations. This post builds a hotel reservation application that shows reusable patterns: UUID primary keys, IAM authentication through the Aurora DSQL connector, individual DDL execution, and optimistic concurrency control retry logic.

As your Amazon Aurora DSQL workload scales, DPU consumption and storage costs can grow without a fixed instance ceiling. This post shows how to monitor Aurora DSQL usage and cost with the built-in Usage and cost panel, set up Amazon CloudWatch alarms for hourly DPU spend, and deploy the alarms as code with AWS CloudFormation.

Amazon Aurora DSQL supports foreign key constraints, letting you enforce referential integrity directly in the database. This post covers defining foreign keys, immediate versus deferred enforcement, adding constraints to existing tables, and how optimistic concurrency control resolves conflicts in distributed workloads.

Build a high-concurrency async Python rideshare application with Tortoise ORM and Amazon Aurora DSQL. This post walks through the key adaptations: UUID primary keys, IAM-authenticated asyncpg connections with a connection-pool patch, individual DDL execution, and optimistic concurrency control (OCC) retry logic.

Amazon Aurora DSQL offers time-based observability through Amazon CloudWatch Database Insights. Learn how the DSQL observability model, DASH, Database Insights, PromQL, and the system diagnostics AI skill help you find performance bottlenecks and connect session time directly to cost.

Stream Amazon Aurora DSQL change data capture (CDC) events into Apache Iceberg tables on Amazon S3 with Amazon Data Firehose, then query them using Amazon Athena. This post walks through a two-table design that keeps a full audit trail and a current-state view, plus deployment and a dashboard for exploring the results.

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.

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, 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.