6 key features of Amazon Aurora DSQL & when leaders should use it

Amazon Aurora DSQL has quickly gained traction within the AWS community, particularly among AWS Heroes and cloud leaders who are excited about its potential to transform data management with distributed SQL databases.

But what exactly is Aurora DSQL? Why is it generating so much enthusiasm? And most importantly, when should leaders consider exploring it?

In this post, we’ll give an introduction to Amazon Aurora DSQL, break down its features, and explore how it fits into modern data architectures. Whether you’re a cloud data leader, a manager guiding your team’s cloud strategy, or an individual contributor interested in new AWS capabilities, this guide will help you understand the value Aurora DSQL brings to the table.

Amazon Aurora DSQL is currently in preview and represents a new serverless, distributed SQL database within the Amazon Aurora ecosystem. It’s designed to support high-availability SQL operations without the hassle of managing infrastructure. 

Think of it as a way to scale compute, I/O, and storage automatically based on workload demands rather than manually sharding databases or upgrading instances. That’s a big deal for organizations dealing with unpredictable workloads.

Learn more about Amazon Aurora in this Pluralsight course.

We’ve all been there—scaling relational databases can be a pain, especially when you’re dealing with availability requirements across regions. Traditional databases often fall short when it comes to scalability, high availability, and multi-region deployment. 

Aurora DSQL tackles these challenges by offering a serverless, active-active architecture that supports horizontal scaling without the typical headaches. It’s designed for those of us who want to build scalable applications without worrying about performance bottlenecks, manual scaling, or downtime during failover.

Amazon Aurora DSQL offers a variety of new features for cloud and data professionals to take advantage of. Here are six of the most important ones.

Aurora DSQL automatically scales to meet workload demands—no server management required. This serverless architecture means that you don't have to worry about provisioning, patching, or maintaining database instances. AWS handles it for you. The distributed nature ensures your database can scale horizontally and automatically add resources as needed, which is crucial for handling varying traffic loads​.

Aurora DSQL supports consistent reads and writes from any regional endpoint. This is achieved through its active-active architecture, which automatically synchronizes data across multiple Availability Zones (AZs). In multi-region clusters, Aurora DSQL offers two strongly consistent regional endpoints, allowing clients to read or write from either endpoint without worrying about data inconsistency or replication lag​.

Aurora DSQL uses familiar SQL syntax, so your team doesn’t have to learn a new querying language. It’s compatible with PostgreSQL version 16, enabling you to use standard PostgreSQL drivers, tools, and libraries. This compatibility makes migration easier and allows developers to leverage existing skills without adapting to a new database environment​.

Aurora DSQL is perfect for multi-region applications with automatic data replication and consistency. Multi-region clusters provide resilient, strongly consistent database access, even in scenarios where one AWS Region becomes inaccessible. This makes it a strong choice for global applications that demand uninterrupted access and data consistency across continents.

Forget about provisioning, patching, or managing database servers. Aurora DSQL handles infrastructure tasks automatically, so you can focus on building applications rather than managing database maintenance. This not only reduces operational overhead but also ensures that your database environment is always up to date with the latest improvements and security patches.

Aurora DSQL adapts to changes in workload seamlessly without impacting your application architecture. It automatically scales compute, I/O, and storage based on workload demands, making it ideal for applications that experience unpredictable traffic spikes. This elasticity allows your application to maintain performance even during peak usage periods.

To make the most of Aurora DSQL, it’s essential to understand where it fits—and where it doesn’t. Here are a few misconceptions to clear up.

Aurora DSQL is designed for transactional workloads rather than large-scale analytical processing. Sure, it’s great for OLTP (Online Transaction Processing) tasks with ACID transactions, joins, and secondary indexes. But if you’re looking to crunch massive datasets or run complex aggregations, it’s not the right tool. For that, stick with Amazon Redshift—it’s built to handle the heavy lifting in data warehousing.​

If your application needs sub-second response times, Aurora DSQL might not be the best fit. Built with availability and scalability in mind, it focuses more on consistency and reliability. So, if real-time data streaming or ultra-low latency is a priority, you’ll want to consider other specialized solutions.

Aurora DSQL can handle transactional data just fine, but it’s not the best choice for complex data transformations or ETL workflows. If your data processing pipeline involves heavy data cleaning, aggregation, or preparation, you’re better off with a tool like AWS Glue. It’s built for that kind of heavy-duty data work​.

While Aurora DSQL is resilient and scalable, it has some limitations when it comes to handling high-frequency, complex transactions. For instance, during the preview phase, it can only modify up to 10,000 rows per transaction, and each transaction can’t exceed 10 MiB. If your workload involves updating a ton of rows all at once, you might hit some roadblocks. For those high-throughput, complex scenarios, consider a more performance-optimized database.

Leaders should definitely explore Aurora DSQL when planning cloud-native or highly available applications. Here are a few scenarios where it makes sense.

If your business depends on continuous availability—like global e-commerce or financial platforms—Aurora DSQL’s active-active model is a game changer.

Serving customers across the globe? Aurora DSQL’s multi-region support helps maintain performance and consistency wherever your users are.

If you’re experimenting with microservices or event-driven architectures, Aurora DSQL offers a flexible, resilient database foundation without the operational headaches.

Like any new service, Aurora DSQL has a few caveats during the preview phase.

• Feature limitations: Missing PostgreSQL features like foreign keys, triggers, and temporary tables might be a dealbreaker for some applications.
• Session limitations: Sessions are capped at 1 hour, requiring reconnection for long-lived processes.
• Mixed DDL and DML restrictions: You can’t mix data definition (DDL) and data manipulation (DML) statements within a single transaction.
• Performance considerations: High-volume transactional workloads may see variability during the preview phase.

If you’re ready to explore Aurora DSQL, follow these steps:

1. Request preview access: Visit the AWS Aurora DSQL page to sign up.
2. Set up permissions: Use IAM roles to configure secure access.
3. Run basic tests: Start with simple SQL operations to see how it handles your workloads.
4. Monitor performance: Use AWS CloudWatch to track how the preview version performs in your environment.
5. Stay updated: Watch for announcements as AWS refines and improves Aurora DSQL.

Amazon Aurora DSQL is shaping up to be a game-changer for building serverless, high-availability applications. It’s still in preview, so while it’s tempting to jump in, focus on prototyping and testing rather than full production deployment.

As AWS continues to evolve this service, it’s worth keeping an eye on how Aurora DSQL develops. Early adopters, like AWS Heroes, are already finding value, and it’s only going to get better from here.

Get your teams up to speed with Pluralsight AWS courses.