AWS Aurora: Full Guide with Features, Benefits, and Pros and Cons

If the naturally occurring aurora borealis, or “northern lights,” are the result of millions of particles from the Sun and the Earth’s atmosphere colliding to create something beautiful, think of AWS Aurora as the product of millions of data points clustering together to create something beautiful. Or, if not beautiful, then at least actionable. 

We’re not totally sure if Amazon had the northern lights in mind when they were thinking of a name for this product. What we do know is that AWS Aurora is a powerful relational database engine designed to fuel the highest-performing applications out there. 

Compatible with the fast-acting MySQL and PostgreSQL database types, Aurora works quietly but intensively in the background to ensure your data is moved, replicated, and stored effectively. Because it’s fully managed in this way, you can be sure that your time will be less spent on data administration, and more on building great applications or insightful analytics tools. 

Here, we’re going to give you an overview of what it’s like to work with this high-throughput, affordable, and scalable database engine. We’ll go over everything you need to know about Aurora’s primary capabilities and features, what makes Aurora great, and what makes it not so great. We’ll also give you some tips on starting out with Aurora and learning how to use it. 

If you’re eagerly on the hunt for a better way to manage your applications, and the data that go along with it, read on to find out more about how AWS Aurora can help you! 

5 Must-know Facts about AWS Aurora

• AWS Aurora is a fully-managed relational database service that brings high performance and scalability at a fraction of the cost of high-end database tools. 
• Aurora is compatible with MySQL and PostgreSQL, which makes it even easier to migrate existing data operations to Aurora. 
• With Multi-AZ deployment provisioned for Aurora users, you can be sure of some of the highest regional availability out there. 
• Aurora has fail-safe systems in place to keep your data safe when issues arise: automated backups, point-in-time recovery, and the ability to replicate databases across multiple regions. 
• For further functionality, Aurora integrates with tons of other AWS services, such as CloudWatch, IAM, Lambda, S3, Redshift, and SageMaker. 

What is AWS Aurora: Explained

Built by Amazon, Aurora is a fully-managed relational database engine that works with MySQL and PostgreSQL. Why MySQL and PostgreSQL? Primarily because these open-source databases are recognized for delivering speed and reliability on par with costly high-end databases. In other words, Aurora strives to be powerful yet cost-effective. 

Aurora has its own storage subsystem baked into it, which is distributed and designed to automatically scale up as your data grows. On top of handling data distribution and growth, Aurora also takes on the challenging task of replicating and clustering data all on its own. 

Amazon Aurora operates through the Amazon Relational Database Service (RDS), a service that facilitates the process of setting up and running a cloud-based relational database. Essentially, this means that you use Aurora within RDS’s Management Console interface and have access to RDS’ pre-packaged CLI commands and built-in API functions to handle provisioning, patching, backup, recovery, failure detection, and repair. 

The following video from Amazon Web Services provides an excellent brief overview of Amazon Aurora in just 1:45 minutes.

Why is AWS Aurora a Good Choice for Your Data?

Many data pros would argue that when choosing a DB option within AWS RDS, Aurora is your best choice if you want the greatest possible performance, availability, resiliency, storage capacity, and scalability. 

In terms of performance, Aurora uses innovative storage design to give up to five times the throughput available on similarly competitive database engines. And thanks to Amazon’s global distribution, Aurora lets you truly maximize the number of Availability Zones you can tap into. This not only increases your potential storage capacity—which is up to 128 tebibytes—but it also improves reliability by providing greater room for replication. 

Finally, Amazon Aurora intends to provide automatic scaling, which is not something that any other RDS engine can do, nor is it something that many DB options outside of AWS can handle. 

Now that we’ve given you an introduction to Aurora’s powerful capabilities, let’s dive into some of its main advantages in further detail. 

Performance and Scalability 

Aurora combines best-in-class hardware and software techniques to maximize the DB engine’s use of compute, memory, and networking resources. What’s more, its Input/Output operations utilize quorums and other distributed systems techniques to ensure high performance is delivered on a consistent basis. Such are the ingredients that enable Aurora to deliver five times the throughput of MySQL and three times that of PostgreSQL. 

With Aurora’s top-tier performance features also come excellent scaling capabilities. Through either RDS APIs or the AWS Management Console, users have the ability to scale computing operations with the click of a button, provisioning instances to move either up or down as needed. 

In terms of data storage, Aurora performs this job automatically. Aurora expands the volume of your database in 10 GB increments without you having to provision excess storage.  

Availability

Aurora provides you access to three Availability Zones so that you can provision replicas in the event of an instance failure. If you have provisioned replicas, Amazon RDS Multi-AZ technology will automate failover for you. If not, RDS will automatically try to create a new Aurora instance for you. 

For further dependency, Aurora lets you tap into Global Database, in which a single Aurora database gets distributed across multiple AWS regions, thus creating faster local data reads and quicker data recovery in the event of a failure. Global Database also replicates data across AWS regions automatically, and you can even designate a secondary region as a full backup option—ensuring access to all your data should a regional outage occur. 

Security

With up to 128 TB of data storage possible through Aurora, Amazon has also taken steps to ensure you get the best possible protection on the cloud. For instance, Aurora operates through Amazon Virtual Private Cloud (VPC), thus automatically isolating your database in an exclusive virtual network. Plus, Aurora integrates with Amazon Identity and Access Management as a way of allowing you to control access and permissions across your resources. 

As some added security bonuses, Aurora keeps your databases encrypted through Amazon’s Key Management Service. It also uses CloudWatch to help you audit activity on your databases and employs Amazon GuardDuty to identify threats or suspicious activity pertaining to your Aurora DBs. 

How to use AWS Aurora

Ready to start using Aurora? Well, starting out with it shouldn’t present a great challenge. You will, however, need to be sure you have an active AWS account. Once you do, sign in to the AWS Management Console. 

From the console, choose Aurora. Then, you’ll need to select a region in which you want your Aurora DB cluster to become active. 

After you’ve selected an appropriate region, you also need to pick which version of Aurora you wish to launch with. The major versions are either MySQL or PostgreSQL. If you’ve already been working with either of these databases in the past, we suggest you go with whatever you’ve already been using, since Amazon lets you transfer your past work onto Aurora. 

Now that you’ve selected your region and version, all you need to do is create your first Aurora DB cluster! 

How to Learn AWS Aurora

Great, you’ve got Aurora up and running. But now you need to learn how to work with this tool. Since this is a database engine, it would be ideal if you had an understanding of relational databases and SQL. 

If you don’t, there’s no shortage of sites out there that can teach you the skills you need to be successful with data technologies. Udemy, Datacamp, and W3 Schools are some of the best-known resources for courses and tutorials, but there are plenty more options out there. 

Beyond having foundational knowledge of working with databases, you also need to learn about the features and benefits of Aurora that are most significant to your work. To do this, you can make use of Amazon’s extensive documentation and tutorials. 

Now, Amazon provides a lot of documentation and learning resources for you. So, it’s best not to get overwhelmed by trying to go over all the material. Instead, simply focus on the topics that interest you the most. 

AWS Aurora: When is it Not The Best Choice?

AWS Aurora is an economical option for taking advantage of the performance and reliability that’s generally only available through high-end database services. It’s easy to scale your applications up and down as needed. And, because it’s compatible with MySQL and PostgreSQL, it’s also easy to migrate existing work to Aurora. 

These advantages aside, there are some drawbacks to using Aurora. For one thing, Aurora is a complex tool to use and manage. If your organization lacks team members with the necessary specialized knowledge and expertise, you might run into a lot of obstacles, and Amazon doesn’t necessarily make it easy to get hands-on support. 

Another downside involves vendor lock-in. Amazon designs most of its products to integrate with other proprietary services. Aurora is no exception. Using Aurora also often involves using adjacent tools like S3, SageMaker, Redshift, CloudWatch, and so on. If you’re unhappy with one of these resources, it could be hard to find a replacement since they’re all enmeshed. 

If you feel you require greater customer service or are anxious about the AWS ecosystem locking you in, you may want to consider alternatives. Here are just a couple.  

MongoDB Atlas

Well-known name, powerful tool, easy to set up and integrate, and scalable. These are all positive attributes associated with MongoDB’s data management technology. Unlike Amazon, MongoDB provides for automatic infrastructure provisioning and ongoing maintenance, making your data operations a less complex journey than it might be with Aurora. 

MongoDB also provides greater flexibility in terms of which tools you can use in tandem with it. Along with proprietary tools, MongoDB also offers compatibility with popular frameworks like Django and Flask, as well as RTEs like Docker and Node.js. 

Finally, the community also highly regards MongoDB for its responsive customer service. It is a more expensive product than Amazon. But you get what you pay for—hands-on support and ongoing maintenance of your databases. 

Oracle

Maybe not quite the titan that Amazon is, but Oracle is no lightweight, either. Oracle has built up a strong reputation for itself as a leader in cloud data management. Some of the benefits of using Oracle DB include exceptional performance, easy scalability, strong security, and plenty of support for backup and replication of critical data. 

Though it is a complex tool to use, there’s a robust developer community behind Oracle, along with Oracle’s own reliable and responsive customer support team. If you’re interested in using some third-party tools alongside Oracle DB, you have that option as well. However, Oracle does not appear to be quite as flexible as MongoDB in that regard. 

AWS Aurora: Release History

Amazon introduced Aurora at the AWS re:Invent conference in November 2014. From the start, Aurora boasted high availability and scalability, and also threw in compatibility with MySQL as an added perk. 

A major update to Aurora came in 2017 with the arrival of Aurora Serverless—a deployment option that allows for automatic scaling of database capacity up or down, depending on your usage. The following year, Amazon also announced Aurora’s compatibility with PostgreSQL for further flexibility and power. 

At Amazon’s re:Invent conference in 2018, the company announced a major leap in Aurora’s scope with the addition of Global Database. Global Database has made it possible for organizations to deploy and replicate databases across multiple regions, thereby adding greater capacity to data usage and ensuring backup resources in the event of a system failure. 

To this day, Amazon continues to support and add new features to Aurora. So, it is safe to say this reliable database management engine is not going anywhere anytime soon.

The image featured at the top of this post is ©Sundry Photography/Shutterstock.com.