Relational databases power most of the world’s applications, but the relational model is also decades old. In traditional RDBMS deployments, the strain is beginning to show. Traditional RDBMS engines running on-premises in customers’ data centers increasingly face problems scaling to match the demands of modern applications.
The increased pressure on traditional relational databases comes from a fundamental change in the applications we’re building. Today’s software has to serve an order of magnitude more users than it did when relational databases first appeared, while maintaining fast performance for a demanding audience that’s often distributed worldwide. They must support far greater numbers of connections from a user base that hammers the database with queries around the clock. Those queries can also spike based on factors ranging from seasonal fluctuations to short-term business events, not all of which are predictable.
In addition, the data explosion shows no sign of stopping. The tech analyst firm IDC estimates the amount of digital data worldwide will double in four years, growing by a CAGR of 22.9 percent through 2025. As Jeffrey Hojlo, an IDC program director, writes, “the deluge of data that every company is contending with, from connected products, assets, processes, and customers, is difficult to leverage without the applications in place that enable collation of information and rapid decision making.”
These drivers compelled Amazon to reinvent the RDBMS for a new generation of web-scale applications in the form of Aurora, a relational database built for the cloud. It’s built on open source, extending the functionality of MySQL and PostgreSQL to meet the demands of modern applications.