While Apache Hive was a very viable SQL engine, it wasn’t cut out for larger-scale operations that Facebook needed when developing Presto SQL.

Presto SQL can handle large data centers at a much faster pace than any of its competing technologies. The reason why PrestoDB can operate at such a quick pace lies within its framework, which enables the SQL engine to process a SQL query using multiple stages that run simultaneously. That’s known as the “push” model running concurrently.

The data flow is organized in a linear manner, where the upper level of data receives updates from the lower level of data, meaning that the data in-between effectively travels at a much faster rate.

One of the main innovations that PrestoDB brought to the table with its invention is the ability to take very complex queries that take a long time to process and simplify them. This efficiency makes previously large and intricate queries bite-sized, easily processable, and streamlined in the SQL process.

Not only does this speed everything up, but it’s also a crucial part of streamlining ad-hoc analytics, giving PrestoDB yet another use case.

Traditional SQL engines aren’t that powerful for things such as data management and analytics. Still, Presto DB’s unique framework allows it to combine analysis, computing power, and querying to deliver data in a streamlined way.

Relevant Source: https://pandio.com/blog/story-of-prestod/