Every day, more than 18M riders key their location and destination into the Uber mobile app to request a ride or a meal. Unbeknownst to the drivers, passengers, and workers making deliveries, each transaction is plugged into extremely complex algorithms, which are built on insight gleaned from petabytes of data. The results come back within seconds and offer the most optimal recommendations for nearby drivers and pricing based on supply and demand economics.
Data and analytics drive Uber to growth, profitability, and expansion into new business areas. To power this mammoth analytical undertaking, Uber chose the open-source Presto distributed query engine. Presto is a federated, distributed SQL query engine that runs on a cluster of machines. It enables interactive, ad-hoc analytics on large amounts of data.
In this case study you’ll learn more about why this ride-share company chose Presto and how they are using the open source SQL query engine to run their mission-critical analytics to power many of their analytical use cases.