At PrestoCon Day we heard from Bolt, a ride sharing app with 100 million users across 45 countries in Eastern Europe, who shared why they chose Presto to underpin their data architecture platform. By leveraging Presto’s capabilities, Bolt was able to address scalability limits, cost efficiency, and workload management challenges. In this blog we’ll recap…
1. Reporting and dashboarding This includes serving custom reporting for both internal and external developers for business insights and also many organizations using Presto for interactive A/B testing analytics. A defining characteristic of this use case is a requirement for low latency. It requires tens to hundreds of milliseconds at very high QPS, and not…
Overview Presto was originally designed to run interactive queries against data warehouses, but now it has evolved into a unified SQL engine on top of open data lake analytics for both interactive and batch workloads. Popular workloads on data lakes include: 1. Reporting and dashboarding This includes serving custom reporting for both internal and external…
Presto defines a connector API that allows Presto to query any data source that has a connector implementation. The existing connector API provides basic predicate pushdown functionality allowing connectors to perform filtering at the underlying data source. However, there are certain limitations with the existing predicate pushdown functionality that limits what connectors can do. The…