Migrating to Presto – How Bolt Built a Data Platform Architecture for Scalability and Cost Efficiency

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…

Presto Parquet Column Encryption

Introduction Apache Parquet modular encryption provides encryption at-rest and in-transit at finer-grained. In big data world, data analytic tables are usually very wide with hundreds of columns, while only a small number of columns need to be protected. So the finer-grained access control is a better fit than coarse-grained one like table level access control….

Faster Presto Queries with Parquet Page Index

Introduction Today’s data is growing very fast, which creates challenges for query engines like Presto. Presto is a popular interactive query engine, because of its scalability, high performance, and smooth integration with Hadoop. As the volume of data grows, Presto needs to read larger chunks of data and load them into memory, which causes higher…

What is Presto on Spark?

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…

Scaling with Presto on Spark

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…