Keynote Panel: Presto at Scale – Shradha Ambekar, Gurmeet Singh, Neerad Somanchi & Rupa Gangatirkar

Keynote Panel: Presto at Scale – Shradha Ambekar, Gurmeet Singh, Neerad Somanchi & Rupa Gangatirkar

Over the last decade Presto has become one of the most widely adopted open source SQL query engines. In use at companies large and small, Presto’s performance, reliability, and efficiency at scale have become critical to many companies’ data infrastructures. In this panel we’ll hear from three of the largest companies running Presto at scale – Meta, Uber, and Intuit. They’ll share more about their learnings, some of their impressive performance metrics with Presto, and what they envision going forward for Presto at their respective companies.

The Past, Present, and Future of Presto – Philip Bell, Meta

The Past, Present, and Future of Presto – Philip Bell, Meta

PrestoDB recently underwent major architectural updates as the Presto Foundation grows membership and is looking to vastly grow the number of new commits and forks. Achieving this desired end state required successful refactoring and improving of Presto’s already impressive speed, efficiency, reliability, and extensibility. Establishing PrestoDB as a premier Open Source project required a major commitment of time and resources from Meta to ensure the community can benefit from this project for years to come, as well as positioning PrestoDB to evolve beyond what Meta alone could create. Members of the Presto Foundation need more of you to be involved in this major evolution in Presto’s history and core components, and bring your own inventive ideas to the mix.

Presto On Spark: Scaling not Failing with Spark – Ariel Weisberg, Meta & Shradha Ambekar, Intuit

Presto On Spark: Scaling not Failing with Spark – Ariel Weisberg, Meta & Shradha Ambekar, Intuit

Presto on Spark is an integration between Presto and Spark that leverages Presto’s compiler/evaluation as a library and Spark’s large scale processing capabilities. It enables a unified SQL experience between interactive and batch use cases. A unified option for batch data processing and ad hoc is very important for creating the experience of queries that scale instead of fail without requiring rewrites between different SQL dialects. In this session, we’ll talk about Presto On Spark architecture, why it matters and its implementation/usage at Intuit.