Implementing Lakehouse Architecture with Presto at Bolt – Kostiantyn Tsykulenko, Bolt.eu

Implementing Lakehouse Architecture with Presto at Bolt – Kostiantyn Tsykulenko, Bolt.eu

Bolt.eu is the first European mobility super-app. We have over 100M users across Europe and Africa and have to deal with data at a large scale on a daily basis (over 100k queries daily). Previously we were using a traditional data warehouse solution based on Redshift but we’ve faced scalability issues that were hard to overcome and after doing our research we chose Presto as the solution. In just a single year we’ve managed to migrate to the Lakehouse architecture using AWS, Presto, Spark and Delta lake. We would like to talk about our journey, some of the challenges we’ve encountered and how we solved them.

Presto on Kafka at Scale – Yang Yang & Yupeng Fu, Uber

Presto on Kafka at Scale – Yang Yang & Yupeng Fu, Uber

Presto is a popular distributed SQL query engine for running interactive analytic queries. Presto provides a Connector API that allows plugins to dozens of data sources, and thus positions itself as a single point of access to a wide variety of data. At Uber, we significantly improved Presto’s Kafka connector to meet Uber’s scale. For example, the new connector allows dynamic Kafka cluster and topic discovery so users can directly query existing Kafka topics without any registration and onboarding process; dynamic schema discovery allows fetching the latest schema without any Presto restart or deployment; smart time range suggestions to users based on Kafka metadata analysis to avoid large-range scans and thus keep the query interactive.