# Trino for large scale ETL at Lyft

DevFeed: [Trino for large scale ETL at Lyft](<https://devfeed.tech/articles/trino-for-large-scale-etl-at-lyft-8702.md>)

Original publisher: [Read original article](<https://trino.io/blog/2022/12/12/trino-summit-2022-lyft-recap.html>)

Author: Charles Song, Ritesh Varyani, Brian Olsen

Published: 2022-12-12T00:00:00Z

Content type: article

Language: en

Sources: [Trino Blog](<https://devfeed.tech/sources/trino-blog.md>)

Topics: [data](<https://devfeed.tech/topics/data.md>), [Amazon EC2](<https://devfeed.tech/topics/amazon-ec2.md>)

Tags: [airflow](<https://devfeed.tech/tags/airflow.md>), [analytics](<https://devfeed.tech/tags/analytics.md>), [best-practices](<https://devfeed.tech/tags/best-practices.md>), [data](<https://devfeed.tech/tags/data.md>), [java](<https://devfeed.tech/tags/java.md>), [lyft](<https://devfeed.tech/tags/lyft.md>), [scale](<https://devfeed.tech/tags/scale.md>), [testing](<https://devfeed.tech/tags/testing.md>)

## AI overview

A Trino Summit recap describes Lyft's large-scale ETL deployment, including operational scale, cluster efficiency, rollback handling, workload separation, and query-testing practices.

## Source excerpt

Buckle up, for the next post in the Trino Summit 2022 recap series. In this post, we're covering the talk given by Lyft engineers, Charles and Ritesh, on how they have not only scaled Trino as adoption grew, but with less nodes and more effective usage. They also started moving to utilizing Trino more for ETL rather than just interactive analytics. Get ready for a smooth ride as Lyft brings you large scale ETL with Trino.