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Trino Summit 2022 will be legendary


Commander Bun Bun is back and this year we have an exciting lineup of speakers. Topics range from architectures like data mesh and data lakehouse, to running Trino at scale with fault-tolerant execution, and query federation. This conference is free and takes place on November 10th. The summit is a hybrid event for in-person and virtual attendance. Find out more details below!

Register for the summit

This years Trino Summit will be hosted at the Commonwealth Club in San Francisco, CA. In-person registration is limited to 250 seats so make sure you register quickly before spots run out!

Trino Summit 2022 teaser

Get ready to federate them all this year! Many times when folks think of Trino, their first instinct is to consider the data lake use case where it replaces Hive or other data lakehouse query engines. However, this summit will also drill into the lesser discussed query federation use case. Federate ‘em all!

Announcing the first sessions and speakers

We have a full roster planned but here is a glance at a few full confirmed sessions. Stay tuned for future blog posts as we announce more session as they are confirmed!

State of Trino keynote

Hear the latest on the state of the open source Trino project. Trino is the award-winning MPP SQL query engine. In this session, Trino creators discuss the latest features that have landed in the last year, the roadmap for the year ahead, and community growth highlights.

  • Martin Traverso, Co-Creator of Trino and CTO, Starburst

  • Dain Sundstrom, Co-Creator of Trino and CTO, Starburst

  • David Phillips, Co-Creator of Trino and CTO, Starburst

Trino for large scale ETL at Lyft

At Lyft, we are processing petabytes of data daily through Trino for various use cases. A single query can execute as long as 4 hours with terabytes of memory reserved. There are quite many challenges to operate Trino ETL at such a scale: how to make all queries as performant as possible with low failures rates; how should we define clusters, routing groups and resource groups for changing volume across a day; how to keep commitment to user SLOs during unexpected spikes, etc.

We’ll share what we’ve done with our config tunings, large query/user identifications, autoscaling and fault tolerant features to execute Trino at such a scale. We’ll also share our upcoming challenges and plans to move steps further with Trino adoption across the company.

Charles Song, Senior Software Engineer at Lyft

Rewriting history: Migrating petabytes of data to Apache Iceberg using Trino

Dataset interoperability between data platform components continues to be a difficult hurdle to overcome. This short coming often results in siloed data and frustrated users. Although open table formats like Apache Iceberg aim to break down these silos by providing a consistent and scalable table abstraction, migrating your pre-existing data archive to a new format can still be daunting. This talk will outline challenges we faced when rewriting petabytes of Shopify’s data into Iceberg table format using the Trino engine. A rapidly evolving landscape, I will highlight recent contributions to Trino’s Iceberg integration that made our work possible while also illustrating how we designed our system to scale. Topics will include: what to consider when designing your migration strategy, how we optimized Trino’s write performance and how to recover from corrupt table states. Finally, I will compare the query performance of old and migrated datasets using Shopify’s datasets as benchmarks.

Marc Laforet, Senior Data Engineer at Shopify

Federating them all on Starburst Galaxy!

You’ve federated them all on Trino, but to beat the elite four at Indigo Plateau, every data trainer needs help. In this talk, I will cover how Starburst Galaxy is the fastest path to query federation and cover a demo that trainers can follow later. We’ll also cover cool features like schema discovery and fault-tolerance execution. The queries we’ll run will be with Pokémon data so that you don’t have to witness yet another taxi cab or iris data set.

Monica Miller, Developer Advocate at Starburst

Using Trino with Apache Airflow for (almost) all your data problems

Trino is incredibly effective at enabling users to extract insights quickly and effectively from large amount of data located in dispersed and heterogeneous federated data systems. However, some business data problems are more complex than interactive analytics use cases, and are best broken down into a sequence of interdependent steps, a.k.a. a workflow. For these use cases, dedicated software is often required in order to schedule and manage these processes with a principled approach. In this session, we will look at how we can leverage Apache Airflow to orchestrate Trino queries into complex workflows that solve practical batch processing problems, all the while avoiding the use of repetitive, redundant data movement.

Philippe Gagnon, Solutions Architect at Astronomer

Conclusion

Stay tuned for new developments in upcoming blog posts, don’t forget to register, and always federate them all!