When Trino (formerly PrestoSQL) arrived on the scene almost 10 years ago, it immediately became known as the much faster alternative to the data warehouse of big data, Apache Hive. The use cases that you, as the community, have built had far exceeded anything we had imagined in complexity. Together we’ve made Trino not only the fastest way to interactively query large data sets, but also a convenient way to run federated queries across data sources to make moving all the data optional.
At Cinco de Trino, we came full circle back to the next iteration of analytics architecture with the data lake. This conference offers advice from industry thought leaders about how to use best lakehouse tools with Trino to manage that data complexity. Hear from industry thought leaders like Martin Traverso (Trino), Dain Sundstrom (Trino), James Campbell (Great Expectations), Jeremy Cohen (DBT Labs), Ryan Blue (Iceberg), Denny Lee (Delta Lake), Vinoth Chandar (Hudi). You can watch the talks on-demand on the Cinco de Trino playlist.
In this post, I’d like to cover the key items from each talk you won’t want to miss.
Keynote: Trino as a data lakehouse
Trino co-creator, Martin Traverso, covers where Trino fits into the data lake and brings you a sneak peak of the future of a Trino. Polymorphic Table Functions, adaptive query planning, are some of the many exciting features Martin walks us through.
If you have one takeaway from the conference, let it be this: there’s a new way in town to get 60% cost savings on your Trino deployment. Cory Darby walks through how utilizing the fault-tolerant execution architecture has enabled BlueCat to auto-scale their Trino clusters, and run over spot instances, which yielded massive cost savings. Zebing Lin goes through how this happens behind the scenes, and how you can run resource-intensive ETL jobs using failure recovery delivered by the team behind Project Tardigrade.
Starburst Galaxy lab
Starburst Galaxy enables you to get Trino up and running rather than spending your time focusing on the setup, scaling, and maintaining the infrastructure. Trino co-creator, Dain Sundstrom, walks you through a fun-filled lab that demonstrates how to use Trino as a service solution, Starburst Galaxy, to generate database rankings by ingesting, cleaning, and analyzing Twitter and Stack Overflow data.
Engineering data reliability with Great Expectations
Let’s be honest: when we claim to have run “tests” for our data pipelines, we
usually mean we checked that
input !=NULL, or that the dashboard isn’t broken.
James Campbell showcases the Great Expectations connector for Trino. The
Great Expectations connector is officially launched as the new way to write
expectations (data quality checks) for your code.
What excites us the most?
- The ability to take advantage of far more sophisticated data quality tests than what any of us would write.
- Having a really awesome UI to manage expectations.
- The data source view that makes it easy to dynamically test your custom data quality checks against backends.
Bring your data into your data lake with Airbyte
The first step of doing any analytics is bringing your data into the data lake. Ingestion engines are a gamechanger for centralizing your data in the data lake. Up until recently, there were no open software to choose from in this category. In just 10 minutes, Abhi Vaidyanatha takes us through the journey of taking in data from various places into your choice of data lake.
Transforming your data with dbt
Ever had 300 lines of SQL in front of you, and wasted lots of time sifting through the code to find which part of the code to edit to check for duplicate customers?
Imagine having to update decimal precision used frequently throughout that SQL statement? What we <3 the most about DBT is that data engineering becomes much more like software engineering, where you code in a much more modular way. Along the way, you get many benefits: the one we love the most? Data lineage graph and automatic documentation. That’s stuff we always say is important, but never do.
Even for dbt experts, there’s something new to learn. Jeremy Cohen goes through new capabilities Trino brings to dbt, while showcasing cool features like macros: a flexible alternative to SQL defined functions.
Choosing the best data lakehouse format for you
Ever wonder about all the hype with the new table formats? Why is everyone choosing Iceberg, Delta Lake, Hudi, over Hive? The founders of each of these modern table formats showcase each of these table formats and let you be the judge of which format makes more sense to your architecture. Below are the highlights:
Ryan Blue dives into important elements of your data lakehouse architecture that affect daily operations and slow down developer efficiency. He then covers how Iceberg is the solution he realized to solve those issues.
The two special elements of Iceberg is that it intentionally breaks compatibility with the Hive format to bring you features like same table partition and schema evolution. I’m the surface this may seem trivial as we’ve conditioned our minds to accepting the limitations of hive-like formats.
The second special element is that Iceberg also builds a community-driven specification that enables anyone to build out the same calls to use Iceberg library.
90% of the time that our Trino data pipelines break, it was because someone committed a bad upstream change. With Delta Lake time travel (coming soon!), you won’t need to spend a whole day pinpointing that bad change: just travel back in time and identify which change that was. Denny Lee gives us a compelling argument for why users desire ACID guarantees in their data lakehouse and how Delta Lake solves for that.
Similar to Iceberg, Delta lake offers optimistic concurrency, which allows there to be multiple writers to the same Delta Lake table while maintaining ACID constrains on the data.
Hudi [Coming Soon to Trino]
The coolest part of the talk? Open up a world of new possibilities with near real-time analytics in Trino with Hudi. With Hudi, you get to serve real-time production systems, debug live issues, and more.
Vinoth Chandar showcasing the compelling use cases that drove innovation around Hudi at Uber. He then covers how he views the architecture of data lakes and lakehouses are starting to merge and the implications this has on the open versus proprietary architectures.
Touch, talk, and see your data with Tableau
Tableau is our favorite data visualization tool, and in this session, Vlad Usatin of Tableau shares how to use Tableau to directly visualize your Trino data.
Thank you to all who attended or viewed, we hope to see you again at our upcoming events later this year. Continue the conversation in our Trino Slack.