Summary
Stripe is a company that relies on data to power their products and business. To support that functionality they have invested in Trino and Iceberg for their analytical workloads. In this episode Kevin Liu shares some of the interesting features that they have built by combining those technologies, as well as the challenges that they face in supporting the myriad workloads that are thrown at this layer of their data platform.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
Your host is Tobias Macey and today I'm interviewing Kevin Liu about his use of Trino and Iceberg for Stripe's data lakehouse
Interview
Introduction
How did you get involved in the area of data management?
Can you describe what role Trino and Iceberg play in Stripe's data architecture?
What are the ways in which your job responsibilities intersect with Stripe's lakehouse infrastructure?
What were the requirements and selection criteria that led to the selection of that combination of technologies?
What are the other systems that feed into and rely on the Trino/Iceberg service?
what kinds of questions are you answering with table metadata
what use case/team does that support
comparative utility of iceberg REST catalog
What are the shortcomings of Trino and Iceberg?
What are the most interesting, innovative, or unexpected ways that you have seen Iceberg/Trino used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Stripe's data infrastructure?
When is a lakehouse on Trino/Iceberg the wrong choice?
What do you have planned for the future of Trino and Iceberg at Stripe?
Contact Info
Substack (https://kevinjqliu.substack.com)
LinkedIn (https://www.linkedin.com/in/kevinjqliu)
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning.
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Links
Trino (https://trino.io/)
Iceberg (https://iceberg.apache.org/)
Stripe (https://stripe.com/)
Spark (https://spark.apache.org/)
Redshift (https://aws.amazon.com/redshift/)
Hive Metastore (https://cwiki.apache.org/confluence/display/hive/design#Design-Metastore)
Python Iceberg (https://py.iceberg.apache.org/)
Python Iceberg REST Catalog (https://github.com/kevinjqliu/iceberg-rest-catalog)
Trino Metadata Table (https://trino.io/docs/current/connector/iceberg.html#metadata-tables)
Flink (https://flink.apache.org/)
Podcast Episode (https://www.dataengineeringpodcast.com/apache-flink-with-fabian-hueske-episode-57)
Tabular (https://tabular.io/)
Podcast Episode (https://www.dataengineeringpodcast.com/tabular-iceberg-lakehouse-tables-episode-363)
Delta Table (https: