Data Engineering Podcast

Using Trino And Iceberg As The Foundation Of Your Data Lakehouse

Feb 18

Summary

A data lakehouse is intended to combine the benefits of data lakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Multiple open source projects and vendors have been working together to make this vision a reality. In this episode Dain Sundstrom, CTO of Starburst, explains how the combination of the Trino query engine and the Iceberg table format offer the ease of use and execution speed of data warehouses with the infinite storage and scalability of data lakes.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I'm interviewing Dain Sundstrom about building a data lakehouse with Trino and Iceberg

Interview

Introduction
How did you get involved in the area of data management?
To start, can you share your definition of what constitutes a "Data Lakehouse"?
- What are the technical/architectural/UX challenges that have hindered the progression of lakehouses?
- What are the notable advancements in recent months/years that make them a more viable platform choice?
There are multiple tools and vendors that have adopted the "data lakehouse" terminology. What are the benefits offered by the combination of Trino and Iceberg?
- What are the key points of comparison for that combination in relation to other possible selections?
What are the pain points that are still prevalent in lakehouse architectures as compared to warehouse or vertically integrated systems?
- What progress is being made (within or across the ecosystem) to address those sharp edges?
For someone who is interested in building a data lakehouse with Trino and Iceberg, how does that influence their selection of other platform elements?
What are the differences in terms of pipeline design/access and usage patterns when using a Trino/Iceberg lakehouse as compared to other popular warehouse/lakehouse structures?
What are the most interesting, innovative, or unexpected ways that you have seen Trino lakehouses used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on the data lakehouse ecosystem?
When is a lakehouse the wrong choice?
What do you have planned for the future of Trino/Starburst?

Contact Info

LinkedIn
dain on GitHub

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__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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