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
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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