Data Engineering Podcast

This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.

https://www.dataengineeringpodcast.com

support

claim!

report

Using Trino And Iceberg As The Foundation Of Your Data Lakehouse

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
Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster today to get started. Your first 30 days are free!
Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
Join in with the event for the global data community, Data Council Austin. From March 26th-28th 2024, they'll play host to hundreds of attendees, 100 top speakers, and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working togethr to build the future of data. As a listener to the Data Engineering Podcast you can get a special discount of 20% off your ticket by using the promo code dataengpod20. Don't miss out on their only event this year! Visit: dataengineeringpodcast.com/data-council today.
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.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.

Links

Trino
Starburst
Presto
JBoss
Java EE
HDFS
S3
GCS == Google Cloud Storage
Hive
Hive ACID
Apache Ranger
OPA == Open Policy Agent
Oso
AWS Lakeformation
Tabular
Iceberg
- Podcast Episode
Delta Lake
- Podcast Episode
Debezium
- Podcast Episode
Materialized View
Clickhouse
Druid
Hudi
- Podcast Episode

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