Trino, Iceberg and the Battle for the Lakehouse | Justin Borgman, CEO, Starburst
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What is the main topic discussed in this episode?
Hi, I'm Matt Turk from Firstmark.
What is Starburst and how does it enable fast SQL analytics across data sources?
Welcome to the Matt Podcast. As AI takes over the world, the battle for domination of the data layer is more intense than ever. My guest today is Justin Borgman, the CEO of Starburst, one of the key players shaping how enterprises handle their exploding data needs. We covered a lot of ground in this conversation, including in particular Starburst's key strategic moves over the years.
How did the open‑source project Presto evolve into Trino and why does it matter?
Building Galaxy, we actually built it twice. We wanted to take a more, I'll call it like a more of an Apple approach that basically took another year.
The critical moment they had to restart
their open source project pretty much from scratch. It was tricky because you're sort of starting from zero in terms of name recognition.
The early bet on the Lake House are
Architecture and Apache Iceberg. You really need fast read performance. Lake houses are the future.
Lakehouse vs. Data Lake vs. Data Warehouse – what’s the difference and why does it matter today?
And while there's been widespread embrace around open formats and iceberg in particular, we've been doing this forever though. We have that sort of end to end life cycle around iceberg, and and we call that the ice house.
The decision to support on-prem deployments when many others bet everything on the cloud.
had dinner with the CEO of one of the largest banks in the country. He told me point blank, We're never gonna move everything to the cloud.
And lessons learned from building their big partnership with Dell.
Where are we? What are the negotiation points? What are we working through?
Why did Starburst back the lakehouse architecture from the start?
It really needs to be that kind of priority on both sides to make it work.
What are the three Starburst offerings – Enterprise, Galaxy, and the Dell partnership?
Now Justin is a very thoughtful serial founder in the data infrastructure space and is particularly great at explaining complex technical concepts in simple terms. Please enjoy this great conversation with Justin. Hey Justin, welcome. Thank you for having me. You're the CEO of Starburst. Let's just start with the uh one minute elevator pitch, just to frame the conversation. What does Starburst do? Sure.
So Starburst is a data platform for analytics, building data apps, and now increasingly incorporating AI in the applications that you build. We're the creators of an open source project called Trino, which is a pretty popular project used by a lot of the big internet companies like Netflix and Airbnb and LinkedIn and and and so forth. And it essentially allows you to run fast SQL queries on data in So you can really run analytics across all the data that you have, uh, be it in a traditional database, a data lake, on prem, in the cloud, uh you name it. So I'd love
to start the conversation with um a little bit of um sort of broad of a view uh of the space to make this interesting to you know anyone that may be curious about the world of data infrastructure and then we can go into all sorts of uh technical details. But like to start with what world do you operate in if if we think of all of this as as uh you know databases so databases, data warehouses, how would you sort of compare and contrast the the the the various um databases
uh of the world. So I think of the world, uh, the database world as being really divided into two halves, uh, the analytical side and the transactional side. The analytical side are systems that are built to be very read optimized, so reading data as fast as possible. The transactional uh world being more oriented towards writing data uh fast and consistently. So when you think of a transactional system that's maybe Empowering an application, you know, a a a uh canonical example would be like uh an ATM that needs to record a debit and a credit uh very quickly and has to be consistent every single time. An analytical application would be something like uh how many customers bought product X last last year, and slicing and dicing the demographic profile of your customers and understanding their journey, you know, through the website all the way to a
transaction at the end.
What is Apache Iceberg and how does it become the core “ice‑house” format for Starburst?
Um and so those are those are broadly speaking the the the two worlds.
Yeah. And when you say right, uh what matters most is to like never lose any data and the data needs to be like hundred percent correct a hundred percent of the time.
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Chapters
8 chapters
1
What is the main topic discussed in this episode?
0:00–0:01
2
What is Starburst and how does it enable fast SQL analytics across data sources?
0:01–0:23
3
How did the open‑source project Presto evolve into Trino and why does it matter?
0:23–0:45
4
Lakehouse vs. Data Lake vs. Data Warehouse – what’s the difference and why does it matter today?
0:45–1:12
5
Why did Starburst back the lakehouse architecture from the start?
1:12–1:16
6
What are the three Starburst offerings – Enterprise, Galaxy, and the Dell partnership?
1:16–3:36
7
What is Apache Iceberg and how does it become the core “ice‑house” format for Starburst?
3:36–4:17
8
How are data applications and AI (RAG, model training) built on top of Starburst’s platform?
4:17–1:06:24
Speakers
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