Understanding Data Engineering in 2025 | Ben Rogojan, Seattle Data Guy
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What is the main topic discussed in this episode?
Hi, I'm Matt Turk from Firstmark. Welcome back to the Matt Podcast. Today, my guest is Seattle Data Guy, aka Ben Rogojan.
What is data engineering and why is it becoming crucial in 2025?
Now, Seattle Data Guy has become a big brand in the data engineering space with a major following on YouTube and across social media.
How do data engineering, AI, and ML overlap in modern data workflows?
We had a fun chat packed with knowledge and insights, both for technical practitioners in the space and also non-technical people looking to better understand the basics of data engineering.
What’s the difference between a data analyst, data engineer, and data scientist?
The first part of the conversation is
What does a typical day look like for a data engineer?
is a deep dive into data engineering as a profession and the day to day reality of it.
Data engineering is like taking all this data and making it usable for people and now machines.
How can someone transition into a data‑engineering career and what skills are required?
And on top of that, you're probably gonna be spending some time fixing broken data pipelines here and there because things break all the time.
We then went into a recap of the big stories of 2024 in data engineering. Rock
set got purchased out by OpenAI. Databricks had bought Tabular,
large
acquisition
there.
What were the biggest data‑engineering stories of 2024 (e.g., Iceberg, acquisitions)?
And then we closed with Ben's predictions for 2025.
Why is Apache Iceberg considered a game‑changer for open table formats?
SQL isn't going anywhere. I feel like someone's gonna tell us it's time to revamp Datalake V1 again, dump everything in there, and we'll let an LLM figure it out, which I don't think will work. That's why we'll still see
SQL kind of be around. Please enjoy this very thoughtful discussion about all things data engineering with the excellent Seattle data guy. Hey Ben, welcome. Hey, hey there, Matt. Thank you so much for having me on. So we are going to talk about uh all things data engineering in twenty twenty five. And uh it sort of feels like twenty twenty five is gonna be a huge year for data engineering given everything that's happening in AI.
Yeah, you know, it it uh it kind of feels like more of the same in the sense that we we repeat the same lessons, I I think. Uh people all uh kind of go through the same iterations. Um new people come to the space or possibly maybe it's more the business side get really excited uh by the prospect of what AI or data science or neural networks, whatever iteration we're in, can do. Uh, and then we kind of get down the line and we're like, oh, we we s we need to get certain things in order first to kind of get to a point where um we can actually use data well. Um, so yeah, I think uh it's gonna be a a a big year. Uh I think we're we're set up to Try t try to f actually figure out how these things work, right?
We've had two years to figure out some of the basics and now see where things go.
Yeah, it sort of feels like that moment in the hype cycle or like where like everybody uh got excited about AI and then last year, especially in the enterprise, like particularly big enterprises, there was like POCs and a little bit of like uh okay, what are the use cases? And if this year is the year of implementation, that's the year when like the rubber really meets the road of uh oh, how does that actually work? Where do we get that data and and all the things? So and uh so you know, we we are going to try to make uh this episode interesting to lots of different people interested in data and AI, so both business people and much more technical folks. Uh so um hopefully we can cover some key concepts, some definitions and all the things, as well as going into more technical stuff so that that's a little bit for for everyone.
Um but maybe before we get into this a little bit of For your background, your story. So you're Ben, but you're also known as Seattle Data Guy. So do you wanna uh go into all of this?
Yeah, sure. No, great. Thank you for for the the light intro. So yeah, like I said, uh Seattle Data Guy. Now I now I live in Denver. So uh it's currently it just snowed, I think, uh the other day. So it's a white outside. Uh that's new for me from Seattle. Uh I've been kind of in the data engineering space for nearly a decade at this point. Started at a hospital doing a combination of like data science, data analytics, data warehousing. work. Um, I think like most people around 2012, 2015, I got bit by the data science bug and I was like, I want to do data science.
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Chapters
8 chapters
1
What is the main topic discussed in this episode?
0:00–0:07
2
What is data engineering and why is it becoming crucial in 2025?
0:07–0:13
3
How do data engineering, AI, and ML overlap in modern data workflows?
0:13–0:23
4
What’s the difference between a data analyst, data engineer, and data scientist?
0:23–0:25
5
What does a typical day look like for a data engineer?
0:25–0:34
6
How can someone transition into a data‑engineering career and what skills are required?
0:34–0:48
7
What were the biggest data‑engineering stories of 2024 (e.g., Iceberg, acquisitions)?
0:48–0:51
8
Why is Apache Iceberg considered a game‑changer for open table formats?
0:51–1:02:21
Speakers
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