The PRQL: Solopreneurship, Streaming Data, and Synthetic Testing with Michael Drogalis of ShadowTraffic.io

John Wessel  00:03

Welcome to The Data Stack Show. The Data Stack Show is a podcast where we talk about the technical, business and human challenges involved in data

Eric Dodds  00:13

work. Join our casual conversations with innovators and data professionals to learn about new data technologies and how data teams are run at top companies. Welcome back to the show. We are here with Michael Drogalis of Shadow Traffic. Michael, welcome to The Data Stack Show. Hey, thanks for having me. All right. Well, we have a ton to get into. Of course, I’m passionate about streaming, streaming data, and so we’re going to go deep on that, and we’re going to talk about solopreneurship and a number of other things. But first, just give our guests a brief background. How’d you get into data and end up at Shadow Traffic? Yeah,

Michael Drogalis  00:56

by trade. I’m a software engineer. I think the last thing that kind of inspired me as I was coming out of college was distributed systems and streaming data. They were all kind of really getting started around like 2010 or 2011 and I went out and built an open source project, ended up building a company on top of that. I sold it to Confluent, and then recently I left to go start shadow traffic, which we’ll talk about. It’s sort of the inspiration of all the problems that I’ve seen occurring in the last 10 years or so. And, yeah, awesome.

John Wessel  01:24

So Michael, we were talking before the show, doing a little bit of show prep. So many cool topics here. Eric already mentioned one solopreneur thing I’ve just read a lot about, and people are all like, what? Who’s gonna be the first 100 million dollar solopreneur? So that’s a fun topic. And then the streaming topic is just, it’s a fun one. It’s been going on a long time, and I think a lot’s happening there. What are some topics you’re interested in covering?

Michael Drogalis  01:48

Yeah, it’s always fun, kind of going into the details of the problems around synthetic data. I think people look at it and they think, Well, I can just use chat GPT to create some data or a little script to do it, and in some simple cases, you can. But as you start to go down this path, and you need to build more and more cases that reflect production scenarios, it’s actually a lot harder than you think. And reaching for a tool or it sort of has that defined as a set of abstractions that help you. It’s fun to go into the motivation behind those things and the use cases

Eric Dodds  02:15

and such. Well, let’s dig in. All right, let’s do it.