The PRQL: Redefining Data Roles in an AI World with Barry McCardel of Hex

Welcome to the Data Stack Show prequel. This is a short bonus episode where we preview the upcoming show. You'll get to meet our guest and hear about the topics we're going to cover. If they're interesting to you, you can catch the full-length show when it drops on Wednesday. Welcome back to the Data Stack Show. We have a three-peat guest on, Barry McCardel, one of our favorite people to talk to. So Barry, I think it's Every year our spidey sense kind of says, wait, we haven't had Barry on in a while. So welcome back.

It is great to be back. I think there's something really fun about doing this roughly annually because it's a good chance for me to even look back at like, what were we talking about a year ago? What's changed in the last year? Yeah. It's such a crazy time just in the world and technology that a lot has changed. And so it'd be fun to talk about what's going on.

Yeah, for sure. Well, for those who didn't listen to your last episode, which by the way, if you haven't, we talked about why building AI products is hard. Barry went super deep into a lot of the how-to stuff in the last episode, so definitely pick that up. We'll put it in the show notes. But Barry, for those not familiar with you, just give us the quick flyover of your background and Hex.

Yeah, so I'm Barry McCardel, CEO and co-founder of Hex. I I've been a data person, data nerd, sort of my whole career, did a lot of different things. And then before, and then started Hex with a couple co-founders I'd worked with at Palantir. And Hex now is used by over 1,500 customers globally to do their most interesting and sort of in-depth data work. And I think especially what we'll get into this, but especially in the last year, we've I would say AI and thinking about how AI can vastly improve those workflows has become our primary focus. And, you know, from a company perspective now, I think we really do think of ourselves as sort of the leading AI tool for doing data analytics and data science work.

Awesome. So Barry, I'm excited to talk more about that. One of the topics we talked about before the show I think will be really relevant for a lot of our listeners is all of the change coming in the data space around AI. So specifically people that are concerned they're getting behind, concerned their job's going to be replaced, or maybe just concerned their team size is going to be reduced, you know, due to some efficiencies from AI. So I want to— we're going to dig into that on the show. What else do you want to talk about?

Well, I'd love to start with that because I think there's sort of this macro picture of like what's changing with technology, what's possible today, and in some really exciting ways. As you mentioned though, I think it calls into question a lot of sort of our previous assumptions on what people's jobs look like, what tools are they using, how the lines in the stack are drawn. You know, we're here on the Data Stack Show, right? It's like, even like, what is the data stack anymore?

Right.

What does it mean? So it, you know, I think that's really rich to dig into. And one of the fun and slightly nerve-wracking things about having conversations like this in public is you're basically making a bunch of predictions and statements that, you know, easy to go back and check, but I love that.

I think it's fun.

Yeah.

Yeah, totally.

Well, it'd be fun to unpack that and make some predictions or to muse about this a little bit. And, you know, maybe I can come on another year. We can see, we can talk about all the places in which we were right or wrong.

Yeah, sure. Awesome.

Yeah. And we'll, we'll go back and fact check our previous episodes too. Maybe we'll have like a roundup. That'd be great. Uh, cool. All right. Well, let's hop in.

All right.

That's a wrap for the prequel. The full-length episode will drop Wednesday morning. Subscribe now so you don't miss it.