The PRQL: From Tables to AI: The Future of Data Modeling with Best-Selling Author, Joe Reis of Ternary Data

John Wessel 00:28
All right, welcome back to The Data Stack Show. We’re here today with Joe Reese, a second time guest, Joe, welcome to the show. So bye, guys doing good. Also, Eric is out today, and we’ve got the cynical data guy Matt here as co host,

Matthew Kelliher-Gibson 00:44
just sliding on over from the couch.

John Wessel 00:48
Glad to have you here. So Joe, yeah, catch us up a little bit on what you’ve been up to the last last few months, since we last spoke,

Joe Reis 00:54
not traveling as much, which is good. Yeah. So I’ve been going non-stop globe trotting, which happens in the spring and fall. So I’m just back home in Salt Lake City, working on some projects right now, and that’s about it. It’s just been nice to just, I mean, definitely thankful to travel a lot and see some cool places and meet awesome people, but it’s good to be back for a bit. No,

Matthew Kelliher-Gibson 01:17
Yeah, it sounds really nice. Okay, Joe, we spent a few minutes chatting before the show. I’m excited to dig into a little bit about the book you’re writing, and just maybe get into some cynical takes on what you’re seeing out there in the data world.

Joe Reis 01:34
Yes, well, yeah, I don’t think it’s any secret I’m working on a new book right now. It’s on data modeling, and I can get into why that is, but what the book is about is it’s it’s an end to end treatment of data modeling across different use cases, whether we’re talking applications, analytics, machine learning across different modalities of data, whether we’re talking structured data, semi structured, unstructured. The goal of the book is to really equip practitioners with an understanding of data modeling end to end. And so I think it’s what I consider to be sort of the next phase of where data modeling is going is not just about tables anymore. It’s much more than that. We’re working with different types of data across many different use cases. And so the goal of this book is obviously to equip practitioners with, you know, a body of knowledge of the existing techniques, as well as hopefully introducing some new ones as well. The working title is mixed model Arts, which is sort of a play on words of Mixed Martial Arts, so I can kind of understand where the threat is coming from. And I think the inspiration comes back. I grew up in the 80s, you know. I grew up watching really trashy TV like kung fu theater and wrestling and, yeah, all this stuff, and boxing and, you know. And I think back in the day combat sports, fighting was very one dimensional. You can be a boxer or a pro wrestler in your speedo, or Hyun fu master in the mountains in China or something. But there was always this notion that, you know, the you know, the questions are always like, who would win a fight? Like, would Bruce Lee beat Mike Tyson, right? You know, in a boxing match, or, you know, or under some set of rules. But UFC, you know, they came around the early 90s. There are obviously other things before, like Valley tudo in Brazil, which is early X martial arts. But UFC, I think, was the mainstream. They lit off the notion of being a one dimensional martial artist. Fast forward to today, and you couldn’t tell me that, you know, the best box in the world. If that person gets into the ring and UFC, they would do very well, or any only one dimensional sport. So think about them but if you take sort of the parallels to this with data, we’ve been stuck in the past. We’ve been stuck with these notions that, you know, that’s this one true way to model data. You know, there’s one technique to rule them all. You know, I think we’ve been, like I said, stuck in a table centric view of the world and sort of, you know, it’s almost akin to thinking the universe revolves around the Sun and right? You know, the world’s moved on. You know, we have endless amounts of different ways of storing and querying data. We have different ways of moving data streaming is becoming increasingly popular, and has been for a long time. Machine learning is everywhere, and now it’s AI and so, you know. But I feel like hopefully the world of data modeling and data in general starts catching up to where we are. So that’s part of the effort of the book.

John Wessel 04:18
Yeah, awesome.