The PRQL: Removing the Execution Engine Language Barrier with Aditya Parameswaran of Ponder

Eric Dodds 00:05
Welcome to The Data Stack Show prequel where we replay a snippet from the show we just recorded. Costas. Are you ready to give people a sneak peek? Let’s do it. Kostas, I love how practical that conversation with a Aditya from Ponder was, I mean, we dug into the specifics of pandas. But then we also got pretty theoretical, he has some really interesting ideas about the way that things should operate. And I think my big takeaway, and this is actually from your conversation with him, so I’m going to unashamedly steal your thunder. But there’s this idea, I think, you know, he didn’t necessarily use these terms. But this idea of almost like, artificial scarcity on execution engines, and he envisions this world where the ergonomics are such that you can use whatever language you want. And then plug that into whatever execution engine you want, that makes sense for your business. And currently, there are a lot of limitations based on the languages and then like they’re meeting to different execution engines. But if you remove those barriers, it actually becomes really interesting to thing. And your point about compilers was really interesting. So when you think about sort of going from pandas to maybe like a Snowflake warehouse, doesn’t seem like a normal mode of operation based on sort of typical ml workflows, necessarily when you’re, you know, going into production. But I love the vision. So that was really exciting. And I’ll think about that a lot. Because I think that is where things probably should go.

Kostas Pardalis 01:49
Yeah. Yeah, for me, I mean, it was like a very fascinating conversation, I think. So the whole focus around the user experience or like developer experience, like the opportunities that exist there like to build and deliver value. And all the insights from our TTI around that stuff, like was, was amazing. And also, I think, I don’t know, it’s like, the second professional that we have now on the show after Andy from CMU. And I like kind of like him, it’s really nice like to have the is, okay, kind of unicorns where you have like these very academic people who are also like, also Soda legs off like the meat, so maybe we should do like, call them 100, like both on the show.

Eric Dodds 02:39
Or do a panel o a panel because I think that’s interesting, like they really do. It’s like they’ve summarized a huge legacy of academic research into things that are like very practical in the market. And AI unit is a great way to describe that.

Kostas Pardalis 02:55
And they probably start at the bottom, like their own company. So it’s not like they’re not just like academics that took all the in theory, like, yeah, they have seen how the social decision made, which I think makes them like even more interesting. And I don’t know, it’s also like, the personalities, I think, like, having the two of them like on the panel. It will be interesting.

Eric Dodds 03:18
I need to do it. Yeah. Someone starts out by saying, you know, I’m a database guy, like, it’s probably gonna be a good conversation. And that just sounds cool. Like, I’m a database guy.

Kostas Pardalis 03:29
Yeah, yeah, absolutely. But yeah, a lot of so many sides of the conversation, and I hope like we will spend more time with him to go even deeper in in the things around working with data systems of scale, and like seeing all these things like from the user perspective, well, just like the technology.

Eric Dodds 03:53
Absolutely. Well, thanks for joining The Data Stack Show. Subscribe if you haven’t, tell a friend and we will catch you on the next one.