The PRQL: Graph as a Utility

Eric Dodds 00:00
Hey welcome to The Data Stack Show prequel. We just recorded a really interesting show with Ryan rights from a company called bat DOT, they are the commercialization of an open source technology called Klein. Dan, what an interesting conversation in Costas, we talked a lot about graph as a concept, but then also graph databases. What was your takeaway? Because we haven’t talked about graph on the show a ton. And it was a really helpful conversation, at least for me, but what was your big takeaway? Yeah, I

Kostas Pardalis 00:41
mean, graph databases and graphs in general is like one of these things that there are not that much discussed today. I mean, there are like some technologies out there, like, okay, but I’m pretty well, like people are using them. But it’s almost feel like they’re some kind of like niche. Yeah. So listen, I agree. But look, I really enjoyed, like with the conversation that we had is that it’s a very refreshing, like point of view in terms of like, what’s in how like graphs can be used. And they don’t necessarily have to be would say, these monolithic like database where everything is like all the others do does it like a node in the graph. But we can take like a different take on it. And you’d like more as like a processing party, and a greeting for adding, like, for specific problems and develop, like, on top of existing systems that didn’t look like necessarily gas based systems, right. And I was like, what I found like, extremely, extremely interesting, about like Wayne, and like, the full conversation that we had. And yeah, like, I really, I really, like, enjoy that. And like, I’m really looking forward to see like more examples of these couples, to be honest.

Eric Dodds 02:00
Yeah, I agree. I think the teasers here that I think are really interesting, or that they’re there. The Quine teams take on graph is less of a self contained unit that, you know, does a very specific type of thing really well. And almost looking at it as a utility. And one of my favorite examples on the show was actually using Quine as like a pre Compute Engine, you know, to save costs downstream by leveraging the graph to do like some complex initial compute in a way more cost effective way right. And at that point, it’s almost, you know, really is just sort of a transformation layer in a pipeline. You know, if you think about like the basic data flow graph happens to be like super well suited for that like use case. But to your point, no one really talks about graph like that. So all that is to say definitely check out this episode is one of the most compelling you know sort of conversations I think I’ve heard on a new ways of thinking about using grass and subscribe if you haven’t, of course, and we’ll catch you on the next one.