The PRQL: Cloud Resource Management Is a Data Problem Featuring Lars Kamp of Resoto

Eric Dodds 00:05
Welcome to The Data Stack Show prequel where we replay a snippet from the show we just recorded. Kostas, are you ready to give people a sneak peek? Let’s do it. Kostas, what a fascinating conversation with Lars. From risotto, I learned a huge amount. And I think my big takeaway is that, you know, I kind of went into this conversation, expecting to be, you know, astounded by the complexity of sort of resource management across the entire ecosystem of infrastructure and tooling, which I was, it’s a very large scope complex problem. But the, the bigger thing was how similar the issue is actually, two sort of a standard data flow in terms of the solution, right? And so LoRa is kind of describe it as you’re, you’re sort of ingesting inputs, you’re doing some sort of modeling, and then you’re pushing those back out, right. And so when we think about the modern data stack, I mean, that’s, you know, bread and butter for a data engineer dealing with customer data, for example. So, it really struck me that sort of, you know, there’s an elegant architecture that already exists for solving this, like, pretty complex problem.

Kostas Pardalis 01:22
Yeah, yeah. 100% I think I’ll try to like proving today that shows bondsman the ease of data problem, I think we also prove that like everyone is a data engineer, right, like every software engineer as a data engineer at the end, like you need to in a way like many of the problems that we are talking about like solving actually relate, but sort of like contain a big part of like data engineering work has to be done, like data has to be exported, there has to be transformed somehow models. And, of course, being like, exposed to the data consumer, like for Van like to be created there. And I think like especially like, I mean, okay, he will sound like it has been said, like many times I think like already, like wearing like this, like decade of like everything’s going to be like around data. But I think we start seeing that a lot. And we start seeing that by actually like getting into domains that don’t necessarily feel like they are, you know, data problems or data related, like technologies that has to be built. But at the end, that’s exactly what is happening. Right. And I think, especially with AI and all the stuff that’s happening right now, we are going to see more and more of, let’s say, these domains to come back and like being rebuilt and read this cover that arounds, like the data problems that can be defined there, including like sales, marketing, like pretty much like everything. And yeah, it was super fascinating. Like we should get a lot of back again. He’s a good friend. And I think like whenever we talk with him, we always come up like with very interesting insights.

Eric Dodds 03:14
No, I completely agree so much to learn and would love to have Lars back has such a deep thinker about these problems. Go ahead and subscribe to the show. If you haven’t, look it up on your favorite podcast network until a friend and we will catch you on the next one.