The Data Stack Show - The PRQL: Turning Data Into an API with Matteo Pelati and Vivek Gudapuri of Dozer

Episode Date: July 31, 2023

In this bonus episode, Eric and Kostas preview their upcoming conversation with Matteo Pelati and Vivek Gudapuri of Dozer.

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? I am, of course. Let's do it. Let's do it. Kostas, fascinating conversation with Vivek and Matteo from Dozer. So interesting, the problem space of trying to turn data into an API, right? Think about all the data sources that a company has

and their goal is to turn all of those sources into APIs and actually even combine different sources into a single API, which is where things get really interesting, right? Imagine a sort of a production database, analytical database, an ML database, being able to combine those into a single API that you can access in real time is absolutely fascinating. I think my biggest takeaway, which we didn't actually talk about this explicitly, but I think that they are anticipating what we're already seeing becoming a huge movement, which is that data applications and data products are just going to become the norm, right? Whether you're serving those to an end user in an application, you know, so we talked about a banking application where you need an account balance across,

you know, the mobile app, the, you know, sort of web app, the insurance portal, et cetera. Of course, you need that data there. Or your personalizing experience based on demographics or whatever. All of these are data products. And we haven't talked about that a ton on the show, but I really think that's the way that things are going. And this is really tooling for the teams that are building those data products, whether they're internal or sort of for the end user. And I think APIs make a ton of sense as the way to sort of enable those data products. So that's my big takeaway. How about you? Yeah, a hundred percent. I don't't think an application engineer is going to change the way that they operate. They have their tooling and they should continue working with what they know how to use.

And do it like, I mean, how they do it already in applications. So that's where I think that the opportunity is for tools like Dozer, right? The same way that a data engineer doesn't want to get into all the protobufs and I don't know

what else applications are using to exchange data, right? The same way an application developer shouldn't get into what the data table is. They should care exchange data, right? It's the same way like an application developer shouldn't get into like what's the data table is, like why they should care about that, right? Like, or what like Snowflake is. What they care about is like get access to the data that they need

and the way that it has to be so they can build their stuff. And that's, I think like what is happening. I think it's primarily like a like a developer tooling problem

to be solved it's not like a marketing it's not like a sales ops it's not it's not any of these like

ops kind of like I mean there is space obviously also like for these tools but if we want to

enable let's say if we want to enable, let's say, builders, we need to build also tooling for engineers to go and build on top of that data. And I think we will see more and more of that happening, even in the reverse ETL tools that we've seen coming in the past like two years and you see that like even with what's the name of this one one of these companies high touch census

yes they start like implementing like a caching layer on top of snowflake right like an audience cache yeah for sure yeah but like forget like audience and put like any kind snowflake, right? Like an audience cache. Yeah, for sure. Yeah, but like, forget audience and put any kind of

query result. That's why I'm saying they started from a marketing use case, right? But at the end, what they are building right now is interfaces for application developers to go and build a top of data that lives inside their warehouse.

Right. And I'm sure we'll see more and more of that. But it's interesting to see that like, even like high-touch that started as a company, like very like focused on like the marketing use case. At least that's my understanding. Like when I saw them, like when they started, they're also like moving towards that, which is a good sign. It's a sign that like more technology is coming, exciting tooling and developer tooling. Yeah, I agree.

I think that, you know, we've talked a lot on the show over the last two years about, you know, data engineering, the confluence of data engineering and software engineering, right? And nowhere is this more apparent than, you know, putting an ML model into production or taking data and delivering it to an application that's providing an experience for, you know, an end user. And so we've actually had a lot of conversations around, you know, software development principles and data engineering, you know, or vice versa, right? And tools like those are fascinating because they actually may help create a healthy separation of concerns where there is good specialization, right? Not that, you know, there isn't good, you know, healthy cross-pollination of skill sets there. But, you know, if you have an API that can serve you data that you need as an application developer, that's actually better. You can do your job to the best of your ability without having to sort of co-opt other skill sets or, you know, sort of, you know, deal with a lot of like data engineering concerns, right? And the

other way around. So I think it's super exciting and an interesting shift since we've started the show. So stay tuned if you want more conversation like this, more guests, lots of exciting stuff coming your way, and we'll catch you on the next one.