The Data Stack Show - The PRQL: The Intersection of Physics, Data Science, and Product Development with Santona Tuli of Upsolver

Episode Date: October 16, 2023

In this bonus episode, Eric and Kostas preview their upcoming conversation with Santona Tuli of Upsolver.

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? I am, of course. Let's do it. Let's do it. Always a joy to talk to a nuclear physicist about data, And boy, was that a great episode. There's just something about someone

who's collided particles at insane speeds that, you know, it's just fun to talk to them about almost anything, which was great. So Shantanu from Upsolver was just a delightful guest. And for someone, she is so, so smart on so many levels, right? I mean, nuclear physics, colliding particles at CERN, working in natural language processing, working as an ML engineer. And she's so down to earth and approachable and just really a delight. Like it was really fun to talk to her. I think one of the things that I found really interesting, and actually, I mean, there's so many things about Upsolver that were interesting and sort of, you know, focusing on the developer

as opposed to the data engineer for a pipeline tool was really interesting. But one of the nuggets from the show was how she talked about the differences of working with data as a scientist a physicist and working on data in a startup because there are some similarities that there are a whole lot of differences and her perspective on that was so interesting and i think that it was interesting because she took learnings from both sides, right? So from her perspective, there are things that the academic community can learn

from startups and then vice versa. So that was a great discussion. Oh, 100%. I totally agree with you. First of all, I think it's hard to find people that can do a really good, even like an average job to be honest across the spectrum of like different disciplines that she has right about someone who has gone from

crunching numbers about some atomic particles at scale right and when i say at scale i mean not just like a lot of like the infrastructure needed there but like at scale. And when I say at scale, I mean not just the infrastructure needed there, but at the scale of the teams. It's literally thousands of people that they have to cooperate, to come up with these things. And doing data science, doing ML work and becoming a product person, right? He has like, that's like a crazy like spectrum of like skills and competence, like the person needs like to develop to be good at all that stuff, right?

So first of all, I think like just for this, like someone should listen to here because I think it's like on its own, like very unique experience to have. At the same time, I think you taught something about like the differences and the like similarities with about like working with data in different like environments. And I think that's like what is like really fascinating in my opinion, when it comes to data as infrastructure or products or whatever we want to call it. Because data is a kind of like assets

that there's no way that you're not going to end up having a diverse group of people that need to work together in order to turn it into something valuable. I think that the things that we talked with here about, like talking from the engineer who builds the actual product, even like the front-end engineer, you have experience with Rutter Stack, for example, and the work that this person is

doing actually affects people from marketeers, BI, people that might even not know that they are in the company if the company is big enough. They don't care about that.

And you need to build products that can accommodate all these different and becoming the glue that can accommodate like all these different ends, like becoming the glue in a way, like between like all these people to make like this whole process of like generating value out of this data, like as robust as possible. And this is not just like an engineering problem. It's not like just like figuring out the right type of technology. It's like a deeply also, how to say that, like like human problem like because there has to be communication there right so figuring all these things out i think is like

what creates like so much opportunity in this space and it's i i'll keep something that she said that wherever there is challenge there's also opportunity right and like that's i think something that's like super important and there are big challenges right now in this space which means that there are also like big opportunities so i would encourage everyone like to go and listen to here it's a lovely episode and many there are many things they like to like to learn from. Definitely. Definitely want to check out. Subscribe if you haven't, tell a friend, and tune in to learn about nuclear physics and data. And we'll catch you on the next one.