Sheel Choksi

Yeah, sure. So the the final straw was, well, not not as intentional from the Maven site, as I wish I could say it was but basically, after Luma got acquired by Google, they were sort of ending redshift support us and that kind of left us without really a vendor in place. But what I will say is even outside of that final straw, some of the other big pain point that was happening is Because it was sort of a stream based transform, you either kind of transform it at that moment, or you kind of let that data go. You know, obviously, there's restreams, and that sort of thing, but it wasn't super easy to do. And so it led us to this kind of quarter mentality, in that, you know, transform everything, keep everything because this is the shot, whether or not it's actually useful whether or not anybody even understands what this column means or what this event means. And let's just hold on to it. Let's just get it in redshift, so that we have it, which sort of inevitably led us to this sort of extremely bloated, both illuma and redshift with tons of tables and tons of columns that didn't necessarily have a lot of business value, and added to just kind of clutter and confusion. You know, when we were adding new people onto the team, there was just this enormous ramp up of like, what is all of this stuff? What do I actually need to use? Should I use this table or that table they both look similar? And so I would say that's the other major one that we were kind of looking to solve. Once we realized that we didn't have a And so that kind of helped frame our perspective of kind of what we were looking for in the search. You know, as we kind of started looking through vendors, we found that at a rough level, we could kind of group them into three categories. One, we kind of called the stream based category. So that was sort of the illumines I think there's a similar one called hebo data these days, kind of bucketed those all into the stream based vendors, then we found kind of the, the kind of the more declarative vendors, that's what we kind of consider ascend, which is sort of, you know, kind of more describe what it is that you want, and not necessarily so imperative as like, we're going to take this event, we're going to split it this way, in that way. And so, you know, some others that we found that we bucketed in that category might be like a, like an upsell for folks like that. And then the kind of the third category that we saw was more of these kind of either legacy one and that legacy, I should say, but, you know, maybe some of these older generation ones, you know, like the Pintos and and then the this very specific like that. You based what have, you know, maybe like a stitch data or five trend where, you know, they're very opinionated and sort of inform how exactly this is all going to go. And, you know, kind of we were used to the flexibility that illuma provided us, you know, they allow you to write kind of arbitrary Python. And so we knew we didn't want to go in that direction of maybe one that didn't allow us to be very flexible, you know, such as, like a five trainer, that sort of thing. And so that sort of left us like, you know, we could do a one to one stream based migration, or, you know, we could take a look at some of these newer providers and what they're up to. And as soon as we started looking at these kind of newer providers, ascent, obviously being our final choice there, we immediately sort of just saw that value add, and how I summarize that value add is it sort of made change. So as I mentioned in illuma, if all of a sudden we didn't map a column, and you know, we need to go back and get that column that was important. You know, that was just really expensive. You had to wait a while you had to go figure out how you're going to go get the old data again, you had to wait for it to slowly one event at a time kind of restream in you had to deal with the deduplication problem at the end. You know, it was just sort of a long Sort of piecemeal steps that sort of made change expensive, right. But as we started looking at these newer providers, that's what we saw as the value add is we didn't need to kind of hoard all the data exactly as we needed it as redshift, but rather, we could treat redshift exactly as more it was meant to be us. Let's prep these tables for how we want them. But let's bring in the columns that had meaning to us. And let's not fear that, you know, maybe we made a mistake, or more importantly, you know, data will change schemas will change, let's be able to adapt.