The Data Stack Show - Shop Talk: Snowflake Summit Recap

Episode Date: July 21, 2023

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Welcome to the Data Sack Show, Shop.Costas. We have talked with people who've built amazing data technology at companies like Netflix, Uber, and LinkedIn. But you and I actually don't record our talks about data very much, but we actually talk about data together a ton. And so Brooks had this amazing idea of just recording some of the conversations that you and I have before and after the show about data and our opinions on it. And really, this has been one of my favorite things that we do. So welcome to Shop Talk. It is where Costas and I share opinions and thoughts on a personal level about what we're seeing in the data space. And it really is simple. We ask one another a question and the other one tries to answer it.

So without further ado, here is Shop Talk. Welcome back to the Data Sack Show. We are going to talk shop. Costas, this is one of my favorite things, where we just sort of get to go off script and talk about whatever, and I think we talked about AI last time,

and I may have brought that subject up, so I think it's your turn to ask the question. Yeah, and so I think it's like a great time, because I think you just came back from the Snowflake Summit. And I wasn't there. So I'm really interested to learn more about what happened there.

Both in terms of what Snowflake, let's say, had to say about Snowflake and the industry in general. But also, I'd love to hear what you've learned and what you experienced there might have changed or not. Like your perspective in where the industry is going. Yeah, I think I'll try to give a high level summary of the sense that I got. You know, as someone who works in product marketing, I think I can tend to be cynical. Maybe, I don't know, maybe I'm just a cynical person in general when it comes to marketing. But, you know, for a long time, I mean, relatively long time, Snowflake has been kind of pushing this concept of a data cloud.

And what was funny about that in practice was that everyone just still thought of it as a data warehouse and even called it a data warehouse, right? Practically on the ground, just, I mean, Snowfl like data warehouse is just the vernacular that people would use, data practitioners would use when they talk about it, right? Because that was for the first, you know, however, you know, for the first, you know, since it launched and everything, it was used as a data warehouse and still is largely used as a data warehouse, right? I mean, analytical workflows are, that's, you know, sort of the most common thing you run into. And so there was a little bit, for a while there, there was a little bit of dissonance between talking about the data cloud and practically people just thinking about Snowflake and using Snowflake as a data warehouse. And I really felt like this Snowflake

Summit was where you could feel it shift to actually there being a lot more weight to the concept of a data cloud. Now, obviously, there are really smart people and visionary people sort of establishing that as a concept and product roadmaps are driving towards that. So I'm not saying that it was fake beforehand. I'm just saying that there was, I think, more dissonance for the average person who probably categorized Snowflake as a data warehouse practically in their day-to-day job. But there were just a lot of things where it really made it feel like a platform with lots of options right so the nvidia announcement obviously was huge right so that's going to be

pretty significant for the development of really large scale really large scale ai models which feels very different from the way that people would traditionally sort of categorize Snowflake as part of their data infrastructure, which is really interesting. The other big thing I think is container services. And so, you know, they, several companies announced, you know, actual sort of native integration with container services. So you can essentially run sort of these products within container services within the Snowflake Data Cloud,

which is really interesting, especially when you think about SaaS apps that have data in them, but then you can actually sort of operational then you can actually operationalize that data within containers. It's very interesting, right? And so now all of a sudden you have all of these ideas, I think, rushing to people's heads around things that you can do and build that maybe seemed more theoretical more theoretical before so i don't know i don't know if i'm sure i'm missing some things but i really left summit this year with a strong conviction that there's a lot of infrastructure

and there are other things that they released that were you know really neat but with a strong conviction of like, man, we're going to see an explosion of people building really interesting things on Snowflake far beyond the bounds of typical analytical workflows. So, I don't know. There's my high-level summary. Yeah, that's

super interesting. I think it kind of makes sense that That's super interesting. I think it kind of makes sense that it's the data cloud and it's the cloud at the end. It's the infrastructure to go and build in general. It's not just dashboards. It's very interesting to see

the path and the journey that Snowflake has because yeah, like it's turning into like a cloud provider in a way, right? Yeah. Which probably is like, okay, like the only way to justify also the multiples, the market, right? Sure, sure. But okay. So you mentioned like NVIDIA.

What's about NVIDIA. What's about NVIDIA? Like, what is the announcement there? Like, what did they describe, and what is, like, how's the vision that they have with working, like, and providing, like, access to

NVIDIA hardware? Yeah, well, actually, so I wasn't at the keynote um i wasn't at the keynote so that's a full disclosure but i did i did talk to people who were at the keynote which actually is almost a more interesting like i don't know in some ways maybe this is more interesting to some people maybe not but like i talked to several people who were at the keynote and asked them what really stuck out to them and this may sound funny but i think one of the biggest things as confidence that there's enough horsepower there to actually do really large-scale

machine learning workflows and sort of develop like really large-scale, you know, so let's just say like enterprise-level ML production workflows, right? Because like I said before, people just didn't normally think about that, right? And so the people that I talked to who came from the keynote who were really excited, you know, who worked for, you know, some of these people work for very large, you know, sort of maybe like

Fortune 1000-ish type companies, right? And they didn't really talk about nvidia specifically right or like the technical undercarriage of like what the partnership means they more were just like wow like maybe we can build some really big stuff on Snowflake's platform now, right? Which was really interesting. And again, it kind of goes back to what I was saying earlier is that they sort of, it was almost like a confidence thing of the horsepower actually existing. I don't know.

That's, that was, that's my takeaway. And I'm probably distilling some of that wrong because I wasn't actually like I like to be honest and that was like a follow-up question that I I had for you it was about like the interactions that you had like with the people there because you were not you know as a vendor you also going there as a vendor, you also have like the opportunity to have like a very, I'd say like almost like a interesting in between a position, right? Like you are not a potential customer of like Snowflake there.

And you are not Snowflake also. So you have like, as a vendor always, you have like a very unique kind of like perspective and way of interacting with the visitors and people who are visiting. So what was the... I mean, you already said like some stuff about the confidence that you said that they had on the ML side of things. In general, what's your take from what you had from people visiting there? What they were asking, what they were looking for, how they felt, what the vibes that you got from them as practitioners, right?

They are not vendors. They are not Snowflake. Yeah. This probably isn't going to surprise you but i would if i had to simplify it as much as possible i would create two general groups of people the first one is actually the bigger one which we've talked about this before on the show a lot, which is people who are just trying to build a high quality data practice within a company and who are trying to solve the basic challenges that you have when you're trying to do that. And that is, I need to get a lot of different disparate data sources into one place.

Obviously, the people at Snowflake are doing that in Snowflake's environment. And then I need to try to create some sort of value with that collected data. And in many ways, that kind of characterizes a lot of the traditional thinking about Snowflake as a data warehouse, right? It's a data store that allows you to easily get all of your data into Snowflake. And then the separation of storage and compute allows you to, you know, make smarter decisions about how you actually try to begin creating value out of that for different use cases. And I just think when you talk to people who are coming by the booth and just ask them, how are you using Snowflake? It's just easy for us to forget that a lot of companies, especially larger companies, it's just really hard to get over the initial hump of doing the basic stuff right like

collecting data and even like driving really good analytics is still a very difficult problem at a lot of companies so that's sort of the first group now i will say one thing that was interesting was the ecosystem of tools provided by snowflake To do that was talked about way more. So like the Snowpipe streaming infrastructure and other things like that, where it's like, you know, you're seeing Snowflake actually now have the ability to replace what traditionally would be

sort of a, you know, complicated set of Kafka pipelines and maybe like homegrown APIs and stuff. So that was kind of interesting. So I think that some people certainly felt like they had more options from Snowflake that were really viable for sort of replacing some of those traditional data flows. Anyways, that's sort of group one. And again, I would say that's a larger group, right? Because as much as we'd like to tell ourselves that every day to practice is like super modern and sophisticated

a lot of them are still trying to do basic stuff but again that's getting easier the second group were I would say this a really interesting characteristic about people in the second group they were thinking about all the new capabilities

of Snowflake. And there was a lot of discussion around consolidating workflows, right? That's a huge problem. And especially with the traditional split between analytics workflows and ML ops, those, I guess maybe a good way to say it would be like, there are people who in their daily job are starting to see those things converge from a cultural standpoint at the company. And I think a lot of that's accelerated by AI, right? And prioritizing machine learning, right?

And so you're starting to see like analytics and ML meld. And a lot of people on the ground there are there to figure out how to get more value out of their snowflake investment, right? Like, how can we use this platform to create more value inside of our company? And so it's really interesting to see them, you know, they may not have used this exact phrase, but if I had to distill it and put words in all these people's mouths, which is always very dangerous, but you see their gears turning around consolidation of workflows, which is pretty compelling, actually, right? So if you think about, let's say there's someone who's ahead of data, and they have a really mature analytics practice, and then a more immature ML practice, but they can actually leverage a lot of the analytics work that's already done as a running start for ML. And the infrastructure is already there to essentially, like you don't really have to do a big infrastructure project to migrate data, move that data, run complex transformations on that data.

It's actually just there and you can start doing ML. That is very exciting to people. And I think it should be because, I mean, that's pretty sweet if you're someone in that position. Yeah, 100%. And I think a big problem that data infrastructure has right now is fragmentation.

And there's a lot of replication also that is happening. At the end, there are, let's say, common patterns that exist regardless of what you are doing. Like if it is ML or like reporting or whatever. And I don't think we have reached the point where, you know, there is like a

robustness in the architectures, like to provide, let's say, the best possible experience at the end, because people might think that it's more about cost because you don't want like to, you know, like duplicate things, but at the end. Because people might think that it's more about cost because you don't want to duplicate things. But at the end, it's not like the cost in terms of money. What people don't understand is that these things, even if

they were, let's say, for free, they just don't scale to the size of the problem that they are trying to solve. And actually, having such a brittle infrastructure, it almost halts down the whole process. That's why we ended up in getting this kind of fragmentation. I know people at some point, they just had to move much faster

than the rest of the infrastructure there because things were happening, and they couldn't wait for the rest of the infrastructure to change, right? Yeah. So that's why we had like all these things. But at some point, if you want to operationalize all these things, you need to have like a common infrastructure like to work on top. And that's where like the, this whole concept of the data cloud or whatever

you want to call it, like makes sense. Right? Now who's going like to own these and if it's going to be one or multiple companies, I don't know, but that's, I think, where we are heading towards. I think it's going to be very fascinating. I'd love to also, okay, we're close to the end here. We talked about Snowflake. There was another summit that was happening at the same time, right?

Yep. I'd love to see... I wasn't there, but I'd love to find someone who was there and do a shop talk and also give a quick update of what happened there. So let's try to figure this out and make it happen. Let's do it.

And I would say one other thing, I know that there are probably a lot of data vendors who listen to this, but it's always a really good reminder that there are so many vendors for doing very similar things. And it's hard for people to sort through all of the options they have to do very similar functions, right? And that's actually getting worse because of Snowflake's advantage of building Snowflake native apps, right? Your options are actually proliferating even within the Snowflake environment itself, which is a great thing for Snowflake, but is creating an interesting complication for people out there who are trying

to decide which sort of tool sets to put together. So I think it'll be interesting to see how vendors sort of respond to that from, you know, a communication standpoint, content standpoint, all that. All right, well, we are at the buzzer. And that was my brief overview. I'm sure someone will email and tell me about all the things that I missed. But we'll get someone from the Data AI Conference on the show soon. And we'll catch you on the next one. You know, Costas, we learned so much from the data leaders that we talked to, but I learned so much from picking your brain. And actually, your questions really make me think really hard. So I appreciate ShopTalk. I think it makes me a sharper thinker. Well, it's, it's fun.

Like, I think it's good to just sit and chat about the stuff that we experience. And yeah, I think like, I hope like people enjoy it. That's why I'll keep asking for people to reach out. Please do this. Come up with like, you can do that. Like send an email. Yeah. Let us know how you feel and like, what are your opinions of like your experience with the show.

So please do that. So me and Derek, we can keep being happy. Please. Of course. And of course we try to take the same types of questions to, you know, Eric, we can't keep being happy. Of course. And of course, we try to take the same types of questions to data leaders from all sorts of companies, large and small. So definitely subscribe to the main show

if you haven't yet. Tons of really good episodes there and tons of really good thoughts from data leaders really around the world. So definitely subscribe if you haven't and we'll catch you on the next Shop Talk.