411: Jeremy Burton, GM of Observability at Snowflake: The Data Layer Was Never a Commodity. It’s the Whole Game

00:22:46 · Jeremy BurtonPlay from here

[00:22:46] S0: Yeah. I mean, I I think fundamentally that the the the data foundation has never been more important, and it's maybe not surprising to hear... If you're in the frontier model business, it's always easy to sort of look look down your nose and say, well, yeah, all of that stuff beneath us is a commodity. I've been doing this a long time. Managing massive volumes of data, particularly critical business data, never has been a commodity, and I don't believe will be a commodity now or anytime in the near future. I think what folks are also realizing is that if if I have 10 source of truth in my enterprise, which almost every enterprise does, an an agent does not reconcile those across those sources of truth. It might average across them, but it it it would it will give you the wrong answer in a confident way. The the the... I think what what enterprise is realizing is, okay. But if I I can reconcile that source of truth, right, and and if I can provide some semantics around that data, then the agent can do it work, and it will be game changing, because what we've always wanted is sort of, I think right back to Microsoft in my formative years in the mid nineties, you want information at your fingertips, and that has been an elusive dream, I think, until now. But you only get enterprise information at your fingertips if you do some legwork on the data foundation. I would also tell you, for the longest time, Snowflake in the early days, was all about just putting the data inside Snowflake and reasoning about data inside Snowflake. I think what is interesting right now is Snowflake quite early jumped on iceberg. I think most large enterprises are gonna look to Iceberg as sort of the OpenTable format. And so what that means is that the catalog, Snowflake, you know, introduced that probably a year or so ago, becomes fundamentally important. Because if you've got a vast quantity of data in Iceberg format, some of it may have been written by Snowflake, some of it may have been written by something else, You you need to know, like, hey. What what... Where is the data that I need to query to to do my thing? And and the catalog, I think, starts to play, you know, quite a critical role. And then I think more broadly, you know, Snowflake is, I think, aggressively adopting coding agents.

00:25:23 · Jeremy Burton, Dan TurchinPlay from here

I mean, Coco, you know, is their coding agent, and you think, well, okay, we're not using it to generate Python code to build applications so much, but if you wanna set up pipelines, data infrastructure required to lay down that data foundation, I think the sense is, it's never been easier to do it. We can take all the legwork out of that, which is quite involved and hairy, and sorta no one values that much the plumbing, but you've gotta do it. It's like, I've got lots of plumbing in my house. I spend a lot of money doing that plumbing, but actually nobody sees it. And and I think that that was the feeling around, you know, any data foundation, which is I gotta do a lot of legwork, and I gotta migrate data. The promise, I think, is that AI and things like Cocoa are just gonna make this very, very easy. And there's quite a massive library of skills that Snowflake has produced around, you know, data engineering and data operations just to make life much easier. And I think that that is now starting to show up in the business. Right? It's much easier and faster to do things. It's much easier and faster to ask questions. That drives the query volume, which is really what drives Snowflake's business. [00:26:47] S1: That's what I thought you would say. I was hoping you were gonna give Coco these examples. So thanks for sharing that. [00:26:51] S0: Yeah.

00:26:54 · Dan TurchinPlay from here

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00:28:51 · Jeremy BurtonPlay from here

[00:28:51] S0: Yeah. I think, obviously, the volume goes up. Right? And and and I think when... With the advent of Kubernetes probably, you know, ten, fifteen years ago, is it is it really that long? You you saw probably a three to five x growth in telemetry volumes. I I firmly believe you're see that again in the AI era. I I think then the the volumes of data then are are obviously so vast. To to ask a poor old human to help you figure that all out and find root cause when the rocket explodes, there's a problem, it's like, no, you're gonna need AI to help troubleshoot the data that has been generated quite often by AI. So needless to say, we think that this process of structuring and organizing messy machine generated data, AI can help with that. If I go back, observe one of the unique innovations that we produced was something called a knowledge graph, and we did this back in 2018. And the knowledge graph really is just an abstraction that sits on top of this machine generated mess. But it but it... We originally designed it to enable humans to navigate that data much more quickly. Instead of diving into a dumpster of logs and looking for a customer ID, you could say, oh, look, here's all my customers. Here's the sessions. Here's the pods they were running on. Here's the containers. So it turns out that we didn't know it at the time. It turns out that we were actually total geniuses, because it is that structuring and that knowledge graph that we use to give context to the agent. So, wind the clock forward now, okay, yes, agents can use that knowledge graph, but what if we could use agent to build that knowledge graph? Today, when we build it, you know, if if if you had an application, we would sit down with you and say, oh, and and you're... When you're troubleshooting, what kind of questions do you ask? Because every business is a little bit different. You know, our customer Dialpad looks at Kohl's. Topgolf looks at Gulf Bays. These are all different entities that businesses wanna ask questions around, and we would have our implementation guy sit down with the SRE team and ask these kind of questions, and then we would extend this knowledge graph to better reflect their business. But you start to think, okay, what is the art of the possible now? We could just ask those questions. We could analyze the query results.

00:31:30 · Jeremy BurtonPlay from here

The agent could suggest structures or entities extracted from that data, and then as we further ramp queries, it could optimize the query performance. So I think AI agents will essentially end up creating that graph. That graph then becomes the basis for other agents to drive the investigation flow. So, you know, you can start to see, yes, there's more telemetry flowing in the bottom. The good news is we don't have humans creating entities and structure, and we have agents doing that. Oh, and by the way, that structure they created is going to help other agents advance the troubleshooting flow more quickly. So, yes, there's going to be humans involved. There's there's unknown problems you've never seen before that, you know, will need some, you know, sort of human insight. But I think the goal here is that 90% plus of the legwork here is is really done by a collection of AI agents.

00:32:31 · Dan TurchinPlay from here

[00:32:31] S1: Makes sense. In a pre AI world, as much as it sounds like an insult, a lot of observability could be performed using heuristics. Back in the day, we didn't think of that as an insult. That was just, you know, it was a nice reliable way to, observe applications. Now as we introduce AI, kind of these probabilistic or nondeterministic outcomes, there's a different level of responsibility. It's it's like... Observe such Snowflake as a vendor. It's less about just verifying the integrity of the data or, you know, its state and more about taking responsibility for some of these actions, the decisions that are made. How do you think about the shifting role of what you do with respect to these, you know, probabilistic outcomes?