>> Narrator: From "theCUBE" studios in Palo Alto, in Boston, connecting with thought leaders all around the world, this is "theCUBE" conversation. >> Hi everybody, this is Dave Vellante. and as you know, we've been tracking the next generation of cloud. Sometimes we call it cloud 2.0, Frank Slootman is here to really unpack this with me. Frank, great to see you. Thanks for coming on. >> Yeah, you as well Dave, good to see you. >> Yeah so, obviously hot off your IPO, a lot of buzz around that, that's fine. We could talk about that, but I really want to talk about the future. What, before we get off the IPO though, was something you told me when you were CEO of ServiceNow. You said, "Hey, we're priced to perfection." So it looks like Snowflake is going to be priced to perfection, it's a marathon though. You made that clear. I presume it's not any different here for you. >> Well, I think, you know, the ServiceNow journey was different in the sense that we were kind of underdogs and people sort of discovered over the years, the full potential of the company. And, I think with Snowflake, they pretty much just discovered it day one (laughs). It's a little bit more, sometimes it's nice to be an underdog or a bit of an overdog in this particular scenario, but yeah, it is what it is. And, it's all about execution, delivering the results, being great with our customers and, hopefully the (indistinct) where they may at that point. >> Yeah, you're a poorly kept secret at this point, Frank, after a while. I've got some excerpts of your book that I've been reading and of course I've been following your career since the 2000's. You're off sailing. You mentioned in your book that you were kind of retired, you were done, and then you got sucked back in. Now, why? Are you in this for the sport? What's the story here? >> Actually, that's not a bad way of characterizing it. I think I am in it, for the sport, the only way to become the best version of yourself is to be under the gun, every single day. And that's certainly what we are. It sort of has its own rewards, building great products, building great companies, regardless of what the spoils may be, it has its own reward. It's hard for people like us to get off the field and hang it up, so here we are. >> You're putting forth this vision now, the data cloud, which obviously it's good marketing, but I'm really happy because I don't like the term enterprise data warehouse. I don't think it reflects, what you're trying to accomplish. EDW, it's slow, only a few people really know how to use it, the time value of data is gone by the time, your business is moving faster than the data in EDW. And it really became, as the savior because of Sarbanes-Oxley, that's really, what became of a reporting mechanism. So I have never seen what you guys are doing as EDW. So I want you to talk about the data cloud, I want to get into the vision a little bit and maybe challenge you on a couple things so our audience can better understand it. >> Yeah, so the notion of a data cloud is actually a type of cloud that we haven't had. Data has been fragmented and locked up in a million different places, in different clouds, different cloud regions, obviously, on premise. And for data science teams, they're trying to drive analysis across datasets, which is incredibly hard, which is why a lot of this resorts to programming and things of that sort. It's hardly scalable because the data is not optimized, the economics are not optimized, there's no governance model and so on. But the data cloud is actually the ability to loosely couple and lightly federate data, regardless of where it is, so it doesn't have scale limitations or performance limitations the way traditional data warehouses have had it. So we really have a fighting chance of really killing the silos and unlocking the bunkers and allowing the full promise of data sciences and ML and AI to really happen. A` lot of the analysis that happens on data is on a single dataset because it's just too damn hard to drive analysis across multiple datasets. When we talk to our customers, they have very precise designs on what they're trying to do. They say, "Look, we are trying to discover through deep learning what the patterns are that lead to transactions, whether it's... If you're a streaming company, maybe it's that you're signing up for a channel or you're buying a movie or whatever it is. What is the pattern of datapoints that leads us to that desired outcome?" Once you have a very accurate description of the data relationships that result in that outcome, you can then search for it and scale it tens of million times over. That's what digital enterprises do, right? So in order to discover these patterns, enrich the data to the point where the patterns become incredibly predictive, that's what Snowflake is for, right? But it requires a completely federated data model because you're not going to find a data pattern in a single dataset, per se, right? So that's what it's all about. The outcomes of a data cloud are very, very closely related to the business outcomes that the user is seeking, right? It's not some infrastructure process. that has a very remote relationship with business outcome. This is very, very closely related. >> So it doesn't take a brain surgeon to look at the trillionaires' club. (chuckles) So I can see that. I can see the big trillion dollars, Apple, $2 trillion market cap companies, they get data at the core. Whereas most companies, most incumbents, it might be a bottling plant at the core or some manufacturing or some of the process, and they put data rounded in these silos. It seems like you're trying to really bring that innovation and put data at the core and you've got an architecture to do that. You're talking about your multi cluster shared storage architecture. You mentioned data sharing. Will this, in your opinion enable, for instance, incumbents to do what a lot of the startups were able to do with the cloud days. hey got access to data centers which they couldn't have before the cloud. Are you trying to do something similar with data? >> Yeah, so obviously there's no doubt that the cloud is a critical enabler. This wouldn't be happening without it. At the same time, the trails that have been blazed by Alexa, Facebook and Google. The reason that those enterprises are so extraordinarily valuable is because of what they know through data and how they can monetize what they know through data. But that power is now becoming available to every single enterprise out there, right. Because the data platforms and the underlying cloud capabilities, we are now delivering that to anybody who wants it. Now you still need to have strong data engineering, data science capabilities. It's not like falling off of a log, but fundamentally those capabilities are now broadly accessible in the marketplace. >> So we talking up front about some of the differences between what you've done early in your career, like I said, you're the worst kept secret, Data Domain I would say it was somewhat of a niche market. You blew it up until it was very disruptive, but it was somewhat limited in what could be done. And maybe some of that limitation, you know, wouldn't have occurred if you stayed an independent company. ServiceNow, you mopped the table up 'cause you really had no competition there. Not the case here. You've got some of the biggest competitors in the world. So talk about that and what gives you confidence that you can continue to dominate? >> It's actually interesting that you bring up these companies. Data Domain, it was a scenario where we were constrained on market and we were a data backup company as you recall, we needed to move into backup software, needed to move into primary storage. While we knew it, we couldn't execute on it because it took tremendous resources which, back in the day, it was much harder than what it is right now. So we ended up selling the company to EMC and now part of Dell, but we're left with some trauma from that experience in the sense that, why couldn't we execute on that transformation? So coming to ServiceNow, we were extremely, and certainly me personally, extremely attuned to the challenges that we had endured in our prior company, and one of the reasons why you saw ServiceNow break out at scale, at tremendous growth rates is because of what we learned from the prior journey. We were not going to ever get caught again in the situation where we could not sustain our markets and sustain our growth. So ServiceNow is very much, the execution model, very much a reaction to what we had encountered in the prior company. Now coming into Snowflake a totally different deal because not only is this a large market this is a developing market. I think you've pointed out in some of your broadcasting, that this market is very much influx. And the reason is that technology is now capable of doing things for people and enterprises that they could never do before. So people are spending way more resources than they ever thought possible on these new capabilities. So you can't think in terms of static markets and static data definitions, it means nothing. Okay, these things are so in transition right now. It's very difficult for people to scope the scale of this opportunity. >> Yeah, I want to understand your thinking around and, you know, I've written about the TAM and can Snowflake grow into it's valuation and the way I drew it, I said, okay, I've got data lakes and you've got an enterprise data warehouse, that's pretty well understood but I called it data as a service company the closest analogy to your data cloud. And then even beyond that when you start bringing in the Edge and real time data. Talk about how you're thinking about that TAM what you have to do to participate. Do you have to bring adjacent capabilities? Or is it this read data sharing that will get you there? In other words, you're not like a transaction system. You hear people talking about converged databases. You're going to talk about real time inference at the Edge that today anyway, isn't what Snowflake is about. Does that vision of data sharing in the data cloud, does that allow you to participate in that massive multi hundred billion dollar TAM that I laid out and probably others as well? >> Yeah, well, it's always difficult to define markets based on historical concept that probably not going to apply a whole lot or for much longer. I mean the way we think of it is that data is the beating heart of the digital enterprise and digital enterprises today, what are you looking at people like the car door dash or so on. They were built from the ground up to be digital enterprises. And data is the beating heart of their operation, data operations is their manufacturing if you will. Every other enterprise out there is working very hard to become digital or part digital and is going to learn to develop a data platform like what we're talking about here to data cloud as well as the expertise in terms of data engineering and data sciences to really fully become a digital enterprise, right? So we view data as driving operations, all the all the digital enterprise, that's really what it is, right? And it's completely data driven end-to-end. There's no people involved and the people are developing and supporting the process but in the execution, it is end-to-end data driven. Meaning that data is the signal that initiates the process he's taking, but as they're, as they're being detected, and then they fully execute the entire machinery, programmatic machinery if you will, all of the processes have been designed. Now for example, I may fit a certain pattern, that leads to some transactional law context, but that's not fully completed, that pattern until I click on some link and all of a sudden, poof, I have become a prime prospect. System detects that in the real time and then unleashes all its outreach and capabilities to get me to transact. You and I are experiencing this every day. When we're, when we're online, you just may not fully realize (laughs) that that's what's happening behind the scenes. That's really what this is all about. So to me, this is sort of the new online transaction processing is an end to end data digital process that is continually acquiring, analyzing and acting on data. >> Well, you've talked about the time, time value of, of data. It loses value over time. And to the extent that you can actually affect decisions, maybe prior, before you lose the customer, before you lose the patient, even even more importantly, or before you lose the battle. There's all kinds of mental models that you can apply this. So automation is a key part of that and then again, I think a lot of people, like you said, if you just try to look at historical markets, you can't really squint through those and apply them. You really have to open up your mind and think about the new possibilities. And so I could see >> Exactly. >> Your component of automation. I see what's happening in the RPA space, and I could see these just massive opportunities to really change society, change business. Your last thoughts. >> While there's just no scenario that I can envision where data is not completely core and central to a digital enterprise period. >> Yeah, I think I really do think Frank, your vision is misunderstood somewhat. I think people say, "Okay hey, we'll bet on Slootman, "Scott Pelley, the team." That's great to do that, but I think this is going to unfold in a way that people maybe haven't predicted and maybe you guys yourselves and your founders you know haven't, aren't able to predict as well, but you've got that good, strong architectural philosophy that you're pursuing and it just kind of feels right, doesn't it? >> One of the harder conversations and the this is one of the reasons why we also wrote our book "The Rise of the Data Cloud" is to convey to the marketplace that this is not an incremental evolution. It is just not sort of building on the past. There is a real step function here. And the way to think about it is that typically enterprises and institutions will look at a platform like Snowflake from a workload context. In other words, I have this business, I have this workload, which is very much historically defined by the way, and then they benchmark us against what they're already doing on some legacy platform and they decide, "Yeah, this is a good fit, we're going to put Snowflake here, maybe there." But, it's still very workload centric which means that we are, essentially, perpetuating the mentality of the past. We were doing it one workload at a time, we're creating the new silos and the new bunkers of data in the process. And we're really not approaching this with the level of vision that the data scientists really require to drive maximum benefit from data. So our argument, and this is not an easy argument, is to say to CIOs and any other C-level person that wants to listen and say, "Look, just thinking about operational context and operational excellence, it's like we have to have a platform that allows use unfettered access to the data that we may need to bring the analytical power to." If you have to bring analytical power to a diversity of datasets, how are we going to do that? The data lives in 500 different places, it's just not possible, other than with insane amounts of programming and complexity and then we don't have the performance and we don't have the economics and we don't have the governance and so on. So you really want to set yourself up with a data cloud so that you can unleash your data science capabilities, your machine learning, your deep learning capabilities and then really get the full throttle advantage of what the technology can do. If you going to perpetuate the silo-ing and bunkering of data by doing it one workload at a time, five, 10 years from now, we're having the same conversations we've been having over the last 40 years. >> Yeah, operationalizing your data is going to require busting down those silos and it's going to require something like the data cloud to really power that to the next decade and beyond. Frank Slootman, thanks so much for coming to "theCUBE" and helping us do a preview here of what's to come. >> You bet Dave, thanks. >> All right, thank you for watching everybody. This is Dave Vellante from the "theCUBE". We'll see you next time.