 Okay, we're back at Caesar Forum, the Snowflake Summit 2022. The Cube's continuous coverage is day two, wall-to-wall coverage. We're so excited to have the analyst panel here, some of my colleagues that we've done a number, you've probably seen some power panels that we've done. David Menegar is here, he's the Cedar Vice President and Research Director at Ventana Research. To his left is Tony Baer, Principal at DB Insight and my in the co-host seat, Sanjeev Mohan, Principal at Sanjimoh. Guys, thanks so much for coming on, I'm glad to be here. Glad to be here. Thanks for having us. You're very welcome. I wasn't able to attend the analyst's action because I've been doing this all day, every day. But let me start with you, Dave. What have you seen that's kind of interested you, pluses, minuses, concerns? Sure. Right on. Well, how about if I focus on what's, I think, valuable to the customers of Snowflake? Right. And our research shows that the majority of organizations, the majority of people, do not have access to analytics. And so a couple of the things they've announced, I think, address those or help to address those issues very directly. So Snowpark, in support for Python and other languages, is a way for organizations to embed analytics into different business processes. And so I think that'll be really beneficial to try and get analytics into more people's hands. And I also think that the native applications, as part of the marketplace, is another way to get applications into people's hands rather than just analytical tools because most people in the organization are not analysts. They're doing some line of business function. They're HR managers, they're marketing people, they're sales people, they're finance people. They're not sitting there mucking around in the data. They're doing a job and they need analytics in that job. So, Tony, thank you. I've heard a lot of data mesh talk this week. It's kind of funny. I can't seem to get away from it. Yeah, I can't see it. It seems to be gathering momentum, but what have you seen that's been interesting? Well, what I have noticed is that unfortunately, because the rooms are too small, you just can't get into the data mesh sessions. So there's a lot of interest in it. It's still very, I don't think there's very much understanding of it, but I think the idea that you can put all the data in one place, which to me, Snowflake, it seems to be sort of in a way, it sounds like almost like the Enterprise Data Warehouse, Cloud Native Edition, bring it all in one place again. I think it's providing sort of, I think for these folks, the thing, this might be kind of like a linchpin for that. I think there are several other things that actually, that really have made a bigger impression on me actually at this event. One is basically, is very much their move with Unistore. And it's kind of interesting coming from MongoDB last week. And I see it's like these two companies seem to be going, converging towards the same place at different speeds. I think Snowflake is going to get there faster than Mongo for a number of different reasons. But I see like a number of common threads here. I mean, one is that Mongo was a company, it's always been towards developers. They need to start cultivating data people. These guys are going the other way. Exactly, bingo. And the thing is that, but I think where they're converging is the idea of operational analytics and trying to serve all constituencies. The other thing, which also is, in terms of serving multiple constituencies, is how Snowflake has laid out Snowpark. And what I'm finding is like, there's an interesting dichotomy. On one hand, you have this very ingrained integration of Anaconda, which I think is pretty ingenious. On the other hand, you speak, let's say the data robot, folks, and say, you know something, our folks want to work, data scientists, we want to work in our environment and use Snowflake in the background. So I see those as kind of some interesting sort of cross-cutting trends. So Sanjeev, I mean, Frank Sloobin will talk about, there's definite benefits into going into the walled garden. Yeah. And I don't think we dispute that, but we see them making moves and adding more and more open source capabilities like Apache iceberg. Is that a move to sort of counteract the narrative that Databricks has put out there? Is that customer-driven? What's your take on that? Primarily, I think it is to contract this whole notion that once you move data into Snowflake, it's a proprietary format. So I think that's how it started, but it's hugely beneficial to the customers, to the users, because now if you have large amounts of data in park air files, you can leave it on S3, but then you're using the Apache iceberg table format in Snowflake, you get all the benefits of Snowflake's optimizer. So for example, you get the micro partitioning, you get the metadata. So in a single query, you can join, you can do select from a Snowflake table union and select from an iceberg table. And you can do store procedure, user-defined functions. So I think what they've done is extremely interesting. Iceberg by itself still does not have multi-table transactional capabilities. So if I'm running a workload, I might be touching 10 different tables. So if I use Apache iceberg in a raw format, they don't have it, but Snowflake does. Right, hence the delta. And maybe that closes over time. I want to ask you, as you look around this, I mean, the ecosystem is pretty vibrant. I mean, it reminds me of like re-invent in 2014, you know? But then I'm struck by the complexity of the last big data era and Hadoop and all the different tools. And is this different or is it the sort of same wine new bottle? You guys have any thoughts on that? I think it's different. How so? And I'll tell you why I think it's different because it's based around SQL. So back to Tony's point, these vendors are coming at this from different angles, right? You've got data warehouse vendors and you've got data lake vendors and they're all going to meet in the middle. So in your case, you're taught operational analytical, but same thing is true with data lake and data warehouse. And Snowflake no longer wants to be known as a data warehouse. They're a data cloud. And our research again, I like to base everything off of the research. I love when you bring your research in. Our research shows that two thirds of organizations have SQL skills and one third have big data skills. So, you know, they're going to meet in the middle, but it sure is a lot easier to bring along those people who know SQL already to that midpoint than it is to bring big data people to that midpoint. I remember Amarara Adala, who was one of the founders of Cloudera said to me one time, John Furrier in the cube, that SQL is the killer app for Hadoop. Yeah. Yeah. The difference with Snowflake is that you don't have to worry about taming the zoo animals. They really have thought out the ease of use. You know, I mean, they thought about, I mean, from the get go, they've thought of tooth in two poles. One is ease of use and the other is scale. And they've had, and that's basically a very, you know, I think very much differentiates it. I mean, Hadoop had the scale, but it didn't have the ease of use. But don't I still need, like if I have, you know, governance from this vendor or, you know, data prep from, you know, don't I still have to have expertise that's sort of distributed in those worlds, right? I mean, go ahead, Sanjeev. Yeah, so the way I see it is, Snowflake is adding more and more capabilities right into the database. So for example, they've gone ahead and added security and privacy. So you can now create policies and do even set level masking, dynamic masking. But most organizations have more than Snowflake. So what we are starting to see all around here is that there's a whole series of data catalog companies, a bunch of companies that are doing dynamic data masking, security and governance, data observability, which is not a space Snowflake has gone into. So there's a whole ecosystem of companies that is mushrooming, although, you know, so they're using the native capabilities of Snowflake, but they are at a level higher. So if you have a data lake and a cloud data warehouse and you have other like relational databases, you can run these cross-platform capabilities in that layer. So that way, you know, Snowflake's done a great job of enabling that ecosystem. How about the Streamlet acquisition? Did you see anything here that indicated they're making strong progress there? Are you excited about that? Are you skeptical? Go ahead. I think it's like the last mile, essentially. In other words, it's like, okay, you have folks that are basically that are very, you know, very comfortable with Tableau, but you do have developers who don't want to have to shell out to a separate tool. And so this is where Snowflake is essentially working to address that constituency. To Sanjeev's point, I think part of it, this kind of plays into it, is what makes this different from the Adoop era is the fact that all these capabilities, you know, a lot of vendors are taking it very seriously to make, you know, put this native, and obviously Snowflake acquired Streamlet so we can expect that the Streamlet capabilities are going to be native. You know, the other thing too about the Adoop ecosystem is Cloudera had to help fund all those different projects and got really, really spread thin. I want to ask you guys about the SuperCloud. We use SuperCloud as this sort of metaphor for the next wave of cloud. You've got infrastructure, AWS, Azure, Google. It's not multi-cloud, but you've got that infrastructure. You're building a layer on top of it that hides the underlying complexities of the primitives and the APIs, and you're adding new value, in this case, the data cloud or super data cloud. And now what we're seeing now is that Snowflake putting forth the notion that they're adding a super pass layer. So you can now build applications that you can monetize, which to me is kind of exciting. It makes this platform even less discretionary. We had a lot of talk on Wall Street about discretionary spending, and that's not discretionary if you're monetizing it. What do you guys think about that? Is this something that's real? Is it just a figment of my imagination, or do you see a different way of coming? Any thoughts on that? So in effect, they're trying to become a data operating system. Yeah, yes. And I think that's wonderful. It's ambitious. I think they'll experience some success with that. As I said, applications are important. That's a great way to deliver information. You can monetize them. So there's a good economic model around it. I think they will still struggle, however, with bringing everything together into one platform. That's always the challenge. Can you become the platform? That's hard to predict. I think this is pretty exciting, right? A lot of energy, a lot of a large ecosystem. There is a network effect already. Can they succeed in being the only place where data exists? I think that's going to be a challenge. I mean, the fact is, I mean, this is the classic best of read versus the umbrella play. And the thing is, this is nothing new. I mean, this is like the old days with enterprise applications where basically Oracle and SAP vacuumed up all these, all these applications in their ecosystem. Whereas with Snowflake, and if you look at the cloud, the hyperscalers, they'll build out their own portfolios as well. Some hyperscalers are more partner friendly than others. What Snowflake is saying is that we're going to give all of you folks who basically are competing against the hyperscalers in various areas like data catalog and pipelines and all that sort of wonderful stuff. We'll make you basically all equal citizens. The burden is on you to basically, we will lay out the APIs. We'll allow you to basically integrate natively to us so you can provide as good experience, but the onus is on your back. Should the ecosystem be concerned as they were back to re-invent 2014 that Amazon was going to nibble away at them or is it different? I find what they're doing is different. For example, data sharing. They were the first ones out the door with data sharing at a large scale. Then everybody has jumped in and said, oh, we also do data sharing. All the hyperscalers came in, but now what Snowflake has done is they've taken it to the next level. Now they're saying it's not just data sharing, it's app sharing. And not only app sharing, you can stream the thing, you can build, test, deploy, and then monetize it, make it discoverable through your marketplace. You can monetize it. Yes, yeah. So I think what they're doing is they are taking it a step further than what hyperscalers are doing. Because it's like what they said is becoming like the data operating system. You log in and you have all of these different functionalities. You can do machine learning now, you can do data quality, you can do data preparation, and you can do monetization of data. Who do you think is Snowflake's biggest competitor? What do you guys think? It's a hard question, isn't it? It is. Because you're like, because we all get the, we separate compute from storage, we have a cloud database and you go, yeah, okay. That's nice, but it's this. I'll take a crack. That's uniqueness. I mean, put it this way. In the old days, it would have been the on-prem household names. I think today's the hyperscalers. And the idea with, I mean, again, this comes down to the best of breed versus get it all from one source. So where is your comfort level? So I think their co-opetition are the hyperscalers. Okay, so it's not Databricks, because why, they're smaller? Well, put it, there are some, okay, now within the best of breed area, yes, there is competition. The obvious is Databricks coming in from a data engineering angle, you know, basically, you know, Snowflake coming from the data analyst angle. I think what, you know, another potential competitor, and I think Snowflake basically, you know, as admitted as such, potentially is MongoDB. I was just going to say, yeah, I mean. Exactly, so I mean, yes, there are two different levels of competition. They're sort of on a longer term collision course. Exactly, exactly. Sort of service now and Salesforce. The only thing that was the reaction I get when I say that, and a lot of people just laugh, I was like, no, you're kidding, there's no way, and I said, excuse me. Well, but then you see Mongo last week, you know, we were adding some analytics capabilities, and always been developers, as you say. They trash SQL, but yet they finally have started to write their first real SQL. We have MC, MQL, now we have SQL, so. What were those numbers, Dave? Two thirds of one third. So the hyperscalers, but the hyperscalers, are you going to trust your hyperscalers to do your cross-cloud? I mean, maybe Google, maybe, I mean, Microsoft perhaps, AWS not there yet, right? I mean, how important is cross-cloud, multi-cloud, super-cloud, whatever you want to call it? What is your data showing? Cross-cloud is important. If I remember correctly, our research shows that three-quarters of organizations are operating in the cloud, and 52% are operating across more than one cloud. So, you know, two thirds of the organizations that are in the cloud are doing multi-cloud. So that's pretty significant. And now they may be operating across clouds for different reasons. Maybe one application runs in one cloud provider, and another application runs in another cloud provider. But I do think organizations want that leverage over the hyperscalers, right? They want to be able to tell the hyperscaler, I'm going to move my workloads over here if you don't, you know, give us a better rate. Yeah, I mean, I think, you know, from a database standpoint, I think you're right. I mean, they're competing against some really well-funded, and you look at BigQuery, rarely solid platform, Redshift, for all its faults, has really done an amazing job of moving forward. But to David's point, you know, those, to me anyway, those hyperscalers aren't going to solve that cross-cloud cloud problem. Right, right, no. Certainly not as quickly. No, no. Or with as much zeal. Right. We'll operate across cloud, but we're going to operate better on our clouds. Exactly, yes, yes. Even when we talk about multi-cloud, the many, many definitions, like you, you know, multi-cloud can mean anything. So the way Snowflake does multi-cloud and the way MongoDB do are very different. So Snowflake says we run on all the hyperscalers, but you have to replicate your data. What MongoDB is claiming is that one cluster can have nodes in multiple different clouds. That is, you know, quite something. Yeah, right, I mean, again, we got to go, but last question, Snowflake. Undervalued, overvalued, or just about right? In the stock market, or in customers' minds. Yeah. Well, but, you know, I'm not sure that's the right question. Well, but no, that's the question I'm asking, you know. No, I'll say the question is undervalued or overvalued for customers, right? That's really what matters. There's a different audience who cares about the investor side of it. Some of those are watching. But I believe that from the customer's perspective, it's probably valued about right. Because the reason I ask it is because it has so hyped. Yes. It had a $100 billion value. It surpassed service now's value, which is crazy. For this, now it's obviously come back quite a bit. It's below its IPO price. So, but you guys are at the financial analyst meeting. Scarpelli laid out the 20, 29 projections, signed up for $10 billion, 25% free cash flow, 20% operating profit. I mean, they better be worth more than they are today if they do that. If I see the momentum here this week, I think they're undervalued. But before this week, I probably would have thought they are at the right valuation. I would say they're probably more at the right valuation, especially because the IPO valuation was just such a false valuation, so hyped. Yeah. Guys, I could go on for another 45 minutes. Thanks so much. David, Tony, Sanjeev. Always great to have you on. We'll have you back for sure. Thanks for having us. Thank you. Keep it right there. We're wrapping up day two on theCUBE at Snowflake Summit 2022. Right back.