(upbeat music) >> Welcome back to theCUBE's continuing coverage of Snowflake Summit '22 live from Caesar's Forum in Las Vegas. I'm Lisa Martin, my cohost Dave Vellante, we've been here the last day and a half unpacking a lot of news, a lot of announcements, talking with customers and partners, and we have another great session coming for you next. We've got a customer and a partner talking tech and data mash. Please welcome Mitesh Shah, VP in market strategy at Elation. >> Great to be here. >> and Ash Naseer great, to have you, senior director of data engineering at Warner Brothers Discovery. Welcome guys. >> Thank you for having me. >> It's great to be back in person and to be able to really get to see and feel and touch this technology, isn't it? >> Yeah, it is. I mean two years or so. Yeah. Great to feel the energy in the conference center. >> Yeah. >> Snowflake was virtual, I think for two years and now it's great to kind of see the excitement firsthand. So it's wonderful. >> Th excitement, but also the boom and the number of customers and partners and people attending. They were saying the first, or the summit in 2019 had about 1900 attendees. And this is around 10,000. So a huge jump in a short time period. Talk a little bit about the Elation-Snowflake partnership and probably some of the acceleration that you guys have been experiencing as a Snowflake partner. >> Yeah. As a snowflake partner. I mean, Snowflake is an investor of us in Elation early last year, and we've been a partner for, for longer than that. And good news. We have been awarded Snowflake partner of the year for data governance, just earlier this week. And that's in fact, our second year in a row for winning that award. So, great news on that front as well. >> Repeat, congratulations. >> Repeat. Absolutely. And we're going to hope to make it a three-peat as well. And we've also been awarded industry competency badges in five different industries, those being financial services, healthcare, retail technology, and Median Telcom. >> Excellent. Okay. Going to right get into it. Data mesh. You guys actually have a data mesh and you've presented at the conference. So, take us back to the beginning. Why did you decide that you needed to implement something like data mesh? What was the impetus? >> Yeah. So when people think of Warner brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of HBO, you think of TNT, you think of CNN, we have 30 plus brands in our portfolio and each have their own needs. So the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against as an example, HBO, if Game of Thrones is going on. >> So, okay. So the, the impetus was to serve those lines of business better. Actually, given that you've got these different brands, it was probably easier than most companies. Cause if you're, let's say you're a big financial services company, and now you have to decide who owns what. CNN owns its own data products, HBO. Now, do they decide within those different brands, how to distribute even further? Or is it really, how deep have you gone in that decentralization? >> That's a great question. It's a very close partnership, because there are a number of data sets, which are used by all the brands, right? You think about people browsing websites, right? You know, CNN has a website, Warner brothers has a website. So for us to ingest that data for each of the brands to ingest that data separately, that means five different ways of doing things and you know, a big environment, right? So that is where our team comes into play. We ingest a lot of the common data sets, but like I said, any unique data sets, data sets regarding theatrical as an example, you know, Warner brothers does it themselves, you know, for streaming, HBO Max, does it themselves. So we kind of operate in partnership. >> So do you have a centralized data team and also decentralized data teams, right? >> That's right. >> So I love this conversation because that was heresy 10 years ago, five years ago, even, cause that's inefficient. But you've, I presume you've found that it's actually more productive in terms of the business output, explain that dynamic. >> You know, you bring up such a good point. So I, you know, I consider myself as one of the dinosaurs who started like 20 plus years ago in this industry. And back then, we were all taught to think of the data warehouse as like a monolithic thing. And the reason for that is the technology wasn't there. The technology didn't catch up. Now, 20 years later, the technology is way ahead, right? But like, our mindset's still the same because we think of data warehouses and data platforms still as a monolithic thing. But if you really sort of remove that sort of mental barrier, if you will, and if you start thinking about, well, how do I sort of, you know, federate everything and make sure that you let folks who are building, or are closest to the customer or are building their products, let them own that data and have a partnership. The results have been amazing. And if we were only sort of doing it as a centralized team, we would not be able to do a 10th of what we do today. So it's that massive scale in, in our company as well. >> And I should have clarified, when we talk about data mesh are we talking about the implementing in practice, the octagon sort of framework, or is this sort of your own sort of terminology? >> Well, so the interesting part is four years ago, we didn't have- >> It didn't exist. >> Yeah. It didn't exist. And, and so we, our principle was very simple, right? When we started out, we said, we want to make sure that our brands are able to operate independently with some oversight and guidance from our technology teams, right? That's what we set out to do. We did that with Snowflake by design because Snowflake allows us to, you know, separate those, those brands into different accounts. So that was done by design. And then the, the magic, I think, is the Snowflake data sharing where, which allows us to sort of bring data in here once, and then share it with whoever needs it. So think about HBO Max. On HBO Max, You not only have HBO Max content, but content from CNN, from Cartoon Network, from Warner Brothers, right? All the movies, right? So to see how The Batman movie did in theaters and then on streaming, you don't need, you know, Warner brothers doesn't need to ingest the same streaming data. HBO Max does it. HBO Max shares it with Warner brothers, you know, store once, share many times, and everyone works at their own pace. >> So they're building data products. Those data products are discoverable APIs, I presume, or I guess maybe just, I guess the Snowflake cloud, but very importantly, they're governed. And that's correct, where Elation comes in? >> That's precisely where Elation comes in, is where sort of this central flexible foundation for data governance. You know, you mentioned data mesh. I think what's interesting is that it's really an answer to the bottlenecks created by centralized IT, right? There's this notion of decentralizing that the data engineers and making the data domain owners, the people that know the data the best, have them be in control of publishing the data to the data consumers. There are other popular concepts actually happening right now, as we speak, around modern data stack. Around data fabric that are also in many ways underpinned by this notion of decentralization, right? These are concepts that are underpinned by decentralization and as the pendulum swings, sort of between decentralization and centralization, as we go back and forth in the world of IT and data, there are certain constants that need to be centralized over time. And one of those I believe is very much a centralized platform for data governance. And that's certainly, I think where we come in. Would love to hear more about how you use Elation. >> Yeah. So, I mean, elation helps us sort of, as you guys say, sort of, map, the treasure map of the data, right? So for consumers to find where their data is, that's where Elation helps us. It helps us with the data cataloging, you know, storing all the metadata and, you know, users can go in, they can sort of find, you know, the data that they need and they can also find how others are using data. So it's, there's a little bit of a crowdsourcing aspect that Elation helps us to do whereby you know, you can see, okay, my peer in the other group, well, that's how they use this piece of data. So I'm not going to spend hours trying to figure this out. You're going to use the query that they use. So yeah. >> So you have a master catalog, I presume. And then each of the brands has their own sub catalogs, is that correct? >> Well, for the most part, we have that master catalog and then the brands sort of use it, you know, separately themselves. The key here is all that catalog, that catalog isn't maintained by a centralized group as well, right? It's again, maintained by the individual teams and not only in the individual teams, but the folks that are responsible for the data, right? So I talked about the concept of crowdsourcing, whoever sort of puts the data in, has to make sure that they update the catalog and make sure that the definitions are there and everything sort of in line. >> So HBO, CNN, and each have their own, sort of access to their catalog, but they feed into the master catalog. Is that the right way to think about it? >> Yeah. >> Okay. And they have their own virtual data warehouses, right? They have ownership over that? They can spin 'em up, spin 'em down as they see fit? Right? And they're governed. >> They're governed. And what's interesting is it's not just governed, right? Governance is a, is a big word. It's a bit nebulous, but what's really being enabled here is this notion of self-service as well, right? There's two big sort of rockets that need to happen at the same time in any given organization. There's this notion that you want to put trustworthy data in the hands of data consumers, while at the same time mitigating risk. And that's precisely what Elation does. >> So I want to clarify this for the audience. So there's four principles of database. This came after you guys did it. And I wonder how it aligns. Domain ownership, give data, as you were saying to the, to the domain owners who have context, data as product, you guys are building data products, and that creates two problems. How do you give people self-service infrastructure and how do you automate governance? So the first two, great. But then it creates these other problems. Does that align with your philosophy? Where's alignment? What's different? >> Yeah. Data products is exactly where we're going. And that sort of, that domain based design, that's really key as well. In our business, you think about who the customer is, as an example, right? Depending on who you ask, it's going to be, the answer might be different, you know, to the movie business, it's probably going to be the person who watches a movie in a theater. To the streaming business, to HBO Max, it's the streamer, right? To others, someone watching live CNN on their TV, right? There's yet another group. Think about all the franchising we do. So you see Batman action figures and T-shirts, and Warner brothers branded stuff in stores, that's yet another business unit. But at the end of the day, it's not a different person, it's you and me, right? We do all these things. So the domain concept, make sure that you ingest data and you bring data relevant to the context, however, not sort of making it so stringent where it cannot integrate, and then you integrate it at a higher level to create that 360. >> And it's discoverable. So the point is, I don't have to go tap Ash on the shoulder, say, how do I get this data? Is it governed? Do I have access to it? Give me the rules of it. Just, I go grab it, right? And the system computationally automates whether or not I have access to it. And it's, as you say, self-service. >> In this case, exactly right. It enables people to just search for data and know that when they find the data, whether it's trustworthy or not, through trust flags, and the like, it's doing both of those things at the same time. >> How is it an enabler of solving some of the big challenges that the media and entertainment industry is going through? We've seen so much change the last couple of years. The rising consumer expectations aren't going to go back down. They're only going to come up. We want you to serve us up content that's relevant, that's personalized, that makes sense. I'd love to understand from your perspective, Mitesh, from an industry challenges perspective, how does this technology help customers like Warner Brothers Discovery, meet business customers, where they are and reduce the volume on those challenges? >> It's a great question. And as I mentioned earlier, we had five industry competency badges that were awarded to us by Snowflake. And one of those four, Median Telcom. And the reason for that is we're helping media companies understand their audiences better, and ultimately serve up better experiences for their audiences. But we've got Ash right here that can tell us how that's happening in practice. >> Yeah, tell us. >> So I'll share a story. I always like to tell stories, right? Once once upon a time before we had Elation in place, it was like, who you knew was how you got access to the data. So if I knew you and I knew you had access to a certain kind of data and your access to the right kind of data was based on the network you had at the company- >> I had to trust you. >> Yeah. >> I might not want to give up my data. >> That's it. And so that's where Elation sort of helps us democratize it, but, you know, puts the governance and controls, right? There are certain sensitive things as well, such as viewership, such as subscriber accounts, which are very important. So making sure that the right people have access to it, that's the other problem that Elation helps us solve. >> That's precisely part of our integration with Snowflake in particular, being able to define and manage policies within Elation. Saying, you know, certain people should have access to certain rows, doing column level masking. And having those policies actually enforced at the Snowflake data layer is precisely part of our value product. >> And that's automated. >> And all that's automated. Exactly. >> Right. So I don't have to think about it. I don't have to go through the tap on their shoulder. What has been the impact, Ash, on data quality as you've pushed it down into the domains? >> That's a great question. So it has definitely improved, but data quality is a very interesting subject, because back to my example of, you know, when we started doing things, we, you know, the centralized IT team always said, well, it has to be like this, Right? And if it doesn't fit in this, then it's bad quality. Well, sometimes context changes. Businesses change, right? You have to be able to react to it quickly. So making sure that a lot of that quality is managed at the decentralized level, at the place where you have that business context, that ensures you have the most up to date quality. We're talking about media industry changing so quickly. I mean, would we have thought three years ago that people would watch a lot of these major movies on streaming services? But here's the reality, right? You have to react and, you know, having it at that level just helps you react faster. >> So data, if I play that back, data quality is not a static framework. It's flexible based on the business context and the business owners can make those adjustments, cause they own the data. >> That's it. That's exactly it. >> That's awesome. Wow. That's amazing progress that you guys have made. >> In quality, if I could just add, it also just changes depending on where you are in your data pipeline stage, right? Data, quality data observability, this is a very fast evolving space at the moment, and if I look to my left right now, I bet you I can probably see a half-dozen quality observability vendors right now. And so given that and given the fact that Elation still is sort of a central hub to find trustworthy data, we've actually announced an open data quality initiative, allowing for best-of-breed data quality vendors to integrate with the platform. So whoever they are, whatever tool folks want to use, they can use that particular tool of choice. >> And this all runs in the cloud, or is it a hybrid sort of? >> Everything is in the cloud. We're all in the cloud. And you know, again, helps us go faster. >> Let me ask you a question. I could go on forever in this topic. One of the concepts that was put forth is whether it's a Snowflake data warehouse or a data bricks, data lake, or an Oracle data warehouse, they should all be inclusive. They should just be a node on the mesh. Like, wow, that sounds good. But I haven't seen it yet. Right? I'm guessing that Snowflake and Elation enable all the self-serve, all this automated governance, and that including those other items, it's got to be a one-off at this point in time. Do you ever see you expanding that scope or is it better off to just kind of leave it into the, the Snowflake data cloud? >> It's a good question. You know, I feel like where we're at today, especially in terms of sort of technology giving us so many options, I don't think there's a one size fits all. Right? Even though we are very heavily invested in Snowflake and we use Snowflake consistently across the organization, but you could, theoretically, could have an architecture that blends those two, right? Have different types of data platforms like a teradata or an Oracle and sort of bring it all together today. We have the technology, you know, that and all sorts of things that can make sure that you query on different databases. So I don't think the technology is the problem, I think it's the organizational mindset. I think that that's what gets in the way. >> Oh, interesting. So I was going to ask you, will hybrid tables help you solve that problem? And, maybe not, what you're saying, it's the organization that owns the Oracle database saying, Hey, we have our system. It processes, it works, you know, go away. >> Yeah. Well, you know, hybrid tables I think, is a great sort of next step in Snowflake's evolution. I think it's, in my opinion, I, think it's a game changer, but yeah. I mean, they can still exist. You could do hybrid tables right on Snowflake, or you could, you know, you could kind of coexist as well. >> Yeah. But, do you have a thought on this? >> Yeah, I do. I mean, we're always going to live in a time where you've got data distributed in throughout the organization and around the globe. And that could be even if you're all in on Snowflake, you could have data in Snowflake here, you could have data in Snowflake in EMEA and Europe somewhere. It could be anywhere. By the same token you might be using. Every organization is using on-premises systems. They have data, they naturally have data everywhere. And so, you know, this one solution to this is really centralizing, as I mentioned, not just governance, but also metadata about all of the data in your organization so that you can enable people to search and find and discover trustworthy data no matter where it is in your organization. >> Yeah. That's a great point. I mean, if you have the data about the data, then you can, you can treat these independent nodes. That's just that. Right? And maybe there's some advantages of putting it all in the Snowflake cloud, but to your point, organizationally, that's just not feasible. The whole, unfortunately, sorry, Snowflake, all the world's data is not going to go into Snowflake, but they play a key role in accelerating, what I'm hearing, your vision of data mesh. >> Yeah, absolutely. I think going forward in the future, we have to start thinking about data platforms as just one place where you sort of dump all the data. That's where the mesh concept comes in. It is going to be a mesh. It's going to be distributed and organizations have to be okay with that. And they have to embrace the tools. I mean, you know, Facebook developed a tool called Presto many years ago that that helps them solve exactly the same problem. So I think the technology is there. I think the organizational mindset needs to evolve. >> Yeah. Definitely. >> Culture. Culture is one of the hardest things to change. >> Exactly. >> Guys, this was a masterclass in data mesh, I think. Thank you so much for coming on talking. >> We appreciate it. Thank you so much. >> Of course. What Elation is doing with Snowflake and with Warner Brothers Discovery, Keep that content coming. I got a lot of stuff I got to catch up on watching. >> Sounds good. Thank you for having us. >> Thanks guys. >> Thanks, you guys. >> For Dave Vellante, I'm Lisa Martin. You're watching theCUBE live from Snowflake Summit '22. We'll be back after a short break. (upbeat music)

(upbeat music) >> Hey everyone, welcome back to theCUBE's three day coverage of Snowflake Summit 22. Lisa Martin here with Dave Vellante. We have been here as I said for three days. Dave, we have had an amazing three days. The energy, the momentum, the number of people still here speaks volumes for- >> Yeah, I was just saying, you look back, theCUBE, when it started, early days was a big part of the Hadoop ecosystem. You know Cloudera kind of got it started, the whole big data movement, it was awesome energy, and that whole ecosystem has been, I think, just hoovered into the Snowflake ecosystem. They've taken over as the data company, the data cloud, I mean, that was Cloudera, it could have been Cloudera, and now they didn't, they missed it, it was a variety of factors, but Snowflake has nailed it. And now it's theirs to lose. Benoit talked about that on our previous segment, how he knew that technically Hadoop was too complex, and was going to fail, and they didn't know it was going to do this. They were going to turn their company into what we see here. But the event itself, Lisa, is almost 10,000 people, the right people, people are doing business, we've had a number of people tell us that they're booking deals. That's why people come to face-to-face shows, right? That's the criticism of virtual. It takes too long to close business. Salespeople want to be belly-to-belly. And this is a belly-to belly-show. >> It absolutely is. When you and I were trying to get into the keynote on Tuesday, we finally got in standing room only, multiple overflow rooms, and we're even hearing that, so this is day four of the summit for them, there are still queues to get into breakout sessions. The momentum, but the appetite for this flywheel, and what they're creating, but also they're involving this massively growing ecosystem in its evolution. It's that synergy was really very much heard, and echoed throughout pretty much all of our segments the last couple days. >> Yeah, it was amazing actually. So we like to go, we want to be in the front row in the keynotes, we're taking notes, we always do that. Sometimes we listen remotely, but when you listen remotely, you miss some things. When you're there, you can see the executives, you can feel their energy, you can chit chat to them on the side, be seen, whatever. And it was crazy, we couldn't get in. So we had to do our thing, and sneak our way in, and "Hey, we're media." "Oh yeah, come on in." And then no, they were taking us to a breakout room. We had to sneak in a side door, got like the last two seats, and wow, I'm glad we were in there because it gave us a better sense. When you're in the remote watching rooms you just can't get a sense of the energy. That's why I like to be there, I know you do too. And then to your point about ecosystem. So we've said many times that what Snowflake is developing is what we call supercloud. It's not just a SaaS, it's not just a cloud database, it's a new layer that they're creating. And so what are the attributes of that layer? Well, it hides the underlying complexity of the underlying primitives of the cloud. We've said that ad nauseam, and it adds new value on top. Well, what's that value that they're adding? Well, they're adding value of being able to share data, collaborate, have data that's governed, and secure, globally. And now the other hallmark of a cloud company is ecosystem. And so they're building that ecosystem much more rapidly than we saw at ServiceNow, which is Slootman's previous company. And the key to me is they've launched an application development platform, essentially a super PaaS, so that you can develop applications on top of the data cloud. And we're hearing tons about monetization. Duh, you could actually make money with data. You can package data into data products, and data services, or feed data products and services, and actually sell that in a cloud, in a supercloud. That's exactly what's happening here. So that's critical. I think my one question mark if I had to lay one out, is the other hallmark of a cloud is startup, startups come into that cloud. And I think we're seeing that, maybe not at the pace that AWS did, it's a little different. Snowflake are, they're whale hunters. They're after big companies. But it looks to me like they're relying on the ecosystem to be the startup innovators. That's the important thing about cloud, cloud brings scale. It definitely brings lower cost 'cause you're eliminating all this undifferentiated labor, but it also brings innovation through startups. So unlike AWS, who sold the startups directly, and startups built businesses on AWS, and by paying AWS, it's a little bit indirect, but it's actually happening where startups in the ecosystem are building products on the data cloud, and that ultimately is going to drive value for customers, and money for Snowflake, and ultimately AWS, and Google, and Azure. The other thing I would say is the criticism or concern that the cost of goods sold for cloud are going to be so high that it's going to force people to come back on-prem. I think it's a step in the wrong direction. I think cloud, and the cloud operating model is here to stay. I think it's going to be very difficult to replicate that on-prem. I don't think you can do cloud without cloud, and we'll see what the edge brings. >> Curious what your thoughts are. We were just at Dell technologies world a month or so ago when the big announcement, the Snowflake partnership there, cloud native companies recognizing, ah, there's still a lot of data that lives on-prem. Given that, and everything that we've heard the last couple of days, what are your thoughts around that and their partnerships there? >> So Dell is, I think finally, now maybe they weren't publicly talking like this, but certainly their marketing was defensive. But in the last year or so, Dell has really embraced cloud, not just the cloud operating model, Dell has said, "Look, we can build value on top of all these hyperscalers." And we saw some examples at Dell Tech World of them stepping their toe into supercloud. Project Alpine is an example, and there are others. And then of course the Snowflake deal, where Snowflake and Dell got together, I asked Frank Slootman how that deal came about. And 'cause I said, "Did the customer get you into a headlock?" 'Cause I presume that was the case. Customer said, "You got to do this or we're not going to do business with you." He said, "Well, no, not really. Michael and I had a chat, and that's how it started." Which was my other scenario, and that's exactly what happened I guess. The point being that those worlds are coming together. And so what it means for Dell is as they embrace cloud, as they develop supercloud capabilities, they're going to do a lot of business. Dell for sure knows how to sell, they know how to execute. What I would be doing if I were Dell, is I would be trying to substantially replicate what's happening in the cloud on-prem with on-prem data. So what happens with that Snowflake deal is, it's read-only data, you read the data into the cloud, the compute is in the cloud. And I should've asked Terry this, I mean Benoit. Can there be an architecture on-prem? We've seen at Vertica has one, it's called Vertica Eon where you separate compute from storage. It doesn't have unlimited elasticity, but you can grow, compute, and storage independently, and have a lot more. With Dell doing APEX on demand, it's cloudlike, they could begin to develop a little mini data cloud, or a big data cloud within on-prem that connects to the public cloud. So what Snowflake is missing, a big part of their TAM that they're missing is the on-prem. The Dell and Pure deals are forays into that, but this on-prem is massive, and Dell is the on-prem poster child. So I think again what it means for them is they've got to continue to embrace it, they got to do more in software, more in data management, they got to push on APEX. And I'd say the same thing for HPE. I think they're both well behind this in terms of ecosystems. I mean they're not even close. But they have to start, and they got to start somewhere, and they've got resources to make it happen. >> You said in your breaking analysis that you published just a few days ago before the event that Snowflake plans to create a de facto standard in data platforms. What we heard from our guests on this program, your mainstage session with Frank Slootman. Still think that? >> I do. I think it more than I believed it coming in. And the reason I called it that is because I am a super fan of Zhamak Dehghani and her data mesh. And what her vision is, it's kind of the Immaculate Conception, where she wants everything to be open, open standards, and those don't exist today. And I think she perfectly realizes the practicality of de facto standards are going to get to market, and add value sooner than open standards. Now open standards over time, and I'll come back to that, may occur, but that's clear to me what Snowflake is creating, is the de facto standard for data platforms, the data cloud, the supercloud. And what's most impressive, or I think really important, is they're layering applications now on top of that. The metric to me, and I don't know if we can even count this, but VMware used to use it. For every dollar spent on VMware license, $15 was spent in the ecosystem. It started at 1 to 1.5, 1 to 2, 1 to 10, 1 to 15, I think it went up to 1 to 30 at the max. I don't know how they counted that, but it's countable. Reasonable people can make estimates like that. And I think as the ecosystem grows, what Snowflake's doing is it's in many respects modeling the cloud, what the cloud has. Cloud has ecosystems, we talked about startups, and the cloud also has optionality. And optionality means open source. So what you saw with Apache Iceberg is we're going to extend to open technologies. What you saw with Hybrid tables is we're going to extend a new workloads like transactions. The other thing about Snowflake that's really impressive is you're seeing the vertical focus. Financial services, healthcare, retail, media and entertainment. It's very rare for a company in this tenure, they're only 10 years old, to really start going vertical with their go-to-market, and building expertise around that. I think what's going to happen is the GSIs are going to come in, they love to eat at the trough, the trough here is maybe not big enough for them yet, but it will be. And they're going to start to align with the GSIs, and they're going to do really well within those industries, connecting people, collaborating with data. But I think it's a killer strategy, but they're executing on it. >> Right, and we heard a lot of great customer stories from all of those four verticals that you talked about, and then some, that that direction and that pivot from a customer perspective, from a sales and marketing perspective is all aligned. And that was kind of one of the themes as well that Frank talked about in his keynote is mission alignment, mission alignment with customers, but also with the ecosystem. And I feel that I heard that with every customer conversation, with every partner conversation, and Snowflake conversation that we had over the last I think 36 segments, Dave. >> Yeah, I mean, yeah, it's the power of many versus the resources of one. And even though Snowflake tell you they have $5 billion in cash, and assets on the balance sheet, and that's fine, that's nothing compared to what an ecosystem has. And Amazon's part of that ecosystem. Azure is part of that ecosystem. Google is part of that ecosystem. Those companies have huge resources, and Snowflake it seems has figured out how to tap those resources, and build value on top of it. To me they're doing a better job than a lot of the cloud databases out there. They don't necessarily have a better database, in fact, I could argue that their database is less functional. And I would argue that actually in many cases. Their database is less functional if you just want a database. But if you want a data cloud, and an ecosystem, and develop applications on top of that, and to be able to monetize, that's unique, and that is a moat that they're building that is highly differentiable, and being able to do that relatively easily. I mean, I think they overstate the simplicity with which that is being done. We talked to some customers who said, he didn't say same wine, new bottle. I did ask him that, about Hadoop complexity. And he said, "No, it's not that bad." But you still got to put this stuff together. And I think in the early parts of a market that are immature, people get really excited because it's so much easier than what was previous. So my other question is, okay, what's somebody working on now, that's looking at what Snowflake's doing and saying, I can improve on that. And what's going to be really interesting to see is, can they improve on it in a way, and can they raise enough capital such that they can disrupt, or is Snowflake going to keep staying paranoid, 'cause they got good leaders, and keep executing? And then I think the other wild card is edge. Snowflake doesn't really have an edge strategy right now. I think they will develop one. >> Through the ecosystem? >> And I don't think they're missing the boat, and they'll do it through the ecosystem, exactly. I don't think they're missing the boat, I think they're just like, "Well, we don't know what to do today." It's all distributed data, and it's ephemeral, and nobody's storing the data. You know anything that comes back to the cloud, we get. But new architectures are emerging on the edge that are going to bring new economics. There's new silicon, you see what's happening with Apple, and the M1, the M1 Ultra, and the new systems that they've just developed. What Tesla is doing with custom silicon, and amazing things, and programmability of the arm model. So it's early days, but semiconductors are the mainspring of innovation in this industry. Without chips, you got nothing. And when you get innovations in silicon, it drives innovations in software, because developers go, "Wow, I can do that now?" I can do things in parallel, I can do things faster, I can do things more simply, and programmable at scale. So that's happening. And that's going to bring a new set of economics that the premise is that will eventually bleed into the data center. It will, it always does. And I guess the other thing is every 15 years or so, the world gets disrupted, the tech world. We're about 15, 16 years in now to the cloud. So at this point, everybody's like, "Wow this is insurmountable, this is all we'll ever see. Everything that's ever been invented, this is the model of the future." We know that's not the case. I don't know how it's going to get disrupted, but I think edge is going to be part of that. It could be public policy. Governments could come in and take big tech on, seems like Sharekhan wants to do that. So that's what makes this industry so fun. >> Never a dull moment, Dave. This has been a great three days hosting this show with you. We've uncovered a lot. Your breaking analysis was great to get me prepared for the show. If you haven't seen it, check it out on siliconangle.com. Thanks, Dave, I appreciate all of your insights. >> Thank you, Lisa, It's been a pleasure working with you. >> Always good to work with you. >> Awesome, great job. >> Likewise. Great job to the team. >> Yes, thank you to our awesome production team. They've kept us going for three days. >> Yes, and the team back, Kristin, and Cheryl, and everybody back at the office. >> Exactly, it takes a village. For Dave Vellante, I am Lisa Martin. We are wrappin' up three days of wall-to-wall coverage at Snowflake Summit 22 from Vegas. Thanks for watching guys, we'll see you soon. (upbeat music)

(upbeat music) >> Welcome back everyone, theCUBE's three days of wall to wall coverage of Snowflake Summit '22 is coming to an end, but Dave Vellante and I, Lisa Martin are so pleased to have our final guest as none other than the co-founder and president of products at Snowflake, Benoit Dageville. Benoit, thank you so much for joining us on the program. Welcome. >> Thank you. Thank you, thank you. >> So this is day four, 'cause you guys started on Monday. This is Thursday. The amount of people that are still here speaks volumes. We've had close to 10,000 people here. >> Yeah. >> Could you ever have imagined back in the day, 10 years ago that it would come to something like this in such a short period of time? >> Absolutely not. And I always say if I had imagined that I might not have started Snowflake, right. This is somehow scary. I mean and yeah, it's huge. And you can feel the excitement of everyone. It is like mind boggling and the fact that so many people are still there after four days is great. >> Your keynote on Tuesday was fantastic. Your energy was off the charts. It was standing room only. There were overflow rooms. Like we just mentioned, a lot of people are still here. Talk about the evolution of Snowflake, this week's announcements and what it means for the future of the data cloud. >> Yeah, so evolution, I mean, I will start with the evolution. It's true that that's what we have announced. This week is not where we started necessarily. So we started really very quickly with big data combined with data warehouse as one thing. We saw that the world was moving into fragmented siloing data and we thought with Thierry, we are going to combine big data and data warehouse in one system for the cloud with this elasticity and this service simplicity. So simplicity, amazing elasticity, which is this multi workload architecture that I was explaining during the keynotes and really extreme simplicity with the service. Then we realized that there is one other attribute in the cloud, which is unique, which doesn't exist on-premise, which is collaboration. How you can connect different tenets of the platform together. And Google showed that with Google Docs. I always say to me, it was amazing that you could share document and have direct access to document that you didn't produce and you can collaborate on this document. So we wanted to do the same thing for data and this is where we created the data cloud and the marketplace where you can have all these data sets available and really the next evolution I would say is really about applications that are (indistinct) by that data, but are way simpler to use for all the tenets of the data cloud. And this is the way you can share expertise also, including, ML model, everyone talks about ML and the democratization of ML. How are you going to democratize ML? It's not by making necessary training super easy. Such that everyone can train their ML for themselves. It's by having very specialized application where data and ML is at the core, which are shared, through the marketplace and we shall leverage by many tenets of this marketplace that have no necessary knowledge about building this ML models. So that's where, yeah. >> When you and Thierry started the company, I go back to the improbable rise of Kubernetes and there were other more sophisticated container management systems back then, but they chose to focus on simplicity. And you've told me before, that was our main tenet. We are not going to worry about all the complex database stuff. You knew how to do that, but you chose not to. So my question is, did you envision solving those complex problems over time yourselves or through an ecosystem? Was this by design or did you... As you started to get into it, say let's not even try to go there let's partner to go there. >> Yeah, I mean, it's both. It's a combination of both. Snowflake, the simplicity of the platform is really important because if our partners are struggling to put their solution and build solution on top of Snowflake they will not build it. So it's very important that number one, our platform is really easy to use from day one. And that really has to be built inside the platform. You cannot build simplicity on top. You cannot have a complex solution and all of a sudden realize that, oh, this is complex. I need to build another layer on top of it to make it simpler, that will not work. So it had to be built from day one, but you're right. What is going to be Snowflake? I always say in 10 years from now, we just turn 10 years old or we are going to turn 10 years old in few months. Actually a few months, yes. >> Right. >> So for the next 10 years I really believe that most of Snowflake will not be built by Snowflake. And that's the power of the partners and these applications. When you are going to say I'm using Snowflake, actually, probably you are not going to use directly code developed by Snowflake. That code will leverage our platform, but you will use a solution that has been built on top of Snowflake. And this is the way we are going to decouple, the effort of Snowflake and multiply it. >> It's an interesting balance, isn't it? When I think of what you did with Apache Iceberg, if I use Iceberg and I'm not going to get as much functionality, but I may want that openness, but I'm going to get more functionality inside of the data cloud. And I don't know, but if you know the answer to what's going to happen. >> No, that's a super good question. So to explain what we did with Apache Iceberg, and the fact that now it's a native format for us. So everything that you can do with our internal formats, you can do it with Apache Iceberg, including security, defining masking, data masking all the governors that we have, fine grain security aspects, the replications you can define you can use (indistinct) on top of... >> But there's a but, right? But if I do that with native Snowflake tools, I'm going to get an even greater advantage, am I not? >> Yes. So that's what I'm saying. So that's why we embraced Iceberg, because I think we can bring all the benefit of Snowflake to people who have decided to use Iceberg, I mean open formats. Iceberg is a table format. So and why it was important because people had massive investments in open source in Hadoop. And we had a lot of companies saying, we love Snowflake. We want to be a Snowflake customer, but we cannot really migrate all our data. I mean, it will be really costly. And we have a lot of tools that need access, direct access. So this is why we created Iceberg because we can really... I mean, we really think that we can bring the benefit of Snowflake to this data. >> Gives customers optionality. Okay. I use this term super cloud. You don't use the term, but that's okay. And I get a lot of heat for it. But to me, what you're doing is quite a bit different than multicloud because you're creating that abstraction layer. You're bringing value above it. My question to you is, the most of the heat I get is, oh, that's just SaaS. Are you just SaaS? >> No. I mean, no, absolutely not. I mean, you're right we are a super cloud. I mean it's a much better word than saying we are multicloud. Multicloud is often viewed as oh, I have my system and now I can run this system in the different cloud providers. Snowflake is different. We have one single platform for the world, which happens to have some regions are AWS region, some regions are Azure, some regions are GCP, Google and we merge them together. We have this Snowgrid technology that connects all our regions together so that we have really one platform for the world. And that's very important because when you talk about connections of data and expertise applications you want to have global reach, right. It doesn't exist. We are not siloed by region of the world, right? You have a lot of companies which are multinational that have presence everywhere. And you want to have this global reach. The world is not a independent set of regions and countries, right. And that's the realization. So we had to create this global platform for our customers. >> And now you have people building clouds on top of your data cloud, well that to me is the next signal. In your keynote, you talked about seven pillars, all data, all workloads, global architecture, self-managed, programmable, marketplace, governance, which ones are the most important? >> All of them. It's like when you have kids, you don't want to pick and say, this one is my preferred one, so they are really important. All of them, as I said without data, there is no Snowflake, right? So all data is so important that we can reach every data, wherever it is. And Iceberg is a part of that, but all workload is really important because you don't want to put your data in one platform, if you cannot run all your workloads and workloads are much broader than just data warehousing, there is data engineering, data science, ML engineering, (indistinct) all these workloads applications. So that's critical. Programmable is where we are moving, right. We want to be the place where data applications are built. And we think we have a lot of advantages because data application needs to use many workloads at once, right? It's not that that application will do only data warehousing, they need to store their states, they need to use this new workload that we define, which is Unistore. They need to do data engineering because they need to get data, right. They have to save this data. So they need to combine many workload and if they have to stitch this workload, because the platform was not designed as one single product where everything is consistent and works together, that you have to stitch, it's complicated for this application to make it work. So Snowflake is we believe an ideal platform to run these data applications. So all workloads, programmable, obviously, so that you can program. And programmable has two aspects, which is big part of our announcement. Is both data programmability, which is running Python against petabyte, terabytes of data at scale and doing it scale out. So that's what we call data programmability. So both Java, Python and (indistinct), but also running applications like UI. And we had this acquisition of Streamlit. Streamlit now has been fully integrated in Snowflake. We announced that such that not only you can have this data programmability, but you can expose your data through this nice UIs, interactive UI to business users potentially. So it goes all the way there. Global is super important. As we say, we want to be one platform for the world. And of course, as I said, the last pillar, which is somehow critical for us, because we are cloud, we need to have governance. We need to have security of our data. And why it took us so long to do Python is not because it's out to run Python, right? Everyone can run Python it's because we had to secure it. And I talk about it creating this amazing sandboxing technology, such that when you include third party libraries and third party codes, you are guaranteed that this third party code will not reach to infiltrate your data, right. We control the environment that Snowflake provides. >> Can you share us some of the feedback from the customer? You probably had many customer conversations over the last four days. >> Look at that smile. (interviewer laughing) (Lisa laughing) >> Actually not because I was so busy everywhere. Unfortunately, I didn't speak to many customers. Saying that, I had everyone stopping me and talking about what they heard and yeah, there is a huge excitement about all of this. >> What's been the feedback around the theme of the event? The world of data collaboration. Data collaboration is so critical as every company these days must be a data company to compete, to win. What's been from just some of the feedback that you've had customers really embracing data collaboration, what Snowflake is enabling. >> Yeah. I mean, almost every company which is using Snowflake, is collaborating with data. You have heard, the number of stable edges that we have, and there is a real need for that because your data alone... You cannot make sense of your data if it is just alone. It needs to be connected with other data. You haven't not generated. So all data, when you say the first pillar of Snowflake is all data is not only about your data, but is about all the data that's created around you. That puts perspective on your own data. And that's critical and it's so painful to get. I mean, even your data is difficult to have access to your data, but imagine data that you didn't produce. And so yes, so the data collaboration is critical, and then now we expanded it to application and expertise, sharing models, for example, That's going to have a huge impact. >> All data includes now transaction data, right? >> Yes. >> That's a big part of the announcements that you guys made. >> Yeah. So and that's the motivation for that was really, if we want to run application, full application, we announced native applications, which are fully executed and run inside the (indistinct) data cloud, right. They need all the services that application need and in particular managing their states. And so we created Unistore, which is a new workload, which allows you to combine transactional data, which are generated by this application. And at the same time being able to do analytics directly on this data. So we call it Hybrid Table because it has this hybrid aspect. You can do both transactional access to this data and at the same time analytic here without having data pipeline and moving data and transforming it from the transactional system to the analytical system, right. Snowflake is one system. Again, in the spirit of simplifying everything, this is the Snowflake (indistinct). >> I can ask the same question I ask at first, (indistinct) when was the aha moment that you and Thierry had that said, this is not just a better data warehouse, it's actually more than that. You probably didn't call it a data cloud until later on, but did you know that from the beginning or was that something you kind of stumbled into? >> No. So as I said, we founded Snowflake in 2012 and Thierry and I, we locked in my apartment and we were doing the blueprint of Snowflake and trying to find what is the revolution with the cloud for this data warehouse system and analytical system, both big data and data warehouse. And the aha moment was but of course cloud, okay. What is cloud? It's elasticity, it's service and later collaboration. So in the elasticity aspect, when you ask database people, what is elasticity, they will tell you, oh, you have a cluster of nodes. Like if it is Oracle, it would be a (indistinct) cluster. And the elasticities that you can add one node, two node to this cluster without having too much impact on the existing workload, because you need to shuffle data, right. It's hard and doing it online, right, that's elasticity. If you can do that, you are elastic. We thought that that was not very interesting to do that. What is interesting with elasticity is to plug new workloads. You can plug a workload like that and that workload is running without having any impact on other workloads, which are running on the platform. So elasticity for us was having dedicated computer resources to workloads. And these computer resources could start and be part as soon as the workload starts and will shut down when the workload finishes and they will be sized exactly for the demand of that workload. And we thought the aha moment was, okay if we can do that, now we can run a workload with, let's say 10X more computer resources than what you would have used or 100X more. Okay, let's say 100X more because we paralyzed things. Now this workload can run 100X faster, right? That's assuming we do a good job in the scale, which is our IP. And if we can do that, now the computer resources that you have used, you have used them for 100 times less. So you have used 100 times more resources because you have more nodes, but because you go fast, you use them for less time, right? So if you multiply the two it's constant. So you can run and accelerate workload dramatically 10X, 100X for the same price. Even if we are not better in efficiency than competition, just having that was the magic, right? >> You know how Google founders originally had trouble raising money because who needs another search engine? Did you get from original, like when you started going to raise money, Amazon's got a database, so who needs another cloud database? Did you get that early on or was it just obvious Speiser and companies as well. >> Speiser is a little bit on the crazy side and ambitious and so Speiser is Speiser. And of course he had no doubt, but even him was saying Benoit, Thierry, Hadoop, right. Everyone is saying Hadoop is going to be the revolution. And you guys are betting actually against Hadoop because we told Speiser, Hadoop is a bad system, it's going to fail, but at the time everyone was so bullish about Hadoop, everyone was implementing Hadoop that it didn't look like it was going to fail and we were probably wrong. So there was a lot of skepticism about not leveraging Hadoop and not being an Hadoop. Okay, something being on top of Hadoop. That was number one. There was no cloud warehouse at the time we started. Redshift was not started. It was the pioneer somewhere when Snowflake was founded. So creating a data warehouse in the cloud sounded crazy to people. How am I going to move my data over there? And security and what about security, the cloud is not secure. So that was another... >> So you guys predated that Parexel move by... >> Yes. >> Okay, so that's interesting. And I thought when Redshift... I mean, Amazon announced Redshift, I was sure that Mike Speiser will come and say, guys it's too sad, but they beat you guys and they build something and actually it was the reverse. Mike Speiser was super excited and so it was interesting to me. >> Wow, that's amazing. 'Cause John Furrier and I, we were early with theCUBE. when theCUBE started it was like the beginning of Hadoop. And so we brought theCUBE to, I think it was the second Hadoop World and we was rubbing nickels together at the time. And I was so excited bring compute to storage and it made so much sense. But I remember and I won't say who it was, but an early Hadoop committer told me this is going to fail. And I'm like, what? And he started going age basis crap and all this stuff. And I was sad because I was so excited, but it turned out that you had the same (indistinct). >> Because of complexity. Okay, Hadoop failed for two reasons. One is because they decided that, oh, a lot of this database thing, you don't need transaction, you don't need SQL, you don't necessarily, you don't need to go fast. It'll be batch, normal real time interaction with data, no one needs that. >> Cheap storage. >> So a lot of compromise on the very important technology. And at the same time, extreme complexity and complexity for me was, where I was I knew that it was going to fail big time and we bet Snowflake on the failure of Hadoop indeed. >> And there was no cloud early on in Hadoop. >> And there was no cloud too. >> And that was what killed it. That was like... >> You're right. And the model that Hadoop had for data didn't work on block storage. Block storage is not as efficient as HGFS. So that was also another figure. >> Do you ever sit back and think about... So you think about how much money has poured in to separating compute from storage and cloud databases and you started it all. (interviewer laughing) >> Yeah. No, this is... >> Pretty amazing. >> Yeah. >> Right, so that's good. That means that you're onto a good idea, but a lot of people get confused that again, they think that you're a cloud data warehouse and you're not, I mean, you're much more than that. >> Yeah, I hate that. I have to say, because from day one we were not a cloud data warehouse. As I said, it was all about combining the big data, massive amount of unstructured data, petabytes stored as files. Okay, that's very important, store as files where it's very easy to drop data in the system without... Very low cost to combine with data warehouse, full multi statement transaction when people will tell you today, oh, now we are a data warehouse. They don't have multi statement transaction, right. So we had from day one multi statement transaction really efficient SQL. You could run your dashboard. So combining these two worlds was I think the crazy thing, that's the crazy innovation that Snowflake did initially. >> Yeah. >> And I know it's really easy to build data warehouse somewhere, because if you don't think about big data, petabytes, extremely structured data, you remove a lot of complexity. >> This is why Lisa, when you get excited about technology, but you always have to have a, somebody who really deeply understands technology to stink test it, all right so awesome. Thank you for sharing that story. >> Yeah. >> Fantastic. So over 5,900 customers now. I saw over 500 in the Forbes G2K, over almost 10,000 people here this year. If we think back to 2019, there was about what? Less than 2000 people. >> Yeah. >> What do you think is going to happen next year? >> I don't know. I don't like to think about next year. I mean, I always say, Snowflake is so exciting to me because it is like a TV show, right. Where you wait the next season and we have one season every year. So I'm really excited to know what is going to happen next year. And I don't want to project what I think will happen, but all these movements to the Snowflake being the platform for data application. I want to see what people are going to build on our platform. I mean, that's the excitement. >> Season 11 coming up. >> Yes. Season 11. Yes. >> No binge watching here. Benoit, it's been a pleasure to have you on the program. >> Thank you. >> Congratulations on incredible success, the momentum, the energy is contagious. We love it. (Benoit laughing) >> Thank you so much. >> Thank you. >> Bye bye. >> For Benoit Dageville and Dave Vellante, I'm Lisa Martin. You're watching theCUBE's coverage of Snowflake Summit '22. Dave and I will be right back with a wrap. (upbeat music)

>>Good morning, everyone. Welcome back to the Cube's coverage of snowflake summit 22 live from Caesar's forum in Las Vegas. Lisa Martin, here with Dave Valante. This is day three of our coverage. We've had an amazing, amazing time. Great conversations talking with snowflake executives, partners, customers. We're gonna be digging into data mesh with data.world. Please welcome John loins, the chief product officer. Great to have you on the program, John, >>Thank you so much for, for having me here. I mean, the summit, like you said, has been incredible, so many great people, so such a good time, really, really nice to be back in person with folks. >>It is fabulous to be back in person. The fact that we're on day four for, for them. And this is the, the solution showcase is as packed as it is at 10 11 in the morning. Yeah. Is saying something >>Yeah. Usually >>Chopping at the bit to hear what they're doing and innovate. >>Absolutely. Usually those last days of conferences, everybody starts getting a little tired, but we're not seeing that at all here, especially >>In Vegas. This is impressive. Talk to the audience a little bit about data.world, what you guys do and talk about the snowflake relationship. >>Absolutely data.world is the only true cloud native enterprise data catalog. We've been an incredible snowflake partner and Snowflake's been an incredible partner to us really since 2018. When we became the first data catalog in the snowflake partner connect experience, you know, snowflake and the data cloud make it so possible. And it's changed so much in terms of being able to, you know, very easily transition data into the cloud to break down those silos and to have a platform that enables folks to be incredibly agile with data from an engineering and infrastructure standpoint, data out world is able to provide a layer of discovery and governance that matches that agility and the ability for a lot of different stakeholders to really participate in the process of data management and data governance. >>So data mesh basically Jamma, Dani lays out the first of all, the, the fault domains of existing data and big data initiatives. And she boils it down to the fact that it's just this monolithic architecture with hyper specialized teams that you have to go through and it just slows everything down and it doesn't scale. They don't have domain context. So she came up with four principles if I may, yep. Domain ownership. So push it out to the businesses. They have the context they should own the data. The second is data as product. We're certainly hearing a lot about that today this week. The third is that. So that makes it sounds good. Push out the, the data great, but it creates two problems. Self-serve infrastructure. Okay. But her premises infrastructure should be an operational detail. And then the fourth is computational governance. So you talked about data CA where do you fit in those four principles? >>You know, honestly, we are able to help teams realize the data mesh architecture. And we know that data mesh is really, it's, it's both a process in a culture change, but then when you want to enact a process in a culture change like this, you also need to select the appropriate tools to match the culture that you're trying to build the process in the architecture that you're trying to build. And the data world data catalog can really help along all four of those axes. When you start thinking first about, let's say like, let's take the first one, you know, data as a product, right? We even like very meta of us from metadata management platform at the end of the day. But very meta of us. When you talk about data as a product, we track adoption and usage of all your data assets within your organization and provide program teams and, you know, offices of the CDO with incredible evented analytics, very detailed that gives them the right audit trail that enables them to direct very scarce data engineering, data architecture resources, to make sure that their data assets are getting adopted and used properly. >>On the, on the domain driven side, we are entirely knowledge graph and open standards based enabling those different domains. We have, you know, incredible joint snowflake customers like Prologis. And we chatted a lot about this in our session here yesterday, where, because of our knowledge graph underpinnings, because of the flexibility of our metadata model, it enables those domains to actually model their assets uniquely from, from group to group, without having to, to relaunch or run different environments. Like you can do that all within one day catalog platform without having to have separate environments for each of those domains, federated governance. Again, the amount of like data exhaust that we create that really enables ambient governance and participatory governance as well. We call it agile data governance, really the adoption of agile and open principles applied to governance to make it more inclusive and transparent. And we provide that in a way that Confederate across those means and make it consistent. >>Okay. So you facilitate across that whole spectrum of, of principles. And so what in the, in the early examples of data mesh that I've studied and actually collaborated with, like with JPMC, who I don't think is who's not using your data catalog, but hello, fresh who may or may not be, but I mean, there, there are numbers and I wanna get to that. But what they've done is they've enabled the domains to spin up their own, whatever data lakes, data, warehouses, data hubs, at least in, in concept, most of 'em are data lakes on AWS, but still in concept, they wanna be inclusive and they've created a master data catalog. And then each domain has its sub catalogue, which feeds into the master and that's how they get consistency and governance and everything else is, is that the right way to think about it? And or do you have a different spin on that? >>Yeah, I, I, you know, I have a slightly different spin on it. I think organizationally it's the right way to think about it. And in absence of a catalog that can truly have multiple federated metadata models, multiple graphs in one platform, I, that is really kind of the, the, the only way to do it, right with data.world. You don't have to do that. You can have one platform, one environment, one instance of data.world that spans all of your domains, enable them to operate independently and then federate across. So >>You just answered my question as to why I should use data.world versus Amazon glue. >>Oh, absolutely. >>And that's a, that's awesome that you've done now. How have you done that? What, what's your secret >>Sauce? The, the secret sauce era is really an all credit to our CTO. One of my closest friends who was a true student of knowledge graph practices and principles, and really felt that the right way to manage metadata and knowledge about the data analytics ecosystem that companies were building was through federated linked data, right? So we use standards and we've built a, a, an open and extensible metadata model that we call costs that really takes the best parts of existing open standards in the semantics space. Things like schema.org, DCA, Dublin core brings them together and models out the most typical enterprise data assets providing you with an ontology that's ready to go. But because of the graph nature of what we do is instantly accessible without having to rebuild environments, without having to do a lot of management against it. It's, it's really quite something. And it's something all of our customers are, are very impressed with and, and, and, and, you know, are getting a lot of leverage out of, >>And, and we have a lot of time today, so we're not gonna shortchange this topic. So one last question, then I'll shut up and let you jump in. This is an open standard. It's not open source. >>No, it's an open built on open standards, built on open standards. We also fundamentally believe in extensibility and openness. We do not want to vertically like lock you into our platform. So everything that we have is API driven API available. Your metadata belongs to you. If you need to export your graph, you know, instantly available in open machine readable formats. That's really, we come from the open data community. That was a lot of the founding of data.world. We, we worked a lot in with the open data community and we, we fundamentally believe in that. And that's enabled a lot of our customers as well to truly take data.world and not have it be a data catalog application, but really an entire metadata management platform and extend it even further into their enterprise to, to really catalog all of their assets, but also to build incredible integrations to things like corporate search, you know, having data assets show up in corporate Wiki search, along with all the, the descriptive metadata that people need has been incredibly powerful and an incredible extension of our platform that I'm so happy to see our customers in. >>So leasing. So it's not exclusive to, to snowflake. It's not exclusive to AWS. You can bring it anywhere. Azure GCP, >>Anytime. Yeah. You know where we are, where we love snowflake, look, we're at the snowflake summit. And we've always had a great relationship with snowflake though, and really leaned in there because we really believe Snowflake's principles, particularly around cloud and being cloud native and the operating advantages that it affords companies that that's really aligned with what we do. And so snowflake was really the first of the cloud data catalogs that we ultimately or say the cloud data warehouses that we integrated with and to see them transition to building really out the data cloud has been awesome. >>Talk about how data world and snowflake enable companies like per lodges to be data companies. These days, every company has to be a data company, but they, they have to be able to do so quickly to be competitive and to, to really win. How do you help them if we like up level the conversation to really impacting the overall business? >>That's a great question, especially right now, everybody knows. And pro is a great example. They're a logistics and supply chain company at the end of the day. And we know how important logistics and supply chain is nowadays and for them and for a lot of our customers. I think one of the advantages of having a data catalog is the ability to build trust, transparency and inclusivity into their data analytics practice by adopting agile principles, by adopting a data mesh, you're able to extend your data analytics practice to a much broader set of stakeholders and to involve them in the process while the work is getting done. One of the greatest things about agile software development, when it became a thing in the early two thousands was how inclusive it was. And that inclusivity led to a much faster ROI on software projects. And we see the same thing happening in data analytics, people, you know, we have amazing data scientists and data analysts coming up with these insights that could be business changing that could make their company significantly more resilient, especially in the face of economic uncertainty. >>But if you have to sit there and argue with your business stakeholders about the validity of the data, about the, the techniques that were used to do the analysis, and it takes you three months to get people to trust what you've done, that opportunity's passed. So how do we shorten those cycles? How do we bring them closer? And that's, that's really a huge benefit that like Prologis has, has, has realized just tightening that cycle time, building trust, building inclusion, and making sure ultimately humans learn by doing, and if you can be inclusive, it, even, it even increases things like that. We all want to, to, to, to help cuz Lord knows the world needs it. Things like data literacy. Yeah. Right. >>So data.world can inform me as to where on the spectrum of data quality, my data set lives. So I can say, okay, this is usable, shareable, you know, exactly of gold standard versus fix this. Right. Okay. Yep. >>Yep. >>That's yeah. Okay. And you could do that with one data catalog, not a bunch of >>Yeah. And trust trust is really a multifaceted and multi multi-angle idea, right? It's not just necessarily data quality or data observability. And we have incredible partnerships in that space, like our partnership with, with Monte Carlo, where we can ingest all their like amazing observability information and display that in a really like a really consumable way in our data catalog. But it also includes things like the lineage who touch it, who is involved in the process of a, can I get a, a, a question answered quickly about this data? What's it been used for previously? And do I understand that it's so multifaceted that you have to be able to really model and present that in a way that's unique to any given organization, even unique within domains within a single organization. >>If you're not, that means to suggest you're a data quality. No, no supplier. Absolutely. But your partner with them and then that you become the, the master catalog. >>That's brilliant. I love it. Exactly. And you're >>You, you just raised your series C 15 million. >>We did. Yeah. So, you know, really lucky to have incredible investors like Goldman Sachs, who, who led our series C it really, I think, communicates the trust that they have in our vision and what we're doing and the impact that we can have on organization's ability to be agile and resilient around data analytics, >>Enabling customers to have that single source of truth is so critical. You talked about trust. That is absolutely. It's no joke. >>Absolutely. >>That is critical. And there's a tremendous amount of business impact, positive business impact that can come from that. What are some of the things that are next for data.world that we're gonna see? >>Oh, you know, I love this. We have such an incredibly innovative team. That's so dedicated to this space and the mission of what we're doing. We're out there trying to fundamentally change how people get data analytics work done together. One of the big reasons I founded the company is I, I really truly believe that data analytics needs to be a team sport. It needs to go from, you know, single player mode to team mode and everything that we've worked on in the last six years has leaned into that. Our architecture being cloud native, we do, we've done over a thousand releases a year that nobody has to manage. You don't have to worry about upgrading your environment. It's a lot of the same story that's made snowflake. So great. We are really excited to have announced in March on our own summit. And we're rolling this suite of features out over the course of the year, a new package of features that we call data.world Eureka, which is a suite of automations and, you know, knowledge driven functionality that really helps you leverage a knowledge graph to make decisions faster and to operationalize your data in, in the data ops way with significantly less effort, >>Big, big impact there. John, thank you so much for joining David, me unpacking what data world is doing. The data mesh, the opportunities that you're giving to customers and every industry. We appreciate your time and congratulations on the news and the funding. >>Ah, thank you. It's been a, a true pleasure. Thank you for having me on and, and I hope, I hope you guys enjoy the rest of, of the day and, and your other guests that you have. Thank you. >>We will. All right. For our guest and Dave ante, I'm Lisa Martin. You're watching the cubes third day of coverage of snowflake summit, 22 live from Vegas, Dave and I will be right back with our next guest. So stick around.

>>Good morning, everyone. Welcome back to the Cube's coverage of snowflake summit live from Caesar's forum in Las Vegas, Lisa Martin, with Dave Valante. Dave, we have had an action packed two days here, talking with loads of folks. There's been about 10,000 attendees here, the momentum, the excitement for snowflake, what they're building, what they're, what they've announced is huge. >>I'll tell you like this is a getaway day and there's still decent amount of buzz going on in the ecosystem here and the exhibit hall. And I was just saying, when you walk around Las Vegas, you'd never know the economy's about the tank with, you know, inflation is on the rise. I mean, Vegas is packed. >>It is packed it a lot of shows going on here. We are excited to welcome Patrick Barch, the senior director of product management at capital one software to the program. Patrick, it's great to have you. >>Thank you. It's great to be here. >>So we all know capital one. I love the commercials. I'm sure you have a, a large say in how fun and creative they are. Talk to us about capital one software. This is a new business software business. It >>Is. And so, you know, from our founding days in 1994, capital one has always recognized the power of data and technology to create differentiated experiences for our customers. But about 10 years ago, we declared that we were gonna reinvent the way that we build and use technology. One of the key steps in that journey was migrating from our owned and operated data centers to the public cloud. But in order to do that, we needed to build a number of products and platforms to help us operate at scale because the market just wasn't quite there yet. And so capital one software, which we announced last week, Woohoo is our first foray into bringing some of those cloud and data management products to market. >>Talk to us about you. Capital one is one of Snowflake's longest running and largest customers. How does snowflake help facilitate that >>A couple different ways? So first snowflake is a, it's a super powerful platform. They've changed the game when it comes to leveraging data. At scale in the cloud, we were an early investor. We were, we were one of their biggest customers. They've been a great partner along the way, helping us adopt the platform. But for us, when we adopted back in 2018 ish, we realized that with all of this power comes a lot of responsibility. And so we needed to make sure that we were putting good governance and good controls around our usage of snowflake from the start. And so, you know, we, we, we needed to build some, some tools to help us optimize our, our usage of snowflake. >>Okay. So you basically said we're going all in the cloud. You guys have made huge investments in, in AWS and obviously snowflake. And then now you're, you're sort of taking what you did internally and exposing it almost like, like Amazon did Amazon retail and then that's how AWS was born. Okay, awesome. What kind of results did you see internally in terms of the primary benefit? If I understand it is cost savings, but also better data management, right? Is that fair? >>So the, the totality of what we've built internally covers both cost savings, data management, data security, adherence to data privacy legislation. The product that we announced here at summit is really focused on cost optimization for snowflake, right? And so with these tools, we've been able to save about 27% on our projected snowflake costs. We've been able to save our teams about 50,000 hours of manual effort by reducing the number of change orders that they have to execute manually through automated infrastructure management. We've reduced our cost per query by about 43%. And so really what these enabled us to do is just get really efficient with how we use the system. You know, one, one of the challenges you might run into with snowflake is, is unexpected costs. And so by leveraging these tools, we've been able to make sure that our costs are predictable and consistent from month to month, which enables us to budget appropriately. >>And, and that's 50,000 hours person hours over what period of time >>Have to get back to you on the exact amounts? I mean, >>Years, months, several years. Weeks. Yeah. Yeah. Okay. So, but we're talking about tens and tens of millions of dollars, right? If you, I mean, just assume a hundred bucks an hour for, for a person just fully loaded. I mean, I'll just do that math. Okay. And 20% percent on snowflake cost. So here's, here's the question? Well, well, first of all, what's the vision, what's the like gimme a five year vision for, for the software group at capital one, >>We wanna bring capital one's data and cloud management expertise to the masses. Okay. We've spoken to a number of companies that are trying to follow in our footsteps. We've, we've heard again and again, that our challenges are their challenges. Our, the path that we walked is the path that they're trying to walk in. So we are super excited about bringing all of our expertise to the market. >>So start with cost savings, but the vision transcends cost savings, absolutely going into security, privacy, data management, >>Absolutely absolutely workflow. And the, the, you know, the industry's in a super interesting place now where it's very fragmented. There is a galaxy of tools out there. You, you look around here, there's hundreds and hundreds of different solutions, but they're point solutions. They're all going after an individual piece of the management puzzle. And what we found was that we needed to create these integrated experiences that were aligned to our team's jobs to be done, not necessarily in terms of, you know, a capability like cataloging or quality or entitlements, you know, in order to efficiently operate at scale, you need to string those things together in a way that lets your team get their job done. >>So my last question on this flow is, I dunno if you're familiar with you guys, maybe familiar with Sarah Wong and Martin CASAA published a piece that got, you know, pretty wide viewing and discussion. They are out out of Andreesen, a 16 Z that the cost of good sold for SaaS companies who are born in the cloud are gonna become so overwhelming that they're gonna repatriate and start managing themselves. And they use Dropbox as an example. Now Dropbox is storage. So it's very specific niche, you know, and I've talked to many, many companies like snowflake about this, and they're like, eh, that ain't happening anytime soon. How do you feel about that? Because if you look at SAS companies that are born in the cloud, their gross margins are, you know, they don't get to 90%, but they're healthy, you know, 75, you know, sometimes 78% even snowflakes, you know, end of decade forecast Scarelli has it. I think it's 78%. And the reason it's not higher is because of the cloud cost. You gotta pay the cloud bills, my belief and I've argued, this is that's okay. I can negotiate cloud bills. I can work with tools like yours over time to keep those down. And the cloud guys are gonna be competing with each other, but, but what do you make of that Patrick >>Cloud costs? Aren't gonna go down. Data is expanding at an exponential rate. The scale of data today is orders of magnitude versus what it was in on-prem systems. And so, you know, I don't think the cloud providers are too worried because data is exploding at such a, a crazy pace. And so it really becomes about using all of those resources as efficiently as possible. And, and in the cloud where compute is fully elastic, it scales infinitely instantly on demand. You know, it's all about getting it's, it's, it's all about making sure that if you're spending more, you're getting more business value. There's not wastage in the system. >>Same question, but different. Do you feel like strategically organizations generally in capital one specifically will, will, will optimize their time on optimizing or spend their, their effort optimizing the cloud costs? Or do you feel like long term you can actually be cheaper to manage yourself? In other words, our, our cloud benefits of not doing all that heavy lifting offset that potential, you know, cost equation. >>I mean, you saved just so much time and effort and headache, not having to manage physical infrastructure. And so like, you know, snowflake, you can write a sequel command to create a database. You can write a sequel command to create a data warehouse. Like the market will not give up that level of simplicity for managing infrastructure. And so I think at the end of the day, you're gonna, you're gonna see a focus on efficiency because what you really want your teams to be focused on your old, your old DBA and data engineering teams is focused on driving customer value, not in the weeds of infrastructure management. >>And that's why I think you guys, this is a great business that you're starting. And I think you, I, frankly, I think you're gonna get a lot of competition, which is a good thing that says you're in a great business and you guys are first >>Talk about the customer experience. You know, we are also as consumers demanding, we wanna be able to transact ASAP. We wanna make sure that, you know, on the swipe fraud detection happens, how does the Slingshot help facilitate and improve the customer experience if I'm transacting or I'm gonna sign up or I'm getting a mortgage. >>So with Slingshot, we enable your company, regardless of what you do at, at capital one, we're, we're a bank to build more personalized experiences for customers in a more cost effective way. And so Enno is our, our intelligent, personal banking assistant with snowflake. We're enable Enno to do way more than we were previously for less than we would've without some of these tools. >>And that's a huge competitive differentiator because we expect as consumers and of whatever it is. We want a personalized experience, right? That's relevant. That's gonna offer us products and services that might build upon what we've already done. >>It's it's kind of table stakes these days. Yes. And so with these tools and with snowflake, we were able to onboard our business teams were able to onboard over 400 new use cases over, over that same time period. And so really what it's enabled us to do is unlock the innovative power of our company and create more of these customer experiences. >>How does the customer visualize those, those cost savings? And, and, and do, do, do you have some tooling, maybe it's in the works to help them predict what kind of cost savings they have based on some modeling that >>You do. And absolutely. So we enable teams to enforce good governance around infrastructure management, up front by building rules and enabling their teams to create warehouses, create databases. And then once that infrastructure is up and running, we give them a whole bunch of dashboards that show transparency and to spend, we enable chargebacks to lines of business in today's consumption, driven business models. It's hard to reconcile at the end of the month, if you spent what you thought you spent and, and data costs have gone from CapEx to OPEX and, but not everybody is an expert. And so we look at usage data, we look at usage history and we come up with recommendations for how you can save money by, you know, tweaking this or tweaking that or better optimizing your, your compute. >>Should we expect you as you expand your opportunity to take your expertise and aim it at AWS more broadly, maybe Redshift more specifically, Google GCP, big query Azure, what, what should we expect there? >>You know, there's, there's a lot of opportunity to help companies optimize costs across other cloud providers as well. This, this concept of elastic compute, isn't just specific to snowflake. That's certainly one path that we could go down. You know, we have a lot of expertise in, in data management as well, and data privacy, data security. And so that's that, that's another path as well that, that we have expertise in. And so, you know, I think it's, it's an exciting time we're in, we're in an exciting place, but it's early days, >>Did you do a working backwards document? Can you share that with us? >>Fortunately >>Not five, five or 10 years down the road, you may decide to do that, right? >>Yeah. Let me, let me check with my PR person to see if I'm allowed to share here. That's >>I mean, I think this is gonna be a huge success and, and I think it it's, it's, it follows a lot of the things that we've learned from AWS. Yeah. And you guys have been all in there and, and, you know, it's funny, right? We laugh about working backwards, customer obsession, two pizza teams. I mean, it really has changed the sort of way that we think about developing software and, and managing infrastructures. I, I think you're gonna have a, a huge business and I, I wish you the best. >>I, I appreciate that. And the, the thing, a lot of that statement is, you know, internal teams are now starting to demand consumer great experiences for the tools that they use. Yeah, for sure. And so one of the things that we did was treat our internal associates. Like they were external customers, we applied design thinking, we applied product management, we built our experience in terms of what are you trying to accomplish? And what's getting in your way, because that's what people have come to expect with all of these consumer experiences, >>Collaboration. That's right. What last question for you? What would you say to peers in your, whatever, same industry, other industries that are really trying to figure out how to get their hands on data to become a data company, what would you advise them? Why should they choose >>Snowflake gives you so many building blocks out of the box to help you create a, a well-managed data ecosystem? You know, the simplicity with which you can create new infrastructure, define policies for that infrastructure onboard new users. I mean, it, it's one of the platforms in internally capital one that has the highest NPS score. And so, you know, if you're looking to adopt a, a data cloud platform, I mean, snowflake is certainly high up on the list of what you should be looking at. >>That's >>Awesome. How do you, do you consider this a SA, is it a consumption or how do you price for this? >>So we, we don't have published pricing at the moment, but it is, it is a SAS product. You know, what we can share is it'll, it'll be a, you know, small fraction of, of your, of your total credit spend with snowflake and, and >>You're thinking a subscription or, or haven't figured that out yet, >>It it'll likely be a, a consumption model based on, you know. Okay. >>So the, so, so say, you know, it's funny SAS, I get it. Software's a service, but it, but because it's consumption, I think it's like modern SAS. If I can say that, you know, it's cloud >>SAS and it, it, you know, it's more important to make sure right now, because we're so early that we're actually providing the right value to customers. We have a pretty generous trial program going on right now where you can try the, the, the software out for free to make sure it, it fits your needs. So, >>Okay. So you're in trial, right. I should have clarified that you're in trial now. And, and so, yeah, of course you haven't figured out exactly how you're gonna price it yet. But >>The, the, the official posture that we're taking is public preview. We've, we've been in private preview for the last six months. We've onboarded a, a couple of customers who are starting to use the product. And so the, the big announcement this week is we're officially in public preview, come on in. >>So you gotta get product market fit. That's right. Before you figure out your pricing and before you, then you, then you're gonna scale. Great. >>What's been the feedback so far >>Overwhelmingly positive. Somebody stopped by the booth and said, oh my God, that's so cool. We've heard a lot of, wow, we need this right now. You know, it's, I had pretty, pretty high expectations coming in, just based on the value that this is created for capital one, but I've, I've been blown away by, by what I've heard from the people who've stopped by our booth. >>Awesome. Patrick, thank you for joining Dave and me on the program, talking about what you're doing with capital one software seems like you're just in early innings, but so much potential to come. We wish you the best of luck with that. And you have to come back and tell us how it's going. Thanks so much. Thanks for having me, our pleasure for Dave ante. I'm Lisa Martin. You're watching the cube our day three coverage of snowflake summit 22 live from Las Vegas continues after a short break.

(bright music) >> Welcome back to the Cube. Our continuing coverage of Snowflake Summit 22, day two, lots of content as I've said, coming at you the last couple of days. Dave and I, Dave Vellante, and Lisa Martin are here with you. We have an exciting guest here next to talk with us about data discovery. Please welcome Shinji Kim, the founder and CEO at Select Star. Welcome to the program. >> Thanks for having me. >> Dave: Great to see you. >> Excited to be here. >> Talk to us about Select Star. What do you guys do? And then we're going to uncrack data discovery. >> Yeah, why'd you start the company? (Shinji laughing) >> Sure. So, Select Star is, on fully automated data discovery platform, that helps any company to be able to find, understand and manage their data. I started this company because after I sold my last company, Concord Systems to Akamai, I started working with a lot of global enterprise companies that manages a lot of IOT devices like automakers or consumer electronics companies. And it became very clear to me that companies are not going to stop anytime soon about collecting more data, more often, and trying to utilize them as much as they can. And cloud providers, and all the new technologies like Snowflake has really helped them to achieve that goal. But the challenges that, I've started noticing, from a lot of these enterprises, is that they now have 100s or 1000s of data sets that they have to manage. And when you are trying to use that data it's almost impossible to find which specific field which specific data sets that you should use out of 1000s and 100s of 1000s of data sets you have. So, that's why I felt like this is the next problem and challenge that I would like to solve. Also because, I have a background of working as a software engineer, data scientist, product manager, in the stages of creating data, transforming data and also querying data and trying to make business decisions on data. Having a right context about the data, is so important, for me to use that data. So, for us, we are trying to solve that challenge around finding and understanding data, and we call that data discovery. >> Wow. That's music to my ears here because I can't tell you how many meetings I've been in, where somebody presents some data and I say, okay, what's the source of that data? What are the assumptions used to derive data? I have different data, you know, and then it becomes this waste of time. My data's better than your data, or everybody has an agenda. You cut through that. >> Yeah, so, data discovery, in a nutshell, we defining as finding, understanding, and managing your data. So, in Select Star, we will automatically bring out, all your, like the schema information. Where does data exist? We will also analyze the SQL query logs as well as activity logs that's generated by any applications and BI tools that are connected on top of your data warehouse, so that any time you're looking at a database any particular database table, column or dashboard, we will tell you, where did this data come from? Where did it originate from? How was this transformed? And which reporting table does this exist? Who's using this data the most inside the company? How are they using it? And which are the dashboards and reports that are built on top of this data set? So you don't have to go out and ask everybody else, "Hey, I'm looking for this type of data. "Has anybody worked with this?" This is actually something that I realize a lot of data analysts and data scientists waste their time on. So yeah, that's really the, what we call fully automated data context that we provide to our customers so that you can truly use all the data that you have in your data warehouse. >> And you do this by understanding the metadata? Or is it some kind of scanning? Or using math or code? >> It's both. So, first of all, we do connect and bring out all the metadata. So, that's all the information under information schema. And then, we also look at all the query history. So all your select SQL queries, all your create queries, create table queries, create view queries. And based on that, we will also match the metadata, where it exists inside those queries and logs. And based on that, we will generate first and foremost, what we would call column level data lineage. Data lineage is all about showing you the flow of data from where it was originated, how it was transformed, and where it exists now. And also, what we call popularity. Who's using what data? How are they using it? And in aggregate, you can also find out, which are the most important data sets in our company? Which are the data sets that can be deprecated because it was like a duplicate of other data sets and nobody's using it anymore? And we like put a, like a popularity score for every single data asset that you have in your company so you can see how that's being used. >> How do your customers take action on the information that you provide them? Do they ultimately automate it? Do they go through a process of sort of the human in the loop? >> Well, we do the automation for them. >> Yeah. >> And we do also provide them with a, really easy to use user interface so that they can add any semantic level data on top. So, that's like tags. Like whether you want to market as, this is a analyst approved table, or do not use table or if you want to put a PII classification of data you can do that on a column. And we will automatically either propagate those annotations throughout the platform. We will also automatically propagate any same matching documentation that you might want to use within the data warehouse. And we will also provide you with, more of a rich text documentation that you can also add on top as a business glossary or like a Wiki that business users can, get a better understanding of data concepts and models as well. >> How do they tag the data? Do they use another tool that does that or? >> No, they can tag it within Select Star. Any table or column has a little icon, tag icon, so you can click on it. Or, we can also give you a view of every database page will have all the tables in one place. You can add a keyword and bulk tag. >> So humans tag. >> Yeah. So humans tag. So in the beginning, humans tag, and then we will automate the propagation of that tag. So if you already tagged, let's say SSN field as a PII, then we will find all the other columns that may use the exact same data, and also tag the same, just as an example. >> Okay so you, once the human puts it in there then you automate the downstream. 'Cause humans sometimes aren't great at classifying and tagging and inconsistencies and I would think that you could use math to improve that. >> And we do have some plans to add more automated tagging system. For example, we are already, we don't necessarily tag them, but we give our customers filters on top of their search results to see, which are the data sets that nobody's using anymore? Which are the data sets that's being created very recently? And you can also filter by who created them or who are the owners. So these are some of the aspects of the data or even like when's the last time was this data updated? So these are the aspects of the operational metadata that we are starting to automate to put more automated annotation, I would say is more coming up towards the end of the year. But in terms of semantic level tagging, like is this data set around customers? Is this data set for marketing, sales, customer support? That is something that we are giving a really easy to use interface for the data team to be able to easily organize them. >> How are you helping organizations? We think of the all the privacy regulations and legislations. How is Select Star a facilitator of data privacy for your clients? Is it part of that play? >> So, I would say, one of the main use cases of data discovery, is data governance. So, starting this company and starting to work with a lot of fortune 500 companies, as well as I would say more like recently IPOed companies that have grown very fast in Silicon valley. Some of those customers have told us that they initially adopted Select Star because they needed a good data catalog and search platform for their data team. But as they are starting to use Select Star and starting to see all these insights about their own data warehouse, they are all kicking off their new data governance projects, because they get to see a really good lay of the land, of how the data is being accessed today. So, this is why we have a very easy to use and also programmatic API so that you can add tags, ownership, and set access control through a Select Star. We are actually just releasing a beta version of our, what we call policy based access control where you can use either role based and attribute based access control so that different roles of the users get to see different versions of a Select star when they log in. And this is just the beginning. Like PII is for example, any column that's already marked as PII. We will always strip out the value before it gets fully processed within Select Star. So even if anybody might stumble upon any sequel queries that other analysts have run, those values won't be available in Select Star at all. >> And you started the company right before the lockdown, right? That must have been crazy. >> Yes, March, 2020 is our, my incorporation of Select Star. It was a very interesting time to start the company. And in a way, I'm glad I did. We had a lot of focus time to really, go heads down, build out the product, and work closely with our customer. And today it's really awesome to get to, provide that support to more customers today. >> And so, what are you doing with Snowflake? >> So Snowflake has been a great partner for us. Lot of customers and Snowflake is really great for this. Basically building single source of truth of your data by connecting all your source of, databases, as well as like your ERP, CRM systems, ad systems, marketing systems, SaaS platform, you can connect them now all to Snowflake, that will all dump all the data inside. So that, allows data team to be able to actually join and crossmatch the customer data across so many different applications. And what we see from a lot of Snowflake customers, hence they end up with many different schemas and tens of thousands of tables. And for them now they are requiring or needing more of a better data discovery tool so that they can use and leverage Snowflake data that they have. So, in that regard so we are a snowflake data governance accelerator partner. And as part of that accelerator program, one of the things that we've integrated with Snowflake is, what we call Snowflake Tag Sync. So if you create any tags in Select Star, and you marked it as a PII, we will also replicate the same tag, to Snowflake. >> Yeah. Okay. >> And so everything is synced in there. And on top of that, a lot of our customers really like using our column level lineage, because we will show how all the data tables within Snowflake is connected to another. And actually last but not least, we actually just released this feature today, called the auto generated ER diagram. ER diagram stands for Entity Relationship Diagram. ERD is like a blueprint of your data model. When your engineers and data architects start creating tables in databases, this is a diagram that they will put together, to show how they are translating business logic into data models in the databases. And that includes, which are the fields for primary keys, foreign keys, and how are different like when you look at Star schema, how different tables are joined together. When all these tables gets migrated into Snowflake, a lot of them actually lose the, the relationships of primary keys and foreign keys. So, many analysts, what we found, is that they are starting to guess, how to join different tables, how to use different data sets together. But because we know how other analysts have actually joined and used the tables in the past, we can give them the guidance and really nice diagram that they can refer to. So that is the ERD diagram that we are releasing today. Available for all customers including our free customers, where you can select any tables, and we will show you the relationship that table has, that you can use right away in your sequel queries. >> And that will facilitate, that simplifies doing more complex joins, yes? Which is an Achilles heel of Snowflake. That's not really what they are about, but they have to rely on the ecosystem to help them do that, which has always been their strategy. The company founded in March 2020, amazing. And then relatively small still, yes? Or is it self-funded? I mean, you've raised a little bit of money, but what's your status? >> Yeah, we raised our seed funding when I first started the company. We've also raised another round of bridge round last year and we plan to raise our another venture round of funding soon. >> Great. Awesome. >> And we're going to be making those investments. What are some of the key parts of the business that you're going to use that funding for? >> There's a lot to build. (Shinji laughing) >> Dave: Yeah. Engineering. >> Obviously more automation features, but having, I would say right now, we have now built a really good foundation of data discovery and that includes fully automated data cataloging for metadata, column level lineage, and also building the usage model like popularity, who's using what, all that type of stuff. So, now we are starting to build really exciting features that leverages these fundamental aspects of data discovery, like auto propagation of tags. We also do auto propagation of documentation. So you write one column description once, and it will get replicated and changed everywhere throughout your data model. We have also other things that we have in store especially more for next year, are, package support for specific use cases like data governance, self-service analytics and cloud cost management. >> Nice, lots of work-- >> Dave: Impressive, I'm blown away. >> And you've accomplished this during a pandemic that's even more impressive. Thank you so much Shinji for coming on, talking to us about Select Star. What you're enabling organizations to do, really derive the context from that data taking a lot of manual work away. We appreciate your insights and your time and wish you the best of luck. >> Well, thanks so much for having me here. This has been great. >> Good. Thanks so much. For Dave Vellante, I'm Lisa Martin. You're watching the Cube's coverage of Snowflake Summit 22, day two. Stick around. Dave has an industry analyst panel common up next. You won't want to miss it. (soft music)

(upbeat music) (upbeat music) (upbeat music) >> Welcome back to Las Vegas. Lisa Martin here, with Dave Vellante. We are covering day two of our coverage of Snowflake Summit '22. of Snowflake Summit '22. It's been a cannon of content coming your way, the last couple of days. We love talking with customers, with partners. We've got a partner on the program from ThoughtSpot. We're going to be diving into digital transformation with self-service analytics for the modern data stack. Please welcome Kuntal Vahalia, SVP of Channel and Alliances at ThoughtSpot. Welcome Kuntal. >> Thank you, Lisa. Dave, thank you for having us. >> Dave: Good to see you. >> Talk to the audience a little bit about ThoughtSpot. Give 'em an overview, and then de dive into the partnership with Snowflake. >> Yeah, absolutely. So ThoughtSpot is the, what we call live analytics, for the modern data stack, right? We want to be the experience layer for all the data that's getting modernized and moving into the cloud, right? And then specifically to Snowflake, we, of course, we have seen over the last two days here Snowflake has made tremendous innovations where they've accelerated a customer's journey into the cloud, especially the data cloud. Our job is to go really unlock that data, right? Generate that value, make it consumable at the at the experience level layer, right? So what we want to do here with Snowflake is here with Snowflake is make analytics self service for the end users, for the end users, on top of the Snowflake data cloud, right? And we want to empower everyone to create, consume, and operationalize data driven insights. We think if the end users can gender their own insights through live analytics, we could do have a completely different operating model for a business, right? And I think we can do that in accelerated fashion on, sitting on top of Snowflake data cloud. >> End users? Lines of business? >> It's line of business users, so we directly go to end users. That's one of our differentiation, not just IT, not just IT, but as end users as well, so we could be all things to all enterprise, to all enterprise, across our line of businesses. >> So what kind of impact are you seeing with your customers? You know, ones that are leaning into ThoughtSpot and Snowflake and sort of rethinking their data approach? >> Yeah. I mean the impact could be immense, right? As I said, this is not just about analytics. If we are successful in empowering end users, it completely changes the velocity of the business. We are now driving innovation at every node, at every layer in the organization. Not just IT, not just smaller segments in the organization, we are doing this anywhere, in any pocket, right? So I think the impact could be massive, if we do this right. And I think we are starting to see that, we have a lot of customers here actually, joint customers, Capital One, Canadian Tires, Walmart, they're all joint customers, where we have seen starting to see some of those impacts, where we have data getting modernized, the stack being ready, and then we're coming in at the top as the experience layer, which is driving that new digital operating model. >> Describe the maturity curve when you go, you mentioned some of the the the leaders, I mean, take a Walmart. I mean, they kind of invented the whole, you know, beer and diapers thing, right? So obviously a company with tremendous resources and and and advanced technology. Compare. Compare. So some of those leaders with sort of the other end of the spectrum, when you come into a company and you see, okay, here's, okay, here's, what does that spectrum look like? And and what's the upside for the, I don't want to call 'em laggards, but I'll call 'em laggards. >> Yeah, yeah, absolutely. I mean, this, this, I think we are still early on. I mean, as this is not just a exercise in getting the data ready, this is also an exercise in in change management, because now, as I said, we are going beyond IT. We are going to line of business users as well, so a lot of change management required, and we have seen companies that are actually putting this in front of the frontline workers, empowering frontline workers to consume analytics and to drive self-service via search and AI, and AI, they're on a different curve. They are actually being competitive in the market. That's an advantage for them, right? >> Right. >> So we are seeing a lot of companies, like Walmart, already ahead in that journey with us still early days, right? We got to go, land in one line of business, go from there to other line of business till we go enterprise wide. >> Can you, it sounds like you might be a facilitator of connecting heads of business with the IT and the tech folks at ThoughtSpot. >> Absolutely. I mean, that is the Holy Grail. How do we get IT And line of business work frictionless, where everyone has their roles defined, right? And still get to the outcome where innovation is happening now with IT on the data cloud and then go beyond IT into the broader business? So yeah, I think that's definitely one of the our goals and outcomes of what we do. >> So what are the roles there? So the business obviously wants to do more business. Okay. They put analytics in their hands and it helps them get there. What role does IT play? Making sure that those services are available? Are they a service provider? Is it more of a governance and compliance thing? >> Yeah, I mean, step number one is still to get the data ready and I think IT still owns the key to that kingdom, especially around governance, security, so I think IT still has to get the data stack ready, right? Step number two is for IT to really build a framework for how to consume analytics for how to consume analytics for the end users. Step number three then is, is the rule is, Hey, we don't need IT to now deliver dashboards or KPIs to the business every day that that's how traditional dashboards work. In our world, once IT does step number one and step number two the business can take over and they can now go operate the business on their own using live analytics. >> Creating self-serve >> Absolutely. Self-service analytics using service in AI. >> What have you seen, in terms of from the IT folks perspective, we talked about change management a minute ago, It's very challenging to do, but these days every company has to be a data company. >> Kuntal: Yeah. >> They don't have a choice. >> Yeah. >> What are you seeing from a change management perspective within the IT function across your customers and then be willing to let go in some cases? and then be willing to let go in some cases? >> Actually, >> Actually, what we have seen is, you know, think about the the technical debt that IT is owning over the last few years, it's just increasing, right? IT is looking for ways to A. cut cost, to A. cut cost, B. deliver more B. deliver more with probably the same amount of resources they have, so in some ways they welcome this new operating model, as long as they can keep the governance, they can keep the security, they can keep the framework around how business is run, as long as IT has a say in that, they're more than welcome to invite business, to really drive innovation at the edges through self-service analytics, so what we found is IT is a is a welcome partner, in this journey, especially when they have to get the data ready and modernize the data set for us. >> You guys announcing a partnership with Matillion this week, what? Tell us what that's all about. The one earlier. >> We did. So we did announce a partnership, so I think, as I said, step number one is getting the data ready, and I think we have heard from Frank and the rest of this team this week, even Snowflake is taking a best of breed approach on the data stack, right? So we want the computer So we want the computer and the storage to be ready, but for that, the data pipeline has to be ready, which is where Matillion comes in with the low code, no code approach, so we think between Matillion, Snowflake, and ThoughtSpot, we could be the accelerated best of breed approach for customers to realize value and and be live on the, on the modern data stack. >> Is that your, is that your stack? >> As we said, we, we meet the customers where they are, but we think this is accelerated path. >> What are the advantages of, you know, what are you optimizing on in that stack? in that stack? >> First with Matillion, we have, what we concept, we have this concept of Spot Apps, so this is ThoughtSpot's way to really capture the IP and the templates for customers to move fast, right? That's where we bake in a lot of the industry IP, a lot of functional IP around end sources, and and endpoints, so we have some of those spot apps built with Matillion, built with Matillion, so now customers able to ingest data into the so now customers able to ingest data into the into the cloud faster using Matillion, right? So that's, that's something we worked with, same thing with Snowflake, you know, we are now starting to go verticalize with Snowflake, So we are starting to build a lot of IP around financial services, healthcare and whatnot, which is where I think we are, again, accelerating customer's path on the modern data stack, all the way to the experience layer. >> A as a partner of Snowflake's, what does all the narrative around the data cloud, we've been talking about that for a while, a lot of conversation around the data cloud the last couple of days, where do partners fit into that overall narrative? >> Yeah, I think multiple places, right? First thing, First thing, First thing, every layer of the data cloud still needs innovation, still needs partners, and every partner adds a different set of value. Just like we add value at the, at the top layer, which is the experience layer, But I think, you know, we have channel partners we have a lot of SIs and GSIs here, and GSIs here, especially once we take a best of breed approach, to delivering customer outcomes, SIs are the neutral ground. They're the ones who are going to have the Matillion expertise, and the Snowflake expertise, and thoughts for expertise, all baked into one DNA practice, data analytics practice, so I think at every layer, partners have a role to play and every layer partners have role, have value to add. have value to add. >> What's the engagement process like for customers when you you're talking about the the the the three way partnership Matillion, Matillion, ThoughtSpot, and stuff like, how do customers get involved, what's your go to market look like? >> Right. I mean, obviously, I mean, we, we, we are humble, we know where we are. I mean, we, a little bit smaller than, than Snowflake Snowflake has a head start, so they've been about five years ahead of us, so we are largely targeting customers that are that are Snowflake ready, where there is some semblance of data cloud, where data seems to be organized and ready to go, right? so once we think the customer is at that point in the journey, we have very strong partnership across both, across entire organization, at a product level, at a field engagement level, and our field teams really understand the value the joint value between the two organizations, so we, we start to see Snowflake feel, and ThoughtSpot feel, starting to work together on key accounts, once we think the data is ready, and wherever we need to accelerate the data, that's where we bring in Matillion as well, to ingest more data into, into the data cloud, but that's largely been the engagement model between the three companies. >> How do you see the announcements that they made around applications affecting what you guys are doing and your ecosystem? >> Yeah, I mean, I think that's a validation. I think to us, I think to us, we always said step number one is to modernize the data, move into the cloud. That's step number one, but we still have to unlock the data. Like the data still needs to be consumed, And we always said, Hey, we are that app that could drive the consumption of data, but now with some of the announcement we have seen, I think the validation is there saying, "Hey, yes." There, even Snowflake is ready to move in a more accelerated fashion into the application world where they want to drive consumption, not just with the analytics layer, but with lot of other applications that's out there. >> Yeah. >> What are some of the things that you've heard this week, in the last couple of days, that really validate that really validate the the partnership with Snowflake, from your perspective? >> Yeah. I mean, I think the first thing is, is this concept of modern data stack, which is best of breed. I think we have been thinking about that for a long time, for the last year or so. We have seen this come through at this event here, right? We see Matillion, Snowflake, and then the SIs around it, all coming together, so I think to us, that's the biggest validation that the modern data stack is the right approach, especially best of breed, to drive the right customer outcomes, so to me, that's big. Second is this concept of really accelerating applications on top of the data cloud. I think that's, again a validation of what we've been trying to do over the last few years, which is, the data has modernized, let's now drive consumption and adoption of that data, so I think those are the two big take areas. >> So, so the modern data stack, to get to the modern data stack, you got to do some work. >> Yep. >> But so the, the play is to hold out the carrot, which you just kind of just did, 'cause once you get there, then you can really start to hit the steep part of the S-curve, right? >> That's right. >> What, what are the, what would you say are are the sort of prerequisites that customers need to think about to really jump on that modern data stack curve? >> Um, I think they they got to first have a vision around the outcomes, what outcomes we are driving. I think it's one thing to say, "Hey, we just going to move the data over from from legacy into the cloud." I mean, that's just, that's just migration, that doesn't drive the outcomes. To us, what makes sense is, let's start with the right outcomes around supply chain, around retail, around e-commerce, let's name it, right? I think, it starts there. From there on, let's figure out, what do we need? What's what, what technologies do we need in the stack to enable those outcomes, right? It could be ThoughtSpot at the top, it could be something else at the top, and same thing, it's Matillion, and Snowflake, right? But it really starts with what outcomes we going to drive in what industry and what KPIs are important for our customers. >> What's next for ThoughtSpot and Snowflake? I was just looking at the notes here. Over 250 plus joint customers, you mentioned some Disney+, Capital One, I've seen them around here. What's next for these two powerhouses? >> Well, I think we're just getting started, to be honest. I mean those 250 customers, first, we got to go drive success for them. I mean, we are a 10 year old company with a two year runway because we transferred our business transformed our business to cloud, less than two years ago, so this 250 joint logos are actually all happened in the last two years and that's driven us to be in the, probably in the top five adoption drivers for Snowflake, all in the last two years, So goal number one is to really, let's go drive customer success for these joint logos. Second, let's go expand them, right? Consumption is the key criteria, both for Snowflake, as well as ThoughtSpot. We are very well aligned, our pricing models aligned there, our incentives aligned there, We really want customers to go adopt and consume the stack, and then of course, really, we want to go verticalize ourselves, start speaking the language of the customers, and really just get bigger. I mean, we still got to build a machine around this. >> Lisa: Yep. >> Lisa, this is, this is all still early days for us. >> Early innings. A lot of, but a ton of potential. The, the field is ripe. >> The field is right open. I think in, and we will, I think we are, bottom of the third or bottom of the second, I think you still have a long game to play, right? >> Well good. Most people always use bottom the first. I'm glad to hear it's really bottom of the second or third. That's pretty good. >> Yeah, well, 250 logos are there. >> Lisa: Yeah. >> And it's further along 'cause of the, the I don't want to say it like this, but I'm going to say it anyway. The failure of the big data movement, it pushed us along quite, quite a ways, in terms of thinking, putting data at the core, the technology kind of failed us, you know and the, and the, you know and the, and the, the centralization of the architectures, the centralization of the architectures, it failed us, But then the cloud came along. >> That's right. >> We learned a lot and now, you know, technology's advanced I think people's thinking is advanced and they realize increasingly the importance of data >> And ecosystem is coming. I mean, I think you look around here, this is a secret sauce for the future. >> Dave: Yep. This is what's going to really get us moving faster over the next few innings because now the rest of the ecosystem is coming along. >> Yep. The momentum is here. That flywheel is moving. >> That's right. >> Definitely. Kuntal, thank you very much for joining David and me on the program talking about >> Kuntal: Lisa, Dave, thank you so much for your time. >> what ThoughtSpot's all about, what you're up to, a lot of momentum. We wish you the best of luck as you progress into those later innings. >> Thank you >> For Dave Vellante. I'm Lisa Martin. You're watching theCube. We are live in Las Vegas at Snowflake Summit '22. Dave and I are going to be right back with our next guest, so stick around. (mellow techno music) (mellow techno music) (mellow techno music) (mellow techno music)

(upbeat music) >> Welcome back to theCUBE's continuing coverage of Snowflake Summit 22 live from Caesars Forum in Las Vegas. I'm Lisa Martin with Dave Vellante. We've got a couple of guests joining us now. We're going to be talking about financial services. Rinesh Patel joins us, the Global Head of Financial Services for Snowflake, and Jack Berkowitz, Chief Data Officer at ADP. Guys, welcome to the program. >> Thanks, thanks for having us. >> Thanks for having us. >> Talk to us about what's going on in the financial services industry as a whole. Obviously, we've seen so much change in the last couple of years. What does the data experience look like for internal folks and of course, for those end user consumers and clients? >> So, one of the big things happening inside of the financial services industry is overcoming the COVID wait, right? A lot of banks, a lot of institutions like ours had a lot of stuff on-prem. And then the move to the Cloud allows us to have that flexibility to deal with it. And out of that is also all these new capabilities. So the machine learning revolution has really hit the services industry, right? And so it's affecting how our IT teams or our data teams are building applications. Also really affecting what the end consumers get out of them. And so there's all sorts of consumerization of the experience over the past couple of years much faster than we ever expected it to happen. >> Right, we have these expectations as consumers that bleed into our business lives that I can do transactions. It's going to be on the swipe in terms of checking authenticity, fraud detection, et cetera. And of course we don't want things to go back in terms of how brands are serving us. Talk about some of the things that you guys have put in place with Snowflake in the last couple of years, particularly at ADP. >> Yeah, so one of the big things that we've done, is, one of the things that we provide is compensation data. So we issue a thing called the National Employment Report that informs the world as to what's happening in the U.S. economy in terms of workers. And then we have compensation data on top of that. So the thing that we've been able to do with Snowflake is to lower the time that it takes us to process that and get that information out into the fingertips of people. And so people can use it to see what's changed in terms of with the worker changes, how much people are making. And they can get it very, very quickly. And we're able to do that with Snowflake now. Used to take us weeks, now it's in a matter of moments we can get that updated information out to people. >> Interesting. It helps with the talent war and- >> Helps in the talent war, helps people adjust, even where they're going to put supply chain in reaction to where people are migrating. We can have all of that inside of the Snowflake system and available almost instantaneously. >> You guys announced the Financial Data Cloud last year. What was that like? 'Cause I know we had Frank on early, he clearly was driving the verticalization of Snowflake if you will, which is kind of rare for a relatively new software company but what's that been like? Give us the update on where you're at and biggest vertical, right? >> Absolutely, it's been an exciting 12 months. We're a platform, but the journey and the vision is more. We're trying to bring together a fragmented ecosystem across financial services. The aim is really to bring together key customers, key data providers, key solution providers all across the different Clouds that exist to allow them to collaborate with data in a seamless way. To solve industry problems. To solve industry problems like ESG, to solve industry problems like quantitative research. And we're seeing a massive groundswell of customers coming to Snowflake, looking at the Financial Services Data Cloud now to actually solve business problems, business critical problems. That's really driving a lot of change in terms of how they operate, in terms of how they win customers, mitigate risk and so forth. >> Jack, I think, I feel like the only industry that's sometimes more complicated than security, is data. Maybe not, security's still maybe more fragmented- >> Well really the intersection of the two is a nightmare. >> And so as you look out on this ecosystem, how do you as the chief data officer, how do you and your organization, what process do you use to decide, okay, which of the, like a chef, which of these ingredients am I going to put together for my business. >> It's a great question, right? There's been explosion of companies. We kind of look at it in two ways. One is we want to make sure that the software and the data can interoperate because we don't want to be in the business of writing bridge code. So first thing is, is having the ecosystem so that the things are tested and can work together. The other area is, and it's important to us is understanding the risk profile of that company. We process about 20% of the U.S. payroll, another 25% of the taxes. And so there's a risk to us that we have an imperative to protect. So we're looking at those companies are they financed, what's their management team. What's the sales experience like, that's important to us. And so technology and the experience of the company coming together are super important to us. >> What's your purview as a chief data officer, I mean, a lot of CDOs that I know came out of the back office and it was a compliance or data quality. You come out of industry from a technology company. So you're sort of the modern... You're like the modern CDO. >> Thanks. Thanks. >> Dave: What's your role? >> I appreciate that. >> You know what I'm saying though? >> And for a while it was like, oh yeah, compliance. >> So I actually- >> And then all of a sudden, boom, big deal. >> Yeah, I really have two jobs. So I have that job with data governance but a lot of data security. But I also have a product development unit, a massive business in monetization of data or people analytics or these compensation benchmarks or helping people get mortgages. So providing that information, so that people can get their mortgage, or their bank loans, or all this other type of transactional data. *So it's both sides of that equation is my reading inside. >> You're responsible for building data products? >> That's right. >> Directly. >> That's right. I've got a massive team that builds data products. >> Okay. That's somewhat unique in your... >> I think it's where CDOs need to be. So we build data products. We build, and we assist as a hub to allow other business units to build analytics that help them either optimize their cost or increase their sales. And then we help with all that governance and communication, we don't want to divide it up. There's a continuum to it. >> And you're a peer of the CIO and the CISO? >> Yeah, exactly. They're my peers. I actually talk to them almost every day. So I've got the CIO as a peer. >> It's a team. >> I've got the security as a peer and we get things done together. >> Talk about the alignment with business. We've been talking a lot about alignment with the data folks, the business folks, the technical folks to identify the right solutions, to be able to govern data, to monetize it, to create data products. What does that... You mentioned a couple of your cohorts, but on the business side, who are some of those key folks? >> So we're like any other big, big organization. We have lots of different business units. So we work directly with either the operational team or the heads of those business units to divine analytic missions that they'll actually execute. And at the same time, we actually have a business unit that's all around data monetization. And so I work with them every single day. And so these business units will come together. I think the big thing for us is to define value and measure that value as we go. As long as we're measuring that value as we go, then we can continue to see improvements. And so, like I said, sometimes it's bottom line, sometimes it's top line, but we're involved. Data is actually a substrate of the company. It's not a side thing to the company. >> Yeah, you are. >> ADP. >> Yeah but if they say data first but you really are data first. >> Yeah. I mean, our CEO says- >> Data's your product. >> Data's our middle name. And it literally is. >> Well, so what do you do in the Snowflake financial services data Cloud? Are you monetizing? >> Yeah. >> What's the plan? >> Yeah, so we have clients. So part of our data monetization is actually providing aggregate and anonymized information that helps other clients make business decisions. So they'll take it into their analytics. So, supply chain optimization, where should we actually put the warehouses based on the population shifts? And so we're actually using the file distribution capabilities or the information distribution, no longer files, where we use Snowflake to actually be that data cloud for those clients. So the data just pops up for our other clients. >> I think the industry's existed a lot with the physical movement of data. When you physically move data, you also physically move the data management challenges. Where do you store it? How do you map it? How do you concord it? And ultimately data sharing is taking away that friction that exists. So it's easier to be able to make informed decisions with the data at hand across two counterparties. >> Yeah, and there's a benefit to us 'cause it lowers our friction. We can have a conversation and somebody can be... Obviously the contracts have to be signed, but once they get done, somebody's up and running on it within minutes. And where it used to be, as you were saying, the movement of data and loss of control, we never actually lose control of it. We know where it is. >> Or yeah, contracts signed, now you got to go through this long process of making sure everything's cool, or a lot of times it could slow down the sale. >> That's right. >> Let's see how that's going to... Let's do a little advanced work. Now you're working without a contract. Here, you can say, "Hey, we're in the Snowflake data cloud. It's governed, you're a part of the ecosystem." >> Yeah, and the ecosystem we announced, oh gee, I think it's probably almost a year and a half ago, a relationship with ICE, Intercontinental Exchange, where they're actually taking our information and their information and creating a new data product that they in turn sell. So you get this sort of combination. >> Absolutely. The ability to form partnerships and monetize data with your partners vastly increases as a consequence. >> Talk to us about the adoption of the financial services data cloud in the last what, maybe nine months or so, since it was announced? And also in terms of the its value proposition, how does the ADP use case articulate that? >> So, very much so. So in terms of momentum, we're a global organization, as you mentioned, we are verticalized. So we have increasingly more expertise and expertise experience now within financial services that allows us to really engage and accelerate our momentum with the top banks, with the biggest asset managers by AUM, insurance companies, sovereign wealth funds on Snowflake. And obviously those data providers and solution providers that we engage with. So the momentum's really there. We're really moving very, very fast in a great market because we've got great opportunity with the capabilities that we have. I mean, ADP is just one of many use cases that we're working with and collaborations that we're taking to market. So yeah, the opportunity to monetize data and help our partners monetize the data has vastly increased within this space. >> When you think about... Oh go ahead, please. >> Yeah I was just going to say, and from our perspective, as we were getting into this, Snowflake was with us on the journey. And that's been a big deal. >> So when you think about data privacy, governance, et cetera, and public policy, it seems like you have, obviously you got things going on in Europe, and you got California, you have other states, there's increasing in complexity. You guys probably love that. (Dave laughs) More data warehouses, but where are we at with that whole? >> It's a great question. Privacy is... We hold some of the most critical information about people because that's our job to help people get paid. And we respect that as sort of our prime agenda. Part of it deals with the technology. How do you monitor, how do you see, make sure that you comply with all these regulations, but a lot of it has to do with the basic ethics of why you're doing and what you're doing. So we have a data and AI ethics board that meets and reviews our use cases. Make sure not only are we doing things properly to the regulation, but are these the types of products, are these the types of opportunities that we as a company want to stand behind on behalf of the consumers? Our company's been around 75 years. We talk about ourselves as a national asset. We have a trust relationship. We want to ensure that that trust relationship is never violated. >> Are you in a position where you can influence public policy and create more standards or framework. >> We actually are, right. We issue something every month called the National Employment Report. It actually tells you what's happening in the U.S. economy. We also issue it in some overseas countries like France. Because of that, we work a lot with various groups. And we can help shape, either data policy, we're involved in understanding although we don't necessarily want to be out in the front, but we want to learn about what's happening with federal trade commission, EOC, because at the end of the day we serve people, I always joke ADP, it's my grandfather's ADP. Well, it was actually my grandfather's ADP. (Dave laughs) He was a small businessman, and he used a ADP all those years ago. So we want to be part of that conversation because we want to continue to earn that trust every day. >> Well, plus your observation space is pretty wide. >> And you've got context and perspective on that that you can bring. >> We move somewhere between two, two and a half trillion dollars a year through our systems. And so we understand what's happening in the economy. >> What are some of the, oh sorry. >> Can your National Employment Report combined with a little Snowflake magic tell us what the hell's going to happen with this economy? >> It's really interesting you say that. Yeah, we actually can. >> Okay. (panelists laugh) >> I think when you think about the amount of data that we are working with, the types of partners that we're working with, the opportunities are infinite. They really, really are. >> So it's either a magic eight ball or it's a crystal ball, but you have it. >> We think- >> We've just uncovered that here on theCUBE. >> We think we have great partners. We have great data. We have a set of industry problems out there that we're working, collaboration with the community to be able to solve. >> What are some of the upcoming use cases Rinesh, that excite you, that are coming up in financial services- >> Great question. >> That snowflake is just going to knock out of the park. >> So look, I think there's a set of here and now problems that the industry faces, ESG's a good one. If you think about ESG, it means many different things from business ethics, to diversity, to your carbon footprint and every asset manager has to make sure they have now some form of green strategy that reflects the values of their investors. And every bank is looking to put in place sustainable lending to help their corporate customers transition. That's a big data problem. And so we're very much at the center of helping those organizations support those informed investors and help those corporates transition to a more sustainable landscape. >> Let me give you an example on Snowflake, we launched capabilities about diversity benchmarks. The first time in the industry companies can understand for their industry, their size, their location what their diversity profile looks like and their org chart profile looks like to differentiate or at least to understand are they doing the right things inside the business. The ability for banks to understand that and everything else, it's a big deal. And that was built on Snowflake. >> I think it's massive, especially in the context of the question around regulation 'cause we're seeing more and more disclosure agreements come out where regulators are making sure that there's no greenwashing taking place. So when you have really strong sources of data that are standardized, that allow that investment process to ingest that data, it does allow for a better outcome for investors. >> Real data, I mean, that diversity example they don't have to rely on a survey. >> It's not a survey. >> Anecdotes. >> It's coming right out of the transactional systems and it's updated, whenever those paychecks are run, whether it's weekly, whether it's biweekly or monthly, all that information gets updated and it's available. >> So it sounds like ADP is a facilitator of a lot of companies ESG initiatives, at least in part? >> Well, we partner with companies all the time. We have over 900,000 clients and all of them are... We've never spoken to a client who's not concerned about their people. And that's just good business. And so, yeah we're involved in that and we'll see where it goes over time now. >> I think there's tremendous opportunity if you think about the data that the ADP have in terms of diversity, in terms of gender pay gap. Huge, huge opportunity to incorporate that, as I said into the ESG principles and criteria. >> Good, 'cause that definitely is what needs to be addressed. (Lisa laughs) Guys thank you so much for joining Dave and me on the program, talking about Snowflake ADP, what you're doing together, and the massive potential that you're helping unlock with the value of data. We appreciate your insights and your time. >> Thank you for having us. >> Dave: Thanks guys. >> Thank you so much. >> For our guests, and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, live in Las Vegas at Snowflake Summit 22. Dave and I will be right back with our next guest. (upbeat music)

(upbeat music) >> Welcome back to theCUBE's continuing coverage of Snowflake Summit '22 live from Caesar's Forum in Las Vegas. I'm Lisa Martin, my cohost Dave Vellante, we've been here the last day and a half unpacking a lot of news, a lot of announcements, talking with customers and partners, and we have another great session coming for you next. We've got a customer and a partner talking tech and data mash. Please welcome Mitesh Shah, VP in market strategy at Elation. >> Great to be here. >> and Ash Naseer great, to have you, senior director of data engineering at Warner Brothers Discovery. Welcome guys. >> Thank you for having me. >> It's great to be back in person and to be able to really get to see and feel and touch this technology, isn't it? >> Yeah, it is. I mean two years or so. Yeah. Great to feel the energy in the conference center. >> Yeah. >> Snowflake was virtual, I think for two years and now it's great to kind of see the excitement firsthand. So it's wonderful. >> Th excitement, but also the boom and the number of customers and partners and people attending. They were saying the first, or the summit in 2019 had about 1900 attendees. And this is around 10,000. So a huge jump in a short time period. Talk a little bit about the Elation-Snowflake partnership and probably some of the acceleration that you guys have been experiencing as a Snowflake partner. >> Yeah. As a snowflake partner. I mean, Snowflake is an investor of us in Elation early last year, and we've been a partner for, for longer than that. And good news. We have been awarded Snowflake partner of the year for data governance, just earlier this week. And that's in fact, our second year in a row for winning that award. So, great news on that front as well. >> Repeat, congratulations. >> Repeat. Absolutely. And we're going to hope to make it a three-peat as well. And we've also been awarded industry competency badges in five different industries, those being financial services, healthcare, retail technology, and Median Telcom. >> Excellent. Okay. Going to right get into it. Data mesh. You guys actually have a data mesh and you've presented at the conference. So, take us back to the beginning. Why did you decide that you needed to implement something like data mesh? What was the impetus? >> Yeah. So when people think of Warner brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of HBO, you think of TNT, you think of CNN, we have 30 plus brands in our portfolio and each have their own needs. So the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against as an example, HBO, if Game of Thrones is going on. >> So, okay. So the, the impetus was to serve those lines of business better. Actually, given that you've got these different brands, it was probably easier than most companies. Cause if you're, let's say you're a big financial services company, and now you have to decide who owns what. CNN owns its own data products, HBO. Now, do they decide within those different brands, how to distribute even further? Or is it really, how deep have you gone in that decentralization? >> That's a great question. It's a very close partnership, because there are a number of data sets, which are used by all the brands, right? You think about people browsing websites, right? You know, CNN has a website, Warner brothers has a website. So for us to ingest that data for each of the brands to ingest that data separately, that means five different ways of doing things and you know, a big environment, right? So that is where our team comes into play. We ingest a lot of the common data sets, but like I said, any unique data sets, data sets regarding theatrical as an example, you know, Warner brothers does it themselves, you know, for streaming, HBO Max, does it themselves. So we kind of operate in partnership. >> So do you have a centralized data team and also decentralized data teams, right? >> That's right. >> So I love this conversation because that was heresy 10 years ago, five years ago, even, cause that's inefficient. But you've, I presume you've found that it's actually more productive in terms of the business output, explain that dynamic. >> You know, you bring up such a good point. So I, you know, I consider myself as one of the dinosaurs who started like 20 plus years ago in this industry. And back then, we were all taught to think of the data warehouse as like a monolithic thing. And the reason for that is the technology wasn't there. The technology didn't catch up. Now, 20 years later, the technology is way ahead, right? But like, our mindset's still the same because we think of data warehouses and data platforms still as a monolithic thing. But if you really sort of remove that sort of mental barrier, if you will, and if you start thinking about, well, how do I sort of, you know, federate everything and make sure that you let folks who are building, or are closest to the customer or are building their products, let them own that data and have a partnership. The results have been amazing. And if we were only sort of doing it as a centralized team, we would not be able to do a 10th of what we do today. So it's that massive scale in, in our company as well. >> And I should have clarified, when we talk about data mesh are we talking about the implementing in practice, the octagon sort of framework, or is this sort of your own sort of terminology? >> Well, so the interesting part is four years ago, we didn't have- >> It didn't exist. >> Yeah. It didn't exist. And, and so we, our principle was very simple, right? When we started out, we said, we want to make sure that our brands are able to operate independently with some oversight and guidance from our technology teams, right? That's what we set out to do. We did that with Snowflake by design because Snowflake allows us to, you know, separate those, those brands into different accounts. So that was done by design. And then the, the magic, I think, is the Snowflake data sharing where, which allows us to sort of bring data in here once, and then share it with whoever needs it. So think about HBO Max. On HBO Max, You not only have HBO Max content, but content from CNN, from Cartoon Network, from Warner Brothers, right? All the movies, right? So to see how The Batman movie did in theaters and then on streaming, you don't need, you know, Warner brothers doesn't need to ingest the same streaming data. HBO Max does it. HBO Max shares it with Warner brothers, you know, store once, share many times, and everyone works at their own pace. >> So they're building data products. Those data products are discoverable APIs, I presume, or I guess maybe just, I guess the Snowflake cloud, but very importantly, they're governed. And that's correct, where Elation comes in? >> That's precisely where Elation comes in, is where sort of this central flexible foundation for data governance. You know, you mentioned data mesh. I think what's interesting is that it's really an answer to the bottlenecks created by centralized IT, right? There's this notion of decentralizing that the data engineers and making the data domain owners, the people that know the data the best, have them be in control of publishing the data to the data consumers. There are other popular concepts actually happening right now, as we speak, around modern data stack. Around data fabric that are also in many ways underpinned by this notion of decentralization, right? These are concepts that are underpinned by decentralization and as the pendulum swings, sort of between decentralization and centralization, as we go back and forth in the world of IT and data, there are certain constants that need to be centralized over time. And one of those I believe is very much a centralized platform for data governance. And that's certainly, I think where we come in. Would love to hear more about how you use Elation. >> Yeah. So, I mean, elation helps us sort of, as you guys say, sort of, map, the treasure map of the data, right? So for consumers to find where their data is, that's where Elation helps us. It helps us with the data cataloging, you know, storing all the metadata and, you know, users can go in, they can sort of find, you know, the data that they need and they can also find how others are using data. So it's, there's a little bit of a crowdsourcing aspect that Elation helps us to do whereby you know, you can see, okay, my peer in the other group, well, that's how they use this piece of data. So I'm not going to spend hours trying to figure this out. You're going to use the query that they use. So yeah. >> So you have a master catalog, I presume. And then each of the brands has their own sub catalogs, is that correct? >> Well, for the most part, we have that master catalog and then the brands sort of use it, you know, separately themselves. The key here is all that catalog, that catalog isn't maintained by a centralized group as well, right? It's again, maintained by the individual teams and not only in the individual teams, but the folks that are responsible for the data, right? So I talked about the concept of crowdsourcing, whoever sort of puts the data in, has to make sure that they update the catalog and make sure that the definitions are there and everything sort of in line. >> So HBO, CNN, and each have their own, sort of access to their catalog, but they feed into the master catalog. Is that the right way to think about it? >> Yeah. >> Okay. And they have their own virtual data warehouses, right? They have ownership over that? They can spin 'em up, spin 'em down as they see fit? Right? And they're governed. >> They're governed. And what's interesting is it's not just governed, right? Governance is a, is a big word. It's a bit nebulous, but what's really being enabled here is this notion of self-service as well, right? There's two big sort of rockets that need to happen at the same time in any given organization. There's this notion that you want to put trustworthy data in the hands of data consumers, while at the same time mitigating risk. And that's precisely what Elation does. >> So I want to clarify this for the audience. So there's four principles of database. This came after you guys did it. And I wonder how it aligns. Domain ownership, give data, as you were saying to the, to the domain owners who have context, data as product, you guys are building data products, and that creates two problems. How do you give people self-service infrastructure and how do you automate governance? So the first two, great. But then it creates these other problems. Does that align with your philosophy? Where's alignment? What's different? >> Yeah. Data products is exactly where we're going. And that sort of, that domain based design, that's really key as well. In our business, you think about who the customer is, as an example, right? Depending on who you ask, it's going to be, the answer might be different, you know, to the movie business, it's probably going to be the person who watches a movie in a theater. To the streaming business, to HBO Max, it's the streamer, right? To others, someone watching live CNN on their TV, right? There's yet another group. Think about all the franchising we do. So you see Batman action figures and T-shirts, and Warner brothers branded stuff in stores, that's yet another business unit. But at the end of the day, it's not a different person, it's you and me, right? We do all these things. So the domain concept, make sure that you ingest data and you bring data relevant to the context, however, not sort of making it so stringent where it cannot integrate, and then you integrate it at a higher level to create that 360. >> And it's discoverable. So the point is, I don't have to go tap Ash on the shoulder, say, how do I get this data? Is it governed? Do I have access to it? Give me the rules of it. Just, I go grab it, right? And the system computationally automates whether or not I have access to it. And it's, as you say, self-service. >> In this case, exactly right. It enables people to just search for data and know that when they find the data, whether it's trustworthy or not, through trust flags, and the like, it's doing both of those things at the same time. >> How is it an enabler of solving some of the big challenges that the media and entertainment industry is going through? We've seen so much change the last couple of years. The rising consumer expectations aren't going to go back down. They're only going to come up. We want you to serve us up content that's relevant, that's personalized, that makes sense. I'd love to understand from your perspective, Mitesh, from an industry challenges perspective, how does this technology help customers like Warner Brothers Discovery, meet business customers, where they are and reduce the volume on those challenges? >> It's a great question. And as I mentioned earlier, we had five industry competency badges that were awarded to us by Snowflake. And one of those four, Median Telcom. And the reason for that is we're helping media companies understand their audiences better, and ultimately serve up better experiences for their audiences. But we've got Ash right here that can tell us how that's happening in practice. >> Yeah, tell us. >> So I'll share a story. I always like to tell stories, right? Once once upon a time before we had Elation in place, it was like, who you knew was how you got access to the data. So if I knew you and I knew you had access to a certain kind of data and your access to the right kind of data was based on the network you had at the company- >> I had to trust you. >> Yeah. >> I might not want to give up my data. >> That's it. And so that's where Elation sort of helps us democratize it, but, you know, puts the governance and controls, right? There are certain sensitive things as well, such as viewership, such as subscriber accounts, which are very important. So making sure that the right people have access to it, that's the other problem that Elation helps us solve. >> That's precisely part of our integration with Snowflake in particular, being able to define and manage policies within Elation. Saying, you know, certain people should have access to certain rows, doing column level masking. And having those policies actually enforced at the Snowflake data layer is precisely part of our value product. >> And that's automated. >> And all that's automated. Exactly. >> Right. So I don't have to think about it. I don't have to go through the tap on their shoulder. What has been the impact, Ash, on data quality as you've pushed it down into the domains? >> That's a great question. So it has definitely improved, but data quality is a very interesting subject, because back to my example of, you know, when we started doing things, we, you know, the centralized IT team always said, well, it has to be like this, Right? And if it doesn't fit in this, then it's bad quality. Well, sometimes context changes. Businesses change, right? You have to be able to react to it quickly. So making sure that a lot of that quality is managed at the decentralized level, at the place where you have that business context, that ensures you have the most up to date quality. We're talking about media industry changing so quickly. I mean, would we have thought three years ago that people would watch a lot of these major movies on streaming services? But here's the reality, right? You have to react and, you know, having it at that level just helps you react faster. >> So data, if I play that back, data quality is not a static framework. It's flexible based on the business context and the business owners can make those adjustments, cause they own the data. >> That's it. That's exactly it. >> That's awesome. Wow. That's amazing progress that you guys have made. >> In quality, if I could just add, it also just changes depending on where you are in your data pipeline stage, right? Data, quality data observability, this is a very fast evolving space at the moment, and if I look to my left right now, I bet you I can probably see a half-dozen quality observability vendors right now. And so given that and given the fact that Elation still is sort of a central hub to find trustworthy data, we've actually announced an open data quality initiative, allowing for best-of-breed data quality vendors to integrate with the platform. So whoever they are, whatever tool folks want to use, they can use that particular tool of choice. >> And this all runs in the cloud, or is it a hybrid sort of? >> Everything is in the cloud. We're all in the cloud. And you know, again, helps us go faster. >> Let me ask you a question. I could go on forever in this topic. One of the concepts that was put forth is whether it's a Snowflake data warehouse or a data bricks, data lake, or an Oracle data warehouse, they should all be inclusive. They should just be a node on the mesh. Like, wow, that sounds good. But I haven't seen it yet. Right? I'm guessing that Snowflake and Elation enable all the self-serve, all this automated governance, and that including those other items, it's got to be a one-off at this point in time. Do you ever see you expanding that scope or is it better off to just kind of leave it into the, the Snowflake data cloud? >> It's a good question. You know, I feel like where we're at today, especially in terms of sort of technology giving us so many options, I don't think there's a one size fits all. Right? Even though we are very heavily invested in Snowflake and we use Snowflake consistently across the organization, but you could, theoretically, could have an architecture that blends those two, right? Have different types of data platforms like a teradata or an Oracle and sort of bring it all together today. We have the technology, you know, that and all sorts of things that can make sure that you query on different databases. So I don't think the technology is the problem, I think it's the organizational mindset. I think that that's what gets in the way. >> Oh, interesting. So I was going to ask you, will hybrid tables help you solve that problem? And, maybe not, what you're saying, it's the organization that owns the Oracle database saying, Hey, we have our system. It processes, it works, you know, go away. >> Yeah. Well, you know, hybrid tables I think, is a great sort of next step in Snowflake's evolution. I think it's, in my opinion, I, think it's a game changer, but yeah. I mean, they can still exist. You could do hybrid tables right on Snowflake, or you could, you know, you could kind of coexist as well. >> Yeah. But, do you have a thought on this? >> Yeah, I do. I mean, we're always going to live in a time where you've got data distributed in throughout the organization and around the globe. And that could be even if you're all in on Snowflake, you could have data in Snowflake here, you could have data in Snowflake in EMEA and Europe somewhere. It could be anywhere. By the same token you might be using. Every organization is using on-premises systems. They have data, they naturally have data everywhere. And so, you know, this one solution to this is really centralizing, as I mentioned, not just governance, but also metadata about all of the data in your organization so that you can enable people to search and find and discover trustworthy data no matter where it is in your organization. >> Yeah. That's a great point. I mean, if you have the data about the data, then you can, you can treat these independent nodes. That's just that. Right? And maybe there's some advantages of putting it all in the Snowflake cloud, but to your point, organizationally, that's just not feasible. The whole, unfortunately, sorry, Snowflake, all the world's data is not going to go into Snowflake, but they play a key role in accelerating, what I'm hearing, your vision of data mesh. >> Yeah, absolutely. I think going forward in the future, we have to start thinking about data platforms as just one place where you sort of dump all the data. That's where the mesh concept comes in. It is going to be a mesh. It's going to be distributed and organizations have to be okay with that. And they have to embrace the tools. I mean, you know, Facebook developed a tool called Presto many years ago that that helps them solve exactly the same problem. So I think the technology is there. I think the organizational mindset needs to evolve. >> Yeah. Definitely. >> Culture. Culture is one of the hardest things to change. >> Exactly. >> Guys, this was a masterclass in data mesh, I think. Thank you so much for coming on talking. >> We appreciate it. Thank you so much. >> Of course. What Elation is doing with Snowflake and with Warner Brothers Discovery, Keep that content coming. I got a lot of stuff I got to catch up on watching. >> Sounds good. Thank you for having us. >> Thanks guys. >> Thanks, you guys. >> For Dave Vellante, I'm Lisa Martin. You're watching theCUBE live from Snowflake Summit '22. We'll be back after a short break. (upbeat music)

>>Hey everyone. Welcome back to the cube. Lisa Martin, with Dave ante. We're here in Las Vegas with snowflake at the snowflake summit 22. This is the fourth annual there's close to 10,000 people here. Lots going on. Customers, partners, analysts, cross media, everyone talking about all of this news. We've got a couple of guests joining us. We're gonna unpack snow park. Torston grabs the director of product management at snowflake and Joe. No NTY AI and MDM architect at Allegis group. Guys. Welcome to the program. Thank >>You so much for having >>Us. Isn't it great to be back in person? It is. >>Oh, wonderful. Yes, it >>Is. Indeed. Joe, talk to us a little bit about Allegis group. What do you do? And then tell us a little bit about your role specifically. >>Well, Allegis group is a collection of OPCA operating companies that do staffing. We're one of the biggest staffing companies in north America. We have a presence in AMEA and in the APAC region. So we work to find people jobs, and we help get 'em staffed and we help companies find people and we help individuals find >>People incredibly important these days, excuse me, incredibly important. These days. It is >>Very, it very is right >>There. Tell me a little bit about your role. You are the AI and MDM architect. You wear a lot of hats. >>Okay. So I'm a architect and I support both of those verticals within the company. So I work, I have a set of engineers and data scientists that work with me on the AI side, and we build data science models and solutions that help support what the company wants to do, right? So we build it to make business business processes faster and more streamlined. And we really see snow park and Python helping us to accelerate that and accelerate that delivery. So we're very excited about it. >>Explain snow park for, for people. I mean, I look at it as this, this wonderful sandbox. You can bring your own developer tools in, but, but explain in your words what it >>Is. Yeah. So we got interested in, in snow park because increasingly the feedback was that everybody wants to interact with snowflake through SQL. There are other languages that they would prefer to use, including Java Scala and of course, Python. Right? So then this led down to the, our, our work into snow park where we're building an infrastructure that allows us to host other languages natively on the snowflake compute platform. And now here, what we're, what we just announced is snow park for Python in public preview. So now you have the ability to natively run Python code on snowflake and benefit from the thousands of packages and libraries that the open source community around Python has contributed over the years. And that's a huge benefit for data scientists. It is ML practitioners and data engineers, because those are the, the languages and packages that are popular with them. So yeah, we very much look forward to working with the likes of you and other data scientists and, and data engineers around the Python ecosystem. >>Yeah. And, and snow park helps reduce the architectural footprint and it makes the data pipelines a little easier and less complex. We have a, we had a pipeline and it works on DMV data. And we converted that entire pipeline from Python, running on a VM to directly running down on snowflake. Right. We were able to eliminate code because you don't have to worry about multi threading, right? Because we can just set the warehouse size through a task, no more multi threading, throw that code away. Don't need to do it anymore. Right. We get the same results, but the architecture to run that pipeline gets immensely easier because it's a store procedure that's already there. And implementing that calling to that store procedure is very easy. The architecture that we use today uses six different components just to be able to run that Python code on a VM within our ecosystem to make sure that it runs on time and is scheduled and all of that. Right. But with snowflake, with snowflake and snow park and snowflake Python, it's two components. It's the store procedure and our ETL tool calling it. >>Okay. So you've simplified that, that stack. Yes. And, and eliminated all the other stuff that you had to do that now Snowflake's doing, am I correct? That you're actually taking the application development stack and the analytics stack and bringing them together? Are they merging? >>I don't know. I think in a way I'm not real sure how I would answer that question to be quite honest. I think with stream lit, there's a little bit of application that's gonna be down there. So you could maybe start to say that I'd have to see how that carries out and what we do and what we produce to really give you an answer to that. But yeah, maybe in a >>Little bit. Well, the reason I asked you is because you talk, we always talk about injecting data into apps, injecting machine intelligence and ML and AI into apps, but there are two separate stacks today. Aren't they >>Certainly the two are getting closer >>To Python Python. It gets a little better. Explain that, >>Explain, explain how >>That I just like in the keynote, right? The other day was SRE. When she showed her sample application, you can start to see that cuz you can do some data pipelining and data building and then throw that into a training module within Python, right down inside a snowflake and have it sitting there. Then you can use something like stream lit to, to expose it to your users. Right? We were talking about that the other day, about how do you get an ML and AI, after you have it running in front of people, we have a model right now that is a Mo a predictive and prescriptive model of one of our top KPIs. Right. And right now we can show it to everybody in the company, but it's through a Jupyter notebook. How do I deliver it? How do I get it in the front of people? So they can use it well with what we saw was streamlet, right? It's a perfect match. And then we can compile it. It's right down there on snowflake. And it's completely easier time to delivery to production because since it's already part of snowflake, there's no architectural review, right. As long as the code passes code review, and it's not poorly written code and isn't using a library that's dangerous, right. It's a simple deployment to production. So because it's encapsulated inside of that snowflake environment, we have approval to just use it. However we see fit. >>It's very, so that code delivery, that code review has to occur irrespective of, you know, not always whatever you're running it on. Okay. So I get that. And, and, but you, it's a frictionless environment you're saying, right. What would you have had to do prior to snowflake that you don't have to do now? >>Well, one, it's a longer review process to allow me to push the solution into production, right. Because I have to explain to my InfoSec people, right? My other it's not >>Trusted. >>Well, well don't use that word. No. Right? It got, there are checks and balances in everything that we do, >>It has to be verified. And >>That's all, it's, it's part of the, the, what I like to call the good bureaucracy, right? Those processes are in place to help all of us stay protected. >>It's the checklist. Yeah. That you >>Gotta go to. >>That's all it is. It's like fly on a plane. You, >>But that checklist gets smaller. And sometimes it's just one box now with, with Python through snow park, running down on the snowflake platform. And that's, that's the real advantage because we can do things faster. Right? We can do things easier, right? We're doing some mathematical data science right now and we're doing it through SQL, but Python will open that up much easier and allow us to deliver faster and more accurate results and easier not to mention, we're gonna try to bolt on the hybrid tables to that afterwards. >>Oh, we had talk about that. So can you, and I don't, I don't need an exact metric, but when you say faster talking 10% faster, 20% faster, 50% path >>Faster, it really depends on the solution. >>Well, gimme a range of, of the worst case, best case. >>I, I really don't have that. I don't, I wish I did. I wish I had that for you, but I really don't have >>It. I mean, obviously it's meaningful. I mean, if >>It is meaningful, it >>Has a business impact. It'll >>Be FA I think what it will do is it will speed up our work inside of our iterations. So we can then, you know, look at the code sooner. Right. And evaluate it sooner, measure it sooner, measure it faster. >>So is it fair to say that as a result, you can do more. Yeah. That's to, >>We be able do more well, and it will enable more of our people because they're used to working in Python. >>Can you talk a little bit about, from an enablement perspective, let's go up the stack to the folks at Allegis who are on the front lines, helping people get jobs. What are some of the benefits that having snow park for Python under the hood, how does it facilitate them being able to get access to data, to deliver what they need to, to their clients? >>Well, I think what we would use snowflake for a Python for there is when we're building them tools to let them know whether or not a user or a piece of talent is already within our system. Right. Things like that. Right. That's how we would leverage that. But again, it's also new. We're still figuring out what solutions we would move to Python. We are, we have some targeted, like we're, I have developers that are waiting for this and they're, and they're in private preview. Now they're playing around with it. They're ready to start using it. They're ready to start doing some analytical work on it, to get some of our analytical work out of, out of GCP. Right. Because that's where it is right now. Right. But all the data's in snowflake and it just, but we need to move that down now and take the data outta the data wasn't in snowflake before. So there, so the dashboards are up in GCP, but now that we've moved all of that data down in, down in the snowflake, the team that did that, those analytical dashboards, they want to use Python because that's the way it's written right now. So it's an easier transformation, an easier migration off of GCP and get us into snow, doing everything in snowflake, which is what we want. >>So you're saying you're doing the visualization in GCP. Is that righting? >>It's just some dashboarding. That's all, >>Not even visualization. You won't even give for. You won't even give me that. Okay. Okay. But >>Cause it's not visualization. It's just some D boardings of numbers and percentages and things like that. It's no graphic >>And it doesn't make sense to run that in snowflake, in GCP, you could just move it into AWS or, or >>No, we, what we'll be able to do now is all that data before was in GCP and all that Python code was running in GCP. We've moved all that data outta GCP, and now it's in snowflake and now we're gonna work on taking those Python scripts that we thought we were gonna have to rewrite differently. Right. Because Python, wasn't available now that Python's available, we have an easier way of getting those dashboards back out to our people. >>Okay. But you're taking it outta GCP, putting it to snowflake where anywhere, >>Well, the, so we'll build the, we'll build those, those, those dashboards. And they'll actually be, they'll be displayed through Tableau, which is our enterprise >>Tool for that. Yeah. Sure. Okay. And then when you operationalize it it'll go. >>But the idea is it's an easier pathway for us to migrate our code, our existing code it's in Python, down into snowflake, have it run against snowflake. Right. And because all the data's there >>Because it's not a, not a going out and coming back in, it's all integrated. >>We want, we, we want our people working on the data in snowflake. We want, that's our data platform. That's where we want our analytics done. Right. We don't want, we don't want, 'em done in other places. We when get all that data down and we've, we've over our data cloud journey, we've worked really hard to move all of that data. We use out of existing systems on prem, and now we're attacking our, the data that's in GCP and making sure it's down. And it's not a lot of data. And we, we fixed it with one data. Pipeline exposes all that data down on, down in snowflake now. And we're just migrating our code down to work against the snowflake platform, which is what we want. >>Why are you excited about hybrid tables? What's what, what, what's the >>Potential hybrid tables I'm excited about? Because we, so some of the data science that we do inside of snowflake produces a set of results and there recommendations, well, we have to get those recommendations back to our people back into our, our talent management system. And there's just some delays. There's about an hour delay of delivering that data back to that team. Well, with hybrid tables, I can just write it to the hybrid table. And that hybrid table can be directly accessed from our talent management system, be for the recruiters and for the hiring managers, to be able to see those recommendations and near real time. And that that's the value. >>Yep. We learned that access to real time. Data it in recent years is no longer a nice to have. It's like a huge competitive differentiator for every industry, including yours guys. Thank you for joining David me on the program, talking about snow park for Python. What that announcement means, how Allegis is leveraging the technology. We look forward to hearing what comes when it's GA >>Yeah. We're looking forward to, to it. Nice >>Guys. Great. All right guys. Thank you for our guests and Dave ante. I'm Lisa Martin. You're watching the cubes coverage of snowflake summit 22 stick around. We'll be right back with our next guest.

(upbeat techno music) >> Hey everyone, and welcome back to theCUBE's coverage of Snowflake Summit '22 live from Caesar's Forum in beautiful, warm, and sunny Las Vegas. I'm Lisa Martin. I got the Chris and Chris show, next. Bear with me. Chris Degnan joins us again. One of our alumni, the Chief Revenue Officer at Snowflake. Good to have you back, Chris. >> Thank you for having us. >> Lisa: Chris Grusz also joins us. Director of Business Development AWS Marketplace and Service Catalog at AWS. Chris and Chris, welcome. >> Thank you. >> Thank you. >> Thank you. Good to be back in person. >> Isn't it great. >> Chris G: It's so much better. >> Chris D: Yeah. >> Nothing like it. So let's talk. There's been so much momentum, Chris D, at Snowflake the last few years. I mean the momentum at this show since we launched yesterday, I know you guys launched the day before with partners, has been amazing. A lot of change, and it's like this for Snowflake. Talk to us about AWS working together with Snowflake and some of the benefits in it from your customer. And then Chris G, I'll go to you for the same question. >> Chris G: Yep. >> You know, first of all, it's awesome. Like, I just, you know, it's been three years since I've had a Snowflake Summit in person, and it's crazy to see the growth that we've seen. You know, I can't, our first cloud that we ever launched on top of was, was AWS, and AWS is our largest cloud, you know, in in terms of revenue today. And they've been, they just kind of know how to do it right. And they've been a wonderful partner all along. There's been challenges, and we've kind of leaned in together and figured out ways to work together, you know, and to solve those challenges. So, been a wonderful partnership. >> And talk about it, Chris G, from your perspective obviously from a coopetition perspective. >> Yep. >> AWS has databases, cloud data forms. >> Chris G: Yeah. >> Talk to us about it. What was the impetus for the partnership with Snowflake from AWS's standpoint? >> Yeah, well first and foremost, they're building on top of AWS. And so that, by default, makes them a great partner. And it's interesting, Chris and I have been working together for, gosh, seven years now? And the relationship's come a really long way. You know, when we first started off, we were trying to sort out how we were going to work together, when we were competing, and when we're working together. And, you know, you fast forward to today, and it's just such a good relationship. Because both companies work backwards from customers. And so that's, you know, kind of in both of our DNA. And so if the customer makes that selection, we're going to support them, even from an AWS perspective. When they're going with Snowflake, that's still a really good thing for AWS, 'cause there's a lot of associated services that Snowflake either integrates to, or we're integrating to them. And so, it's really kind of contributed to how we can really work together in a co-sell motion. >> Talk to us, talk about that. The joint GOTO market and the co-selling motion from Snowflake's perspective, how do customers get engaged? >> Well, I think, you know, typically we, where we are really good at co-selling together is we identify on premise systems. So whether it's, you know, some Legacy UDP system, some Legacy database solution, and they want to move to the cloud? You know, Amazon is all in on getting everyone to the cloud. And I think that's their approach they've taken with us is saying we're really good at accelerating that adoption and moving all these, you know, massive workloads into the cloud. And then to Chris's point, you know, we've integrated so nicely into things like SageMaker and other tool sets. And we, we even have exciting scenarios where they've allowed us to use, you know, some of their Amazon.com retail data sets that we actually use in data sharing via the partnership. So we continue to find unique ways to partner with our great friends at Amazon. >> Sounds like a very deep partnership. >> Chris D: Yeah. Absolutely. >> Chris G: Oh, absolutely, yeah. We're integrating into Snowflake, and they're integrating to AWS. And so it just provides a great combined experience for our customers. And again, that's kind of what we're both looking forward from both of our organizations. >> That customer centricity is, >> Yeah. >> is I think the center of the flywheel that is both that both of you, your companies have. Chris D, talk about the the industry's solutions, specific, industry-specific solutions that Snowflake and AWS have. I know we talked yesterday about the pivot from a sales perspective >> Chris D: Yes. >> That snowflake made in recent months. Talk to us about the industries that you are help, really targeting with AWS to help customers solve problems. >> Yeah. I think there's, you know, we're focused on a number of industries. I think, you know, some of the examples, like I said, I gave you the example of we're using data sharing to help the retail space. And I think it's a really good partnership. Because some of the, some companies view Amazon as a competitor in the retail space, and I think we kind of soften that blow. And we actually leverage some of the Amazon.com data sets. And this is where the partnership's been really strong. In the healthcare space, in the life sciences space, we have customers like Anthem, where we're really focused on helping actually Anthem solve real business problems. Not necessarily like technical problems. It's like, oh no, they want to get, you know, figure out how they can get the whole customer and take care of their whole customer, and get them using the Anthem platform more effectively. So there's a really great, wonderful partnership there. >> We've heard a lot in the last day and a half on theCUBE from a lot of retail customers and partners. There seems to be a lot of growth in that. So there's so much change in the retail market. I was just talking with Click and Snowflake about Urban Outfitters, as an example. And you think of how what these companies are doing together and obviously AWS and Snowflake, helping companies not just pivot during the pandemic, but really survive. I mean, in the beginning with, you know, retail that didn't have a digital presence, what were they going to do? And then the supply chain issues. So it really seems to be what Snowflake and its partner Ecosystem is doing, is helping companies now, obviously, thrive. But it was really kind of like a no-go sort of situation for a lot of industries. >> Yeah, and I think the neat part of, you know, both the combined, you know, Snowflake and AWS solution is in, a good example is DoorDash, you know. They had hyper growth, and they could not have handled, especially during COVID, as we all know. We all used DoorDash, right? We were just talking about it. Chipotle, like, you know, like (laughter) and I think they were able to really take advantage of our hyper elastic platforms, both on the Amazon side and the Snowflake side to scale their business and meet the high demand that they were seeing. And that's kind of some of the great examples of where we've enabled customer growth to really accelerate. >> Yeah. Yeah, right. And I'd add to that, you know, while we saw good growth for those types of companies, a lot of your traditional companies saw a ton of benefit as well. Like another good example, and it's been talked about here at the show, is Western Union, right? So they're a company that's been around for a long time. They do cross border payments and cross currency, you know, exchanges, and, you know, like a lot of companies that have been around for a while, they have data all over the place. And so they started to look at that, and that became an inhibitor to their growth. 'Cause they couldn't get a full view of what was actually going on. And so they did a lengthy evaluation, and they ended up going with Snowflake. And, it was great, 'cause it provided a lot of immediate benefits, so first of all, they were able to take all those disparate systems and pull that into Snowflake. So they finally had a single source of the truth, which was lacking before that. So that was one of the big benefits. The second benefit, and Chris has mentioned this a couple times, is the fact that they could use data sharing. And so now they could pull in third data. And now that they had a holistic view of their entire data set, they could pull in that third party data, and now they could get insights that they never could get before. And so that was another large benefit. And then the third part, and this is where the relationship between AWS and Snowflake is great, is they could then use Amazon SageMaker. So one of the decisions that Western Union made a long time ago is they use R for their data science platform, and SageMaker supports R. And so it really allowed them to dovetail the skill sets that they had around data science into SageMaker. They could now look across all of Snowflake. And so that was just a really good benefit. And so it drove the cost down for Western Union which was a big benefit, but the even bigger benefit is they were now able to start to package and promote different solutions to their customers. So they were effectively able to monetize all the data that they were now getting and the information they were getting out of Snowflake. And then of course, once it was in there, they could also use things like Tableau or ThoughtSpot, both of which available in AWS Marketplace. And it allowed them to get all kinds of visualization of data that they never got in the past. >> The monetization piece is, is interesting. It's so challenging for organizations, one, to get that single source view, to be able to have a customer 360, but to also then be able to monetize data. When you're in customer conversations, how do you help customers on that journey, start? Because the, their competitors are clearly right behind them, ready to take first place spot. How do you help customers go, all right this is what we're going to do to help you on this journey with AWS to monetize your data? >> I think, you know, it's everything from, you know, looking at removing the silos of data. So one of the challenges they've had is they have these Legacy systems, and a lot of times they don't want to just take the Legacy systems and throw them into the cloud. They want to say, we need a holistic view of our customer, 360 view of our customer data. And then they're saying, hey, how can we actually monetize that data? That's where we do everything from, you know, Snowflake has the data marketplace where we list it in the data marketplace. We help them monetize it there. And we use some of the data sets from Amazon to help them do that. We use the technologies like Chris said with SageMaker and other tool sets to help them realize the value of their data in a real, meaningful way. >> So this sounds like a very strategic and technical partnership. >> Yeah, well, >> On both sides. >> It's technical and it's GOTO market. So if you take a look at, you know, Snowflake where they've built over 20 integrations now to different AWS services. So if you're using S3 for object storage, you can use Snowflake on top of that. If you want to load up Snowflake with Glue which is our ETL tool, you can do that. If you want to use QuickSite to do your data visualization on top of Snowflake, you can do that. So they've built integration to all of our services. And then we've built integrations like SageMaker back into Snowflake, and so that supports all kinds of specific customer use cases. So if you think of people that are doing any kind of cloud data platform workload, stuff like data engineering, data warehousing, data lakes, it could be even data applications, cyber security, unistore type things, Snowflake does an excellent job of helping our customers get into those types of environments. And so that's why we support the relationship with a variety of, you know, credit programs. We have a lot of co-sell motions on top of these technical integrations because we want to make sure that we not only have the right technical platform, but we've got the right GOTO market motion. And that's super important. >> Yeah, and I would add to that is like, you know one of the things that customers do is they make these large commitments to Amazon. And one of the best things that Amazon did was allow those customers to draw down Snowflake via the AWS Marketplace. So it's been wonderful to his point around the GOTO market, that was a huge issue for us. And, and again, this is where Amazon was innovative on identifying the ways to help make the customer have a better experience >> Chris G: Yeah. >> Chris D: and put the customer first. And this has been, you know, wonderful partnership there. >> Yeah. It really has. It's been a great, it's been really good. >> Well, and the customers are here. Like we said, >> Yep. >> Yes. Yes they are. >> we're north of 10,000 folks total, and customers are just chomping at the bit. There's been so much growth in the last three years from the last time, I think I heard the 2019 Snowflake Summit had about 1500 people. And here we are at 10,000 plus now, and standing-room-only keynote, the very big queue to get in, people turned away, pushed back to an overflow area to be able to see that, and that was yesterday. I didn't even get a chance to see what it was like today, but I imagine it was probably the same. Talk about the, when you're in customer conversations, where do you bring, from a GTM perspective, Where do you bring Snowflake into the conversation? >> Yeah >> Obviously, there's Redshift there, what does that look like? I imagine it follows the customer's needs, challenges. >> Exactly. >> Compelling events. >> Yeah. We're always going to work backwards from the customer need, and so that is the starting point for kindling both organizations. And so we're going to, you know, look at what they need. And from an AWS perspective, you know, if they're going with Snowflake, that's a very good thing. Right? 'Cause one of the things that we want to support is a selection experience to our AWS customers and make sure that no matter what they're doing, they're getting a very good, supported experience. And so we're always going to work backwards from the customer. And then once they make that technology decision, then we're going to support them, as I mentioned, with a whole bunch of co-sell resources. We have technical resources in the field. We have credit programs and in, you know, and, of course, we're going to market in a variety of different verticals as well with Snowflake. If you take a look at all the industry clouds that Snowflake has spun up, financial services and healthcare, and media entertainment, you know, those are all very specific use cases that are very valuable to an AWS customer. And AWS is going more and more to market on a vertical approach, and so Snowflake really just fits right in with our overall strategy. >> Right. Sounds like very tight alignment there. That mission alignment that Frank talked about yesterday. I know he was talking about that with respect to customers, but it sounds like there's a mission alignment between AWS and Snowflake. >> Mission alignment, yeah. >> I live that every week. (laughter) >> Sorry if I brought up a pain point. >> Yeah. Little bit. No. >> Guys, what's, in terms of use cases, obviously we've been here for a couple days. I'm sure you've had tremendous feedback, >> Chris G: Yeah. >> from, from customers, from partners, from the ecosystem. What's next, what can we expect to hear next? Maybe give us a preview of re:Invent in the few months. >> Preview of re:Invent. Yeah. No, well, one of the things we really want to start doing is just, you know, making the use case of, of launching Snowflake on AWS a lot easier. So what can we do to streamline those types of experiences? 'Cause a lot of times we'll find that customers, once they buy a third party solution like Snowflake, they have to then go through a whole series of configuration steps, and what can we do to streamline that? And so we're going to continue to work on that front. One of the other places that we've been exploring with Snowflake is how we work with channel partners. And, you know, when we first launched Marketplace it was really more of an app store model that was ISVs on one side and channel partners on the other, and there wasn't really a good fit for channel partners. And so four years ago we retrofitted the platform and have opened it up to resellers like an SHI or SIs like Salam or Deloitte who are top, two top SIs for Snowflake. And now they can use Marketplace to resell those technologies and also sell their services on top of that. So Snowflake's got a big, you know, practice with Salam, as I mentioned. You know, Salam can now sell through Marketplace and they can actually sell that statement of work and put that on the AWS bill all by virtue of using Marketplace, that automation platform. >> Ease of use for customers, ease of use for partners as well. >> Yes. >> And that ease of use is it's no joke. It's, it's not just a marketing term. It's measurable and it's about time-to-value, time-to-market, getting customers ahead of their competition so that they can be successful. Guys, thanks for joining me on theCUBE today. Talking about AWS and >> Nice to be back. Nice to be back in person. >> Isn't it nice to be back. It's great to be actually sitting across from another human. >> Exactly. >> Thank you so much for your insights, what you shared about the partnership and where it's going. We appreciate it. >> Thank you. >> Cool. Thank you. >> Thank you. >> All right guys. For Chris and Chris, I'm Lisa Martin, here watching theCUBE live from Las Vegas. I'll be back with my next guest momentarily, so stick around. (Upbeat techno music)

>>Welcome back to Vegas. Lisa Martin here covering snowflake summit 22. We are live at Caesar's forum. A lot of guests here about 10,000 attendees, actually 10,000 plus a lot of folks here at the momentum and the buzz. I gotta tell you the last day and a half we've been covering this event is huge. It's probably some of the biggest we've seen in a long time. We're very pleased to welcome back. One of our cube alumni to the program, Ron Tayo principal and chief data officer at Deloitte one. It's great to have you joining us. >>Yeah, no, thank you. Super excited to be here with you today. >>Isn't it great to be back in person? Oh, >>I love it. I mean the, the energy, the, you know, connections that we're making definitely, definitely loving and loving the experience. >>Good experience, but the opportunity to connect with customers. Yes. I'm hearing a lot of conversations from snowflake folks from their partners like Deloitte from customers themselves. Like it's so great to be back in person. And they're really talking about some of the current challenges that are being faced by so many industries. >>That's right. Oh, that, that is, you know, I would say as a consultant, you know, it all comes down to that personal connection and that relationship. And so I am, I'm all for this and love, you know, being able to connect with our customers. >>Yeah. Talk to me about the Deloitte snowflake partnership. Obviously a ton of news announced from snowflake yesterday. Snowflake is a rocket ship. Talk to us about the partnership, what you guys do together, maybe some joint customer examples. >>Yeah. I mean, so snowflake is a strategic Alliance partner. We won the, you know, SI partner of the year award and for us, the, the shift and the opportunity to help our clients modernize and achieve a level of data maturity in their journey is, is strategically it's super important. And it's really about how do we help them leverage, you know, snowflake has underlying data platform to ultimately achieve, you know, broader goals around, you know, their business strategy. And our approach is always very much connected to overarching business strategies and sense of, is it a finance transformation, a supply chain transformation, a customer transformation, and what are the goals of those transformations and how do we ensure that data is a critical component to enabling that and with, you know, technologies and vendors and partners like snowflake, allowing us to even do that at a faster, better, cheaper pace only increases the overall business case and the value and the impact that it generates. >>And so we are super, super excited about our partnership with snowflake and we believe, you know, the journey is very, very bright. You know, we, this is the future, you know, often tell folks that, you know, data has and will continue to be more valuable than sort of the systems that own it and manage it. And I think we're starting to see that. I think the topic that I discussed today around data collaboration and data sharing is an example of how we're starting to see, you know, the importance and the value of data, you know, become way more important and more of the focus around the strategy for, for organizations >>As the chief data officer, what do data sharing and data collaboration mean to somebody in your position and what are some of the conversations you have with customer other CDOs at customer organizations? >>Yeah, so, so my role is, is sort of twofold. I, I am responsible for our internal data strategy. So when you think about Deloitte as a professional service organization, across four unique businesses, I am a customer of snowflake in our own data modernization journey, and we have our own strategy on how and what we share, not only internally across our businesses, but also externally across, you know, our partners. So, so I bring that perspective, but then I also am a client service professional and serve our clients in their own journey. So I often feel very privileged in, in the opportunity to be able to sort of not only share my own experience from a Deloitte perspective, but also in how we help our clients >>Talk about data maturation. You mentioned, you know, the volume of data just only continues to grow. We've seen so much growth in the last two years alone of data. We've seen all of us be so dependent on things like media and entertainment and retail, eCommerce, healthcare, and life sciences. What, how do you define data maturation and how does Deloitte and snowflake help companies create a pathway to get there? >>Yeah. Yeah. So I would say step one for us is all about the overarching business strategy. And when you sort of double click on the big, broad business strategy and what that means from a data strategy perspective, we have to develop business models where there is an economical construct to the value of data. And it's extremely important specifically when we talk about sharing and collaborating data, I would say the, the, the, the assumption or the, or, or, or, or the posture typically seems to be, it's a one way relationship, our strategy and what we're pushing, you know, again, not only internally within ourselves, but also with our clients, is it has to be a bidirectional relationship. And so you, you hear of, of the concepts of, you know, the, the, the data clean room where you have two partners coming together and agreeing with certain terms to share data bidirectionally. Like I do believe that is the future in how we need to do, you know, more data collaboration, more data sharing at a scale that we've not quite seen. Yes. Yet >>The security and privacy areas are increasingly critical. We've seen the threat landscape change so dramatically the last couple of years, it's not, will we get hit by a cyber talk? It's when yes. For every industry, right? The privacy legislation that just we've seen it with GDPR, CCPA is gonna become CPR in California, other states doing the same thing. How do you help customers kind of balance that line of being able to share data equitably between organizations between companies do so in a secure way, and in a way that ensures data privacy will be maintained. >>Yeah. Yeah. So first absolutely recognizing, evolving, recognize the evolving regulatory landscape. You mentioned, you know, California, there's actually now 22 states that have a, is it 22 now? Right? Yeah. 22 states that have a privacy act enacted. And our projection is in the next 12 to 18 months, all states will have one. And so absolutely a, a perceived challenge, but one that I think is, is addressable. And, and I think that gets to the spirit of the question for us. There's, there's four dimensions that an organization needs to work through when it comes to data sharing. The first one is back to the, the business goal and objective, like, is there truly a business need? And is there value in sharing data? And it needs to have a very solid business model. Okay. So, so that's the first step. The second step is what are the legal terms? >>What are the legal terms? What can you do? What can't you do? Do you have primary rights, secondary rights? The third dimension is around risk. What is the risk and exposure, not only from a data security perspective, but what is the risk if someone uses a data inappropriately, and then the fourth one is around ethics and the ethical use of data. And we see lots of examples where an organization has consent has rights to the data, but the way they used it might have not necessarily been, you know, among the kind of ethical framing. And so for us, those four dimensions is what guides us and our clients in developing a very robust data, sharing data collaboration framework that ensures it's connected to the overall business strategy, but it provides enough of the guardrails to minimize legal and ethical risk. So >>With that in mind, what do the customer conversations look like? Cause you gotta have a lot of players, the business folks, the data folks, every line of business needs data for its functions. Talk to us about how the customer conversations and projects have evolved as data is increasingly important to every line of business. >>Yes. I would say the biggest channel, or maybe the, the, the denominator at this point that we're seeing bring the, let's say diversity of needs to more common denominator has been AI. So every organization at this point is driving massive AI programs. And in order to really scale AI, you know, the, the algorithm cannot execute without data. Yeah. And so for us, at least in our experience with our customers, AI has almost been the, the, the mechanism to have these conversations across the different business stakeholders and do it in a way that, you know, you're not necessarily boiling the ocean, cuz I think that's the other element that makes this a bit hard is, well, what, what data do you want me to share and for what purpose? And when you start to bring it into sort of more individual swim lanes and, and, and our experience with our customers is AI has sort of been that mechanism to say, am I automating, you know, our factory floor? Am I bringing AI and how we engage and serve our customers? Right? Like it be, it be begins to sort of bring a little bit more of, of that repeatability at a, at an individual level. So that's been a, a really good strategy for us in our customers >>In terms of the customer's strategy and kind of looking forward, what are some of the things that excite you about the, the future of data collaboration, especially given all of the news that snowflake announced just yesterday? >>Yes. Yeah. I think for me, and this is both the little bit of the ambition, as well as the push, it's no longer a question of should it's it's how and for what? And so, so yes, I mean the, the, the snowflake data cloud is a network that allows us to integrate, you know, disparate and unique data assets that have never, you know, been possible before. Right. So we're in this network, it's now a matter of figuring out how to use that and for what purpose. And so I, I go back to, we, each individual organization needs to be figuring out the how, and for what not, when this is the future, we all need it. Yeah. And we just need to figure out how that fits in our individual businesses >>In terms of the, how that's such an interesting, I love how you bring that up. It's not, it's not when it's definitely how, because there's gonna be another competing business or several right there in the rear view mirror, ready to take your place. Yep. If you don't act quickly, how does Deloitte and snowflake help customers achieve the, how quickly enough to be able to really take advantage of data sharing and data collaboration so that they can be very competitive? >>Yeah. So there's two main, maybe even three driving forces in this. What we see is when there's a common purpose across director, indirect competitors and the need to share data. So I think the poster child of this was the pandemic, and we started to see organizations again, either competitively or non-com competitively share data in ways for a greater good, right. When there was a purpose, we believe when that element exists, the ability to share data is going to increase. We believe the next big sort of common purpose out there in the world is around ESG. And so that's gonna be a big driver for sharing data. So that's one element. The other one is the concept of developing integrated value chains. So when you think about any individual business and sort of where they are in that piece of the value chain, developing more integrated value across, let's say a manufacturer of goods with a distributor of those goods that ultimately get to an end customer. >>They're not sharing data in a meaningful way to really maximize their overall, you know, profitability. And so that's another really good, meaningful example that we're seeing is where there's value across, you know, a, what appears to be a siloed set of steps, and really looking at it more as an integrated value chain, the need to share data is the only way to unlock that. And so that's, that's the second one. The, the third one I would say is, is around the need to address the consumer across sort of the multiple personas that we all individually sit. Right? So I go into a bank and I'm, I'm a client. I walk into a retail store and I'm a customer. I walk into my physician's office and I'm a patient at the end of the day. I am still the same person. I am still one. And so that consumer element and the convergence of how we are engaging and serving that consumer is the third, big shift that is really going to bring data collaboration and sharing to the next level. >>Do you think snowflake is, is the right partner of the defacto for delight to do that with? >>Absolutely. I think, you know, the head start of the cloud, the data cloud platform and the network that it's already established with all the sort of data privacy and security constraints around it. Like that's a big, that's a big, you know, check right. That we don't have to worry about. It's there for sure. >>Awesome. Sounds like a great partnership, Juan. Thank you so much for joining me on the program. It's great to have you back on the cube in person sharing what Deloitte and snowflake are doing and how you're really helping to transform organizations across every industry. We appreciate >>Your insights. Yeah. No, thank you for having me here. My pleasure. Always a pleasure. Thank you. >>All right. For Juan. I am Lisa Martin. You're watching the cube live from snowflake summit 22 at Caesar's forum. You write back with our next guest.

(upbeat music) >> Welcome back to Vegas, baby. Lisa Martin here with theCUBE. We are live at Caesar's Forum covering Snowflake Summit 22. This is day two of our wall to wall coverage on theCUBE you won't want to miss. We've got an exciting customer story to talk to you about next with Novartis and Snowflake. Please welcome two guests to theCUBE. Loïc Giraud, Global head digital delivery, Novartis. I hope I got the name right. >> Yes. Hi, thank you. >> I did my best. >> Absolutely. >> Lisa: (laughs) Jesse Cugliotta also joins us. Global Industry Lead, Healthcare and Life Sciences at Snowflake. Welcome with theCUBE, gentlemen. >> Thank you for having us. Good morning. >> So it was great to hear Novartis is a household word now, especially with what's gone on in the last two years. I had a chance to see the Keynote yesterday, heard Novartis mention in terms of a massive outcome that Snowflake is delivering that we're going to get to. But Loic talk to us about Novartis global 500 organization. You rank among the world's top companies investing in R&D, the massive portfolio and you're reaching nearly 800 million patients worldwide. That's huge, but there's been a lot of change in the healthcare and life sciences industry, especially recently. Talk to us about the industry landscape. What are you seeing? >> As you described, Novartis is one of the top life science company in the world. We are number three. We operate in 150 countries, and we have almost 120,000 employees. Our purpose is actually to reimagine medicine for the use of data science and technology and to extend people's life. And we really mean it. I think, as you mentioned, we treat eight or 9 million patient per year with our drugs. We expect to treat more than a billion patients in near time soon. Over the last few years, especially during COVID, our digital transformation help us to accelerate the drug discovery and then the commiseration of our drug to markets. As it was mentioned in the Keynote yesterday, we have actually been able to reduce our time to market. It used to take us up to 12 years and cost around 1.2 billion to discover and commercialize drug. And now we've actually use of technology like Snowflake, we have been able to reduce by two to three years, which ultimately is a benefit for our patients. >> Absolutely. Well, we're talking about life and death situations. Talk about... You mentioned Novartis wants to reimagine medicine. What does that look like? Where is data in that and how is Snowflake an enabler of reimagining medicine? >> So data is core for our asset, is a core of enterprise process. So if you look at our enterprise, we are using data from the research, for drug development, in manufacturing process, and how do we market and sell our product through HCPs and distribute it to reach our patients. If you build through our digital transformation we have created this integrated data ecosystem, where Snowflake is a core component. And through that ecosystem, we are able to identify compounds and cohorts, perform clinical trials, and engage HCPs and HGOs so that can prescribe drugs to serve our patient needs. >> Jesse, let's bring you into the conversation. Snowflake recently launched its healthcare and life sciences data cloud. I believe that was back in March. >> It was. >> Just a couple of months ago. Talk to us about the vertical focus. Talk to us about what this healthcare and life sciences data cloud is aiming to help customers like Novartis achieve. >> Well, as you mentioned there, Snowflake has made a real pivot to kind of focus on the various different industries that we serve in a new way. I think historically, we've been engaged in really, all of the industries across the major sectors where we participate today. But historically we've been often engaging with the office of IT. And there was a recognition as a company that we really need to be able to better speak the language of our customers in with our respective industries. So the entire organization has really made a pivot to start to build that capability internally. That's part of the team that I support here at Snowflake. And with respect to healthcare and life sciences, that means being able to solve some of the challenges that Loic was just speaking about. In particular, we're seeing the industry evolve in a number of ways. You bring up clinical research in the time that it takes to actually bring a drug to market. This is a big one that's really changed a lot over the last couple of years. Some of the reasons are obvious and other ones are somewhat opportunistic. When we looked at what it takes to get a drug to market, there's several stages of clinical research that have to be participated in, and this can often take years. What we saw in the last couple of years, is that all of a sudden, patients didn't want to physically participate in those anymore, because there was fear of potential infection and being in a healthcare facility. So the entire industry realized that it needed to change in terms of way that it would engage with patients in that context. And we're now seeing this concept of decentralized clinical research. And with that, becomes the need to potentially involve many different types of organizations beyond the traditional pharma, their research partners, but we're starting to see organizations like retail pharmacies, like big box retailers, who have either healthcare delivery or pharmaceutical arms actually get involved in the process. And of course, one of the core things that happens here is that everyone needs a better way to collaborate and share data amongst one another. So bringing this back to your original question, this concept of being able to do exactly that is core to the healthcare and the life sciences data cloud. To be able to collaborate and share data amongst those different types of organizations. >> Collaboration and data sharing. It seems to me to be a differentiator for Snowflake, in terms of being able to deliver secure, governed powerful analytics and data sharing to customers, partners to the ecosystem. You mentioned an example of the ecosystem there and how impactful to patients' lives, that collaboration and data sharing can be. >> That's absolutely right. It's something that if you think about all of the major challenges that the industry has had historically, whether it is high costs, whether it are health inequities, whether it is physicians practicing defensive medicine or repeat testing, what's core to each one of these things is kind of the inability to adequate collaborate and share data amongst all of the different players. So the industry has been waiting for the capability or some sort of solution to be able to do this, I think for a long, long time. And this is probably one of the most exciting parts of the conversations that we have with our customers, is when they realize that this is possible. And not only that it's possible within our platform, but that most of the organizations that they work with today are also Snowflake customers. So they realize that everyone's already here. It's just a matter of who else can we work with and how do we get started? >> Join the party. >> Exactly. >> Loic talk to us about Novartis's data journey. I know you guys have been, I believe using Snowflake since 2017 pre pandemic. But you had a largely on-premises infrastructure. Talk to us about the decision of Novartis to go to the cloud, do it securely and why you chose to partner with Snowflake. >> So when we started our journey in 2018, I think the ambition that our CEO, was to transform all enterprise processes for the use of digital tech. And at the core of this digital tech is data foundation. So we started with a large program called Formula One, which aim to integrate all our internal and external data asset into an integrated platform. And for that, I think we've built this multicloud and best upgrade platform, where Snowflake is a core component. And we've been able to integrate almost 1,000 data asset, internal and external for the platform to be able to accelerate the use of data to create insight for our users. In that transformation, we've realized that Snowflake could be a core component because of the scalability and the performance with large dataset. And moreover, when Snowflake started to actually open collaboration for their marketplace, we've been able to integrate new data set that are publicly available at the place that we could not do on ourself, on our own. So that is a core component of what we are trying to do. >> Yeah, and I think that's a great example of really what we're talking about here is that, he's mentioning that they're going out to our marketplace to be able to integrate data more easily with some of the vendors there. And that is kind of this concept of the healthcare and life sciences data cloud realized, where all of a sudden, acquiring and bringing data in and making it ready for analysis becomes much faster, much easier. We continually see more and more vendors coming to us saying, I get it now, I want in. Who else can I work with in this space? So I think that's a perfect example of how this starts to become real for folks. >> Well, it sounds like the marketplace has been an enabler, Loic, of the expansion of use cases. You've grown this beyond drug development. I read that you're developing new products and services for healthcare providers to personalize treatments for patients, which we all are demanding patients. We want that personalized care. But talk about the marketplace as a facilitator of those expanding use cases that Snowflake is powering. >> Yes. That's right. I mean we have currently almost 65 use cases in production and we are in advanced progress for over 200 use cases and they go across all our business sector. So if you look at drug development, we are monitoring our clinical trials using Snowflake. If you look at our omnichannel marketing, we are looking at personalization of information with our HCPs and HGOs using snowflake. If you look at our manufacturing process, we are looking at yet management, freight optimization, inventory, insight. So almost across all the industry sectors that we have, I think we are using the platforms to be able to deliver faster information to our users. >> And that's what we all want. Faster information. I think in the pandemic we learned that access to real time data in every industry wasn't a nice to have. That was a- >> Necessity. >> Absolute necessity. >> Yeah. >> And made the difference for companies that survived and thrived and those that didn't. That's something that we learned. But we also learned that the volume of data just continues to proliferate. Loic, you've been in the industry a couple of decades. What do you see? And you've got, obviously this great foundation now with Snowflake. You've got 65 use cases you said in production. What's the future of the data culture in healthcare and life sciences from your perspective? >> So my perspective. It is time now we give the access to our business technologies to be able to be self-sufficient using digital product. We need to consumerize digital technology so they can be self-sufficient. The amount of problems that we have to solve, and we can now solve with new technology has never been there. And I think where in the past, where in the next few years that you will see an accelerated generation of insight and an accelerated process of medicine by empowering the business technologies to use a technology that like Snowflake and over progress. >> What are your thoughts Loic, of some of the, obviously a lot of news coming out yesterday from Snowflake, we mentioned standing room only in the Keynote. This I believe is north of 10,000 attendees. People are ready to engage in person with Snowflake, but some of the news coming out, what is your perspective? You've been a partner of theirs for a while. What do you see from Snowflake in terms of the news, the volume of customers it's adding, all that good stuff? >> I must say I was blown away yesterday when Frank was talking about the ramp up of customers using Snowflake. But also, and I think in Benoit and Christian, and they talk about the innovation. When you look at native application or you look at hybrid tables, we saw a thing there. And the expansion of the marketplace by monetization application, that is something that is going to accelerate the expansion, not only on the company, but the integration and the utilization of customers. And to Jesse's point, I think that it is key that people collaborate using the platform. I think we want to collaborate with suppliers and providers and they want to collaborate with us. But we want to have a neutral environment where we can do that. And Snowflake can be that environment. >> And do it securely, right? Security is absolutely- >> Of course. I mean that's really table stake for this industry. And I think the point that you just made Loic, is very important, is that, the biggest question that we're often asked by our customers is who else is a customer within this industry that I can collaborate with? I think as Loic here will attest to, one of the challenges within life sciences in particular is that it is a highly regulated industry. It is a highly competitive industry, and folks are very sensitive about referenceability. So about things like logo usage. So to give some ideas here, people often have no idea that we're working with 28 of the top 50 global pharma today, working with seven of the top 12 global medical device companies today. The largest CROs, the largest distributors. So when I say that the party is here, they really are. And that's why we're so excited to have events like these, 'cause people can physically introduce themselves to one another and meet, and actually start to engage in some of these more collaborative discussions that they've been waiting for. >> Jesse, what's been some of the feedback that you've heard the last couple of days on the healthcare and life sciences data cloud? You've obviously finally gotten back to engaging with customers in person. But what are some of the things, feed on this street have said that you've thought, we made the absolute right decision on this pivot? >> Yeah, well I think some of it speaks to the the point I was just speaking about, is that they had no idea that so many of their peers were actually working with Snowflake already and that how mature their implementations have actually been. The other thing that folks are realizing is that, a lot of the technologies that serve this ecosystem, whether they're in the health tech space, whether they're clinical management or commercial engagement or supply chain planning technologies, those companies are also now pivoting to Snowflake, where they're either building a part or the entirety of their platform on top of ours. So it offers this great way to start to collaborate with the ecosystem through some of those capabilities that we spoke about. And that's driving new use cases in commercial, in supply chain, in pharmacovigilance, in clinical operations. >> Well, I think you just sum up beautifully why the theme of this conference is the world of data collaboration. >> Yes, absolutely. >> The potential there, that Snowflake is unleashing to the world is I think is what's captivating to me. That you just scratch on the surface about connecting and facilitating this collaboration and this data sharing in a secure way across industries. Loic, last question for you. Take us home with what is next for Novartis. You've done a tremendous amount of digitalization. 65 use cases in production with Snowflake. What's next for the company? >> See, I think that in next year's to come, open collaboration with the ecosystem, but also personalization. If you look at digital medicine and access to patient's informations, I think this is probably the next revolution that we are entering into. >> Excellent. And of course those demanding patients aren't going to want anything slower or less information. Guys, thank you for joining me on the program talking about the Novartis-Snowflake collaboration. The partnership, the outcomes that you're achieving and how this is really dramatically impacting the lives of hundreds of millions of people. We appreciate your time and your insights. >> Thank you for having us. This was fun. >> My pleasure. >> Thank you. >> For my guests, I'm Lisa Martin. You're watching theCUBE. This is live from Las Vegas, day two of our coverage of Snowflake Summit 22. I'll be right back with my next guest, so stick around. (upbeat music)

(Upbeat music) >> Hey everyone. Welcome back to theCUBE's continuing coverage day two Snowflake Summit '22 live from Caesar's forum in Las Vegas. Lisa Martin here with Dave Vellante, bringing you wall to wall coverage yesterday, today, and tomorrow. We're excited to welcome Matthew Carroll to the program. The CEO of Immuta, we're going to be talking about removing barriers to secure data access security. Matthew, welcome. >> Thank you for having me, appreciate it. >> Talk to the audience a little bit about Immuta you're a Snowflake premier technology partner, but give him an overview of Immuta what you guys do, your vision, all that good stuff. >> Yeah, absolutely, thanks. Yeah, if you think about what Immunta at it's core is, we're a data security platform for the modern data stack, right? So what does that mean? It means that we embed natively into a Snowflake and we enforce policies on data, right? So, the rules to be able to use it, to accelerate data access, right? So, that means connecting to the data very easily controlling it with any regulatory or security policy on it as well as contractual policies, and then being able to audit it. So, that way, any corporation of any size can leverage their data and share that data without risking leaking it or potentially violating a regulation. >> What are some of the key as we look at industry by industry challenges that Immuta is helping those customers address and obviously quickly since everything is accelerating. >> Yeah. And it's, you're seeing it 'cause the big guys like Snowflake are verticalizing, right? You're seeing a lot of industry specific, you know, concepts. With us, if you think of, like, where we live obviously policies on data regulated, right? So healthcare, how do we automate HIPAA compliance? How do we redesign clinical trial management post COVID, right? If you're going to have billions of users and you're collecting that data, pharmaceutical companies can't wait to collect that data. They need to remove those barriers. So, they need to be able to collect it, secure it, and be able to share it. Right? So, double and triple blinded studies being redesigned in the cloud. Government organizations, how do we share security information globally with different countries instantaneously? Right? So these are some of the examples where we're helping organizations transform and be able to kind of accelerate their adoption of data. >> Matt, I don't know if you remember, I mean, I know you remember coming to our office. But we had an interesting conversation and I was telling Lisa. Years ago I wrote a piece of you know, how to build on top of, AWS. You know, there's so much opportunity. And we had a conversation, at our office, theCUBE studios in Marlborough, Massachusetts. And we both, sort of, agreed that there was this new workload emerging. We said, okay, there's AWS, there's Snowflake at the time, we were thinking, and you bring machine learning, at time where we were using data bricks, >> Yeah. >> As the example, of course now it's been a little bit- >> Yeah. Careful. >> More of a battle, right, with those guys. But, and so, you see them going in their different directions, but the premise stands is that there's an ecosystem developing, new workloads developing, on top of the hyper scale infrastructure. And you guys play a part in that. So, describe what you're seeing there 'cause you were right on in that conversation. >> Yeah. Yeah. >> It's nice to be, right. >> Yeah. So when you think of this design pattern, right, is you have a data lake, you have a warehouse, and you have an exchange, right? And this architecture is what you're seeing around you now, is this is every single organization in the world is adopting this design pattern. The challenge that where we fit into kind of a sliver of this is, the way we used to do before is application design, right? And we would build lots of applications, and we would build all of our business logic to enforce security controls and policies inside each app. And you'd go through security and get it approved. In this paradigm, any user could potentially access any data. There's just too many data sources, too many users, and too many things that can go wrong. And to scale that is really hard. So, like, with Immuta, what we've done, versus what everyone else has done is we natively embedded into every single one of those compute partners. So ,Snowflake, data breaks, big query, Redshift, synapse on and on. Natively underneath the covers, so that was BI tools, those data science tools hit Snowflake. They don't have to rewrite any of their code, but we automatically enforce policy without them having to do anything. And then we consistently audit that. I call that the separation of policy from platform. So, just like in the world in big data, when we had to separate compute from storage, in this world, because we're global, right? So we're, we have a distributed workforce and our data needs to abide by all these new security rules and regulations. We provide a flexible framework for them to be able to operate at that scale. And we're the only ones in the world doing it. >> Dave Vellante: See the key there is, I mean, Snowflake is obviously building out its data cloud and the functions that it's building in are quite impressive. >> Yeah. >> Dave Vellante: But you know at some point a customer's going to say, look I have other stuff, whether it's in an Oracle database, or data lake or wherever, and that should just be a node on this global, whatever you want to call it, mesh or fabric. And then if I'm hearing you right, you participate in all of that. >> Correct? Yeah We kind of, we were able to just natively inject into each, and then be able to enforce that policy consistently, right? So, hey, can you access HIPAA data? Who are you? Are you authorized to use this? What's the purpose you want to query this data? Is it for fraud? Is it for marketing? So, what we're trying to do as part of this new design paradigm is ensure that we can automate nearly the entire data access process, but with the confidence and de-risk it, that's kind of the key thing. But the one thing I will mention is I think we talk a lot about the core compute, but I think, especially at this summit, data sharing is everything. Right? And this concept of no copy data sharing, because the data is too big and there's too many sets to share, that's the keys to the kingdom. You got to get your lake and your warehouse set with good policy, so you can effectively share it. >> Yeah, so, I wanted to just to follow up, if I may. So, you'd mentioned separating compute from storage and a lot of VC money poured into that. A lot of VC money poured into cloud database. How do you see, do you see Snowflake differentiating substantially from all the other cloud databases? And how so? >> I think it's the ease of use, right? Apple produces a phone that isn't much different than other competitors. Right? But what they do is, end to end, they provide an experience that's very simple. Right? And so yes. Are there other warehouses? Are there other ways to, you know you heard about their analytic workloads now, you know through unistore, where they're going to be able to process analytical workloads as well as their ad hoc queries. I think other vendors are obviously going to have the same capabilities, but I think the user experience of Snowflake right now is top tier. Right? Is I can, whether I'm a small business, I can load my debt in there and build an app really quickly. Or if I'm a JP Morgan or, you know, a West Farmer's I can move legacy, you know monolithic architectures in there in months. I mean, these are six months transitions. When think about 20 years of work is now being transitioned to the cloud in six months. That's the difference. >> So measuring ease of views and time to value, time to market. >> Yeah. That's it's everything is time to value. No one wants to manage the infrastructure. In the Hudup world, no one wants to have expensive customized engineers that are, you know, keeping up your Hudup infrastructure any longer. Those days are completely over. >> Can you share an example of a joint customer, where really the joint value proposition that Immuta and Snowflake bring, are delivering some pretty substantial outcomes? >> Yeah. I, what we're seeing is and we're obviously highly incentivized to get them in there because it's easier on us, right? Because we can leverage their row and com level security. We can leverage their features that they've built in to provide a better experience to our customers. And so when we talk about large banks, they're trying to move Terra data workloads into Snowflake. When we talk about clinical trial management, they're trying to get away from physical copies of data, and leverage the exchanges of mechanism, so you can manage data contracts, right? So like, you know, when we think of even like a company like Latch, right? Like Latch uses us to be able to oversee all of the consumer data they have. Without like a Snowflake, what ends up happening is they end up having to double down and invest on their own people building out all their own infrastructure. And they don't have the capital to invest in third party tools like us that keep them safe, prevent data leaks, allow them to do more and get more value out of their data, which is what they're good at. >> So TCO reduction I'm hearing. >> Matthew Carroll: Yes, exactly. >> Matt, where are you as a company, you've obviously made a lot of progress since we last talked. Maybe give us the update on you know, the headcount, and fundraising, and- >> Yeah, we're just at about 250 people, which scares me every day, but it's awesome. But yeah, we've just raised 100 million dollars- >> Lisa Martin: Saw that, congratulations. >> Series E, thank you, with night dragon leading it. And night dragon was very tactical as well. We are moving, we found that data governance, I think what you're seeing in the market now is the catalog players are really maturing, and they're starting to add a suite of features around governance, right? So quality control, observability, and just traditional asset management around their data. What we are finding is is that there's a new gap in this space, right? So if you think about legacy it's we had infrastructure security we had the four walls and we protect our four walls. Then we moved to network security. We said, oh, the adversary is inside zero trust. So, let's protect all of our endpoints, right? But now we're seeing is data is the security flaw data could be, anyone could potentially access it in this organization. So how do we protect data? And so what we have matured into is a data security company. What we have found is, there's this next generation of data security products that are missing. And it's this blend between authentication like an, an Okta or an AuthO and auth- I'm sorry, authorization. Like Immuta, where we're authorizing certain access. And we have to pair together, with the modern observability, like a data dog, to provide an a layer above this modern data stack, to protect the data to analyze the users, to look for threats. And so Immuta has transformed with this capital. And we brought Dave DeWalt onto our board because he's a cybersecurity expert, he gives us that understanding of what is it like to sell into this modern cyber environment. So now, we have this platform where we can discover data, analyze it, tag it, understand its risk, secure it to author and enforce policies. And then monitor, the key thing is monitoring. Who is using the data? Why are they using the data? What are the risks to that? In order to enforce the security. So, we are a data security platform now with this raise. >> Okay. That, well, that's a new, you know, vector for you guys. I always saw you as an adjacency, but you're saying smack dab in the heart >> Matthew Carroll: Yes. Yeah. We're jumping right in. What we've seen is there is a massive global gap. Data is no longer just in one country. So it is, how do we automate policy enforcement of regulatory oversight, like GDPR or CCPA, which I think got this whole category going. But then we quickly realized is, well we have data jurisdiction. So, where does that data have to live? Where can I send it to? Because from Europe to us, what's the export treaty? We don't have defined laws anymore. So we needed a flexible framework to handle that. And now what we're seeing is data leaks, upon data leaks, and you know, the Snowflakes and the other cloud compute vendors, the last thing they ever want is a data leak out of their ecosystem. So, the security aspects are now becoming more and more important. It's going to be an insider threat. It's someone that already has access to that and has the rights to it. That's going to be the risk. And there is no pattern for a data scientist. There's no zero trust model for data. So we have to create that. >> How are you, last question, how are you going to be using a 100 million raised in series E funding, which you mentioned, how are you going to be leveraging that investment to turn the volume up on data security? >> Well, and we still have also another 80 million still in the bank from our last raise, so 180 million now, and potentially more soon, we'll kind of throw that out there. But, the first thing is M and A I believe in a recessing market, we're going to see these platforms consolidate. Larger customer of ours are driving us to say, Hey, we need less tools. We need to make this easier. So we can go faster. They're, even in a recessing market, these customers are not going to go slower. They're moving in the cloud as fast as possible, but it needs to be easier, right? It's going back to the mid nineties kind of Lego blocks, right? Like the IBM, the SAP, the Informatica, right? So that's number one. Number two is investing globally. Customer success, engineering, support, 24 by seven support globally. Global infrastructure on cloud, moving to true SaaS everywhere in the world. That's where we're going. So sales, engineering, and customer success globally. And the third is, is doubling down on R and D. That monitor capability, we're going to be building software around. How do we monitor and understand risk of users, third parties. So how do you handle data contracts? How do you handle data use agreements? So those are three areas we're focused on. >> Dave Vellante: How are you scaling go to market at this point? I mean, I presume you are. >> Yeah, well, I think as we're leveraging these types of engagements, so like our partners are the big cloud compute vendors, right? Those data clouds. We're injecting as much as we can into them and helping them get more workloads onto their infrastructure because it benefits us. And then obviously we're working with GSIs and then RSIs to kind of help with this transformation, but we're all in, we're actually deprecating support of legacy connectors. And we're all in on cloud compute. >> How did the pivot to all in on security, how did it affect your product portfolio? I mean, is that more positioning or was there other product extensions that where you had to test product market fit? >> Yeah. This comes out of customer drive. So we've been holding customer advisory boards across Europe, Asia and U.S. And what we just saw was a pattern of some of these largest banks and pharmaceutical companies and insurance companies in the world was, hey we need to understand who is actually on our data. We have a better understanding of our data now, but we don't actually understand why they're using our data. Why are they running these types of queries? Is this machine, you know logic, that we're running on this now, we invested all this money in AI. What's the risk? They just don't know. And so, yeah, it's going to change our product portfolio. We modularized our platform to the street components over the past year, specifically now, so we can start building custom applications on top of it, for specific users like the CSO, like, you know, the legal department, and like third party regulators to come in, as well as as going back to data sharing, to build data use agreements between one or many entities, right? So an SMP global can expose their data to third parties and have one consistent digital contract, no more long memo that you have to read the contract, like, Immuta can automate those data contracts between one or many entities. >> Dave Vellante: And make it a checkbox item. >> It's just a checkbox, but then you can audit it all, right? >> The key thing is this, I always tell people, there's negligence and gross negligence. Negligence, you can go back and fix something, gross negligence you don't have anything to put into controls. Regulators want you to be at least negligent, grossly negligent. They get upset. (laughs) >> Matthew, it sounds like great stuff is going on at Immuta, lots of money in the bank. And it sounds like a very clear and strategic vision and direction. We thank you so much for joining us on theCUBE this morning. >> Thank you so much >> For our guest and Dave Vellante, I'm Lisa Martin, you're watching theCUBE's coverage of day two, Snowflake Summit '22, coming at ya live, from the show floor in Las Vegas. Be right back with our next guest. (Soft music)

(bright music) >> Hey everyone, welcome back to theCUBE's continuing coverage of Snowflake Summit 22, live from Caesar's Forum in Las Vegas. I'm Lisa Martin here with about 7,000 plus folks, and this next Cube segment, two words, girl power. Please welcome one of our alumni back to the program, Colleen Kapase, SVP, Worldwide Partners and Alliances at Snowflake and Poornima Ramaswamy, EVP of Global Partnerships and Chief of Staff to the CEO. Ladies, welcome to the program! >> Thank you, very happy to be here, amazing event! >> Isn't it? It's so great to see this many people. Yesterday, the keynote, we got in barely, standing room only. I know there was at least one overflow room, maybe two. People are chomping at the bit to hear what Snowflake and its ecosystem has been up to the last three years, since 2019. >> It's been phenomenal! Since the last time we met together, as humans coming together, and then seeing the step function growth three years later, I don't think, we didn't grow gradually. We just jumped three years ahead, and people have just been hungry for the information and the sharing and the joint education, so it's been a phenomenal show. >> It has been, Poornima, talk to us about the Qlik partnership with Snowflake. What's it all about? What's your joint vision, your joint strategy? Give us all that good stuff. >> Sure, so speaking of three years, this relationship has been in existence for the last three years. We were at the last Snowflake Conference in 2019, and I liked what Frank said, even though we were not in-person in life the innovation has continued and our relationship has strengthened over the last three years as well. So it's interesting that everything that Frank and everything that was mentioned at the keynote yesterday is completely in alignment with Qlik's vision and strategy as well. We are focused on making data available for quick decision making, in a timely manner, for in the moment business decisions as such. The world has gone topsy-turvy in the last two years, so you want to know things that are changing as they happen and not one day late, one month late or one quarter late, because then the world's already passed you, that business moment has passed you. That's been our focus. We've got a dual product strategy and portfolio. We collaborate really strongly with Snowflake on both of those to make the most amount of data, made available on the Snowflake platform in the shortest amount of time, so that it's fresh, and it's timely for business decision makers to get access to it, to make decisions as they are dealing with supply chain challenges and people challenges and so on and can make those moments count as such. >> They have to, one of the things that we've learned in the pandemic is access to real-time data is no longer a, oh, that's great, nice to have. It's table stakes for businesses in every industry. Consumer expectations have risen to a level we've probably never seen, and let's face it, they're not going to go down. Nobody's going to want less data, slower. (laughs) Colleen, talk about the Qlik partnership from your Snowflake's perspective. >> Yeah, it's been fabulous, and we started on the BI side and keep evolving it, frankly with more technology, more solutions, making that real-time access, not just the the BI side of having the business intelligence and seeing the data but moving beyond that to the governance side, and that's such a huge piece of the relationship as well, and the trustworthy that executives have with the data, who's seeing it and how are we leveraging it, and we keep expanding that too and having some fun too. I know you guys have been making some acquisitions. >> Talk to us about what's going on at Qlik and some news today as well, acquisitions news, what's the deal? >> Yeah, so like I mentioned, we have a dual product strategy, a Qlik data integration platform and a Qlik analytics platform. And we are strengthening, making sure that we align with Snowflake's vision of all workloads, SaaS only and governed. So the announcement today was we do provide real-time data using our Qlik data integration platform into Snowflake, but that real-time data has to make its way into the hands of the business decision makers as well. So we launched what we call as direct query into Snowflake, so as and when data gets into the Snowflake platform, now customers for specific use cases can choose to access that data as it comes in by accessing it directly on Snowflake. And there are other use cases where the data's already been prepared and so on, and they'll continue using the Qlik analytics platform, but this direct query access will make a world of difference in terms of that active intelligence, in-the-moment decision making. The second announcement that we did was the SaaS first and going all into SaaS, so we are doing our data movement investments in our SaaS platform, and one of our first investments is on the Snowflake platform, going direct into Snowflake, and our data ingestion now, our data replication real-time is going to be available natively into the Snowflake platform through our SaaS data transformation investment that we've made. So those are the two big announcements, and governance has been the cornerstone for our platform end-to-end, right from the beginning, and that strength continues, and that's, again, completely in alignment with the vision that Snowflake has as well. >> I couldn't agree more, that native integration, we used to think about bringing the data to the work, and now it's bring the work to the data, because that's the secure environment, the governed environment, and that's what we're seeing with our product roadmaps together and where we're going, and it gives customers just peace of mind. When you're bringing the work to the data, it's more secure, it's more governed, and that real-time access, it's speed, because boy, so many executives have to make real-time decisions quickly. The world is moving faster than it ever has before, and I've never had an executive say, "Oh yeah, I'll just wait and get the data later." That's not a conversation they have. I need it, and I need it now, and I need it at my fingertips, and I need more of my entire organization to have access to that data, what I feel secure and safe to share with them. And so, having Qlik make that possible is just fantastic. >> The security piece is absolutely critical. We've seen such changes to the threat landscape in the last couple of years. It's no longer now a, if we get hit by a cyber attack, it's a matter of when. And the volume of data just keeps proliferating, proliferating, proliferating, which obviously is not going to slow down either. So having the governance factor, the ability to share data securely, leveraging powerful analytics across to customers and partners and ecosystem, it sounds like to me a pretty big differentiator of what Snowflake is delivering to its customers and the ecosystem. >> It is, and I would say one of the things that has held folks back from moving to the cloud before, was governance. Is this just going to be a free for all, Lisa? I'm not feeling secure with that. And so, having the ability to extend our ecosystem and work on that governance together gives executives peace of mind, that they can easily determine who's going to have access to what, which makes a transition to the cloud faster. And that's what we're looking for, because to have our customers experience the benefits of cloud and the moving up and moving down from a data perspective and really getting access to the data cloud, that's where the nirvana is, and so you guys are helping make that possible and provide that peace of mind, so it's amazing. >> You talk about peace of mind, and it's one of those things we think, oh, it's a marketing term or it's a soft term. It's actually not, it's completely measurable, and it's something that I talk to a lot of C-suite, and the statement of "I sleep better at night," is real. There's gravity with it, knowing that they can trust where the data is. The access is governed. It just keeps getting more and more critical every day. >> Colleen: Well, it's a newsworthy event, frankly- >> Absolutely, nobody wants don't to be a headline. >> If things don't go right, that's people's jobs on the line that's reputations, and that's careers, so that is so important, and I think with a lot of our customers that's our conversations directly of how can you ensure that this is going to be a secure experience? And it's Snowflake and some of our superpowers, and frankly, some of our partners superpowers too, together it's better. >> I can bring this home with a customer example, a couple of customer examples. So Urban Outfitters, I think they're a well-known brand. They've got about 650 stores, to your point on governed autonomy is what I call it. But then it's not just about helping with decision making at the top. You want to be able to make decision making at all levels, so we speak about data democratization. It's about not just strategic decisions that you make for a two-year timeframe or a five-year timeframe. It's about decisions that you want to make today in the first half of the day versus the second half of the day. So Urban Outfitters is a common customer, and during the pandemic they had to change their in-stores into distribution centers. They had to look at their supply chain landscape, because there were supply chain bottlenecks that are still happening today. So, with the power of both Qlik data integration and Qlik analytics, but then the combined power of Qlik and Snowflake, the customer actually was able to make insights available to their in-store managers, to their distribution centers, and from a time perspective, what used to take them days, or, in fact, sometimes even weeks, they're now able to get data in 15 minutes refresh time for their operational decision makers, their distribution centers and their order taking systems, so they're able to make decisions on which brands are moving, not moving. Do they need to change the product position in their stores? Do they need to change their suppliers today? Because, for what's going to be in their inventory one month later, because they are foreseeing, they're able to predict the supply chain bottlenecks that are coming in. They're able to do all of that today because that power of a governed autonomous environment that we've built but real-time data making fresh data available through Snowflake and easy-to-use dashboards and visualization through the analytics platform that we've got. And another customer ABB, 37 different SAP source systems being refreshed every two minutes, worldwide for B2B transactions to be able to make all of those decisions. >> And what you're talking about there, especially with their Urban Outfitters example, I think that's one that everybody as a consumer of clothing and apparel, what you just described, what Qlik and Snowflake enabled there, that could have very well saved that organization. We saw a lot of retailers that were not able to make that pivot. >> Poornima: Yep, no, and it did. >> You are exactly right. I think the differentiation on a lot of our core customers together of combing through, not just surviving but thriving through the pandemic, access to data and supply chain management, and it's these types of solutions that are game changing, and that's why Snowflake's not being sold just to the IT department, it's the business decision makers where they have to make decisions, and one of the things that surprised us the most was we had the star schema COVID data up on our data marketplace and the access to that, that we had our customers to determine supply chain management. What's open? What are the rules per state, per region? Where should we put supply? Where should we not? It was phenomenal. So when you have tools like what Qlik offers together with that data coming through the community, I think that's where a lot of executives experience the power of the data cloud, and that's what we want to see. And we're helping real businesses. We say we want to drive outcomes. Supply chain management was a massive outcome that we helped over the last two years. >> And that was critical, obviously we're still in that from a macro economic perspective. It's still a challenge for a lot of folks, but it was life and death. It was, initially, how do we survive this? And to your point, Colleen, now we've got this foundation, now we can thrive, and we can leave the competition who wasn't able to move this fast in the dust behind us. >> A foreseen function for change, really, and then that change wasn't just different, it was better. >> Yeah, it is better, and it now sets the foundation for the next stage of innovation, which is auto ML and AI ML. You're looking back, you're saying, "Okay this is all the data, "so these are the decisions I had to make in the moment." But then now they can start looking at what are the midterm and the long term strategic decisions I have to make, because I can now predict what are the interconnectedness or the second secondary level and the tertiary level impact for worldwide events. There's a pandemic. We are passed the pandemic. There's flood somewhere. There's fire somewhere. China shuts down every so often. You need new suppliers. How do you get out of your way in terms of making daily decisions, but start planning ahead? I think auto ML, AI ML, and data's going to be the foundation for that and real-time data at that. So what Snowflake's doing in terms of the investment in that space, and Qlik has acquired companies in the auto ML space and driving more automation, that time-to-business value and time-to-predictive insights is going to become very key. >> Absolutely key and also really a lifeline for organizations to be able to do that. >> And I have to say, it's a source of pride for us to see our partners growing and thriving in this environment too. Like some of these acquisitions they're making, Lisa, in the machine learning space, it's awesome. This is where customers want to go. They've got all this fabulous data. They now know how to access it real time. How do I use queries to make me smarter? How do I use this machine learning to look at a vast amount of data in a very real time fashion and make business decisions from? That's the future, that's where we're going. So to see you guys expand from BI, to governance, to machine learning, we're really, Lisa, watching companies in our ecosystem grow as we grow, and that's the piece I take a lot of personal pride in, and it's the fun part of the job, frankly. >> Yeah, as you should take part in that, and that's something too, that's been thematic the last... We were recovering this show yesterday and today that the growth and the substance of the Snowflake ecosystem. You see it, you feel it, and you hear it. >> Yeah, well in Frank Slootman's book, "Amp It Up," there's actually a section that he talks about, because I think he has some amazing lifelong advice on his journey of growth, and he tells us that, "Hey you can attach your company, "your personal career energy to an elevator going up "and a company and a high growth story "or a flat or declining." And it's harder in a flat and declining space, and Snowflake we certainly see as an elevator skyrocketing up and these organizations surrounding us with their technologies and capabilities to have joint outcomes, they're doing fantastic too. I've heard this story over and over again this week. I love seeing this story too with Qlik, and it's just amazing. >> I bet, Ladies, thank you so much for joining me, talking about the Snowflake-Qlik partnership, the better together power, and also, you're just scratching the surface. The future, the momentum, you can feel it. >> Yeah, I love it. >> We appreciate your insights and your time and good luck! >> Thank you, thank you. >> And let's let the girl bosses go! (laughs) >> Exactly! (laughs) For my girl boss guests, I'm Lisa Martin. You're watching theCUBE's coverage of Snowflake Summit 22, live from Caesar's Forum in Las Vegas. I'll be right back with my next guest. (bright music)