>> Narrator: Live from San Jose, it's theCUBE. Presenting Big Data, Silicon Valley. Brought to you by SiliconANGLE Media and its ecosystem partners. >> Hi, I'm George Gilbert, and we are broadcasting from the Strata Data Conference, we're right around the corner at the Forager Tasting Room & Eatery. We have this wonderful location here, and we are very lucky to have with us Michael Nixon, from Snowflake, which is a leading cloud data warehouse. And David Abercrombie from Sharethrough which is a leading ad tech company. And between the two of them, they're going to tell us some of the most advance these cases we have now for cloud-native data warehousing. Michael, why don't you start with giving us some context for how on a cloud platform one might rethink a data warehouse? >> Yeah, thank you. That's a great question because let me first answer it from the end-user, business value perspective, when you run a workload on a cloud, there's a certain level of expectation you want out of the cloud. You want scalability, you want unlimited scalability, you want to be able to support all your users, you want to be able to support the data types, whatever they may be that comes in into your organization. So, there's a level of expectation that one should expect from a service point of view once you're in a cloud. So, a lot of the technology that were built up to this point have been optimized for on-premises types of data warehousing where perhaps that level of service and currency and unlimited scalability was not really expected but, guess what? Once it comes to the cloud, it's expected. So those on-premises technologies aren't suitable in the cloud, so for enterprises and, I mean, companies, organizations of all types from finance, banking, manufacturing, ad tech as we'll have today, they want that level of service in the cloud. And so, those technologies will not work, and so it requires a rethinking of how those architectures are built. And it requires being built for the cloud. >> And just to, alright, to break this down and be really concrete, some of the rethinking. We separate compute from storage, which is a familiar pattern that we've learned in the cloud but we also then have to have this sort of independent elasticity between-- >> Yes. Storage and the compute, and then Snowflake's taken it even a step further where you can spin out multiple compute clusters. >> Right. >> Tell us how that works and why that's so difficult and unique. >> Yeah, you know, that's taking us under the covers a little bit, but what makes our infrastructure unique is that we have a three-layer architecture. We separate, just as you said, storage from the compute layer, from the services layer. And that's really important because as I mentioned before, you want unlimited capacity, unlimited resources. So, if you scale, compute, and today's world on on-premises MPP, what that really means is that you have to bring the storage along with the compute because compute is tied to the storage so when you scale the storage along with the compute, usually that involves a lot of burden on the data warehouse manager because now they have to redistribute the data and that means redistributing keys, managing keys if you will. And that's a burden, and by the reverse, if all you wanted to do was increase storage but not the compute, because compute was tied to storage. Why you have to buy these additional compute notes, and that might add to the cost when, in fact, all you really wanted to pay for was for additional storage? So, by separating those, you keep them independent, and so you can scale storage apart from compute and then, once you have your compute resources in place, the virtual warehouses that you're talking about that have completed the job, you spun them up, it's done its job, and you take it down, guess what? You can release those resources, and of course, in releasing those resources, basically you can cut your cost as well because, for us, it's pure usage-based pricing. You only pay for what you use, and that's really fantastic. >> Very different from the on-prem model where, as you were saying, tied compute and storage together, so. >> Yeah, let's think about what that means architecturally, right? So if you have an on-premises data warehouse, and you want to scale your capacity, chances are you'll have to have that hardware in place already. And having that hardware in place already means you're paying that expense and, so you may pay for that expense six months prior to need it. Let's take a retailer example. >> Yeah. >> You're gearing up for a peak season, which might be Christmas, and so you put that hardware in place sometime in June, you'll always put it in advanced because why? You have to bring up the environment, so you have to allow time for implementation or, if you will, deployment to make sure everything is operational. >> Okay. >> And then what happens is when that peak period comes, you can't expand in that capacity. But what happens once that peak period is over? You paid for that hardware, but you don't really need it. So, our vision is, or the vision we believe you should have when you move workloads to the cloud is, you pay for those when you need them. >> Okay, so now, David, help us understand, first, what was the business problem you were trying to solve? And why was Snowflake, you know, sort of uniquely suited for that? >> Well, let me talk a little bit about Sharethrough. We're ad tech, at the core of our business we run an ad exchange, where we're doing programmatic training with the bids, with the real-time bidding spec. The data is very high in volume, with 12 billion impressions a month, that's a lot of bids that we have to process, a lot of bid requests. The way it operates, the bids and the bid responses and programmatic training are encoded in JSONs, so our ad exchange is basically exchanging messages in JSON with our business partners. And the JSONs are very complicated, there's a lot of richness and detail, such that the advertisers can decide whether or not they want to bid. Well, this data is very complicated, very high-volume. And advertising, like any business, we really need to have good analytics to understand how our business is operating, how our publishers are doing, how our advertisers are doing. And it all depends upon this very high-volume, very complex JSON event data stream. So, Snowflake was able to ingest our high-volume data very gracefully. The JSON parsing techniques of Snowflake allow me to expose the complicated data structure in a way that's very transparent and usable to our analysts. Our use of Snowflake has replaced clunkier tools where the analysts basically had to be programmers, writing programs in Scala or something to do in analysis. And now, because we've transparently and easily exposed the complicated structures within Snowflake in a relational database, they can use good old-fashioned SQL to run their queries, literally, afternoon analysis is now a five-minute query. >> So, let me, as I'm listening to you describe this. We've had various vendors telling us about these workflows in the sort of data prep and data science tool change. It almost sounds to me like Snowflake is taking semi-structured or complex data and it's sort of unraveling it and normalizing is kind of an overloaded term but it's making it business-ready, so you don't need as much of that manual data prep. >> Yeah, exactly, you don't need as much manual data prep, or you don't need as much expertise. For instance, Snowflake's JSON capabilities, in terms of drilling down the JSON tree with dot path notation, or expanding nested objects is very expressive, very powerful, but still your typical analyst or your BI tool certainly wouldn't know how to do that. So, in Snowflake, we sort of have our cake and eat it too. We can have our JSONs with their full richness in our database, but yet we can simplify and expose the data elements that are needed for analysis, so that an analyst, their first day on the job, they can get right to work and start writing queries. >> So let me ask you about, a little more about the programmatic ad use case. So if you have billions of impressions per month, I'm guessing that means you have quite a few times more, in terms of bids, and then there's the, you know once you have, I guess a successful one, you want to track what happens. >> Correct. >> So tell us a little more about that, what that workload looks like, in terms of, what analytics you're trying to perform, what's your tracking? >> Yeah, well, you're right. There's different steps in our funnel. The impression request expands out by a factor of a dozen as we send it to all the different potential bidders. We track all that data, the responses come back, we track that, we track our decisions and why we selected the bidder. And then, once the ad is shown, of course there's various beacons and tracking things that fire. We'd have to track all of that data, and the only way we could make sense out of our business is by bringing all that data together. And in a way that is reliable, transparent, and visible, and also has data integrity, that's another thing I like about the Snowflake database is that it's a good old-fashioned SQL database that I can declare my primary keys, I can run QC checks, I can ensure high data integrity that is demanded by BI and other sorts of analytics. >> What would be, as you continue to push the boundaries of the ad tech service, what's some functionality that you're looking to add, and Snowflake as your partner, either that's in there now that you still need to take advantage of or things that you're looking to in the future? >> Well, moving forward, of course, we, it's very important for us to be able to quickly gauge the effectiveness of new products. The ad tech market is fast-changing, there's always new ways of bidding, new products that are being developed, new ways for the ad ecosystem to work. And so, as we roll those out, we need to be able to quickly analyze, you know, "Is this thing working or not?" You know, kind of an agile environment, pivot or prove it. Does this feature work or not? So, having all the data in one place makes that possible for that very quick assessment of the viability of a new feature, new product. >> And, dropping down a little under the covers for how that works, does that mean, like you still have the base JSON data that you've absorbed, but you're going to expose it with different schemas or access patterns? >> Yeah, indeed. For instance, we make use of the SQL schemas, roles, and permissions internally where we can have the different teams have their own domain of data that they can expose internally, and looking forward, there's the share house feature of Snowflake that we're looking to implement with our partners, where, rather than sending them data, like a daily dump of data, we can give them access to their data in our database through this top layer that Michael mentioned, the service layer, essentially allows me to create a view grant select onto another customer. So I no longer have to send daily data dumps to partners or have some sort of API for getting data. They can simply query the data themselves so we'll be implementing that feature with our major partners. >> I would be remiss in not asking at a data conference like this, now that there's the tie-in with CuBOL and Spark Integration and Machine Learning, is there anything along that front that you're planning to exploit in the near future? >> Well, yeah, Sharethrough, we're very experimental, playful, we're always examining new data technologies and new ways of doing things but now with Snowflake as sort of our data warehouse of curated data. I've got two petabytes of referential integrity data, and that is reliable. We can move forward into our other analyses and other uses of data knowing that we have captured every event exactly once, and we know exactly where it fits in a business context, in a relational manner. It's clean, good data integrity, reliable, accessible, visible, and it's just plain old SQL. (chuckles) >> That's actually a nice way to sum it up. We've got the integrity that we've come to expect and love from relational databases. We've got the flexibility of machine-oriented data, or JSON. But we don't have to give up the query engine, and then now you have more advanced features, analytic features that you can take advantage of coming down the pipe. >> Yeah, again we're a modern platform for the modern age, that's basically cloud-based computing. With a platform like Snowflake in the backend, you can now move those workloads that you're accustomed to to the cloud and have in the environment that you're familiar with, and it saves you a lot of time and effort. You can focus on more strategic projects. >> Okay, well, with that, we're going to take a short break. This has been George Gilbert, we're with Michael Nixon of Snowflake, and David Abercrombie of Sharethrough listening to how the most modern ad tech companies are taking advantage of the most modern cloud data warehouses. And we'll be back after a short break here at the Strata Data Conference, thanks. (quirky music)

>> live from Las Vegas. It's the queue covering del Technologies. World twenty nineteen. Brought to you by Del Technologies and its ecosystem partners. >> Welcome back to the Cubes Live coverage here in Las Vegas. The Cube covering del technology World twenty nineteen. I'm John for Michael David. Want Dave? Lot of strategy being discussed. A lot of new product introductions, availability of things and beta. Michael Dell on stage of Pat Nelson, You're starting to tell a lot of great things. Jeff Clarke, master of ceremonies and here with us, Matt Baker was senior vice president's strategy and playing. Works for Jeff Clarke. Re to see you. Thanks for coming on. >> Thanks for having me. It's great to be back. >> So you're the man behind the curtain for Jeffrey. You get mall. Who's their devices? You sending all the place? He's Tom Brady. You're Bill Belichick. I >> don't have my >> so pretty strategic couple years for del sure. So take a step back because you know one of the luxuries of doing the queue for ten years. As we get some one on ones with Michael, we seem in the hallways. We get the child, then he's very approachable. We talk to him before you went private when he went private or three. Dellums buying him. See? And then when he bought the emcee. So serious conversations. But it's always had that vision of scale. Benefits of that and the numbers were off the chart. People's eyes were popping out their head. So a lot of strategy coming in with the founder with the team pretty impressive. Take us through the pieces on the board, What happened, what didn't have and what could have happened and how it all transpired. >> That's that's a long journey, and it could take a little weight. And second, I think, you know, Michael is a visionary. He's always had a vision for something bigger and certainly the history of del shows that scale matters. And, you know, we we saw display out with our competitors and our strategy versus their strategy. They got smaller, we got bigger and you know, so we're sitting here today. Following all of those moves was a vision tio become that, as Michael would say, essential infrastructure provider and in addition to that not just the physical infrastructure but the logical infrastructure of the management of all that. So vision that Michael saw through to, you know, the acquisition of the M C along with the constituent parts that were the federation inside of there being bm where an opportunity to really bring forward a solutions powerhouse that was ableto address this broad challenge that our customers were having, Which is how do you manage all of this stuff? And, you know, putting a lasso around that and figuring out how to help our customers with what is becoming increasingly complicated. It's not becoming simpler. The individual components are becoming simpler, and that's our job. To make each of those elements more automated more, you know, easy to use, more approachable for more people. But ultimately, people are trying to achieve so much with technology today that you gotta drain away the complexity of that and bring a higher order platform and a higher order operating environment to allow people to really realize their goals. >> And one of the things that you've got to do and your job is you also got not only look at that but also cut through the hype. Sure, I mean how many times we heard the PC is dead. Many times we heard that if you're not here there, you're out of business somewhat true when you have transformational markets. But then the day that the value activities that they're involved in a company is pretty simple, they have operations. They sell a product, good or service. They give it to a customer, they collect cash. At the end of the day, this workload. So you look at everything all things being equal. This is a developer operational and a workload kind of challenge to do something. Certainly pretty much. It's so operation leads a big advantage. >> Well, I think that's what our customers are struggling with is that there are very good reasons to choose different operating environments. I mean, tohave things in different localities. Simply you might want to preposition content like you guys do. I'm sure somewhere behind the Cube is a cdn right for distributing content and so that service benefits from geographic distribution. But you shouldn't have to create a diverse operating environment in order to operate a geographically diverse environment. What we're creating is the singular operational hub for your entire environment. You can run workloads in azure. You Khun, run workloads in eight of us, you Khun run workloads in the four thousand VC PP partners that that Pat mentioned during the key note. And you could do that all through a single operational framework. I don't want to stay single pane of glass, but in essence, the sink. Thank you. Single operational framework that allows you to orchestrate and manage your entirety of your operations. Which allows you to choose the best horse for the given course. >> So strategy guy? Yeah, dial back a few years, even maybe before the acquisition. How clear wasn't you? Was it to you? And what gave you the confidence that acquiring Because you guys were quiz, It'd Dell was inquisitive, and I say a lot of them didn't really pan out the way you had hoped. And you're going to bring in this giant PMC cleanup. VM wears cloud strategy, leverage it across the portfolio. Really, Dr Scale Become that sort of arms dealer, if you will. Sure. What gave you confidence at the time or was it more? Hey, this is a great asset. We'LL figure it out. Can you share with us? >> I think you need to roll the clock back much further to the fact that We were partners for a long time with the emcee, and we were certainly one of the largest. And year by year, the largest or second largest partner to VM were. So we were incredibly familiar with one another and incredibly familiar with our capabilities. We were the early company that worked on. I can't remember what it was called. It might have been Oh, my God. I can't even remember the name of a guy. Integration. No, no, I'm talking about the early visa and implementations that went to market with Dell. Right? So we have been collaborating on hyper Converged. We've been collaborating on Cloud management. We've been working with the emcee years prior. In fact, some of the folks that are appearing on you know your show today. Our people that were my partners ten years ago at that Del. So I think we had the confidence and Michael certainly had a long history with with the organization. And I think he saw on opportunity that there was sort of ah, confluence of of opportunities around, you know, the markets, you know, our ability to pull a deal like that off. But I think ultimately Michael had the confidence that this was going to create a powerhouse and those assets were undeniable. And to some degree, what you were saying about, you know, the sort of these zero some assumptions is like Okay, well, software to find it's going to disrupt traditional race. To the extent that it's going to be, there are no zero sum outcomes in it. We continue on, and those markets continue to be robust. In fact, the storage market has been growing quite robustly, so sort of like this is a set up of capabilities that people need. We need to get bigger, not smaller. >> I buy that and I buy this not a zero sum. I heard Bill Clinton at Adele World years ago talk about how it's not a zero sum game and that I thought was pretty credible. However, historically the IT business has been a winner. Take most you know the leader gets most of the the prophet the second makes does okay and the third kind of barely breaks even. And there is no fourth, fifth and sixth. So it is sort of a winner. Take All are going to take most market, isn't it? >> I think to some degree, but it depends on how you define markets and Barney we all. We all tend to participate and find opportunities. Teo to move and cross into new market areas. Market extension is a a basic of strategy, right? Like look for adjacent sees expend. So there's plenty of room in this three plus trillion dollars market for us all to really participate. The job of strategists and business leaders is what air those best opportunities and how do we get after them? And certainly Michael is proven to be the strategist to figure this out. Like what? What? You know Dell's acquiring A and C. It's like, Yeah, way are you know that >> nineteen ninety billion You grew fifty fourteen percent last year. What? Yeah, >> it's always good that the founder around always great to have that leadership in the history. But one of the things I really like about the strategy that you guys are taking is one. I love the bigger scale leverage. I think that's right on the money. We called that right out of the gate. I'm here in the Q right when it happened, but there's nothing that's emerging. I want to get your thoughts on what? I see this clearly with Google. Google has a sorry sight, reliable engineer, and they run measurement for the massive interest for themselves. They're clouds. Not yet. Translating is no one's like Google, right? There's no enterprise that actually just Google, so it's like a tailored suit for one person. Google Operational Consistency is a huge message here. This year has been for a while. This is really important. I want you to take a minute to explain why that strategy and what the impact will be for customers. >> Yeah, well, I think that you just sort of it's the analog within our customers to what you just described with our business. Achieving scale allows you to accelerate growth. Achieving scale, innit operations allows you to achieve scale and accelerate. Your digital transformation is a customer. So if you're able to create the equivalent of these sightless reliability, in other words, creating a highly scaleable environment, there is no lack of demand, for it fueled innovation in any business anywhere today. That's why we're so bullish on the future that were in the middle of this massive investment cycle of digitizing codifying business process into application and growing the footprint and sort of surface area of all businesses. So if you're able tto create this consistent operating level rather than spending all day down in the plumbing of it all, automate that layer and then focus on the business value. That's if you go look through our studies around, you know, digital transformation. What our customers air doing. It shows that to some degree they face did transformation stall right there. They're like, How do I get everybody aligned to this tea? I've got the business increasingly were like You all need to come together as an organization and focus up and helping our people free up and scale themselves to get closer to the business and really be a part of that sort of strategic discussion for the company. It's the same thing it's achieved. Scale works everywhere, achieve scale, innit Operations frees up time and investment dollars tto help the debs to help the business >> rival revenue to drive revenue well, not justa cost issue. You take away that cost, which is a cost consolidation, cost, leverage, efficiency, et cetera. But the flip side is like Bank of America was saying on the keynote. They gotta know we gotta run their business and make money. So the APP developers are critical. Well, tonight could be a part of the revenue stream, >> and I think the sort of basics of it all is that this wee keeps throwing around the term digital transformation. But at the end of the day, people are codifying business process and their customer experiences and all of that into technology. And that's how they're delivering new business products, new experiences for their customers. You know, I have a branch list bank that I use, I have. You know, I adopted them because the technology was great, right, because their experience of banking through them is amazing. If Andre never >> they know you that its data model, that actually is not an account number and >> absolutely so I think that that's the point you're asking. Why is a consistent operating model important? It helps it achieve scale. And some people say, Well, it's all about developments like them. Look, it's the It's the interplay between ops and death. There's two words there. It's not know ops. It's Dev and Ops and Achieve Scale. You need operators who can achieve scale to achieve scale, you need consistent capabilities. Tooling >> excited. So what you're saying about revenue scale is really important because most big acquisitions, they talk about synergies. That's a code word for cut. You guys grew fourteen percent last year, so I'm sure you have, you know, costs energies. But they're our revenues, energies as well to your point >> and customers as well. >> I think we've just been successful, showing the power of the full portfolio and and that's turned into will make a bigger and bigger bets with you. And during the the Post keynote press briefing, you know, it was stated. Look, you know, people I don't necessarily want more and more and more more vendors. They want fewer more strategic vendors for certain elements, and then they want to look for new innovation in in other areas on. So I think that it's sort of we're benefitting from the fact that we are the company that you could turn to to solve this broad challenge of it, and then you can work with others around your specific vertical, what you might do. We've got a huge network of partners that we work with that can help customers with the spoke applications for their specific vertical health care retail. So on and so forth. So, yeah, I think we value chain in the valley. Twenty infrastructure >> suppliers to your point about that zero sum. Um, uh, thesis is not being zero. Sum is infrastructure loves automation. Automation loves data. So if you have an end to end architecture, you have better data. Correct. You have opportunities, you know, being around automation machine learning, too. Set the standard for the next layer >> up. Well, and that consistency extends all the way across. Right? So if I can create a consistent model, that model is also four out in the data. And then I can create a consistent engine to consume that data to Dr Automation that continues to add value in value in value. So putting that loop around it is hugely important to driving value for our >> customers. Do your kids play? Would you rather you know, they say we gotta like this Really? The hot desert or freezing cold, right. So, Matt, would you rather be a fighter test pilot or a college professor? >> You know, you must have read something from my Twitter feed. I would rather >> you gotta answer. Definitely. Fighter pilot. Fighter pie. Okay. How about would you rather be a professional hunting and fishing guide or professional ocean racing skipper? >> Probably the ocean racing skipper. Although I'd like to be closer to my family. Both of those. You're out out of >> the way with your top five jobs. >> That was Those are my theoretical jobs. I love my job. I don't want a new job. I love my job. Like I don't I don't want to go anywhere. But if that was purely theoretical of it, >> Matt, thanks for coming on the Cube. I'LL give you the final word. In short, what's the core strategy of Adele Technologies? >> Well, I think it's to continue to drive value across the totality of this entity that has so much power. And I hope that's on display. An obvious to you both that we're really pulling together to create solutions that deliver a massive amount of value to our customers. And I think that's unmatched in the industry. So I appreciate you having me on toe talk through this and give me a little rip me a little about my tweets about my >> friend. Give a quick flight for your video log. The Baker's Dozen. >> Yeah, the Bakers, half dozen >> figures That doesn't give it. What's it about what he wants? The focus. >> It's It's basically a six minute spot that I go through. Six and a half things. Baker's dozens thirteen. It's hard to divide it by two way. End up with a half thing. It's sort of funny, but I just take people through a basic rundown of Hey, what's going on in the marketplace? And I try to make it simple, funny and just sort of poke fun at myself. So it's funny. >> Matt Baker, senior vice president of strategy and planning for Del Technologies. I'm Jeffrey Worry David Lantz. Stay too. From more live coverage of day One of three days of wall to wall coverage to cube sets, A lot of content. The cube cannon blowing out the content here, Del Technologies world. Stay with us. We'LL be right back