>> Narrator: Live from Boston, Massachusetts, this is theCUBE, covering Spark Summit East 2017. Brought to you by Databricks. Now, here are your hosts Dave Vellante and George Gilbert. >> Welcome back to Boston, everybody, this is theCUBE, the leader in live tech coverage. We're here in Boston, in snowy Boston. This is Spark Summit. Spark Summit does a East Coast version, they do a West Coast version, they've got one in Europe this year. theCUBE has been a partner with Databricks as the live broadcast partner. Our friend Bill Peterson is here. He's the head of partner marketing at MapR. Bill, good to see you again. >> Thank you, thanks for having me. >> So how's the show going for you? >> It's great. >> Give us the vibe. We're kind of windin' down day two. >> It is. The show's been great, we've got a lot of traffic coming by, a lot of deep technical questions which is-- >> Dave: Hardcore at the show-- >> It is, it is. I spend a lot of time there smiling and going, "Yeah, talk to him." (laughs) But it's great. We're getting those deep technical questions and it's great. We actually just got one on Lustre, which I had to think for a minute, oh, HPC. It was like way back in there. >> Dave: You know, Cray's on the floor. >> Oh, yeah that's true. But a lot of our customers as well. UnitedHealth Group, Wells Fargo, AMEX coming by. Which is great to see them and talk to them, but also they've got some deep technical questions for us. So it's moving the needle with existing customers but also new business, which is great. >> So I got to ask a basic question. What is MapR? MapR started in the early days of Hadoop distro, vendor, one of the big three. When somebody says to you what is MapR, what do you say? My answer today is MapR is an enterprise software company that delivers a converged data platform. That converged data platform consists of a file system, a NoSQL database, a Hadoop distribution, a Spark distribution, and a set of data management tools. And as a customer of MapR, you get all of those. You can turn 'em all on if you'd like. You can just turn on the file system, for example, if you wanted to just use the file system for storage. But the enterprise software piece of that is all the hardening we do behind the scenes on things like snapshots, mirroring, data governance, multi-tenancy, ease of use performance, all of that baked in to the solution, or the platform as we're calling it now. So as you're kind of alluding to, a year ago now we kind of got out of that business of saying okay, lead 100% with Hadoop and then while we have your attention, or if we don't, hey wait, we got all this other stuff in the basket we want to show you, we went the platform play and said we're going to include everything and it's all there and then the baseline underneath is the hardening of it, the file system, the database, and the streaming product, actually, which I didn't mention, which is kind of the core, and everything plays off of there. And that honestly has been really well-received. And it just, I feel, makes it so much easier because-- It happened here, we get the question, okay, how are you different from Cloudera or Hortonworks? And some of it here, given the nature of the attendees, is very technical, but there's been a couple of business users that I've talked to. And when I talk about us as an enterprise software company delivering a plethora of solutions versus just Hadoop, you can see the light going on sometimes in people's eyes. And I got it today, earlier, "I had no idea you had a file system," which, to me, just drives me insane because the file system is pretty cool, right? >> Well you guys are early on in investing in that file system and recovery capabilities and all the-- >> Two years in stealth writing it. >> Nasty, gnarly, hard stuff that was kind of poo-pooed early on. >> Yeah, yeah. MapR was never patient about waiting for the open source community to just figure it out and catch up. You always just said all right, we're going to solve this problem and go sell. >> And I'm glad you said that. I want to be clear. We're not giving up on open source or anything, right? Open source is still a big piece. 50% of our engineers' time is working on open source projects. That's still super important to us. And then back in November-ish last year we announced the MapR Ecosystem Packs, which is our effort to help our customers that are using open source components to stay current. 'Cause that's a pain in the butt. So this is a set of packages that have a whole bunch of components. We lead with Spark and Drill, and that was by customer request, that they were having a hard time keeping current with Spark and Drill. So the packs allow them to come up to current level within the converged data platform for all of their open source components. And that's something we're going to do at dot Level, so I think we're at 2.1 or 2 now. The dot levels will bring you up on everything and then the big ones, like the 3.0s, the 4.0s, will bring Spark and Drill current. And so we're going to kind of leapfrog those. So that's still a really important part of our business and we don't want to forget that part, but what we're trying here to do is, via the platform, is deliver all of that in one entity, right? >> So the converged data platform is relevant presumably because you've got the history of Hadoop, 'cause you got all these different components and you got to cobble 'em together and they're different interfaces and different environments, you're trying to unify that and you have unified that, right? >> Yeah, yeah. >> So what is your customer feedback with regard to the converged data platform? >> Yeah so it's a great question because for existing customers, it was like, ah, thank you. It was one of those, right, because we're listening. Actually, again, glad you said that. This week, in addition to Spark Summit we're doing our yearly customer advisory board so we've got, like a lot of vendors, we've got a 30 plus company customer advisory board that we bring in and we sit down with them for a couple of days and they give us feedback on what we should and shouldn't be doing and where, directional and all that, which is super important. And that's where a lot of this converged data platform came out of is the need for... There was just too much, it's kind of confusing. I'll give the example of streams, right? We came out with our streaming product last year and okay, I'm using Hadoop, I'm using your file system, I'm using NoSQL, now you're adding streams, this is great, but now, like MEP, the Ecosystem Packages, I have to keep everything current. You got to make it easier for me, you got to make my life easier for me. So for existing customers it's a stay current, I like this, the model, I can turn on and off what I want when I want. Great model for them, existing business. For new business it gets us out of that Hadoop-only mode, right? I kind of jokingly call us Hadoop plus plus plus plus. We keep adding solutions and add it to a single, cohesive data platform that we keep updated. And as I mentioned here, talking to new customers or new prospects, our potential new business, when I describe the model you can just see the light going on and they realize wow, there's a lot more to this than I had imagined. I got it earlier today, I thought you guys only did Hadoop. Which is a little infuriating as a marketer, but I think from a mechanism and a delivery and a message and a story point of view, it's really helped. >> More Cube time will help get this out there. (laughs) >> Well played, well played. >> It's good to have you back on. Okay, so Spark comes along a couple years ago and it was like ah, what's going to happen to Hadoop? So you guys embraced Spark. Talk more specifically about Spark, where it fits in your platform and the ecosystem generally. >> Spark, Hadoop, others as a entity to bring data into the converged data platform, that's one way to think about it. Way oversimplified, obviously, but that's a really great way, I think, to think about it is if we're going to provide this platform that anybody can query on, you can run analytics against. We talk a lot about now converged applications. So taking historical data, taking operational data, so streaming data, great example. Putting those together and you could use the Data Lake example if you want, that's fine. But putting them into a converged application in the middle where they overlap, kind of typical Venn diagram where they overlap, and that middle part is the converged application. What's feeding that? Well, Spark could be feeding that, Hadoop could be feeding that. Just yesterday we announced a Docker for containers, that could be feeding into the converged data platform as well. So we look at all of these things as an opportunity for us to manage data and to make data accessible at the enterprise level. And then that enterprise level goes back to what I was talkin' before, it's got to have all of those things, like multi-tenancy and snapshots and mirroring and data governance, security, et cetera. But Spark is a big component of that. All of the customers who came by here that I mentioned earlier, which are some really good names for us, are all using Spark to drive data into the converged data platform. So we look at it as we can help them build new applications within converged data platform with that data. So whether it's Spark data, Hadoop data, container data, we don't really care. >> So along those lines, if the focus of intense interest right now is on Spark, and Spark says oh, and we work with all these databases, data storers, file systems, if you approach a customer who's Spark first, what's the message relative to all the other data storers that they can get to through, without getting too techy, their API? >> Sure, sure. I think as you know, George, we support a whole bunch of APIs. So I guess for us it's the breadth. >> But I'm thinking of Spark in particular. If someone says specifically, I want to run Databricks, but I need something underneath it to capture the data and to manage it. >> Well I think that's the beauty of our file system there. As I mentioned, if you think about it from an architectural point of view, our file system along the bottom, or it could be our database or our streaming product, but in this instance-- >> George: That's what I'm getting at too, all three. >> Picture that as the bottom layer as your storage-- I shouldn't say storage layer but as the bottom layer. 'Cause it's not just storage, it's more than storage. Middle layer is maybe some of your open source tools and the like, and then above that is what I called your data delivery mechanisms. Which would be Spark, for example, one bucket. Another bucket could be Hadoop, and another bucket could be these microservices we're talking about. Let my draw the picture another way using a partner, SAP. One of the things we've had some success with SAP is SAP HANA sitting up here. SAP would love to have you put all your data in HANA. It's probably not going to happen. >> George: Yeah, good luck. >> Yeah, good luck, right? But what if you, hey customer, what if you put zero to two years worth of data, historical data, in HANA. Okay, maybe the customer starts nodding their head like you just did. Hey customer, what if you put two to five years worth of data in Business Warehouse. Guess what, you already own that. You've been an SAP customer for awhile, you already have it. Okay, the customer's now really nodding their head. You got their attention. To your original question, whether it's Spark or whatever, five plus years, put it in MapR. >> Oh, and then like HANA Vora could do the query. >> Drill can query across all of them. >> Oh, right including the Business Warehouse, okay. >> So we're running in the file system. That, to me, and we do this obviously with our joint SAP MapR customers, that to me is kind of a really cool vision. And to your original question, if that was Spark at the top feeding it rather than SAP, sure, right? Why not? >> What can you share with us, Bill, about business metrics around MapR? However you choose to share it, head count, want to give us gross margins by product, that's great, but-- (laughs) >> Would you like revenues too, Dave? >> We know they're very high because you're a software company, so that's actually a bad question. I've already profit-- (laughs) >> You don't have to give us top line revenues-- >> So what are you guys saying publicly about the company, its growth. >> That's fair. >> Give us the latest. >> Fantastic, number one. Hiring like crazy, we're well north of 500 people now. I actually, you want to hear a funny story? I yesterday was texting in the booth, with a candidate from my team, back and forth on salary. Did the salary negotiation on text right there in the booth and closed her, she starts on the 27th, so. >> Dave: Congratulations. >> I'm very excited about that. So moving along on that. Seven, 800 plus customers as we talk about... We just finished our fiscal year on January 31st, so we're on Feb one fiscal year. And we always do a momentum press release, which will be coming out soon. Hiring, again, like crazy, as I mentioned, executive staff is all filled in and built to scale which we're really excited about. We talk a lot about the kind of uptake of-- it used to be of the file system, Hadoop, et cetera on its own, but now in this one the momentum release we'll be doing, we'll talk about the converged data platform and the uplift we've seen from that. So we obviously can't talk revenue numbers and the like, but everything... David, I got to tell you, we've been doin' this a long time, all of that is just all moving in the right direction. And then the other example I'll give you from my world, in the partner world. Last year I rebranded our partner to the converged partner program. We're going with this whole converged thing, right? And we established three levels, elite, preferred, and affiliate with different levels there. But also, there's revenue requirements at each level, so elite, preferred, and affiliate, and there's resell and influence revenues, we have MDF funds, not only from the big guys coming to us, but we're paying out MDF funds now to select partners as well. So all of this stuff I always talk about as the maturity of the company, right? We're maturing in our messaging, we're maturing in the level of people who are joining, and we're maturing in the customers and the deals, the deal sizes and volumes that we're seeing. It's all movin' in the right direction. >> Dave: Great, awesome, congratulations. >> Bill: Thank you, yeah, I'm excited. >> Can you talk about number of customers or number of employees relative to last year? >> Oh boy. Honestly, George, I don't know off the top of my head. I apologize, I don't know the metric, but I know it's north of 500 today, of employees, and it's like seven, 800 customers. >> Okay, okay. >> Yeah, yeah. >> And a little bit more on this partner, elite, preferred, and affiliate. >> Affiliate, yeah. >> What did you call it, the converged partners program? >> Converged-- Yeah, yeah. >> What are some of the details of that? >> Sure. So the elites are invite only, and those are some of the bigger ones. So for us, we're-- >> Dave: Like, some examples. >> Cisco, SAP, AWS, others, but those are some of the big ones. And they were looking at things like resell and influence revenue. That's what I track in my... I always jokingly say at MapR, even though we're kind of a big startup now, I always jokingly say at MapR you have three jobs. You have the job you were hired for, you have your Thursday night job, and you have your Sunday night job. (Dave and George laugh) In the job that I was hired for, partner marketing, I track influence and resell revenue. So at the elite level, we're doing both. Like Cisco resells us, so this S-Series, we're in their SKU, their sales reps can go sell an S-Series for big data workloads or analytical workloads, MapR, on it, off you go. Our job then is cashing checks, which I like. That's a good job to have in this business. At the preferred level it's kind of that next tier of big players, but revenue thresholds haven't moved into the elite yet. Partners in there, like the MicroStrategies of the world, we're doing a lot with them, Tableau, Talend, a lot of the BI vendors in there. And then the affiliates are the smaller guys who maybe we'll do one piece of a campaign during the year with them. So I'll give you an example, Attunity, you guys know those guys right here? >> Sure >> Yeah, yeah. >> Last year we were doing a campaign on DWO, data warehouse offload. We wanted to bring them in but this was a MapR campaign running for a quarter, and we're typical, like a lot of companies, we run four campaigns a year and then my partner in field stuff kind of opts into that and we run stuff to support it. And then corporate marketing does something. Pretty traditional. But what I try and do is pull these partners into those campaigns. So we did a webinar with Attunity as part of that campaign. So at the affiliate level, the lower level, we're not doing a full go-to-market like we would with the elites at the top, but they're being brought into our campaigns and then obviously hopefully, we hope on the other side they're going to pull us in as well. >> Great, last question. What should we pay attention to, what's comin' up? >> Yeah, so-- >> Let's see, we got some events, we got Strata coming up you'll be out your way, or out MapR way. >> As my Twitter handle says, seat 11A. That's where I am. (laughs) Yeah, I mean the Docker announcement we're really excited about, and microservices. You'll see more from us on the whole microservices thing. Streaming is still a big one, we think, for this year. You guys probably agree. That's why we announced the MapR streaming product last year. So again, from a go-to-market point of view and kind of putting some meat behind streaming not only MapR but with partners, so streaming as a component and a delivery model for managing data in CDP. I think that's a big one. Machine learning is something that we're seeing more and more touching us from a number of customers but also from the partner perspective. I see all the partner requests that come in to join the partner program, and there's been an uptick in the machine learning customers that want to come in and-- Excuse me, partners, that want to be talking to us. Which I think is really interesting. >> Where you would be the sort of prediction serving layer? >> Exactly, exactly. Or a data store. A lot of them are looking for just an easy data store that the MapR file system can do. >> Infrastructure to support that, yeah. >> Commodity, right? The whole old promise of Hadoop or just a generic file system is give me easy access to storage on commodity hardware. The machine learning-- >> That works. >> Right. The existing machine learning vendors need an answer for that. When the customer asks them, they want just an easy answer, say oh, we just use MapR FS for that and we're done. Okay, that's fine with me, I'll take that one. >> So that's the operational end of that machine learning pipeline that we call DevOps for data scientists? >> Correct, right. I guess the nice synergy there is the whole, going back to the Docker microservices one, there's a DevOps component there as well. So, might be interesting marrying those together. >> All right, we got to go, Bill, thanks very much, good to see you again. >> All right, thank you. >> All right, George and I will be back to wrap. We're going to part two of our big data forecast right now, so stay with us, right back. (digital music) (synth music)

>>From New York expecting the signal to nine. It's the cube covering sparks summit east brought to you by spark summit. Now your hosts, Dave Volante and George Gilbert >>Right here in Midtown at the Hilton hotel. This has sparked somebody and this is the cube. The cube goes out to the events. We extract the signal from the noise. Jack Norris is here. He's the CMO of Mapbox, long time cube, alum jackets. It's great to see you again. Hey, if you've been here since the beginning of this whole big data >>Meme and it might've started here, I don't know. I think we've yeah, >>I think you're right. I mean, it really did start it. I think in this building, it was our first big data show at the original, you know, uh, uh, Hadoop world. And, uh, and you guys, like I say, I've been there from the start. Uh, you were kind of impatient early on. You said, you know, we're just going to go build solutions and, uh, and ignore the noise and you built a really nice, nice business. Um, you guys have been growing, you're growing your Salesforce and, uh, and things are good and all of a sudden, boom, the spark thing comes in. So we're seeing the evolution. I remember saying to George and the early days of a dupe, we were geeking out talking to all the bits and bytes and then it turned into a business discussion. It's like we're back to the hardcore bits and bites. So give us the update from Matt bar's point of view, where are we in the whole big data space? >>Well, I think, um, I think it has transitioned. I mean, uh, if you look at the typical large fortune company, the web to Datto's, it's really, how do we best leverage our data and how do we leverage our data in that we can, we can make decisions much faster, right? That high-frequency decision-making process. Um, and typically that involves taking production data and analytics and joining them together so that you're actually impacting business as it happens and to do that effectively requires, um, innovations. So the exciting thing about spark is taking and, uh, and having a distributed compute engine, it's much easier to develop and, uh, in much faster. >>So in the remember the early days we'd be at these shows and the big question was, you know, can you take the humans out of the equation? It's like, no, no humans are the last mile. Um, is that, is that changing or would we still need that human interaction or, >>Um, humans are important part of the process, but increasingly if you can adjust and make, you know, small algorithmic decisions, um, and, and make those decisions at that kind of moment of truth, you got big impact, and I'll give you a few examples. So, um, ad platforms, you know, Rubicon project over a hundred billion ad auctions a day, you know, humans, part of that process in terms of setting that up and reviewing the process, but each, you know, each supply and demand decision, there is an automated decision optimizing that has a huge impact on the bottom line, um, fraud, uh, you know, credit card swiping that transaction and deciding is this fraudulent or not avoiding false positives, et cetera, a big leveraged item. So we're seeing things like that across manufacturing, across retail healthcare. And, um, it isn't about asking bigger questions or doing reports and looking back at, you know, what happened last week. It's more, how can I have an infrastructure in place that allows this organization to be agile? Because it's not the companies with the most data that's going to win. It's the companies that are the most agile and making intelligent. >>So it's so much data. Humans can ingest it any faster. I mean, we just, we can't keep up. So the world needs data scientists that needs trained developers. You've got some news I want to talk about on the training side, but even that we can only throw so many bodies at the problem. So it's really software. That's going to allow us to scale it. Software's hard. Software takes time. So we've seen a lot of the spend in the analytics, big data world on, on services. And obviously you guys and others have been working hard to shift it towards software. I want to come back to that training issue. We heard this morning about, uh, Databricks launched a move. They trained 20,000 people. That's a lot, but still long way to go. You guys are putting some investment into training. Talk about that news. Yeah. >>Yeah. Um, well it starts at the underlying software. If you can do things in the platform to make it much easier and do things that are hard to surround with services, like, uh, data protection, right? If you've lost data, it doesn't matter how many people you throw at it, you can't recover it. Right. So that's kind of the starting point you're gonna get fired. >>The, the, uh, the approach we've taken is, is to take, uh, a software product approach to the training as well. So we rolled out on demand training. So it's free, it's on demand. You work at your own pace. It's got different modules, there's some training associated with that, or some hands-on labs, if you will. Um, we launched that last January. So it's basically coming up the year anniversary. We recently celebrated, we trained 50,000 people, uh, on, on Hadoop and big data. Um, today we're announcing expansion on spark classes. We've got full curriculum around spark, including a certification. So you can get sparked certification through this, this map, our on demand training. Okay. >>Gotcha. You said something really, really intriguing that I want to dive into a little bit is where we were talking about the small decisions that can be made really, really fast for that a human in the loop human might have to train them, but it at runtime now where you said, it's not about asking bigger questions, it's finding faster answers, um, what had to change in your platform or in the underlying technology to make that possible. >>You know, um, there's a lot that into it. It's typically a series of functions, uh, a kind of breadth that needs to be brought to the problem as well as squeezing out latencies. So instead of, um, the traditional approach, which is different applications and different analytic techniques dictate a separate silo, a separate, you know, scheme of data. And you've got those all around the organization and data kind of travels, and you get an answer at the end of some period of time. Uh, it's converging that altogether into a single platform, squeezing out those latencies so that you can have an informed action at the speed of business, if you will. And, >>Um, let's say spark never came along. Would that be possible? >>Yes. Yes. Would you, how would you, so if you look at kind of the different architectures that are out there, there's typically deep analytics in terms of, you know, let's go look at the trends, you know, the last seven years, what happened. And then look, let's look at, um, doing actions on a streaming set, say for instance, storm, and then let's do a real time database operations. So you could do that with, with HBase or map RDB and all of that together. What spark has really done is made that whole development process just much easier and much more streamlined. And that's where a lot of the excitements happen. >>So you mentioned earlier, um, to, to use cases, ad tech and fraud detection. Um, and I want to ask you about those in the state of those. So ad tech obviously has come a long way, but it's still got a ways to go. I mean, you look at, I mean, who's making money on ads. Obviously Google will make tons of money. Everybody else is sorta chasing them Facebook making money. It's probably cause they didn't let Google in. Okay. So how will spark affect sort of that business? Uh, and, and what's map, R's sort of role in evolving that, you know, to the next level. >>So, so, um, there's, there's different kind of compute and the types of things you can do, um, on the data. I think increasingly we're seeing the kind of streaming analytics and making those decisions as the data arrives, right. And then there's the whole ecosystem in terms of how do you coordinate those flows of data? It's not just a simple, here's the origin, here's the destination. There's typically a complex data flow. Um, that's where we've kind of focused on map our streams, this huge publish and subscribe infrastructure so that you can get real-time data to the appropriate location and then do the right operations, a lot of that involved with spark, but not exclusively. >>Okay. And then on fraud detection, um, obviously come a long way. Sampling could have died. Yes. And now, but now we're getting too many false positives. You get the call and, you know, I mean, I get a lot of calls because we can buy so much equipment, but, um, but now what about the next level? What are you guys doing to take fraud detection to the next level? So that when I get on the plane in Boston and I land in London, it knows, um, is that a database problem? Is it an integration problem, a systems problem, and how, what role you guys play in solving that? >>Well, there's, there's, um, you know, there's, there's a lot of details and techniques that probably go, um, beyond, you know, what, what we'll share publicly or what are our customers talk about publicly? I think in general, it's the more data that you can apply to a problem. The more context, the better off you are, that's the way I kind of summarize it so that instead of a sampling or instead of a boy, that's a strange purchase over there, it's understanding, well, this is Dave Valenti and this is the full body of, of, uh, expenditures he's done, then the types of things and here's who he frequently purchases from. And here's kind of a transaction trend started in San Francisco, went to New York, et cetera. So in context it would make more sense. So >>Part of that is more data. And the other part of that is just better algorithms and better, better learnings and applying that on a continuous basis. How are your customers dealing with that, that constraint? I mean, if they got a, a hundred dollars to spend, yeah. They can only spend so much on, on each of those gathering more data, cleaning the data, they spent so much time getting it ready versus making their machine learning algorithms or whatever the other techniques to do. What are you seeing there as sort of best practice? It was probably varies. I'm sure, but give us some color on it. >>Um, I'll actually go back to Google and Google a letter last round, um, you know, excellent, excellent insights coming from Google. They wrote a paper called the unreasonable effectiveness of data and in it, they basically squarely addressed that problem. And given the choice to invest in either the complex model and algorithm or put more data at it, putting more data, had a huge impact. And, um, you know, my simple explanation is if you're sampling the data, you have to have a model that tries to recreate reality. If you're looking at all of the data, then the anomalies can, can pop up and be more apparent. And, um, the more context you can bring, the more data from other sources. So you get around, you know, a better picture of what's happening, the better off you are. And so that requires scale. It requires speed and requires different techniques that can be brought to bear, right? The database operation, here's a streaming operation, here's a deep, you know, file machine learning algorithm. >>So there's a lot of vendors in the sort of big data ecosystem are coming at spark from different angles and, um, are, are trying to add value to it and sort of bathe themselves in sort of the halo. Yep. Now you guys took some time upfront to build a converged platform so that you weren't trying to wrap your arms around 37 different projects. Can you tell us how having perhaps not anticipated spark how this converts platform allows you to add more value to it than other approaches? >>So, so we simplify, if you look at the Hadoop ecosystem, it's basically separated into the components for compute and management on top of the data layer, right? The Hadoop distributed file system. So how do you scale data? How do you protect it? It's very simply what's going on. Spark really does a great job at that top layer. Doesn't do anything about defining the underlying storage layer in the Hadoop community that underlying storage layer is a batch system. So you're trying to do, you know, micro batch kind of streaming operations on top of batch oriented data. What we addressed was to take that whole data layer, make it real time, make it random. Read-write converge enterprise storage together with Hadoop support and spark support on a single platform. And that's basically >>With the difference and to make an enterprise great. You guys were really the first to lead the lecture. You were, everybody started talking about attic price straight after you were kind of delivering it. So you've had a lead there. Do you feel like you still have a lead there, or is that the kind of thing where you sort of hit the top of the S-curve and start innovating elsewhere? >>NC state did a study, uh, just this past year, a recent study identified that only 25% of data corruption issues are identified and properly handled by the Hadoop distributed file system. 42% of those are silent. So there's a huge gap in terms of quote unquote enterprise grade features and what we think. >>Yes, silent data corruption has been a problem for decades now. And you're saying it's no different in the duke ecosystem, especially as, as mainstream businesses start to, uh, to adopt this what's happening in the valley. Uh, we're seeing, you know, in the wall street journal every day you read about down rounds, flat rounds, people can't get B rounds. Uh, you guys are funded, you know, you're growing, you're talking about investments, you know, what do you see? Do you, do you feel like you're achieving escape velocity? Um, maybe give us sort of an update on, uh, the state of the business. >>Yeah. I, I think the state of the business is best represented by the customers, right? And the customers kind of vote, right. They vote in terms of, you know, how well is this technology driving their business? So we've got a recent study, um, that kind of shows the, the returns that customers, um, are getting, uh, we've got a 1% chance, a 99% retention rate with our customers. We've got, uh, an expansion rate. That's, that's unbelievable. We've got multi-million dollar customers in, uh, in seven of the top verticals and nine out of the top $10 million customers. So we're seeing significant investments and more importantly, significant returns on the part of customers where they're not just doing a single application on the platform, but multiple >>Applications, Jack Norris map are always focused. Always a pleasure having you on the cube. Thanks very much for coming on. Appreciate it. Keep right there, buddy. We'll be back with our next guest is the cube we're live from spark somebody's right back. Okay.

(uplifting music) >> Hello, welcome back to theCUBE's coverage of CloudNativeSecurityCon, the inaugural event, in Seattle. I'm John Furrier, host of theCUBE, here in the Palo Alto Studios. We're calling it theCUBE Center. It's kind of like our Sports Center for tech. It's kind of remote coverage. We've been doing this now for a few years. We're going to amp it up this year as more events are remote, and happening all around the world. So, we're going to continue the coverage with this segment focusing on the data stack, entrepreneurial opportunities around all things security, and as, obviously, data's involved. And our next guest is a friend of theCUBE, and CUBE alumni from 2013, entrepreneur himself, turned, now, venture capitalist angel investor, with his own firm, Opher Kahane, Managing Director, Sonoma Ventures. Formerly the founder of Origami, sold to Intuit a few years back. Focusing now on having a lot of fun, angel investing on boards, focusing on data-driven applications, and stacks around that, and all the stuff going on in, really, in the wheelhouse for what's going on around security data. Opher, great to see you. Thanks for coming on. >> My pleasure. Great to be back. It's been a while. >> So you're kind of on Easy Street now. You did the entrepreneurial venture, you've worked hard. We were on together in 2013 when theCUBE just started. XCEL Partners had an event in Stanford, XCEL, and they had all the features there. We interviewed Satya Nadella, who was just a manager at Microsoft at that time, he was there. He's now the CEO of Microsoft. >> Yeah, he was. >> A lot's changed in nine years. But congratulations on your venture you sold, and you got an exit there, and now you're doing a lot of investments. I'd love to get your take, because this is really the biggest change I've seen in the past 12 years, around an inflection point around a lot of converging forces. Data, which, big data, 10 years ago, was a big part of your career, but now it's accelerated, with cloud scale. You're seeing people building scale on top of other clouds, and becoming their own cloud. You're seeing data being a big part of it. Cybersecurity kind of has not really changed much, but it's the most important thing everyone's talking about. So, developers are involved, data's involved, a lot of entrepreneurial opportunities. So I'd love to get your take on how you see the current situation, as it relates to what's gone on in the past five years or so. What's the big story? >> So, a lot of big stories, but I think a lot of it has to do with a promise of making value from data, whether it's for cybersecurity, for Fintech, for DevOps, for RevTech startups and companies. There's a lot of challenges in actually driving and monetizing the value from data with velocity. Historically, the challenge has been more around, "How do I store data at massive scale?" And then you had the big data infrastructure company, like Cloudera, and MapR, and others, deal with it from a scale perspective, from a storage perspective. Then you had a whole layer of companies that evolved to deal with, "How do I index massive scales of data, for quick querying, and federated access, et cetera?" But now that a lot of those underlying problems, if you will, have been solved, to a certain extent, although they're always being stretched, given the scale of data, and its utility is becoming more and more massive, in particular with AI use cases being very prominent right now, the next level is how to actually make value from the data. How do I manage the full lifecycle of data in complex environments, with complex organizations, complex use cases? And having seen this from the inside, with Origami Logic, as we dealt with a lot of large corporations, and post-acquisition by Intuit, and a lot of the startups I'm involved with, it's clear that we're now onto that next step. And you have fundamental new paradigms, such as data mesh, that attempt to address that complexity, and responsibly scaling access, and democratizing access in the value monetization from data, across large organizations. You have a slew of startups that are evolving to help the entire lifecycle of data, from the data engineering side of it, to the data analytics side of it, to the AI use cases side of it. And it feels like the early days, to a certain extent, of the revolution that we've seen in transition from traditional databases, to data warehouses, to cloud-based data processing, and big data. It feels like we're at the genesis of that next wave. And it's super, super exciting, for me at least, as someone who's sitting more in the coach seat, rather than being on the pitch, and building startups, helping folks as they go through those motions. >> So that's awesome. I want to get into some of these data infrastructure dynamics you mentioned, but before that, talk to the audience around what you're working on now. You've been a successful entrepreneur, you're focused on angel investing, so, super-early seed stage. What kind of deals are you looking at? What's interesting to you? What is Sonoma Ventures looking for, and what are some of the entrepreneurial dynamics that you're seeing right now, from a startup standpoint? >> Cool, so, at a macro level, this is a little bit of background of my history, because it shapes very heavily what it is that I'm looking at. So, I've been very fortunate with entrepreneurial career. I founded three startups. All three of them are successful. Final two were sold, the first one merged and went public. And my third career has been about data, moving data, passing data, processing data, generating insights from it. And, at this phase, I wanted to really evolve from just going and building startup number four, from going through the same motions again. A 10 year adventure, I'm a little bit too old for that, I guess. But the next best thing is to sit from a point whereby I can be more elevated in where I'm dealing with, and broaden the variety of startups I'm focused on, rather than just do your own thing, and just go very, very deep into it. Now, what specifically am I focused on at Sonoma Ventures? So, basically, looking at what I refer to as a data-driven application stack. Anything from the low-level data infrastructure and cloud infrastructure, that helps any persona in the data universe maximize value for data, from their particular point of view, for their particular role, whether it's data analysts, data scientists, data engineers, cloud engineers, DevOps folks, et cetera. All the way up to the application layer, in applications that are very data-heavy. And what are very typical data-heavy applications? FinTech, cyber, Web3, revenue technologies, and product and DevOps. So these are the areas we're focused on. I have almost 23 or 24 startups in the portfolio that span all these different areas. And this is in terms of the aperture. Now, typically, focus on pre-seed, seed. Sometimes a little bit later stage, but this is the primary focus. And it's really about partnering with entrepreneurs, and helping them make, if you will, original mistakes, avoid the mistakes I made. >> Yeah. >> And take it to the next level, whatever the milestone they're driving with. So I'm very, very hands-on with many of those startups. Now, what is it that's happening right now, initially, and why is it so exciting? So, on one hand, you have this scaling of data and its complexity, yet lagging value creation from it, across those different personas we've touched on. So that's one fundamental opportunity which is secular. The other one, which is more a cyclic situation, is the fact that we're going through a down cycle in tech, as is very evident in the public markets, and everything we're hearing about funding going slower and lower, terms shifting more into the hands of typical VCs versus entrepreneur-friendly market, and so on and so forth. And a very significant amount of layoffs. Now, when you combine these two trends together, you're observing a very interesting thing, that a lot of folks, really bright folks, who have sold a startup to a company, or have been in the guts of the large startup, or a large corporation, have, hands-on, experienced all those challenges we've spoken about earlier, in turf, maximizing value from data, irrespective of their role, in a specific angle, or vantage point they have on those challenges. So, for many of them, it's an opportunity to, "Now, let me now start a startup. I've been laid off, maybe, or my company's stock isn't doing as well as it used to, as a large corporation. Now I have an opportunity to actually go and take my entrepreneurial passion, and apply it to a product and experience as part of this larger company." >> Yeah. >> And you see a slew of folks who are emerging with these great ideas. So it's a very, very exciting period of time to innovate. >> It's interesting, a lot of people look at, I mean, I look at Snowflake as an example of a company that refactored data warehouses. They just basically took data warehouse, and put it on the cloud, and called it a data cloud. That, to me, was compelling. They didn't pay any CapEx. They rode Amazon's wave there. So, a similar thing going on with data. You mentioned this, and I see it as an enabling opportunity. So whether it's cybersecurity, FinTech, whatever vertical, you have an enablement. Now, you mentioned data infrastructure. It's a super exciting area, as there's so many stacks emerging. We got an analytics stack, there's real-time stacks, there's data lakes, AI stack, foundational models. So, you're seeing an explosion of stacks, different tools probably will emerge. So, how do you look at that, as a seasoned entrepreneur, now investor? Is that a good thing? Is that just more of the market? 'Cause it just seems like more and more kind of decomposed stacks targeted at use cases seems to be a trend. >> Yeah. >> And how do you vet that, is it? >> So it's a great observation, and if you take a step back and look at the evolution of technology over the last 30 years, maybe longer, you always see these cycles of expansion, fragmentation, contraction, expansion, contraction. Go decentralize, go centralize, go decentralize, go centralize, as manifested in different types of technology paradigms. From client server, to storage, to microservices, to et cetera, et cetera. So I think we're going through another big bang, to a certain extent, whereby end up with more specialized data stacks for specific use cases, as you need performance, the data models, the tooling to best adapt to the particular task at hand, and the particular personas at hand. As the needs of the data analysts are quite different from the needs of an NL engineer, it's quite different from the needs of the data engineer. And what happens is, when you end up with these siloed stacks, you end up with new fragmentation, and new gaps that need to be filled with a new layer of innovation. And I suspect that, in part, that's what we're seeing right now, in terms of the next wave of data innovation. Whether it's in a service of FinTech use cases, or cyber use cases, or other, is a set of tools that end up having to try and stitch together those elements and bridge between them. So I see that as a fantastic gap to innovate around. I see, also, a fundamental need in creating a common data language, and common data management processes and governance across those different personas, because ultimately, the same underlying data these folks need, albeit in different mediums, different access models, different velocities, et cetera, the subject matter, if you will, the underlying raw data, and some of the taxonomies right on top of it, do need to be consistent. So, once again, a great opportunity to innovate, whether it's about semantic layers, whether it's about data mesh, whether it's about CICD tools for data engineers, and so on and so forth. >> I got to ask you, first of all, I see you have a friend you brought into the interview. You have a dog in the background who made a little cameo appearance. And that's awesome. Sitting right next to you, making sure everything's going well. On the AI thing, 'cause I think that's the hot trend here. >> Yeah. >> You're starting to see, that ChatGPT's got everyone excited, because it's kind of that first time you see kind of next-gen functionality, large-language models, where you can bring data in, and it integrates well. So, to me, I think, connecting the dots, this kind of speaks to the beginning of what will be a trend of really blending of data stacks together, or blending of models. And so, as more data modeling emerges, you start to have this AI stack kind of situation, where you have things out there that you can compose. It's almost very developer-friendly, conceptually. This is kind of new, but kind of the same concept's been working on with Google and others. How do you see this emerging, as an investor? What are some of the things that you're excited about, around the ChatGPT kind of things that's happening? 'Cause it brings it mainstream. Again, a million downloads, fastest applications get a million downloads, even among all the successes. So it's obviously hit a nerve. People are talking about it. What's your take on that? >> Yeah, so, I think that's a great point, and clearly, it feels like an iPhone moment, right, to the industry, in this case, AI, and lots of applications. And I think there's, at a high level, probably three different layers of innovation. One is on top of those platforms. What use cases can one bring to the table that would drive on top of a ChatGPT-like service? Whereby, the startup, the company, can bring some unique datasets to infuse and add value on top of it, by custom-focusing it and purpose-building it for a particular use case or particular vertical. Whether it's applying it to customer service, in a particular vertical, applying it to, I don't know, marketing content creation, and so on and so forth. That's one category. And I do know that, as one of my startups is in Y Combinator, this season, winter '23, they're saying that a very large chunk of the YC companies in this cycle are about GPT use cases. So we'll see a flurry of that. The next layer, the one below that, is those who actually provide those platforms, whether it's ChatGPT, whatever will emerge from the partnership with Microsoft, and any competitive players that emerge from other startups, or from the big cloud providers, whether it's Facebook, if they ever get into this, and Google, which clearly will, as they need to, to survive around search. The third layer is the enabling layer. As you're going to have more and more of those different large-language models and use case running on top of it, the underlying layers, all the way down to cloud infrastructure, the data infrastructure, and the entire set of tools and systems, that take raw data, and massage it into useful, labeled, contextualized features and data to feed the models, the AI models, whether it's during training, or during inference stages, in production. Personally, my focus is more on the infrastructure than on the application use cases. And I believe that there's going to be a massive amount of innovation opportunity around that, to reach cost-effective, quality, fair models that are deployed easily and maintained easily, or at least with as little pain as possible, at scale. So there are startups that are dealing with it, in various areas. Some are about focusing on labeling automation, some about fairness, about, speaking about cyber, protecting models from threats through data and other issues with it, and so on and so forth. And I believe that this will be, too, a big driver for massive innovation, the infrastructure layer. >> Awesome, and I love how you mentioned the iPhone moment. I call it the browser moment, 'cause it felt that way for me, personally. >> Yep. >> But I think, from a business model standpoint, there is that iPhone shift. It's not the BlackBerry. It's a whole 'nother thing. And I like that. But I do have to ask you, because this is interesting. You mentioned iPhone. iPhone's mostly proprietary. So, in these machine learning foundational models, >> Yeah. >> you're starting to see proprietary hardware, bolt-on, acceleration, bundled together, for faster uptake. And now you got open source emerging, as two things. It's almost iPhone-Android situation happening. >> Yeah. >> So what's your view on that? Because there's pros and cons for either one. You're seeing a lot of these machine learning laws are very proprietary, but they work, and do you care, right? >> Yeah. >> And then you got open source, which is like, "Okay, let's get some upsource code, and let people verify it, and then build with that." Is it a balance? >> Yes, I think- >> Is it mutually exclusive? What's your view? >> I think it's going to be, markets will drive the proportion of both, and I think, for a certain use case, you'll end up with more proprietary offerings. With certain use cases, I guess the fundamental infrastructure for ChatGPT-like, let's say, large-language models and all the use cases running on top of it, that's likely going to be more platform-oriented and open source, and will allow innovation. Think of it as the equivalent of iPhone apps or Android apps running on top of those platforms, as in AI apps. So we'll have a lot of that. Now, when you start going a little bit more into the guts, the lower layers, then it's clear that, for performance reasons, in particular, for certain use cases, we'll end up with more proprietary offerings, whether it's advanced silicon, such as some of the silicon that emerged from entrepreneurs who have left Google, around TensorFlow, and all the silicon that powers that. You'll see a lot of innovation in that area as well. It hopefully intends to improve the cost efficiency of running large AI-oriented workloads, both in inference and in learning stages. >> I got to ask you, because this has come up a lot around Azure and Microsoft. Microsoft, pretty good move getting into the ChatGPT >> Yep. >> and the open AI, because I was talking to someone who's a hardcore Amazon developer, and they said, they swore they would never use Azure, right? One of those types. And they're spinning up Azure servers to get access to the API. So, the developers are flocking, as you mentioned. The YC class is all doing large data things, because you can now program with data, which is amazing, which is amazing. So, what's your take on, I know you got to be kind of neutral 'cause you're an investor, but you got, Amazon has to respond, Google, essentially, did all the work, so they have to have a solution. So, I'm expecting Google to have something very compelling, but Microsoft, right now, is going to just, might run the table on developers, this new wave of data developers. What's your take on the cloud responses to this? What's Amazon, what do you think AWS is going to do? What should Google be doing? What's your take? >> So, each of them is coming from a slightly different angle, of course. I'll say, Google, I think, has massive assets in the AI space, and their underlying cloud platform, I think, has been designed to support such complicated workloads, but they have yet to go as far as opening it up the same way ChatGPT is now in that Microsoft partnership, and Azure. Good question regarding Amazon. AWS has had a significant investment in AI-related infrastructure. Seeing it through my startups, through other lens as well. How will they respond to that higher layer, above and beyond the low level, if you will, AI-enabling apparatuses? How do they elevate to at least one or two layers above, and get to the same ChatGPT layer, good question. Is there an acquisition that will make sense for them to accelerate it, maybe. Is there an in-house development that they can reapply from a different domain towards that, possibly. But I do suspect we'll end up with acquisitions as the arms race around the next level of cloud wars emerges, and it's going to be no longer just about the basic tooling for basic cloud-based applications, and the infrastructure, and the cost management, but rather, faster time to deliver AI in data-heavy applications. Once again, each one of those cloud suppliers, their vendor is coming with different assets, and different pros and cons. All of them will need to just elevate the level of the fight, if you will, in this case, to the AI layer. >> It's going to be very interesting, the different stacks on the data infrastructure, like I mentioned, analytics, data lake, AI, all happening. It's going to be interesting to see how this turns into this AI cloud, like data clouds, data operating systems. So, super fascinating area. Opher, thank you for coming on and sharing your expertise with us. Great to see you, and congratulations on the work. I'll give you the final word here. Give a plugin for what you're looking for for startup seats, pre-seeds. What's the kind of profile that gets your attention, from a seed, pre-seed candidate or entrepreneur? >> Cool, first of all, it's my pleasure. Enjoy our chats, as always. Hopefully the next one's not going to be in nine years. As to what I'm looking for, ideally, smart data entrepreneurs, who have come from a particular domain problem, or problem domain, that they understand, they felt it in their own 10 fingers, or millions of neurons in their brains, and they figured out a way to solve it. Whether it's a data infrastructure play, a cloud infrastructure play, or a very, very smart application that takes advantage of data at scale. These are the things I'm looking for. >> One final, final question I have to ask you, because you're a seasoned entrepreneur, and now coach. What's different about the current entrepreneurial environment right now, vis-a-vis, the past decade? What's new? Is it different, highly accelerated? What advice do you give entrepreneurs out there who are putting together their plan? Obviously, a global resource pool now of engineering. It might not be yesterday's formula for success to putting a venture together to get to that product-market fit. What's new and different, and what's your advice to the folks out there about what's different about the current environment for being an entrepreneur? >> Fantastic, so I think it's a great question. So I think there's a few axes of difference, compared to, let's say, five years ago, 10 years ago, 15 years ago. First and foremost, given the amount of infrastructure out there, the amount of open-source technologies, amount of developer toolkits and frameworks, trying to develop an application, at least at the application layer, is much faster than ever. So, it's faster and cheaper, to the most part, unless you're building very fundamental, core, deep tech, where you still have a big technology challenge to deal with. And absent that, the challenge shifts more to how do you manage my resources, to product-market fit, how are you integrating the GTM lens, the go-to-market lens, as early as possible in the product-market fit cycle, such that you reach from pre-seed to seed, from seed to A, from A to B, with an optimal amount of velocity, and a minimal amount of resources. One big difference, specifically as of, let's say, beginning of this year, late last year, is that money is no longer free for entrepreneurs, which means that you need to operate and build startup in an environment with a lot more constraints. And in my mind, some of the best startups that have ever been built, and some of the big market-changing, generational-changing, if you will, technology startups, in their respective industry verticals, have actually emerged from these times. And these tend to be the smartest, best startups that emerge because they operate with a lot less money. Money is not as available for them, which means that they need to make tough decisions, and make verticals every day. What you don't need to do, you can kick the cow down the road. When you have plenty of money, and it cushions for a lot of mistakes, you don't have that cushion. And hopefully we'll end up with companies with a more agile, more, if you will, resilience, and better cultures in making those tough decisions that startups need to make every day. Which is why I'm super, super excited to see the next batch of amazing unicorns, true unicorns, not just valuation, market rising with the water type unicorns that emerged from this particular era, which we're in the beginning of. And very much enjoy working with entrepreneurs during this difficult time, the times we're in. >> The next 24 months will be the next wave, like you said, best time to do a company. Remember, Airbnb's pitch was, "We'll rent cots in apartments, and sell cereal." Boy, a lot of people passed on that deal, in that last down market, that turned out to be a game-changer. So the crazy ideas might not be that bad. So it's all about the entrepreneurs, and >> 100%. >> this is a big wave, and it's certainly happening. Opher, thank you for sharing. Obviously, data is going to change all the markets. Refactoring, security, FinTech, user experience, applications are going to be changed by data, data operating system. Thanks for coming on, and thanks for sharing. Appreciate it. >> My pleasure. Have a good one. >> Okay, more coverage for the CloudNativeSecurityCon inaugural event. Data will be the key for cybersecurity. theCUBE's coverage continues after this break. (uplifting music)

(upbeat music) >> The stampede to cloud and massive VC investments has led to the emergence of a new generation of object store based data lakes. And with them two important trends, actually three important trends. First, a new category that combines data lakes and data warehouses aka the lakehouse is emerged as a leading contender to be the data platform of the future. And this novelty touts the ability to address data engineering, data science, and data warehouse workloads on a single shared data platform. The other major trend we've seen is query engines and broader data fabric virtualization platforms have embraced NextGen data lakes as platforms for SQL centric business intelligence workloads, reducing, or somebody even claim eliminating the need for separate data warehouses. Pretty bold. However, cloud data warehouses have added complimentary technologies to bridge the gaps with lakehouses. And the third is many, if not most customers that are embracing the so-called data fabric or data mesh architectures. They're looking at data lakes as a fundamental component of their strategies, and they're trying to evolve them to be more capable, hence the interest in lakehouse, but at the same time, they don't want to, or can't abandon their data warehouse estate. As such we see a battle royale is brewing between cloud data warehouses and cloud lakehouses. Is it possible to do it all with one cloud center analytical data platform? Well, we're going to find out. My name is Dave Vellante and welcome to the data platform's power panel on theCUBE. Our next episode in a series where we gather some of the industry's top analysts to talk about one of our favorite topics, data. In today's session, we'll discuss trends, emerging options, and the trade offs of various approaches and we'll name names. Joining us today are Sanjeev Mohan, who's the principal at SanjMo, Tony Baers, principal at dbInsight. And Doug Henschen is the vice president and principal analyst at Constellation Research. Guys, welcome back to theCUBE. Great to see you again. >> Thank guys. Thank you. >> Thank you. >> So it's early June and we're gearing up with two major conferences, there's several database conferences, but two in particular that were very interested in, Snowflake Summit and Databricks Data and AI Summit. Doug let's start off with you and then Tony and Sanjeev, if you could kindly weigh in. Where did this all start, Doug? The notion of lakehouse. And let's talk about what exactly we mean by lakehouse. Go ahead. >> Yeah, well you nailed it in your intro. One platform to address BI data science, data engineering, fewer platforms, less cost, less complexity, very compelling. You can credit Databricks for coining the term lakehouse back in 2020, but it's really a much older idea. You can go back to Cloudera introducing their Impala database in 2012. That was a database on top of Hadoop. And indeed in that last decade, by the middle of that last decade, there were several SQL on Hadoop products, open standards like Apache Drill. And at the same time, the database vendors were trying to respond to this interest in machine learning and the data science. So they were adding SQL extensions, the likes Hudi and Vertical we're adding SQL extensions to support the data science. But then later in that decade with the shift to cloud and object storage, you saw the vendor shift to this whole cloud, and object storage idea. So you have in the database camp Snowflake introduce Snowpark to try to address the data science needs. They introduced that in 2020 and last year they announced support for Python. You also had Oracle, SAP jumped on this lakehouse idea last year, supporting both the lake and warehouse single vendor, not necessarily quite single platform. Google very recently also jumped on the bandwagon. And then you also mentioned, the SQL engine camp, the Dremios, the Ahanas, the Starbursts, really doing two things, a fabric for distributed access to many data sources, but also very firmly planning that idea that you can just have the lake and we'll help you do the BI workloads on that. And then of course, the data lake camp with the Databricks and Clouderas providing a warehouse style deployments on top of their lake platforms. >> Okay, thanks, Doug. I'd be remiss those of you who me know that I typically write my own intros. This time my colleagues fed me a lot of that material. So thank you. You guys make it easy. But Tony, give us your thoughts on this intro. >> Right. Well, I very much agree with both of you, which may not make for the most exciting television in terms of that it has been an evolution just like Doug said. I mean, for instance, just to give an example when Teradata bought AfterData was initially seen as a hardware platform play. In the end, it was basically, it was all those after functions that made a lot of sort of big data analytics accessible to SQL. (clears throat) And so what I really see just in a more simpler definition or functional definition, the data lakehouse is really an attempt by the data lake folks to make the data lake friendlier territory to the SQL folks, and also to get into friendly territory, to all the data stewards, who are basically concerned about the sprawl and the lack of control in governance in the data lake. So it's really kind of a continuing of an ongoing trend that being said, there's no action without counter action. And of course, at the other end of the spectrum, we also see a lot of the data warehouses starting to edit things like in database machine learning. So they're certainly not surrendering without a fight. Again, as Doug was mentioning, this has been part of a continual blending of platforms that we've seen over the years that we first saw in the Hadoop years with SQL on Hadoop and data warehouses starting to reach out to cloud storage or should say the HDFS and then with the cloud then going cloud native and therefore trying to break the silos down even further. >> Now, thank you. And Sanjeev, data lakes, when we first heard about them, there were such a compelling name, and then we realized all the problems associated with them. So pick it up from there. What would you add to Doug and Tony? >> I would say, these are excellent points that Doug and Tony have brought to light. The concept of lakehouse was going on to your point, Dave, a long time ago, long before the tone was invented. For example, in Uber, Uber was trying to do a mix of Hadoop and Vertical because what they really needed were transactional capabilities that Hadoop did not have. So they weren't calling it the lakehouse, they were using multiple technologies, but now they're able to collapse it into a single data store that we call lakehouse. Data lakes, excellent at batch processing large volumes of data, but they don't have the real time capabilities such as change data capture, doing inserts and updates. So this is why lakehouse has become so important because they give us these transactional capabilities. >> Great. So I'm interested, the name is great, lakehouse. The concept is powerful, but I get concerned that it's a lot of marketing hype behind it. So I want to examine that a bit deeper. How mature is the concept of lakehouse? Are there practical examples that really exist in the real world that are driving business results for practitioners? Tony, maybe you could kick that off. >> Well, put it this way. I think what's interesting is that both data lakes and data warehouse that each had to extend themselves. To believe the Databricks hype it's that this was just a natural extension of the data lake. In point of fact, Databricks had to go outside its core technology of Spark to make the lakehouse possible. And it's a very similar type of thing on the part with data warehouse folks, in terms of that they've had to go beyond SQL, In the case of Databricks. There have been a number of incremental improvements to Delta lake, to basically make the table format more performative, for instance. But the other thing, I think the most dramatic change in all that is in their SQL engine and they had to essentially pretty much abandon Spark SQL because it really, in off itself Spark SQL is essentially stop gap solution. And if they wanted to really address that crowd, they had to totally reinvent SQL or at least their SQL engine. And so Databricks SQL is not Spark SQL, it is not Spark, it's basically SQL that it's adapted to run in a Spark environment, but the underlying engine is C++, it's not scale or anything like that. So Databricks had to take a major detour outside of its core platform to do this. So to answer your question, this is not mature because these are all basically kind of, even though the idea of blending platforms has been going on for well over a decade, I would say that the current iteration is still fairly immature. And in the cloud, I could see a further evolution of this because if you think through cloud native architecture where you're essentially abstracting compute from data, there is no reason why, if let's say you are dealing with say, the same basically data targets say cloud storage, cloud object storage that you might not apportion the task to different compute engines. And so therefore you could have, for instance, let's say you're Google, you could have BigQuery, perform basically the types of the analytics, the SQL analytics that would be associated with the data warehouse and you could have BigQuery ML that does some in database machine learning, but at the same time for another part of the query, which might involve, let's say some deep learning, just for example, you might go out to let's say the serverless spark service or the data proc. And there's no reason why Google could not blend all those into a coherent offering that's basically all triggered through microservices. And I just gave Google as an example, if you could generalize that with all the other cloud or all the other third party vendors. So I think we're still very early in the game in terms of maturity of data lakehouses. >> Thanks, Tony. So Sanjeev, is this all hype? What are your thoughts? >> It's not hype, but completely agree. It's not mature yet. Lakehouses have still a lot of work to do, so what I'm now starting to see is that the world is dividing into two camps. On one hand, there are people who don't want to deal with the operational aspects of vast amounts of data. They are the ones who are going for BigQuery, Redshift, Snowflake, Synapse, and so on because they want the platform to handle all the data modeling, access control, performance enhancements, but these are trade off. If you go with these platforms, then you are giving up on vendor neutrality. On the other side are those who have engineering skills. They want the independence. In other words, they don't want vendor lock in. They want to transform their data into any number of use cases, especially data science, machine learning use case. What they want is agility via open file formats using any compute engine. So why do I say lakehouses are not mature? Well, cloud data warehouses they provide you an excellent user experience. That is the main reason why Snowflake took off. If you have thousands of cables, it takes minutes to get them started, uploaded into your warehouse and start experimentation. Table formats are far more resonating with the community than file formats. But once the cost goes up of cloud data warehouse, then the organization start exploring lakehouses. But the problem is lakehouses still need to do a lot of work on metadata. Apache Hive was a fantastic first attempt at it. Even today Apache Hive is still very strong, but it's all technical metadata and it has so many different restrictions. That's why we see Databricks is investing into something called Unity Catalog. Hopefully we'll hear more about Unity Catalog at the end of the month. But there's a second problem. I just want to mention, and that is lack of standards. All these open source vendors, they're running, what I call ego projects. You see on LinkedIn, they're constantly battling with each other, but end user doesn't care. End user wants a problem to be solved. They want to use Trino, Dremio, Spark from EMR, Databricks, Ahana, DaaS, Frink, Athena. But the problem is that we don't have common standards. >> Right. Thanks. So Doug, I worry sometimes. I mean, I look at the space, we've debated for years, best of breed versus the full suite. You see AWS with whatever, 12 different plus data stores and different APIs and primitives. You got Oracle putting everything into its database. It's actually done some interesting things with MySQL HeatWave, so maybe there's proof points there, but Snowflake really good at data warehouse, simplifying data warehouse. Databricks, really good at making lakehouses actually more functional. Can one platform do it all? >> Well in a word, I can't be best at breed at all things. I think the upshot of and cogen analysis from Sanjeev there, the database, the vendors coming out of the database tradition, they excel at the SQL. They're extending it into data science, but when it comes to unstructured data, data science, ML AI often a compromise, the data lake crowd, the Databricks and such. They've struggled to completely displace the data warehouse when it really gets to the tough SLAs, they acknowledge that there's still a role for the warehouse. Maybe you can size down the warehouse and offload some of the BI workloads and maybe and some of these SQL engines, good for ad hoc, minimize data movement. But really when you get to the deep service level, a requirement, the high concurrency, the high query workloads, you end up creating something that's warehouse like. >> Where do you guys think this market is headed? What's going to take hold? Which projects are going to fade away? You got some things in Apache projects like Hudi and Iceberg, where do they fit Sanjeev? Do you have any thoughts on that? >> So thank you, Dave. So I feel that table formats are starting to mature. There is a lot of work that's being done. We will not have a single product or single platform. We'll have a mixture. So I see a lot of Apache Iceberg in the news. Apache Iceberg is really innovating. Their focus is on a table format, but then Delta and Apache Hudi are doing a lot of deep engineering work. For example, how do you handle high concurrency when there are multiple rights going on? Do you version your Parquet files or how do you do your upcerts basically? So different focus, at the end of the day, the end user will decide what is the right platform, but we are going to have multiple formats living with us for a long time. >> Doug is Iceberg in your view, something that's going to address some of those gaps in standards that Sanjeev was talking about earlier? >> Yeah, Delta lake, Hudi, Iceberg, they all address this need for consistency and scalability, Delta lake open technically, but open for access. I don't hear about Delta lakes in any worlds, but Databricks, hearing a lot of buzz about Apache Iceberg. End users want an open performance standard. And most recently Google embraced Iceberg for its recent a big lake, their stab at having supporting both lakes and warehouses on one conjoined platform. >> And Tony, of course, you remember the early days of the sort of big data movement you had MapR was the most closed. You had Horton works the most open. You had Cloudera in between. There was always this kind of contest as to who's the most open. Does that matter? Are we going to see a repeat of that here? >> I think it's spheres of influence, I think, and Doug very much was kind of referring to this. I would call it kind of like the MongoDB syndrome, which is that you have... and I'm talking about MongoDB before they changed their license, open source project, but very much associated with MongoDB, which basically, pretty much controlled most of the contributions made decisions. And I think Databricks has the same iron cloud hold on Delta lake, but still the market is pretty much associated Delta lake as the Databricks, open source project. I mean, Iceberg is probably further advanced than Hudi in terms of mind share. And so what I see that's breaking down to is essentially, basically the Databricks open source versus the everything else open source, the community open source. So I see it's a very similar type of breakdown that I see repeating itself here. >> So by the way, Mongo has a conference next week, another data platform is kind of not really relevant to this discussion totally. But in the sense it is because there's a lot of discussion on earnings calls these last couple of weeks about consumption and who's exposed, obviously people are concerned about Snowflake's consumption model. Mongo is maybe less exposed because Atlas is prominent in the portfolio, blah, blah, blah. But I wanted to bring up the little bit of controversy that we saw come out of the Snowflake earnings call, where the ever core analyst asked Frank Klutman about discretionary spend. And Frank basically said, look, we're not discretionary. We are deeply operationalized. Whereas he kind of poo-pooed the lakehouse or the data lake, et cetera, saying, oh yeah, data scientists will pull files out and play with them. That's really not our business. Do any of you have comments on that? Help us swing through that controversy. Who wants to take that one? >> Let's put it this way. The SQL folks are from Venus and the data scientists are from Mars. So it means it really comes down to it, sort that type of perception. The fact is, is that, traditionally with analytics, it was very SQL oriented and that basically the quants were kind of off in their corner, where they're using SaaS or where they're using Teradata. It's really a great leveler today, which is that, I mean basic Python it's become arguably one of the most popular programming languages, depending on what month you're looking at, at the title index. And of course, obviously SQL is, as I tell the MongoDB folks, SQL is not going away. You have a large skills base out there. And so basically I see this breaking down to essentially, you're going to have each group that's going to have its own natural preferences for its home turf. And the fact that basically, let's say the Python and scale of folks are using Databricks does not make them any less operational or machine critical than the SQL folks. >> Anybody else want to chime in on that one? >> Yeah, I totally agree with that. Python support in Snowflake is very nascent with all of Snowpark, all of the things outside of SQL, they're very much relying on partners too and make things possible and make data science possible. And it's very early days. I think the bottom line, what we're going to see is each of these camps is going to keep working on doing better at the thing that they don't do today, or they're new to, but they're not going to nail it. They're not going to be best of breed on both sides. So the SQL centric companies and shops are going to do more data science on their database centric platform. That data science driven companies might be doing more BI on their leagues with those vendors and the companies that have highly distributed data, they're going to add fabrics, and maybe offload more of their BI onto those engines, like Dremio and Starburst. >> So I've asked you this before, but I'll ask you Sanjeev. 'Cause Snowflake and Databricks are such great examples 'cause you have the data engineering crowd trying to go into data warehousing and you have the data warehousing guys trying to go into the lake territory. Snowflake has $5 billion in the balance sheet and I've asked you before, I ask you again, doesn't there has to be a semantic layer between these two worlds? Does Snowflake go out and do M&A and maybe buy ad scale or a data mirror? Or is that just sort of a bandaid? What are your thoughts on that Sanjeev? >> I think semantic layer is the metadata. The business metadata is extremely important. At the end of the day, the business folks, they'd rather go to the business metadata than have to figure out, for example, like let's say, I want to update somebody's email address and we have a lot of overhead with data residency laws and all that. I want my platform to give me the business metadata so I can write my business logic without having to worry about which database, which location. So having that semantic layer is extremely important. In fact, now we are taking it to the next level. Now we are saying that it's not just a semantic layer, it's all my KPIs, all my calculations. So how can I make those calculations independent of the compute engine, independent of the BI tool and make them fungible. So more disaggregation of the stack, but it gives us more best of breed products that the customers have to worry about. >> So I want to ask you about the stack, the modern data stack, if you will. And we always talk about injecting machine intelligence, AI into applications, making them more data driven. But when you look at the application development stack, it's separate, the database is tends to be separate from the data and analytics stack. Do those two worlds have to come together in the modern data world? And what does that look like organizationally? >> So organizationally even technically I think it is starting to happen. Microservices architecture was a first attempt to bring the application and the data world together, but they are fundamentally different things. For example, if an application crashes, that's horrible, but Kubernetes will self heal and it'll bring the application back up. But if a database crashes and corrupts your data, we have a huge problem. So that's why they have traditionally been two different stacks. They are starting to come together, especially with data ops, for instance, versioning of the way we write business logic. It used to be, a business logic was highly embedded into our database of choice, but now we are disaggregating that using GitHub, CICD the whole DevOps tool chain. So data is catching up to the way applications are. >> We also have databases, that trans analytical databases that's a little bit of what the story is with MongoDB next week with adding more analytical capabilities. But I think companies that talk about that are always careful to couch it as operational analytics, not the warehouse level workloads. So we're making progress, but I think there's always going to be, or there will long be a separate analytical data platform. >> Until data mesh takes over. (all laughing) Not opening a can of worms. >> Well, but wait, I know it's out of scope here, but wouldn't data mesh say, hey, do take your best of breed to Doug's earlier point. You can't be best of breed at everything, wouldn't data mesh advocate, data lakes do your data lake thing, data warehouse, do your data lake, then you're just a node on the mesh. (Tony laughs) Now you need separate data stores and you need separate teams. >> To my point. >> I think, I mean, put it this way. (laughs) Data mesh itself is a logical view of the world. The data mesh is not necessarily on the lake or on the warehouse. I think for me, the fear there is more in terms of, the silos of governance that could happen and the silo views of the world, how we redefine. And that's why and I want to go back to something what Sanjeev said, which is that it's going to be raising the importance of the semantic layer. Now does Snowflake that opens a couple of Pandora's boxes here, which is one, does Snowflake dare go into that space or do they risk basically alienating basically their partner ecosystem, which is a key part of their whole appeal, which is best of breed. They're kind of the same situation that Informatica was where in the early 2000s, when Informatica briefly flirted with analytic applications and realized that was not a good idea, need to redouble down on their core, which was data integration. The other thing though, that raises the importance of and this is where the best of breed comes in, is the data fabric. My contention is that and whether you use employee data mesh practice or not, if you do employee data mesh, you need data fabric. If you deploy data fabric, you don't necessarily need to practice data mesh. But data fabric at its core and admittedly it's a category that's still very poorly defined and evolving, but at its core, we're talking about a common meta data back plane, something that we used to talk about with master data management, this would be something that would be more what I would say basically, mutable, that would be more evolving, basically using, let's say, machine learning to kind of, so that we don't have to predefine rules or predefine what the world looks like. But so I think in the long run, what this really means is that whichever way we implement on whichever physical platform we implement, we need to all be speaking the same metadata language. And I think at the end of the day, regardless of whether it's a lake, warehouse or a lakehouse, we need common metadata. >> Doug, can I come back to something you pointed out? That those talking about bringing analytic and transaction databases together, you had talked about operationalizing those and the caution there. Educate me on MySQL HeatWave. I was surprised when Oracle put so much effort in that, and you may or may not be familiar with it, but a lot of folks have talked about that. Now it's got nowhere in the market, that no market share, but a lot of we've seen these benchmarks from Oracle. How real is that bringing together those two worlds and eliminating ETL? >> Yeah, I have to defer on that one. That's my colleague, Holger Mueller. He wrote the report on that. He's way deep on it and I'm not going to mock him. >> I wonder if that is something, how real that is or if it's just Oracle marketing, anybody have any thoughts on that? >> I'm pretty familiar with HeatWave. It's essentially Oracle doing what, I mean, there's kind of a parallel with what Google's doing with AlloyDB. It's an operational database that will have some embedded analytics. And it's also something which I expect to start seeing with MongoDB. And I think basically, Doug and Sanjeev were kind of referring to this before about basically kind of like the operational analytics, that are basically embedded within an operational database. The idea here is that the last thing you want to do with an operational database is slow it down. So you're not going to be doing very complex deep learning or anything like that, but you might be doing things like classification, you might be doing some predictives. In other words, we've just concluded a transaction with this customer, but was it less than what we were expecting? What does that mean in terms of, is this customer likely to turn? I think we're going to be seeing a lot of that. And I think that's what a lot of what MySQL HeatWave is all about. Whether Oracle has any presence in the market now it's still a pretty new announcement, but the other thing that kind of goes against Oracle, (laughs) that they had to battle against is that even though they own MySQL and run the open source project, everybody else, in terms of the actual commercial implementation it's associated with everybody else. And the popular perception has been that MySQL has been basically kind of like a sidelight for Oracle. And so it's on Oracles shoulders to prove that they're damn serious about it. >> There's no coincidence that MariaDB was launched the day that Oracle acquired Sun. Sanjeev, I wonder if we could come back to a topic that we discussed earlier, which is this notion of consumption, obviously Wall Street's very concerned about it. Snowflake dropped prices last week. I've always felt like, hey, the consumption model is the right model. I can dial it down in when I need to, of course, the street freaks out. What are your thoughts on just pricing, the consumption model? What's the right model for companies, for customers? >> Consumption model is here to stay. What I would like to see, and I think is an ideal situation and actually plays into the lakehouse concept is that, I have my data in some open format, maybe it's Parquet or CSV or JSON, Avro, and I can bring whatever engine is the best engine for my workloads, bring it on, pay for consumption, and then shut it down. And by the way, that could be Cloudera. We don't talk about Cloudera very much, but it could be one business unit wants to use Athena. Another business unit wants to use some other Trino let's say or Dremio. So every business unit is working on the same data set, see that's critical, but that data set is maybe in their VPC and they bring any compute engine, you pay for the use, shut it down. That then you're getting value and you're only paying for consumption. It's not like, I left a cluster running by mistake, so there have to be guardrails. The reason FinOps is so big is because it's very easy for me to run a Cartesian joint in the cloud and get a $10,000 bill. >> This looks like it's been a sort of a victim of its own success in some ways, they made it so easy to spin up single note instances, multi note instances. And back in the day when compute was scarce and costly, those database engines optimized every last bit so they could get as much workload as possible out of every instance. Today, it's really easy to spin up a new node, a new multi node cluster. So that freedom has meant many more nodes that aren't necessarily getting that utilization. So Snowflake has been doing a lot to add reporting, monitoring, dashboards around the utilization of all the nodes and multi node instances that have spun up. And meanwhile, we're seeing some of the traditional on-prem databases that are moving into the cloud, trying to offer that freedom. And I think they're going to have that same discovery that the cost surprises are going to follow as they make it easy to spin up new instances. >> Yeah, a lot of money went into this market over the last decade, separating compute from storage, moving to the cloud. I'm glad you mentioned Cloudera Sanjeev, 'cause they got it all started, the kind of big data movement. We don't talk about them that much. Sometimes I wonder if it's because when they merged Hortonworks and Cloudera, they dead ended both platforms, but then they did invest in a more modern platform. But what's the future of Cloudera? What are you seeing out there? >> Cloudera has a good product. I have to say the problem in our space is that there're way too many companies, there's way too much noise. We are expecting the end users to parse it out or we expecting analyst firms to boil it down. So I think marketing becomes a big problem. As far as technology is concerned, I think Cloudera did turn their selves around and Tony, I know you, you talked to them quite frequently. I think they have quite a comprehensive offering for a long time actually. They've created Kudu, so they got operational, they have Hadoop, they have an operational data warehouse, they're migrated to the cloud. They are in hybrid multi-cloud environment. Lot of cloud data warehouses are not hybrid. They're only in the cloud. >> Right. I think what Cloudera has done the most successful has been in the transition to the cloud and the fact that they're giving their customers more OnRamps to it, more hybrid OnRamps. So I give them a lot of credit there. They're also have been trying to position themselves as being the most price friendly in terms of that we will put more guardrails and governors on it. I mean, part of that could be spin. But on the other hand, they don't have the same vested interest in compute cycles as say, AWS would have with EMR. That being said, yes, Cloudera does it, I think its most powerful appeal so of that, it almost sounds in a way, I don't want to cast them as a legacy system. But the fact is they do have a huge landed legacy on-prem and still significant potential to land and expand that to the cloud. That being said, even though Cloudera is multifunction, I think it certainly has its strengths and weaknesses. And the fact this is that yes, Cloudera has an operational database or an operational data store with a kind of like the outgrowth of age base, but Cloudera is still based, primarily known for the deep analytics, the operational database nobody's going to buy Cloudera or Cloudera data platform strictly for the operational database. They may use it as an add-on, just in the same way that a lot of customers have used let's say Teradata basically to do some machine learning or let's say, Snowflake to parse through JSON. Again, it's not an indictment or anything like that, but the fact is obviously they do have their strengths and their weaknesses. I think their greatest opportunity is with their existing base because that base has a lot invested and vested. And the fact is they do have a hybrid path that a lot of the others lack. >> And of course being on the quarterly shock clock was not a good place to be under the microscope for Cloudera and now they at least can refactor the business accordingly. I'm glad you mentioned hybrid too. We saw Snowflake last month, did a deal with Dell whereby non-native Snowflake data could access on-prem object store from Dell. They announced a similar thing with pure storage. What do you guys make of that? Is that just... How significant will that be? Will customers actually do that? I think they're using either materialized views or extended tables. >> There are data rated and residency requirements. There are desires to have these platforms in your own data center. And finally they capitulated, I mean, Frank Klutman is famous for saying to be very focused and earlier, not many months ago, they called the going on-prem as a distraction, but clearly there's enough demand and certainly government contracts any company that has data residency requirements, it's a real need. So they finally addressed it. >> Yeah, I'll bet dollars to donuts, there was an EBC session and some big customer said, if you don't do this, we ain't doing business with you. And that was like, okay, we'll do it. >> So Dave, I have to say, earlier on you had brought this point, how Frank Klutman was poo-pooing data science workloads. On your show, about a year or so ago, he said, we are never going to on-prem. He burnt that bridge. (Tony laughs) That was on your show. >> I remember exactly the statement because it was interesting. He said, we're never going to do the halfway house. And I think what he meant is we're not going to bring the Snowflake architecture to run on-prem because it defeats the elasticity of the cloud. So this was kind of a capitulation in a way. But I think it still preserves his original intent sort of, I don't know. >> The point here is that every vendor will poo-poo whatever they don't have until they do have it. >> Yes. >> And then it'd be like, oh, we are all in, we've always been doing this. We have always supported this and now we are doing it better than others. >> Look, it was the same type of shock wave that we felt basically when AWS at the last moment at one of their reinvents, oh, by the way, we're going to introduce outposts. And the analyst group is typically pre briefed about a week or two ahead under NDA and that was not part of it. And when they dropped, they just casually dropped that in the analyst session. It's like, you could have heard the sound of lots of analysts changing their diapers at that point. >> (laughs) I remember that. And a props to Andy Jassy who once, many times actually told us, never say never when it comes to AWS. So guys, I know we got to run. We got some hard stops. Maybe you could each give us your final thoughts, Doug start us off and then-- >> Sure. Well, we've got the Snowflake Summit coming up. I'll be looking for customers that are really doing data science, that are really employing Python through Snowflake, through Snowpark. And then a couple weeks later, we've got Databricks with their Data and AI Summit in San Francisco. I'll be looking for customers that are really doing considerable BI workloads. Last year I did a market overview of this analytical data platform space, 14 vendors, eight of them claim to support lakehouse, both sides of the camp, Databricks customer had 32, their top customer that they could site was unnamed. It had 32 concurrent users doing 15,000 queries per hour. That's good but it's not up to the most demanding BI SQL workloads. And they acknowledged that and said, they need to keep working that. Snowflake asked for their biggest data science customer, they cited Kabura, 400 terabytes, 8,500 users, 400,000 data engineering jobs per day. I took the data engineering job to be probably SQL centric, ETL style transformation work. So I want to see the real use of the Python, how much Snowpark has grown as a way to support data science. >> Great. Tony. >> Actually of all things. And certainly, I'll also be looking for similar things in what Doug is saying, but I think sort of like, kind of out of left field, I'm interested to see what MongoDB is going to start to say about operational analytics, 'cause I mean, they're into this conquer the world strategy. We can be all things to all people. Okay, if that's the case, what's going to be a case with basically, putting in some inline analytics, what are you going to be doing with your query engine? So that's actually kind of an interesting thing we're looking for next week. >> Great. Sanjeev. >> So I'll be at MongoDB world, Snowflake and Databricks and very interested in seeing, but since Tony brought up MongoDB, I see that even the databases are shifting tremendously. They are addressing both the hashtag use case online, transactional and analytical. I'm also seeing that these databases started in, let's say in case of MySQL HeatWave, as relational or in MongoDB as document, but now they've added graph, they've added time series, they've added geospatial and they just keep adding more and more data structures and really making these databases multifunctional. So very interesting. >> It gets back to our discussion of best of breed, versus all in one. And it's likely Mongo's path or part of their strategy of course, is through developers. They're very developer focused. So we'll be looking for that. And guys, I'll be there as well. I'm hoping that we maybe have some extra time on theCUBE, so please stop by and we can maybe chat a little bit. Guys as always, fantastic. Thank you so much, Doug, Tony, Sanjeev, and let's do this again. >> It's been a pleasure. >> All right and thank you for watching. This is Dave Vellante for theCUBE and the excellent analyst. We'll see you next time. (upbeat music)

>> From the theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> Well, it looks like hybrid cloud is finally here. We've seen a decade of posturing, marchitecture, slideware and narrow examples of hybrid cloud, but there's little question that the definition of cloud is expanding to include on-premises workloads in hybrid models. Now depending on which numbers you choose to represent IT spending, public cloud only accounts for actually less than 5% of the total pie. So the big question is, how will this now evolve? Customers want control, they want governance, they want security, flexibility and a feature-rich set of services to build their digital businesses. It's unlikely that they can buy all that, so they're going to have to build it with partners, specifically vendors, SI's, consultancies and their own developers. The tug of war to win the new cloud day has finally started in earnest between the hyperscalers and the largest enterprise tech companies in the world. Hello and welcome to this week's Wikibon CUBE insights, powered by ETR. In this Breaking Analysis, we'll walk you through how we see the battle for hybrid cloud, how we got here, where we are and where it's headed. First, I want to go back to 2009, in a blog post by a man named Chuck Hollis. Chuck Hollis, at the time, was a CTO and marketing guru inside of EMC who, remember, owned VMware. Chuck was kind of this hybrid, multi-tool player, pun intended. EMC at the time had a big stake, a lot at stake, as the ascendancy of AWS was threatening the historical models, which had defined enterprise IT. Now around that time, NIST published its first draft of a cloud computing definition which, as I recall, included language, something to the effect of accessing remote services over the public network, i.e., public IP networks. Now, NIST has essentially or since evolved that definition, but the original draft was very favorable to the public cloud. And the vendor community, the traditional vendor community, said hang on, we're in this game too. So that was 2009 when Chuck Hollis published this slide. He termed it Private Cloud, a term which he saw buried inside of a Gartner research post or research note that was not really fleshed out and defined. The idea was pretty compelling. The definition of cloud centered on control, where you, as the customer, had on-prem workloads that could span public and on-prem clouds, if you will, with federated security and a data plan that spanned the states. Essentially, you had an internal and an external cloud with a single point of control. This is basically what the hybrid cloud vision has become. An abstraction layer that spans on-prem and public clouds and we can extend that across clouds and out to the edge, where a customer has a single point of control and federated governance and security. Now we know this is still aspirational, but we're now seeing vendor offerings that put forth this promise and a roadmap to get there from different points of view, that we're going to talk about today. The NIST definition now reads cloud computing is a model for enabling ubiquitous, convenient on-demand network access to a shared pool of configurable computing resources, e.g., network server storage, applications and services, that can be rapidly provisioned and released with minimal management effort or service provider interaction. So there you have it, that is inclusive of on-prem, but it took the industry a decade plus to actually get where we are today. And they did so by essentially going to school with the public cloud offerings. Now in 2018, AWS announced Outposts and that was another wake up call to the on-prem community. Externally, they pointed to the validation that hybrid cloud was real. Hey, AWS is doing it so clearly they've capitulated, but most on-prem vendors at the time didn't have a coherent offering for hybrid, but the point is the on-prem vendors responded as they saw AWS moving past the demilitarized zone into enemy lines. And here's what the competitive landscape of hybrid offerings looks like today. All three US-based hyperscalers have an offering or multiple offerings in various forms, Outposts from Amazon and other services that they offer, Google Anthos and Azure Arc, they're all so prominent, but the real action today is coming from the on-prem vendors. Every major company has an offering. Now most of these stemmed from services-led and finance-led initiatives, but they're evolving to true Azure Service models. HPE GreenLake is prominent and the company's CEO, Antonio Neri, is putting the whole company behind Azure Service. HPE claims to be the first, it uses that in its marketing, with such an Azure Service offering, but actually Oracle was their first with Cloud@Customer. You know, possibly Microsoft could make a claim to being early as well, but it really doesn't matter. Let's see, Dell has responded with Apex and is going hard after this opportunity. Cisco has Cisco Plus and Lenovo has TruScale. IBM also has a long services and finance-led history and has announced pockets of Azure Service in areas like storage. And Pure Storage is an example that we chose of a segment player, of course within storage, that has a strong Azure Service offering, and there are others like that. So the landscape is getting very busy. And so, let's break this down a bit. AWS is bringing its programmable infrastructure model and its own hardware to what it calls the edge. And it looks at on-prem data centers as just another edge node. So that's how they're de-positioning the on-prem crowd, but the fact is, when you really look at what Outposts can do today, it's limited, but AWS will move quickly so expect a continued rapid evolution of their model and the services that are supported on Outposts. Azure gets its hardware from partners and has relationships with virtually everyone that matters. Anthos is, as well, a software layer and Google created Kubernetes as the great equalizer in cloud. And it was a nice open source gift to the industry and has obviously taken off. So the cloud guys have the advantage of owning a cloud. The pure on-prem players, they don't, but the on-prem crowd has rich stacks, much richer and more mature in a lot of areas, as it relates to supporting on-premises workloads and much more so than the cloud players, but they don't have mature cloud stacks. They're kind of just getting started with things like subscription billing and API-based microservices offerings. They got to figure out Salesforce compensation and just the overall Azure service mentality versus the historical product box mentality, and that takes time. And they're each coming at this from their respective different points of view and points of strength. HPE is doing a very good job of marketing and go-to market. It probably has the cleanest model, enabled by the company's split from HP, but it has some gaps that it's needed to fill and it's doing so through acquisitions. Ezmeral, for example, is it's new data play. It just bought Zerto to facilitate backup as a service. And it's expanded partnerships to fill gaps in the portfolio. Some partnerships, which they couldn't do before because it created conflicts inside of HPE or HP. Dell is all about the portfolio, the breadth of the portfolio, the go-to-market prowess and its supply chain advantage. It's very serious about Azure Service with Apex and it's driving hard to win that day. Cisco comes at this from a huge portfolio and of course, a point of strength and networking, which maybe is a bit tougher to offer as a service, but Cisco has a large and fast growing subscription business in collaborations, security and other areas, so it's cloud-like in that regard. And Oracle, of course, has the huge advantage of an extremely rich functional stack and it owns a cloud, which has dramatically improved in the past few years, but Oracle is narrow to the red stack, at least today. Oracle, if it wanted to, we think, could dominate the database cloud, it could be the database cloud, especially if it decided to open its cloud to competitive database offerings and run them in the Oracle cloud. Hmm. Wonder if Oracle will ever move in that direction. Now a big part of this shift is the appeal of OPEX versus CAPEX. Let's take a look at some ETR data that digs a bit deeper into this topic. This data is from an August ETR drill down, asking CIOs and IT buyers how their budgets are split between OPEX and CAPEX. The mid point of the yellow line shows where we are today, 57% OPEX, expecting to grow to 63% one year from now. That's not a huge difference, there's not a huge difference when you drill into global 2000, which kind of surprised me. I thought global 2000 would be heavier CAPEX, but they seem to be accelerating the shift to OPEX slightly faster than the overall base, but not really in a meaningful way. So I didn't really discern big differences there. Now, when you dig further into industries and look at subscription versus consumption models for OPEX, you see about 60/40 favoring subscription models, with most industry slowly moving toward consumption or usage based models over time. There are a couple of outliers, but generally speaking, that's the trend. What's perhaps more interesting is when you drill into subscription versus usage based models by product area, and that's what this chart shows. It shows by tech segment, the percent subscription, that's the blue, versus consumption or usage based, that's the gray bars, yellow being indifferent or maybe it's I don't know. What stands out are two areas that are more usage heavy, consumption heavy. That's database, data warehousing, and IS. So database is surely weighted by companies like Snowflake and offerings like Redshift and other cloud databases from Azure and Google and other managed services, but the IS piece, while not surprising, is, we think, relevant because most of the legacy vendor Azure Service offerings are borrowing from a SaaS-oriented subscription model with a hardware twist. In other words, as a customer, you're committing to a term and a minimum spend over the life of that term. You're locked in for a year or three years, whatever it is, to account for the hardware and headroom the vendor has to install because they want to allow you to increase your usage. So that's the usage based model. See, you're then paying by the drink for that consumption above that minimum threshold. So it's a hybrid subscription consumption model, which is actually quite interesting. And we've been saying, what would really be cool is if one of the on-prem penguins on the iceberg would actually jump in and offer a true consumption model right out of the box, as a disruptive move to the industry and to the cloud players, and take that risk. And I think that might happen once they feel comfortable with the financial model and they have nailed the product market fit, but right now, the model is what it is. And even AWS without post requires a threshold and a minimum commitment. So we'd love to see someone take that chance and offer true cloud consumption pricing to facilitate more experimentation and lower risk for the customer entry points. Now let's take a look at some of these players and see what kind of spending momentum they have. This is our popular XY chart-view that plots net score or spending velocity on the x-axis and market share or pervasiveness in the data set on the... Oh, sorry, net score or spending momentum on the y-axis and pervasiveness or market share on the x-axis. Now this is cut by cloud computing vendors, as defined by the customers responding. There were nearly 1500 respondents in the ETR survey, so a couple of points here. Note the red line is the elevated line. In other words, anything above that is considered really robust momentum. And no surprise, Azure, AWS and Google are above that line. Azure and AWS always battle it out for top share of voice in the x-axis in this survey. Now this, remember, is the July survey, but ETR, they gave me a sneak peek at the October results that they're going to be releasing in the coming week and Dell cloud and VMware cloud, which is VCF and maybe some other components, not VMware cloud and AWS, that's a separate beast, but those two are moving up in the y-axis. So they're demonstrating spending momentum. IBM is moving down and Oracle is at a respectable 20% on the y-axis. Now, interestingly, HPE and Lenovo don't show up in the cloud taxonomy, in that cloud cut, and neither does Cisco. I believe I'm correct in that this is an open-ended question, i.e., who are your cloud suppliers? So the customers are not resonating with that messaging yet, but I'm going to double check on that. Now to widen the aperture a bit, we said let's do a cut of the on-prem and cloud players within cloud accounts, so we can include HPE and Cisco and see how they're doing inside of cloud accounts. So that's what this chart does. It's a filter on 975 customers who identify themselves as cloud accounts. So here we were able to add in Cisco and HPE. Now, Lenovo still doesn't show up on the data. It shows up in laptops and desktops, but not as prominent in the enterprise, not prominent at all, but HPE Ezmeral did show up and it's moving forward in the October survey, again, part of the sneak peek. Ezmeral is HPE's data platform that they've introduced, combining the assets of MapR, BlueData and some other organic development. Now, as you can see, HPE and Cisco, they show up on the chart, as I said, and you can see the rope in the tug of war is starting to get a little bit more taut. The cloud guys have momentum and big account presence, but the on-prem folks also have big footprints, rich stacks and many have strong services arms, and a lot of customer affinity. So let's wrap with some comments about how this will shake out and what's some of the markers we can watch. Now, the first thing I'll say is we're starting to hear the right language come out of the vendor community. The idea that they're investing in a layer to abstract the underlying complexity of the clouds and on-prem infrastructure and turning the world into, essentially, a programmable interface to resources. The question is, what about giving access through that layer to underlying primitives in the public cloud? VMware has been very clear on this. They will facilitate that access. I believe Red Hat as well. So watch to the degree in which the large on-prem players are enabling that access for developers. We believe this is the right direction overall, but it's also very hard and it's going to require lots of resources and R & D. I would say at this point that each company has its respective strengths and weaknesses. I see HPE mostly focused today on making its on-prem offerings work like a cloud, whereas some of the others, VMware, Dell and Cisco, are stressing to a greater degree, in my view, enabling multi-cloud and edge connections, cross connections. Not that HPE isn't open to that when you ask them about it, but its marketing is more on-prem leaning, in my opinion. Now all of the traditional vendors, in my view, are still defensive about the cloud, although I would say much less so each day. Increasingly, they look at the public cloud as an opportunity to build value on top of that abstraction layer, if you will. As I said earlier, these on-prem guys, they all have ways to go. They're in the early stages of figuring out what a cloud operating model looks like, how it works, what services to offer, how to pay sellers and partners, but the public cloud vendors, they're miles ahead in that regard, but at the same time, they're navigating into on-prem territory. And they're very immature, in most cases. So how do they service all this stuff? How do they establish partnerships and so forth? And how do they build stacks on prem that are as rich as they are in the cloud? And what's their motivation to do that? Are they getting pulled, digging their heels in? Or are they really serious about it? Now, in some respects, Oracle is in the best position here in terms of hybrid maturity, but again, it's narrowly focused on the Red Stack. I would say the same for Pure Storage, more mature as a service, but narrowly focused, of course, on storage. Let's talk marketplace and ecosystems. One of the hallmarks of public clouds is optionality of tooling. Just all you do is go to the AWS Marketplace and you'll see what I mean. It's got this endless bevy of choices. It's got one of everything in there and you can buy directly from your AWS Console. So watch how the hybrid cloud plays out in terms of partner inclusion and ease of doing business, that's another sign of maturity. Let's talk developers and edge. This is by far the most important and biggest hole in the hybrid portfolios, outside the public cloud players. If you're going to build infrastructure as code, who do you expect to code it? How are the on-prem players cultivating developer communities? IBM paid 34 billion to buy its way in. Actually, in today's valuation terms, you might say that's looking like a good play, but still, that cash outlay is equal to one third of IBM's revenue. So big, big bet on OpenShift, but IBM's infrastructure strategy is fragmented and its cloud business, as IBM reports in its financial statements, is a services-heavy, kitchen sink set of offerings. It's very confusing. So they got to still do some clean up there, but they're serious about the architectural battle for hybrid cloud, as Arvind Krishna calls it. Now VMware, by cobbling together the misfit developer toys of the remnants from the EMC Federation, including Pivotal, is trying to get there. You know, but when you talk to customers, they're still not all in on VMware's developer affinity. Now Cisco has DevNet, but that's basically CCIE's and other trained networking engineers learning to code in languages like Python. It's not necessarily true devs, although they're upskilling. It's a start and they're investing, Cisco, that is, investing in the community, leveraging their champions, and I would say Dell could do the same with, for example, the numerous EMC storage admins that are out there. Now Oracle bought Sun to get Java, and that's a large community of developers, but even so, when you compare AWS and Microsoft ecosystems to the others, it's not even close in terms of developer affinity. So lots of work to be done there. One other point is Pure's acquisition of Portworx, again, while narrowly focused, is a good move and instructive of the changes going on in infrastructure. Now how does this all relate to the edge? Well, I'm not going to talk much about that today, but suffice to say, developers, in our view, will win the edge. And right now, they're coding in the cloud. Now they're often coding in the cloud and moving work on prem, wrapping them in containers, but watch how sticky that model is for the respective players. The other thing to watch is cadence of offerings. Another hallmark of cloud is a rapid expansion of features. The public cloud players don't appear to be slowing down and the on-prem folks seem to be accelerating. I've been watching HPE and GreenLake and their cadence of offerings, and watch how quickly the newbies of Azure Service can add functionality, I have no doubt Dell is going to be right there as well, as is Cisco and others. Also pay attention to financial metrics, watch how Azure Service impacts the income statements and how the companies deal with that because as you shift to deferred revenue models, it's going to hurt profitability. And I'm not worried about that at all because it won't hurt cashflow, or at least it shouldn't. As long as the companies communicate to Wall Street and they're transparent, i.e., they don't shift reporting definitions every year and a half or two years, but watch for metrics around retention and churn, RPO or Remaining Performance Obligations, billing versus bookings, increased average contract values, cohort selling, the impact on both gross margin and operating margin. These are the things you watch with SaaS companies and essentially, these big hardware players are becoming Azure Service slash SaaS companies. These are going to be the key indicators of success and the proof in the pudding of the transition to Azure Service. It should be positive for these companies, assuming they get the product market fit right, and can create a flywheel effect with their respective ecosystems and partner channels. Now I'm sure you can think of other important factors to watch, but I'm going to leave it here for now. Remember these episodes, they're all available as podcasts, wherever you listen. All you got to do is search Breaking Analysis podcast and please subscribe, check out ETR's website at etr.plus. We also publish a full report every week on wikibon.com and siliconangle.com. You can get in touch with me, email david.vellante@siliconangle.com or you can DM me @dvellante. You can comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week, everybody, stay safe, be well. And we'll see you next time. (soft music)

>> From theCUBE studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is braking analysis with Dave Vellante. >> A new era of data is upon us, and we're in a state of transition. You know, even our language reflects that. We rarely use the phrase big data anymore, rather we talk about digital transformation or digital business, or data-driven companies. Many have come to the realization that data is a not the new oil, because unlike oil, the same data can be used over and over for different purposes. We still use terms like data as an asset. However, that same narrative, when it's put forth by the vendor and practitioner communities, includes further discussions about democratizing and sharing data. Let me ask you this, when was the last time you wanted to share your financial assets with your coworkers or your partners or your customers? Hello everyone, and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we want to share our assessment of the state of the data business. We'll do so by looking at the data mesh concept and how a leading financial institution, JP Morgan Chase is practically applying these relatively new ideas to transform its data architecture. Let's start by looking at what is the data mesh. As we've previously reported many times, data mesh is a concept and set of principles that was introduced in 2018 by Zhamak Deghani who's director of technology at ThoughtWorks, it's a global consultancy and software development company. And she created this movement because her clients, who were some of the leading firms in the world had invested heavily in predominantly monolithic data architectures that had failed to deliver desired outcomes in ROI. So her work went deep into trying to understand that problem. And her main conclusion that came out of this effort was the world of data is distributed and shoving all the data into a single monolithic architecture is an approach that fundamentally limits agility and scale. Now a profound concept of data mesh is the idea that data architectures should be organized around business lines with domain context. That the highly technical and hyper specialized roles of a centralized cross functional team are a key blocker to achieving our data aspirations. This is the first of four high level principles of data mesh. So first again, that the business domain should own the data end-to-end, rather than have it go through a centralized big data technical team. Second, a self-service platform is fundamental to a successful architectural approach where data is discoverable and shareable across an organization and an ecosystem. Third, product thinking is central to the idea of data mesh. In other words, data products will power the next era of data success. And fourth data products must be built with governance and compliance that is automated and federated. Now there's lot more to this concept and there are tons of resources on the web to learn more, including an entire community that is formed around data mesh. But this should give you a basic idea. Now, the other point is that, in observing Zhamak Deghani's work, she is deliberately avoided discussions around specific tooling, which I think has frustrated some folks because we all like to have references that tie to products and tools and companies. So this has been a two-edged sword in that, on the one hand it's good, because data mesh is designed to be tool agnostic and technology agnostic. On the other hand, it's led some folks to take liberties with the term data mesh and claim mission accomplished when their solution, you know, maybe more marketing than reality. So let's look at JP Morgan Chase in their data mesh journey. Is why I got really excited when I saw this past week, a team from JPMC held a meet up to discuss what they called, data lake strategy via data mesh architecture. I saw that title, I thought, well, that's a weird title. And I wondered, are they just taking their legacy data lakes and claiming they're now transformed into a data mesh? But in listening to the presentation, which was over an hour long, the answer is a definitive no, not at all in my opinion. A gentleman named Scott Hollerman organized the session that comprised these three speakers here, James Reid, who's a divisional CIO at JPMC, Arup Nanda who is a technologist and architect and Serita Bakst who is an information architect, again, all from JPMC. This was the most detailed and practical discussion that I've seen to date about implementing a data mesh. And this is JP Morgan's their approach, and we know they're extremely savvy and technically sound. And they've invested, it has to be billions in the past decade on data architecture across their massive company. And rather than dwell on the downsides of their big data past, I was really pleased to see how they're evolving their approach and embracing new thinking around data mesh. So today, we're going to share some of the slides that they use and comment on how it dovetails into the concept of data mesh that Zhamak Deghani has been promoting, and at least as we understand it. And dig a bit into some of the tooling that is being used by JP Morgan, particularly around it's AWS cloud. So the first point is it's all about business value, JPMC, they're in the money business, and in that world, business value is everything. So Jr Reid, the CIO showed this slide and talked about their overall goals, which centered on a cloud first strategy to modernize the JPMC platform. I think it's simple and sensible, but there's three factors on which he focused, cut costs always short, you got to do that. Number two was about unlocking new opportunities, or accelerating time to value. But I was really happy to see number three, data reuse. That's a fundamental value ingredient in the slide that he's presenting here. And his commentary was all about aligning with the domains and maximizing data reuse, i.e. data is not like oil and making sure there's appropriate governance around that. Now don't get caught up in the term data lake, I think it's just how JP Morgan communicates internally. It's invested in the data lake concept, so they use water analogies. They use things like data puddles, for example, which are single project data marts or data ponds, which comprise multiple data puddles. And these can feed in to data lakes. And as we'll see, JPMC doesn't strive to have a single version of the truth from a data standpoint that resides in a monolithic data lake, rather it enables the business lines to create and own their own data lakes that comprise fit for purpose data products. And they do have a single truth of metadata. Okay, we'll get to that. But generally speaking, each of the domains will own end-to-end their own data and be responsible for those data products, we'll talk about that more. Now the genesis of this was sort of a cloud first platform, JPMC is leaning into public cloud, which is ironic since the early days, in the early days of cloud, all the financial institutions were like never. Anyway, JPMC is going hard after it, they're adopting agile methods and microservices architectures, and it sees cloud as a fundamental enabler, but it recognizes that on-prem data must be part of the data mesh equation. Here's a slide that starts to get into some of that generic tooling, and then we'll go deeper. And I want to make a couple of points here that tie back to Zhamak Deghani's original concept. The first is that unlike many data architectures, this puts data as products right in the fat middle of the chart. The data products live in the business domains and are at the heart of the architecture. The databases, the Hadoop clusters, the files and APIs on the left-hand side, they serve the data product builders. The specialized roles on the right hand side, the DBA's, the data engineers, the data scientists, the data analysts, we could have put in quality engineers, et cetera, they serve the data products. Because the data products are owned by the business, they inherently have the context that is the middle of this diagram. And you can see at the bottom of the slide, the key principles include domain thinking, an end-to-end ownership of the data products. They build it, they own it, they run it, they manage it. At the same time, the goal is to democratize data with a self-service as a platform. One of the biggest points of contention of data mesh is governance. And as Serita Bakst said on the Meetup, metadata is your friend, and she kind of made a joke, she said, "This sounds kind of geeky, but it's important to have a metadata catalog to understand where data resides and the data lineage in overall change management. So to me, this really past the data mesh stink test pretty well. Let's look at data as products. CIO Reid said the most difficult thing for JPMC was getting their heads around data product, and they spent a lot of time getting this concept to work. Here's the slide they use to describe their data products as it related to their specific industry. They set a common language and taxonomy is very important, and you can imagine how difficult that was. He said, for example, it took a lot of discussion and debate to define what a transaction was. But you can see at a high level, these three product groups around wholesale, credit risk, party, and trade and position data as products, and each of these can have sub products, like, party, we'll have to know your customer, KYC for example. So a key for JPMC was to start at a high level and iterate to get more granular over time. So lots of decisions had to be made around who owns the products and the sub-products. The product owners interestingly had to defend why that product should even exist, what boundaries should be in place and what data sets do and don't belong in the various products. And this was a collaborative discussion, I'm sure there was contention around that between the lines of business. And which sub products should be part of these circles? They didn't say this, but tying it back to data mesh, each of these products, whether in a data lake or a data hub or a data pond or data warehouse, data puddle, each of these is a node in the global data mesh that is discoverable and governed. And supporting this notion, Serita said that, "This should not be infrastructure-bound, logically, any of these data products, whether on-prem or in the cloud can connect via the data mesh." So again, I felt like this really stayed true to the data mesh concept. Well, let's look at some of the key technical considerations that JPM discussed in quite some detail. This chart here shows a diagram of how JP Morgan thinks about the problem, and some of the challenges they had to consider were how to write to various data stores, can you and how can you move data from one data store to another? How can data be transformed? Where's the data located? Can the data be trusted? How can it be easily accessed? Who has the right to access that data? These are all problems that technology can help solve. And to address these issues, Arup Nanda explained that the heart of this slide is the data in ingestor instead of ETL. All data producers and contributors, they send their data to the ingestor and the ingestor then registers the data so it's in the data catalog. It does a data quality check and it tracks the lineage. Then, data is sent to the router, which persists the data in the data store based on the best destination as informed by the registration. This is designed to be a flexible system. In other words, the data store for a data product is not fixed, it's determined at the point of inventory, and that allows changes to be easily made in one place. The router simply reads that optimal location and sends it to the appropriate data store. Nowadays you see the schema infer there is used when there is no clear schema on right. In this case, the data product is not allowed to be consumed until the schema is inferred, and then the data goes into a raw area, and the inferer determines the schema and then updates the inventory system so that the data can be routed to the proper location and properly tracked. So that's some of the detail of how the sausage factory works in this particular use case, it was very interesting and informative. Now let's take a look at the specific implementation on AWS and dig into some of the tooling. As described in some detail by Arup Nanda, this diagram shows the reference architecture used by this group within JP Morgan, and it shows all the various AWS services and components that support their data mesh approach. So start with the authorization block right there underneath Kinesis. The lake formation is the single point of entitlement and has a number of buckets including, you can see there the raw area that we just talked about, a trusted bucket, a refined bucket, et cetera. Depending on the data characteristics at the data catalog registration block where you see the glue catalog, that determines in which bucket the router puts the data. And you can see the many AWS services in use here, identity, the EMR, the elastic MapReduce cluster from the legacy Hadoop work done over the years, the Redshift Spectrum and Athena, JPMC uses Athena for single threaded workloads and Redshift Spectrum for nested types so they can be queried independent of each other. Now remember very importantly, in this use case, there is not a single lake formation, rather than multiple lines of business will be authorized to create their own lakes, and that creates a challenge. So how can that be done in a flexible and automated manner? And that's where the data mesh comes into play. So JPMC came up with this federated lake formation accounts idea, and each line of business can create as many data producer or consumer accounts as they desire and roll them up into their master line of business lake formation account. And they cross-connect these data products in a federated model. And these all roll up into a master glue catalog so that any authorized user can find out where a specific data element is located. So this is like a super set catalog that comprises multiple sources and syncs up across the data mesh. So again to me, this was a very well thought out and practical application of database. Yes, it includes some notion of centralized management, but much of that responsibility has been passed down to the lines of business. It does roll up to a master catalog, but that's a metadata management effort that seems compulsory to ensure federated and automated governance. As well at JPMC, the office of the chief data officer is responsible for ensuring governance and compliance throughout the federation. All right, so let's take a look at some of the suspects in this world of data mesh and bring in the ETR data. Now, of course, ETR doesn't have a data mesh category, there's no such thing as that data mesh vendor, you build a data mesh, you don't buy it. So, what we did is we use the ETR dataset to select and filter on some of the culprits that we thought might contribute to the data mesh to see how they're performing. This chart depicts a popular view that we often like to share. It's a two dimensional graphic with net score or spending momentum on the vertical axis and market share or pervasiveness in the data set on the horizontal axis. And we filtered the data on sectors such as analytics, data warehouse, and the adjacencies to things that might fit into data mesh. And we think that these pretty well reflect participation that data mesh is certainly not all compassing. And it's a subset obviously, of all the vendors who could play in the space. Let's make a few observations. Now as is often the case, Azure and AWS, they're almost literally off the charts with very high spending velocity and large presence in the market. Oracle you can see also stands out because much of the world's data lives inside of Oracle databases. It doesn't have the spending momentum or growth, but the company remains prominent. And you can see Google Cloud doesn't have nearly the presence in the dataset, but it's momentum is highly elevated. Remember that red dotted line there, that 40% line, anything over that indicates elevated spending momentum. Let's go to Snowflake. Snowflake is consistently shown to be the gold standard in net score in the ETR dataset. It continues to maintain highly elevated spending velocity in the data. And in many ways, Snowflake with its data marketplace and its data cloud vision and data sharing approach, fit nicely into the data mesh concept. Now, a caution, Snowflake has used the term data mesh in it's marketing, but in our view, it lacks clarity, and we feel like they're still trying to figure out how to communicate what that really is. But is really, we think a lot of potential there to that vision. Databricks is also interesting because the firm has momentum and we expect further elevated levels in the vertical axis in upcoming surveys, especially as it readies for its IPO. The firm has a strong product and managed service, and is really one to watch. Now we included a number of other database companies for obvious reasons like Redis and Mongo, MariaDB, Couchbase and Terradata. SAP as well is in there, but that's not all database, but SAP is prominent so we included them. As is IBM more of a database, traditional database player also with the big presence. Cloudera includes Hortonworks and HPE Ezmeral comprises the MapR business that HPE acquired. So these guys got the big data movement started, between Cloudera, Hortonworks which is born out of Yahoo, which was the early big data, sorry early Hadoop innovator, kind of MapR when it's kind of owned course, and now that's all kind of come together in various forms. And of course, we've got Talend and Informatica are there, they are two data integration companies that are worth noting. We also included some of the AI and ML specialists and data science players in the mix like DataRobot who just did a monster $250 million round. Dataiku, H2O.ai and ThoughtSpot, which is all about democratizing data and injecting AI, and I think fits well into the data mesh concept. And you know we put VMware Cloud in there for reference because it really is the predominant on-prem infrastructure platform. All right, let's wrap with some final thoughts here, first, thanks a lot to the JP Morgan team for sharing this data. I really want to encourage practitioners and technologists, go to watch the YouTube of that meetup, we'll include it in the link of this session. And thank you to Zhamak Deghani and the entire data mesh community for the outstanding work that you're doing, challenging the established conventions of monolithic data architectures. The JPM presentation, it gives you real credibility, it takes Data Mesh well beyond concept, it demonstrates how it can be and is being done. And you know, this is not a perfect world, you're going to start somewhere and there's going to be some failures, the key is to recognize that shoving everything into a monolithic data architecture won't support massive scale and agility that you're after. It's maybe fine for smaller use cases in smaller firms, but if you're building a global platform in a data business, it's time to rethink data architecture. Now much of this is enabled by the cloud, but cloud first doesn't mean cloud only, doesn't mean you'll leave your on-prem data behind, on the contrary, you have to include non-public cloud data in your Data Mesh vision just as JPMC has done. You've got to get some quick wins, that's crucial so you can gain credibility within the organization and grow. And one of the key takeaways from the JP Morgan team is, there is a place for dogma, like organizing around data products and domains and getting that right. On the other hand, you have to remain flexible because technologies is going to come, technology is going to go, so you got to be flexible in that regard. And look, if you're going to embrace the metaphor of water like puddles and ponds and lakes, we suggest maybe a little tongue in cheek, but still we believe in this, that you expand your scope to include data ocean, something John Furry and I have talked about and laughed about extensively in theCUBE. Data oceans, it's huge. It's the new data lake, go transcend data lake, think oceans. And think about this, just as we're evolving our language, we should be evolving our metrics. Much the last the decade of big data was around just getting the stuff to work, getting it up and running, standing up infrastructure and managing massive, how much data you got? Massive amounts of data. And there were many KPIs built around, again, standing up that infrastructure, ingesting data, a lot of technical KPIs. This decade is not just about enabling better insights, it's a more than that. Data mesh points us to a new era of data value, and that requires the new metrics around monetizing data products, like how long does it take to go from data product conception to monetization? And how does that compare to what it is today? And what is the time to quality if the business owns the data, and the business has the context? the quality that comes out of them, out of the shoot should be at a basic level, pretty good, and at a higher mark than out of a big data team with no business context. Automation, AI, and very importantly, organizational restructuring of our data teams will heavily contribute to success in the coming years. So we encourage you, learn, lean in and create your data future. Okay, that's it for now, remember these episodes, they're all available as podcasts wherever you listen, all you got to do is search, breaking analysis podcast, and please subscribe. Check out ETR's website at etr.plus for all the data and all the survey information. We publish a full report every week on wikibon.com and siliconangle.com. And you can get in touch with us, email me david.vellante@siliconangle.com, you can DM me @dvellante, or you can comment on my LinkedIn posts. This is Dave Vellante for theCUBE insights powered by ETR. Have a great week everybody, stay safe, be well, and we'll see you next time. (upbeat music)

(bright music) >> Data by its very nature is distributed and siloed, but most data architectures today are highly centralized. Organizations are increasingly challenged to organize and manage data, and turn that data into insights. This idea of a single monolithic platform for data, it's giving way to new thinking. Where a decentralized approach, with open cloud native principles and federated governance, will become an underpinning of digital transformations. Hi everybody. This is Dave Volante. Welcome back to HPE Discover 2021, the virtual version. You're watching theCube's continuous coverage of the event and we're here with Matt Maccaux, who's a field CTO for Ezmeral Software at HPE. We're going to talk about HPE software strategy, and Ezmeral and specifically how to take AI analytics to scale and ensure the productivity of data teams. Matt, welcome to theCube. Good to see you. >> Good to see you again, Dave. Thanks for having me today. >> You're welcome. So talk a little bit about your role as a CTO. Where do you spend your time? >> I spend about half of my time talking to customers and partners about where they are on their digital transformation journeys and where they struggle with this sort of last phase where we start talking about bringing those cloud principles and practices into the data world. How do I take those data warehouses, those data lakes, those distributed data systems, into the enterprise and deploy them in a cloud-like manner? Then the other half of my time is working with our product teams to feed that information back, so that we can continually innovate to the next generation of our software platform. >> So when I remember, I've been following HP and HPE, for a long, long time, theCube has documented, we go back to sort of when the company was breaking in two parts, and at the time a lot of people were saying, "Oh, HP is getting rid of their software business, they're getting out of software." I said, "No, no, no, hold on. They're really focusing", and the whole focus around hybrid cloud and now as a service, you've really retooling that business and sharpened your focus. So tell us more about Ezmeral, it's a cool name, but what exactly is Ezmeral software? >> I get this question all the time. So what is Ezmeral? Ezmeral is a software platform for modern data and analytics workloads, using open source software components. We came from some inorganic growth. We acquired a company called Cytec, that brought us a zero trust approach to doing security with containers. We bought BlueData who came to us with an orchestrator before Kubernetes even existed in mainstream. They were orchestrating workloads using containers for some of these more difficult workloads. Clustered applications, distributed applications like Hadoop. Then finally we acquired MapR, which gave us this scale out distributed file system and additional analytical capabilities. What we've done is we've taken those components and we've also gone out into the marketplace to see what open source projects exist to allow us to bring those cloud principles and practices to these types of workloads, so that we can take things like Hadoop, and Spark, and Presto, and deploy and orchestrate them using open source Kubernetes. Leveraging GPU's, while providing that zero trust approach to security, that's what Ezmeral is all about is taking those cloud practices and principles, but without locking you in. Again, using those open source components where they exist, and then committing and contributing back to the opensource community where those projects don't exist. >> You know, it's interesting, thank you for that history, and when I go back, I have been there since the early days of Big Data and Hadoop and so forth and MapR always had the best product, but they couldn't get it out. Back then it was like kumbaya, open source, and they had this kind of proprietary system but it worked and that's why it was the best product. So at the same time they participated in open source projects because everybody did, that's where the innovation is going. So you're making that really hard to use stuff easier to use with Kubernetes orchestration, and then obviously, I'm presuming with the open source chops, sort of leaning into the big trends that you're seeing in the marketplace. So my question is, what are those big trends that you're seeing when you speak to technology executives which is a big part of what you do? >> So the trends are, I think, are a couplefold, and it's funny about Hadoop, but I think the final nails in the coffin have been hammered in with the Hadoop space now. So that leading trend, of where organizations are going, we're seeing organizations wanting to go cloud first. But they really struggle with these data-intensive workloads. Do I have to store my data in every cloud? Am I going to pay egress in every cloud? Well, what if my data scientists are most comfortable in AWS, but my data analysts are more comfortable in Azure, how do I provide that multi-cloud experience for these data workloads? That's the number one question I get asked, and that's probably the biggest struggle for these chief data officers, chief digital officers, is how do I allow that innovation but maintaining control over my data compliance especially when we talk international standards, like GDPR, to restrict access to data, the ability to be forgotten, in these multinational organizations how do I sort of square all of those components? Then how do I do that in a way that just doesn't lock me into another appliance or software vendor stack? I want to be able to work within the confines of the ecosystem, use the tools that are out there, but allow my organization to innovate in a very structured compliant way. >> I mean, I love this conversation and you just, to me, you hit on the key word, which is organization. I want to talk about what some of the barriers are. And again, you heard my wrap up front. I really do think that we've created, not only from a technology standpoint, and yes the tooling is important, but so is the organization, and as you said an analyst might want to work in one environment, a data scientist might want to work in another environment. The data may be very distributed. You might have situations where they're supporting the line of business. The line of business is trying to build new products, and if I have to go through this monolithic centralized organization, that's a barrier for me. And so we're seeing that change, that I kind of alluded to it up front, but what do you see as the big barriers that are blocking this vision from becoming a reality? >> It very much is organization, Dave. The technology's actually no longer the inhibitor here. We have enough technology, enough choices out there that technology is no longer the issue. It's the organization's willingness to embrace some of those technologies and put just the right level of control around accessing that data. Because if you don't allow your data scientists and data analysts to innovate, they're going to do one of two things. They're either going to leave, and then you have a huge problem keeping up with your competitors, or they're going to do it anyway. And they're going to do it in a way that probably doesn't comply with the organizational standards. So the more progressive enterprises that I speak with have realized that they need to allow these various analytical users to choose the tools they want, to self provision those as they need to and get access to data in a secure and compliant way. And that means we need to bring the cloud to generally where the data is because it's a heck of a lot easier than trying to bring the data where the cloud is, while conforming to those data principles, and that's HPE's strategy. You've heard it from our CEO for years now. Everything needs to be delivered as a service. It's Ezmeral Software that enables that capability, such as self-service and secure data provisioning, et cetera. >> Again, I love this conversation because if you go back to the early days of Hadoop, that was what was profound about a Hadoop. Bring five megabytes of code to a petabyte of data, and it didn't happen. We shoved it all into a data lake and it became a data swamp. And that's okay, it's a one dot oh, you know, maybe in data as is like data warehouses, data hubs, data lakes, maybe this is now a four dot oh, but we're getting there. But open source, one thing's for sure, it continues to gain momentum, it's where the innovation is. I wonder if you could comment on your thoughts on the role that open-source software plays for large enterprises, maybe some of the hurdles that are there, whether they're legal or licensing, or just fears, how important is open source software today? >> I think the cloud native developments, following the 12 factor applications, microservices based, paved the way over the last decade to make using open source technology tools and libraries mainstream. We have to tip our hats to Red Hat, right? For allowing organizations to embrace something so core as an operating system within the enterprise. But what everyone realized is that it's support that's what has to come with that. So we can allow our data scientists to use open source libraries, packages, and notebooks, but are we going to allow those to run in production? So if the answer is no, well? Then if we can't get support, we're not going to allow that. So where HPE Ezmeral is taking the lead here is, again, embracing those open source capabilities, but, if we deploy it, we're going to support it. Or we're going to work with the organization that has the committers to support it. You call HPE, the same phone number you've been calling for years for tier one 24 by seven support, and we will support your Kubernetes, your Spark your Presto, your Hadoop ecosystem of components. We're that throat to choke and we'll provide, all the way up to break/fix support, for some of these components and packages, giving these large enterprises the confidence to move forward with open source, but knowing that they have a trusted partner in which to do so. >> And that's why we've seen such success with say, for instance, managed services in the cloud, versus throwing out all the animals in the zoo and say, okay, figure it out yourself. But then, of course, what we saw, which was kind of ironic, was people finally said, "Hey, we can do this in the cloud more easily." So that's where you're seeing a lot of data land. However, the definition of cloud or the notion of cloud is changing. No longer is it just this remote set of services, "Somewhere out there in the cloud", some data center somewhere, no, it's moving to on-prem, on-prem is creating hybrid connections. You're seeing co-location facilities very proximate to the cloud. We're talking now about the edge, the near edge, and the far edge, deeply embedded. So that whole notion of cloud is changing. But I want to ask you, there's still a big push to cloud, everybody has a cloud first mantra, how do you see HPE competing in this new landscape? >> I think collaborating is probably a better word, although you could certainly argue if we're just leasing or renting hardware, then it would be competition, but I think again... The workload is going to flow to where the data exists. So if the data's being generated at the edge and being pumped into the cloud, then cloud is prod. That's the production system. If the data is generated via on-premises systems, then that's where it's going to be executed. That's production, and so HPE's approach is very much co-exist. It's a co-exist model of, if you need to do DevTests in the cloud and bring it back on-premises, fine, or vice versa. The key here is not locking our customers and our prospective clients into any sort of proprietary stack, as we were talking about earlier, giving people the flexibility to move those workloads to where the data exists, that is going to allow us to continue to get share of wallet, mind share, continue to deploy those workloads. And yes, there's going to competition that comes along. Do you run this on a GCP or do you run it on a GreenLake on-premises? Sure, we'll have those conversations, but again, if we're using open source software as the foundation for that, then actually where you run it is less relevant. >> So there's a lot of choices out there, when it comes to containers generally and Kubernetes specifically, and you may have answered this, you get the zero trust component, you've got the orchestrator, you've got the scale-out piece, but I'm interested in hearing in your words why an enterprise would or should consider Ezmeral instead of alternatives to Kubernetes solutions? >> It's a fair question, and it comes up in almost every conversation. "Oh, we already do Kubernetes, we have a Kubernetes standard", and that's largely true in most of the enterprises I speak to. They're using one of the many on-premises distributions to their cloud distributions, and they're all fine. They're all fine for what they were built for. Ezmeral was generally built for something a little different. Yes, everybody can run microservices based applications, DevOps based workloads, but where Ezmeral is different is for those data intensive, in clustered applications. Those sorts of applications require a certain degree of network awareness, persistent storage, et cetera, which requires either a significant amount of intelligence. Either you have to write in Golang, or you have to write your own operators, or Ezmeral can be that easy button. We deploy those stateful applications, because we bring a persistent storage layer, that came from MapR. We're really good at deploying those stateful clustered applications, and, in fact, we've opened sourced that as a project, KubeDirector, that came from BlueData, and we're really good at securing these, using SPIFFE and SPIRE, to ensure that there's that zero trust approach, that came from Scytale, and we've wrapped all of that in Kubernetes. So now you can take the most difficult, gnarly complex data intensive applications in your enterprise and deploy them using open source. And if that means we have to co-exist with an existing Kubernetes distribution, that's fine. That's actually the most common scenario that I walk into is, I start asking about, "What about these other applications you haven't done yet?" The answer is usually, "We haven't gotten to them yet", or "We're thinking about it", and that's when we talk about the capabilities of Ezmeral and I usually get the response, "Oh. A, we didn't know you existed and B well, let's talk about how exactly you do that." So again, it's more of a co-exist model rather than a compete with model, Dave. >> Well, that makes sense. I mean, I think again, a lot of people, they go, "Oh yeah, Kubernetes, no big deal. It's everywhere." But you're talking about a solution, kind of taking a platform approach with capabilities. You got to protect the data. A lot of times, these microservices aren't so micro and things are happening really fast. You've got to be secure. You got to be protected. And like you said, you've got a single phone number. You know, people say one throat to choke. Somebody in the media the other day said, "No, no. Single hand to shake." It's more of a partnership. I think that's apropos for HPE, Matt, with your heritage. >> That one's better. >> So, you know, thinking about this whole, we've gone through the pre big data days and the big data was all the hot buzzword. People don't maybe necessarily use that term anymore, although the data is bigger and getting bigger, which is kind of ironic. Where do you see this whole space going? We've talked about that sort of trend toward breaking down the silos, decentralization, maybe these hyper specialized roles that we've created, maybe getting more embedded or aligned with the line of business. How do you see... It feels like the next 10 years are going to be different than the last 10 years. How do you see it, Matt? >> I completely agree. I think we are entering this next era, and I don't know if it's well-defined. I don't know if I would go out on an edge to say exactly what the trend is going to be. But as you said earlier, data lakes really turned into data swamps. We ended up with lots of them in the enterprise, and enterprises had to allow that to happen. They had to let each business unit or each group of users collect the data that they needed and IT sort of had to deal with that down the road. I think that the more progressive organizations are leading the way. They are, again, taking those lessons from cloud and application developments, microservices, and they're allowing a freedom of choice. They're allowing data to move, to where those applications are, and I think this decentralized approach is really going to be king. You're going to see traditional software packages. You're going to see open source. You're going to see a mix of those, but what I think will probably be common throughout all of that is there's going to be this sense of automation, this sense that, we can't just build an algorithm once, release it and then wish it luck. That we've got to treat these analytics, and these data systems, as living things. That there's life cycles that we have to support. Which means we need to have DevOps for our data science. We need a CI/CD for our data analytics. We need to provide engineering at scale, like we do for software engineering. That's going to require automation, and an organizational thinking process, to allow that to actually occur. I think all of those things. The sort of people, process, products. It's all three of those things that are going to have to come into play, but stealing those best ideas from cloud and application developments, I think we're going to end up with probably something new over the next decade or so. >> Again, I'm loving this conversation, so I'm going to stick with it for a sec. It's hard to predict, but some takeaways that I have, Matt, from our conversation, I wonder if you could comment? I think the future is more open source. You mentioned automation, Devs are going to be key. I think governance as code, security designed in at the point of code creation, is going to be critical. It's no longer going be a bolt on. I don't think we're going to throw away the data warehouse or the data hubs or the data lakes. I think they become a node. I like this idea, I don't know if you know Zhamak Dehghani? but she has this idea of a global data mesh where these tools, lakes, whatever, they're a node on the mesh. They're discoverable. They're shareable. They're governed in a way. I think the mistake a lot of people made early on in the big data movement is, "Oh, we got data. We have to monetize our data." As opposed to thinking about what products can I build that are based on data that then can lead to monetization? I think the other thing I would say is the business has gotten way too technical. (Dave chuckles) It's alienated a lot of the business lines. I think we're seeing that change, and I think things like Ezmeral that simplify that, are critical. So I'll give you the final thoughts, based on my rant. >> No, your rant is spot on Dave. I think we are in agreement about a lot of things. Governance is absolutely key. If you don't know where your data is, what it's used for, and can apply policies to it. It doesn't matter what technology you throw at it, you're going to end up in the same state that you're essentially in today, with lots of swamps. I did like that concept of a node or a data mesh. It kind of goes back to the similar thing with a service mesh, or a set of APIs that you can use. I think we're going to have something similar with data. The trick is always, how heavy is it? How easy is it to move about? I think there's always going to be that latency issue, maybe not within the data center, but across the WAN. Latency is still going to be key, which means we need to have really good processes to be able to move data around. As you said, govern it. Determine who has access to what, when, and under what conditions, and then allow it to be free. Allow people to bring their choice of tools, provision them how they need to, while providing that audit, compliance and control. And then again, as you need to provision data across those nodes for those use cases, do so in a well measured and governed way. I think that's sort of where things are going. But we keep using that term governance, I think that's so key, and there's nothing better than using open source software because that provides traceability, auditability and this, frankly, openness that allows you to say, "I don't like where this project's going. I want to go in a different direction." And it gives those enterprises a control over these platforms that they've never had before. >> Matt, thanks so much for the discussion. I really enjoyed it. Awesome perspectives. >> Well thank you for having me, Dave. Excellent conversation as always. Thanks for having me again. >> You're very welcome. And thank you for watching everybody. This is theCube's continuous coverage of HPE Discover 2021. Of course, the virtual version. Next year, we're going to be back live. My name is Dave Volante. Keep it right there. (upbeat music)

(upbeat music) >> From theCUBE studios in Palo Alto, in Boston bringing you data-driven insights from theCUBE and ETR. This is braking analysis with Dave Vellante. >> Database is the heart of enterprise computing. The market is both exploding and it's evolving. The major force is transforming the space include Cloud and data, of course, but also new workloads, advanced memory and IO capabilities, new processor types, a massive push towards simplicity, new data sharing and governance models, and a spate of venture investment. Snowflake stands out as the gold standard for operational excellence and go to market execution. The company has attracted the attention of customers, investors, and competitors and everyone from entrenched players to upstarts once in the act. Hello everyone and welcome to this week's Wikibon CUBE Insights powered by ETR. In this breaking analysis, we'll share our most current thinking on the database marketplace and dig into Snowflake's execution. Some of its challenges and we'll take a look at how others are making moves to solve customer problems and try to get a piece of the growing database pie. Let's look at some of the factors that are driving market momentum. First, customers want lower license costs. They want simplicity. They want to avoid database sprawl. They want to run anywhere and manage new data types. These needs often are divergent and they pull vendors and technologies in different direction. It's really hard for any one platform to accommodate every customer need. The market is large and it's growing. Gardner has it at around 60 to 65 billion with a CAGR of somewhere around 20% over the next five years. But the market, as we know it is being redefined. Traditionally, databases have served two broad use cases, OLTP or transactions and reporting like data warehouses. But a diversity of workloads and new architectures and innovations have given rise to a number of new types of databases to accommodate all these diverse customer needs. Many billions have been spent over the last several years in venture money and it continues to pour in. Let me just give you some examples. Snowflake prior to its IPO, raised around 1.4 billion. Redis Labs has raised more than 1/2 billion dollars so far, Cockroach Labs, more than 350 million, Couchbase, 250 million, SingleStore formerly MemSQL, 238 million, Yellowbrick Data, 173 million. And if you stretch the definition of database a little bit to including low-code or no-code, Airtable has raised more than 600 million. And that's by no means a complete list. Now, why is all this investment happening? Well, in a large part, it's due to the TAM. The TAM is huge and it's growing and it's being redefined. Just how big is this market? Let's take a look at a chart that we've shown previously. We use this chart to Snowflakes TAM, and it focuses mainly on the analytics piece, but we'll use it here to really underscore the market potential. So the actual database TAM is larger than this, we think. Cloud and Cloud-native technologies have changed the way we think about databases. Virtually 100% of the database players that they're are in the market have pivoted to a Cloud first strategy. And many like Snowflake, they're pretty dogmatic and have a Cloud only strategy. Databases has historically been very difficult to manage, they're really sensitive to latency. So that means they require a lot of tuning. Cloud allows you to throw virtually infinite resources on demand and attack performance problems and scale very quickly, minimizing the complexity and tuning nuances. This idea, this layer of data as a service we think of it as a staple of digital transformation. Is this layer that's forming to support things like data sharing across ecosystems and the ability to build data products or data services. It's a fundamental value proposition of Snowflake and one of the most important aspects of its offering. Snowflake tracks a metric called edges, which are external connections in its data Cloud. And it claims that 15% of its total shared connections are edges and that's growing at 33% quarter on quarter. This notion of data sharing is changing the way people think about data. We use terms like data as an asset. This is the language of the 2010s. We don't share our assets with others, do we? No, we protect them, we secure or them, we even hide them. But we absolutely don't want to share those assets but we do want to share our data. I had a conversation recently with Forrester analyst, Michelle Goetz. And we both agreed we're going to scrub data as an asset from our phrasiology. Increasingly, people are looking at sharing as a way to create, as I said, data products or data services, which can be monetized. This is an underpinning of Zhamak Dehghani's concept of a data mesh, make data discoverable, shareable and securely governed so that we can build data products and data services that can be monetized. This is where the TAM just explodes and the market is redefining. And we think is in the hundreds of billions of dollars. Let's talk a little bit about the diversity of offerings in the marketplace. Again, databases used to be either transactional or analytic. The bottom lines and top lines. And this chart here describe those two but the types of databases, you can see the middle of mushrooms, just looking at this list, blockchain is of course a specialized type of database and it's also finding its way into other database platforms. Oracle is notable here. Document databases that support JSON and graph data stores that assist in visualizing data, inference from multiple different sources. That's is one of the ways in which adtech has taken off and been so effective. Key Value stores, log databases that are purpose-built, machine learning to enhance insights, spatial databases to help build the next generation of products, the next automobile, streaming databases to manage real time data flows and time series databases. We might've missed a few, let us know if you think we have, but this is a kind of pretty comprehensive list that is somewhat mind boggling when you think about it. And these unique requirements, they've spawned tons of innovation and companies. Here's a small subset on this logo slide. And this is by no means an exhaustive list, but you have these companies here which have been around forever like Oracle and IBM and Teradata and Microsoft, these are the kind of the tier one relational databases that have matured over the years. And they've got properties like atomicity, consistency, isolation, durability, what's known as ACID properties, ACID compliance. Some others that you may or may not be familiar with, Yellowbrick Data, we talked about them earlier. It's going after the best price, performance and analytics and optimizing to take advantage of both hybrid installations and the latest hardware innovations. SingleStore, as I said, formerly known as MemSQL is a very high end analytics and transaction database, supports mixed workloads, extremely high speeds. We're talking about trillions of rows per second that could be ingested in query. Couchbase with hybrid transactions and analytics, Redis Labs, open source, no SQL doing very well, as is Cockroach with distributed SQL, MariaDB with its managed MySQL, Mongo and document database has a lot of momentum, EDB, which supports open source Postgres. And if you stretch the definition a bit, Splunk, for log database, why not? ChaosSearch, really interesting startup that leaves data in S-3 and is going after simplifying the ELK stack, New Relic, they have a purpose-built database for application performance management and we probably could have even put Workday in the mix as it developed a specialized database for its apps. Of course, we can't forget about SAP with how not trying to pry customers off of Oracle. And then the big three Cloud players, AWS, Microsoft and Google with extremely large portfolios of database offerings. The spectrum of products in this space is very wide, with you've got AWS, which I think we're up to like 16 database offerings, all the way to Oracle, which has like one database to do everything not withstanding MySQL because it owns MySQL got that through the Sun Acquisition. And it recently, it made some innovations there around the heat wave announcement. But essentially Oracle is investing to make its database, Oracle database run any workload. While AWS takes the approach of the right tool for the right job and really focuses on the primitives for each database. A lot of ways to skin a cat in this enormous and strategic market. So let's take a look at the spending data for the names that make it into the ETR survey. Not everybody we just mentioned will be represented because they may not have quite the market presence of the ends in the survey, but ETR that capture a pretty nice mix of players. So this chart here, it's one of the favorite views that we like to share quite often. It shows the database players across the 1500 respondents in the ETR survey this past quarter and it measures their net score. That's spending momentum and is shown on the vertical axis and market share, which is the pervasiveness in the data set is on the horizontal axis. The Snowflake is notable because it's been hovering around 80% net score since the survey started picking them up. Anything above 40%, that red line there, is considered by us to be elevated. Microsoft and AWS, they also stand out because they have both market presence and they have spending velocity with their platforms. Oracle is very large but it doesn't have the spending momentum in the survey because nearly 30% of Oracle installations are spending less, whereas only 22% are spending more. Now as a caution, this survey doesn't measure dollar spent and Oracle will be skewed toward the big customers with big budgets. So you got to consider that caveat when evaluating this data. IBM is in a similar position although its market share is not keeping up with Oracle's. Google, they've got great tech especially with BigQuery and it has elevated momentum. So not a bad spot to be in although I'm sure it would like to be closer to AWS and Microsoft on the horizontal axis, so it's got some work to do there. And some of the others we mentioned earlier, like MemSQL, Couchbase. As shown MemSQL here, they're now SingleStore. Couchbase, Reddis, Mongo, MariaDB, all very solid scores on the vertical axis. Cloudera just announced that it was selling to private equity and that will hopefully give it some time to invest in this platform and get off the quarterly shot clock. MapR was acquired by HPE and it's part of HPE's Ezmeral platform, their data platform which doesn't yet have the market presence in the survey. Now, something that is interesting in looking at in Snowflakes earnings last quarter, is this laser focused on large customers. This is a hallmark of Frank Slootman and Mike Scarpelli who I know they don't have a playbook but they certainly know how to go whale hunting. So this chart isolates the data that we just showed you to the global 1000. Note that both AWS and Snowflake go up higher on the X-axis meaning large customers are spending at a faster rate for these two companies. The previous chart had an end of 161 for Snowflake, and a 77% net score. This chart shows the global 1000, in the end there for Snowflake is 48 accounts and the net score jumps to 85%. We're not going to show it here but when you isolate the ETR data, nice you can just cut it, when you isolate it on the fortune 1000, the end for Snowflake goes to 59 accounts in the data set and Snowflake jumps another 100 basis points in net score. When you cut the data by the fortune 500, the Snowflake N goes to 40 accounts and the net score jumps another 200 basis points to 88%. And when you isolate on the fortune 100 accounts is only 18 there but it's still 18, their net score jumps to 89%, almost 90%. So it's very strong confirmation that there's a proportional relationship between larger accounts and spending momentum in the ETR data set. So Snowflakes large account strategy appears to be working. And because we think Snowflake is sticky, this probably is a good sign for the future. Now we've been talking about net score, it's a key measure in the ETR data set, so we'd like to just quickly remind you what that is and use Snowflake as an example. This wheel chart shows the components of net score, that lime green is new adoptions. 29% of the customers in the ETR dataset that are new to Snowflake. That's pretty impressive. 50% of the customers are spending more, that's the forest green, 20% are flat, that's the gray, and only 1%, the pink, are spending less. And 0% zero or replacing Snowflake, no defections. What you do here to get net scores, you subtract the red from the green and you get a net score of 78%. Which is pretty sick and has been sick as in good sick and has been steady for many, many quarters. So that's how the net score methodology works. And remember, it typically takes Snowflake customers many months like six to nine months to start consuming it's services at the contracted rate. So those 29% new adoptions, they're not going to kick into high gear until next year, so that bodes well for future revenue. Now, it's worth taking a quick snapshot at Snowflakes most recent quarter, there's plenty of stuff out there that you can you can google and get a summary but let's just do a quick rundown. The company's product revenue run rate is now at 856 million they'll surpass $1 billion on a run rate basis this year. The growth is off the charts very high net revenue retention. We've explained that before with Snowflakes consumption pricing model, they have to account for retention differently than what a SaaS company. Snowflake added 27 net new $1 million accounts in the quarter and claims to have more than a hundred now. It also is just getting its act together overseas. Slootman says he's personally going to spend more time in Europe, given his belief, that the market is huge and they can disrupt it and of course he's from the continent. He was born there and lived there and gross margins expanded, do in a large part to renegotiation of its Cloud costs. Welcome back to that in a moment. Snowflake it's also moving from a product led growth company to one that's more focused on core industries. Interestingly media and entertainment is one of the largest along with financial services and it's several others. To me, this is really interesting because Disney's example that Snowflake often puts in front of its customers as a reference. And it seems to me to be a perfect example of using data and analytics to both target customers and also build so-called data products through data sharing. Snowflake has to grow its ecosystem to live up to its lofty expectations and indications are that large SIS are leaning in big time. Deloitte cross the $100 million in deal flow in the quarter. And the balance sheet's looking good. Thank you very much with $5 billion in cash. The snarks are going to focus on the losses, but this is all about growth. This is a growth story. It's about customer acquisition, it's about adoption, it's about loyalty and it's about lifetime value. Now, as I said at the IPO, and I always say this to young people, don't buy a stock at the IPO. There's probably almost always going to be better buying opportunities ahead. I'm not always right about that, but I often am. Here's a chart of Snowflake's performance since IPO. And I have to say, it's held up pretty well. It's trading above its first day close and as predicted there were better opportunities than day one but if you have to make a call from here. I mean, don't take my stock advice, do your research. Snowflake they're priced to perfection. So any disappointment is going to be met with selling. You saw that the day after they beat their earnings last quarter because their guidance in revenue growth,. Wasn't in the triple digits, it sort of moderated down to the 80% range. And they pointed, they pointed to a new storage compression feature that will lower customer costs and consequently, it's going to lower their revenue. I swear, I think that that before earnings calls, Scarpelli sits back he's okay, what kind of creative way can I introduce the dampen enthusiasm for the guidance. Now I'm not saying lower storage costs will translate into lower revenue for a period of time. But look at dropping storage prices, customers are always going to buy more, that's the way the storage market works. And stuff like did allude to that in all fairness. Let me introduce something that people in Silicon Valley are talking about, and that is the Cloud paradox for SaaS companies. And what is that? I was a clubhouse room with Martin Casado of Andreessen when I first heard about this. He wrote an article with Sarah Wang, calling it to question the merits of SaaS companies sticking with Cloud at scale. Now the basic premise is that for startups in early stages of growth, the Cloud is a no brainer for SaaS companies, but at scale, the cost of Cloud, the Cloud bill approaches 50% of the cost of revenue, it becomes an albatross that stifles operating leverage. Their conclusion ended up saying that as much as perhaps as much as the back of the napkin, they admitted that, but perhaps as much as 1/2 a trillion dollars in market cap is being vacuumed away by the hyperscalers that could go to the SaaS providers as cost savings from repatriation. And that Cloud repatriation is an inevitable path for large SaaS companies at scale. I was particularly interested in this as I had recently put on a post on the Cloud repatriation myth. I think in this instance, there's some merit to their conclusions. But I don't think it necessarily bleeds into traditional enterprise settings. But for SaaS companies, maybe service now has it right running their own data centers or maybe a hybrid approach to hedge bets and save money down the road is prudent. What caught my attention in reading through some of the Snowflake docs, like the S-1 in its most recent 10-K were comments regarding long-term purchase commitments and non-cancelable contracts with Cloud companies. And the companies S-1, for example, there was disclosure of $247 million in purchase commitments over a five plus year period. And the company's latest 10-K report, that same line item jumped to 1.8 billion. Now Snowflake is clearly managing these costs as it alluded to when its earnings call. But one has to wonder, at some point, will Snowflake follow the example of say Dropbox which Andreessen used in his blog and start managing its own IT? Or will it stick with the Cloud and negotiate hard? Snowflake certainly has the leverage. It has to be one of Amazon's best partners and customers even though it competes aggressively with Redshift but on the earnings call, CFO Scarpelli said, that Snowflake was working on a new chip technology to dramatically increase performance. What the heck does that mean? Is this Snowflake is not becoming a hardware company? So I going to have to dig into that a little bit and find out what that it means. I'm guessing, it means that it's taking advantage of ARM-based processes like graviton, which many ISVs ar allowing their software to run on that lower cost platform. Or maybe there's some deep dark in the weeds secret going on inside Snowflake, but I doubt it. We're going to leave all that for there for now and keep following this trend. So it's clear just in summary that Snowflake they're the pace setter in this new exciting world of data but there's plenty of room for others. And they still have a lot to prove. For instance, one customer in ETR, CTO round table express skepticism that Snowflake will live up to its hype because its success is going to lead to more competition from well-established established players. This is a common theme you hear it all the time. It's pretty easy to reach that conclusion. But my guess is this the exact type of narrative that fuels Slootman and sucked him back into this game of Thrones. That's it for now, everybody. Remember, these episodes they're all available as podcasts, wherever you listen. All you got to do is search braking analysis podcast and please subscribe to series. Check out ETR his website at etr.plus. We also publish a full report every week on wikinbon.com and siliconangle.com. You can get in touch with me, Email is David.vellante@siliconangle.com. You can DM me at DVelante on Twitter or comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week everybody, be well and we'll see you next time. (upbeat music)

>> Okay. Now we're going to dig deeper into HPE Ezmeral and try to better understand how it's going to impact customers. And with me to do that are Robert Christiansen, who is the Vice President of Strategy in the office of the CTO and Kumar Sreekanti, who is the Chief Technology Officer and Head of Software, both of course, with Hewlett Packard Enterprise. Gentlemen, welcome to the program. Thanks for coming on. >> Good seeing you, Dave. Thanks for having us. >> It's always good to see you guys. >> Thanks for having us. >> So, Ezmeral, kind of an interesting name, catchy name, but Kumar, what exactly is HPE Ezmeral? >> It's indeed a catchy name. Our branding team has done fantastic job. I believe it's actually derived from Esmeralda, is the Spanish for emarald. Often it's supposed some very mythical bars, and they derived Ezmeral from there. And we all initially when we heard, it was interesting. So, Ezmeral was our effort to take all the software, the platform tools that HPE has and provide this modern operating platform to the customers and put it under one brand. So, it has a modern container platform, it does persistent storage with the data fabric and it doesn't include as many of our customers from that. So, think of it as a modern container platform for modernization and digitazation for the customers. >> Yeah, it's an interesting, you talk about platform, so it's not, you know, a lot of times people say product, but you're positioning it as a platform so that has a broader implication. >> That's very true. So, as the customers are thinking of this digitazation, modernization containers and Microsoft, as you know, there is, has become the stable all. So, it's actually a container orchestration platform with golfers open source going into this as well as the persistence already. >> So, by the way, Ezmeral, I think Emerald in Spanish, I think in the culture, it also has immunity powers as well. So immunity from lock-in, (Robert and Kumar laughing) and all those other terrible diseases, maybe it helps us with COVID too. Robert, when you talk to customers, what problems do you probe for that Ezmeral can do a good job solving? >> Yeah, that's a really great question because a lot of times they don't even know what it is that they're trying to solve for other than just a very narrow use case. But the idea here is to give them a platform by which they can bridge both the public and private environment for what they do, the application development, specifically in the data side. So, when yo're looking to bring containerization, which originally got started on the public cloud and it has moved its way, I should say it become popular in the public cloud and it moved its way on premises now, Ezmeral really opens the door to three fundamental things, but, you know, how do I maintain an open architecture like you're referring to, to some low or no lock-in of my applications. Number two, how do I gain a data fabric or a data consistency of accessing the data so I don't have to rewrite those applications when I do move them around. And then lastly, where everybody's heading, the real value is in the AI ML initiatives that companies are really bringing and that value of their data and locking that data at where the data is being generated and stored. And so the Ezmeral platform is those multiple pieces that Kumar was talking about stacked together to deliver the solutions for the client. >> So Kumar, how does it work? What's the sort of IP or the secret source behind it all? What makes HPE different? >> Yeah. Continuing on (indistinct) it's a modern glass form of optimizing the data and workloads. But I think I would say there are three unique characteristics of this platform. Number one is that it actually provides you both an ability to run statefull and stateless as workloads under the same platform. And number two is, as we were thinking about, unlike another Kubernete is open source, it actually add, use you all open-source Kurbenates as well as an orchestration behind them so you can actually, you can provide this hybrid thing that Robert was talking about. And then actually we built the workflows into it, for example, they'll actually announced along with it Ezmeral, ML expert on the customers can actually do the workflow management around specific data woakload. So, the magic is if you want to see the secrets out of all the efforts that has been going into some of the IP acquisitions that HPE has done over the years, we said we BlueData, MAPR, and the Nimble, all these pieces are coming together and providing a modern digitization platform for the customers. >> So these pieces, they all have a little bit of a machine intelligence in them, you have people, who used to think of AI as this sort of separate thing, I mean the same thing with containers, right? But now it's getting embedded into the stack. What is the role of machine intelligence or machine learning in Ezmeral? >> I would take a step back and say, you know, there's very well the customers, the amount of data that is being generated and 95% or 98% of the data is machine generated. And it does a series of a window gravity, and it is sitting at the edge and we were the only one that had edge to the cloud data fabric that's built to it. So, the number one is that we are bringing computer or a cloud to the data that taking the data to the cloud, right, if you will. It's a cloud like experience that provides the customer. AI is not much value to us if we don't harness the data. So, I said this in one of the blog was we have gone from collecting the data, to the finding the insights into the data, right. So, that people have used all sorts of analysis that we are to find data is the new oil. So, the AI and the data. And then now your applications have to be modernized and nobody wants write an application in a non microservices fashion because you wanted to build the modernization. So, if you bring these three things, I want to have a data gravity with lots of data, I have built an AI applications and I want to have those three things I think we bring to the customer. >> So, Robert let's stay on customers for a minute. I mean, I want to understand the business impact, the business case, I mean, why should all the cloud developers have all the fun, you've mentioned it, you're bridging the cloud and on-prem, they talk about when you talk to customers and what they are seeing is the business impact, what's the real drivers for that? >> That's a great question cause at the end of the day, I think the recent survey that was that cost and performance are still the number one requirement for this, just real close second is agility, the speed at which they want to move and so those two are the top of mind every time. But the thing we find Ezmeral, which is so impactful is that nobody brings together the Silicon, the hardware, the platform, and all of that stack together work and combine like Ezmeral does with the platforms that we have and specifically, we start getting 90, 92, 93% utilization out of AI ML workloads on very expensive hardware, it really, really is a competitive advantage over a public cloud offering, which does not offer those kinds of services and the cost models are so significantly different. So, we do that by collapsing the stack, we take out as much intellectual property, excuse me, as much software pieces that are necessary so we are closest to the Silicon, closest to the applications, bring it to the hardware itself, meaning that we can interleave the applications, meaning that you can get to true multitenancy on a particular platform that allows you to deliver a cost optimized solution. So, when you talk about the money side, absolutely, there's just nothing out there and then on the second side, which is agility. One of the things that we know is today is that applications need to be built in pipelines, right, this is something that's been established now for quite some time. Now, that's really making its way on premises and what Kumar was talking about with, how do we modernize? How do we do that? Well, there's going to be some that you want to break into microservices containers, and there's some that you don't. Now, the ones that they're going to do that they're going to get that speed and motion, et cetera, out of the gate and they can put that on premises, which is relatively new these days to the on-premises world. So, we think both won't be the advantage. >> Okay. I want to unpack that a little bit. So, the cost is clearly really 90 plus percent utilization. >> Yes. >> I mean, Kumar, you know, even pre virtualization, we know that it was like, even with virtualization, you never really got that high. I mean, people would talk about it, but are you really able to sustain that in real world workloads? >> Yeah. I think when you make your exchangeable cut up into smaller pieces, you can insert them into many areas. We have one customer was running 18 containers on a single server and each of those containers, as you know, early days of new data, you actually modernize what we consider week run containers or microbiome. So, if you actually build these microservices, and you all and you have versioning all correctly, you can pack these things extremely well. And we have seen this, again, it's not a guarantee, it all depends on your application and your, I mean, as an engineer, we want to always understand all of these caveats work, but it is a very modern utilization of the platform with the data and once you know where the data is, and then it becomes very easy to match those two. >> Now, the other piece of the value proposition that I heard Robert is it's basically an integrated stack. So I don't have to cobble together a bunch of open source components, there's legal implications, there's obviously performance implications. I would imagine that resonates and particularly with the enterprise buyer because they don't have the time to do all this integration. >> That's a very good point. So there is an interesting question that enterprises, they want to have an open source so there is no lock-in, but they also need help to implement and deploy and manage it because they don't have the expertise. And we all know that the IKEA desk has actually brought that API, the past layer standardization. So what we have done is we have given the open source and you arrive to the Kubernetes API, but at the same time orchestration, persistent stories, the data fabric, the AI algorithms, all of them are bolted into it and on the top of that, it's available both as a licensed software on-prem, and the same software runs on the GreenLake. So you can actually pay as you go and then we run it for them in a colo or, or in their own data center. >> Oh, good. That was one of my latter questions. So, I can get this as a service pay by the drink, essentially I don't have to install a bunch of stuff on-prem and pay it perpetualized... >> There is a lot of containers and is the reason and the lapse of service in the last discover and knowledge gone production. So both Ezmeral is available, you can run it on-prem, on the cloud as well, a congenital platform, or you can run instead on GreenLake. >> Robert, are there any specific use case patterns that you see emerging amongst customers? >> Yeah, absolutely. So there's a couple of them. So we have a, a really nice relationship that we see with any of the Splunk operators that were out there today, right? So Splunk containerized, their operator, that operator is the number one operator, for example, for Splunk in the IT operation side or notifications as well as on the security operations side. So we've found that that runs highly effective on top of Ezmeral, on top of our platforms so we just talked about, that Kumar just talked about, but I want to also give a little bit of backgrounds to that same operator platform. The way that the Ezmeral platform has done is that we've been able to make it highly active, active with HA availability at nine, it's going to be at five nines for that same Splunk operator on premises, on the Kubernetes open source, which is as far as I'm concerned, a very, very high end computer science work. You understand how difficult that is, that's number one. Number two is you'll see just a spark workloads as a whole. All right. Nobody handles spark workloads like we do. So we put a container around them and we put them inside the pipeline of moving people through that basic, ML AI pipeline of getting a model through its system, through its trained, and then actually deployed to our ML ops pipeline. This is a key fundamental for delivering value in the data space as well. And then lastly, this is, this is really important when you think about the data fabric that we offer, the data fabric itself doesn't necessarily have to be bolted with the container platform, the container, the actual data fabric itself, can be deployed underneath a number of our, you know, for competitive platforms who don't handle data well. We know that, we know that they don't handle it very well at all. And we get lots and lots of calls for people saying, "Hey, can you take your Ezmeral data fabric "and solve my large scale, "highly challenging data problems?" And we say, "yeah, "and then when you're ready for a real world, "full time enterprise ready container platform, "we'd be happy to prove that too." >> So you're saying you're, if I'm inferring correctly, you're one of the values as you're simplifying that whole data pipeline and the whole data science, science project pun intended, I guess. (Robert and Kumar laughing) >> That's true. >> Absolutely. >> So, where does a customer start? I mean, what, what are the engagements like? What's the starting point? >> It's means we're probably one of the most trusted and robust supplier for many, many years and we have a phenomenal workforce of both the (indistinct), world leading support organization, there are many places to start with. One is obviously all these salaries that are available on the GreenLake, as we just talked about, and they can start on a pay as you go basis. There are many customers that actually some of them are from the early days of BlueData and MAPR, and then already running and they actually improvise on when, as they move into their next version more of a message. You can start with simple as well as container platform or system with the store, a computer's operation and can implement as an analyst to start working. And then finally as a big company like HPE as an everybody's company, that finance it's services, it's very easy for the customers to be able to get that support on day to day operations. >> Thank you for watching everybody. It's Dave Vellante for theCUBE. Keep it right there for more great content from Ezmeral.

>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante. >> In June of this year, Snowflake filed a confidential document suggesting that it would do an IPO. Now of course, everybody knows about it, found out about it and it had a $20 billion valuation. So, many in the community and the investment community and so forth are excited about this IPO. It could be the hottest one of the year, and we're getting a number of questions from investors and practitioners and the entire Wiki bond, ETR and CUBE community. So, welcome everybody. This is Dave Vellante. This is "CUBE Insights" powered by ETR. In this breaking analysis, we're going to unpack five critical questions around Snowflake's IPO or pending IPO. And with me to discuss that is Erik Bradley. He's the Chief Engagement Strategists at ETR and he's also the Managing Director of VENN. Erik, thanks for coming on and great to see you as always. >> Great to see you too. Always enjoy being on the show. Thank you. >> Now for those of you don't know Erik, VENN is a roundtable that he hosts and he brings in CIOs, IT practitioners, CSOs, data experts and they have an open and frank conversation, but it's private to ETR clients. But they know who the individual is, what their role is, what their title is, et cetera and it's a kind of an ask me anything. And I participated in one of them this past week. Outstanding. And we're going to share with you some of that. But let's bring up the agenda slide if we can here. And these are really some of the questions that we're getting from investors and others in the community. There's really five areas that we want to address. The first is what's happening in this enterprise data warehouse marketplace? The second thing is kind of a one area. What about the legacy EDW players like Oracle and Teradata and Netezza? The third question we get a lot is can Snowflake compete with the big cloud players? Amazon, Google, Microsoft. I mean they're right there in the heart, in the thick of things there. And then what about that multi-cloud strategy? Is that viable? How much of a differentiator is that? And then we get a lot of questions on the TAM. Meaning the total available market. How big is that market? Does it justify the valuation for Snowflake? Now, Erik, you've been doing this now. You've run a couple VENNs, you've been following this, you've done some other work that you've done with Eagle Alpha. What's your, just your initial sort of takeaway from all this work that you've been doing. >> Yeah, sure. So my first take on Snowflake was about two and a half years ago. I actually hosted them for one of my VENN interviews and my initial thought was impressed. So impressed. They were talking at the time about their ability to kind of make ease of use of a multi-cloud strategy. At the time although I was impressed, I did not expect the growth and the hyper growth that we have seen now. But, looking at the company in its current iteration, I understand where the hype is coming from. I mean, it's 12 and a half billion private valuation in the last round. The least confidential IPO (laughs) anyone's ever seen (Dave laughs) with a 15 to $20 billion valuation coming out, which is more than Teradata, Margo and Cloudera combined. It's a great question. So obviously the success to this point is warranted, but we need to see what they're going to be able to do next. So I think the agenda you laid out is a great one and I'm looking forward to getting into some of those details. >> So let's start with what's happening in the marketplace and let's pull up a slide that I very much love to use. It's the classic X-Y. On the vertical axis here we show net score. And remember folks, net score is an indicator of spending momentum. ETR every quarter does like a clockwork survey where they're asking people, "Essentially are you spending more or less?" They subtract the less from the more and comes up with a net score. It's more complicated than, but like NPS, it's a very simple and reliable methodology. That's the vertical axis. And the horizontal axis is what's called market share. Market share is the pervasiveness within the data set. So it's calculated by the number of mentions of the vendor divided by the number of mentions within that sector. And what we're showing here is the EDW sector. And we've pulled out a few companies that I want to talk about. So the big three, obviously Microsoft, AWS and Google. And you can see Microsoft has a huge presence far to the right. AWS, very, very strong. A lot of Redshift in there. And then they're pretty high on the vertical axis. And then Google, not as much share, but very solid in that. Close to 60% net score. And then you can see above all of them from a vertical standpoint is Snowflake with a 77.5% net score. You can see them in the upper right there in the green. One of the highest Erik in the entire data set. So, let's start with some sort of initial comments on the big guys and Snowflakes. Your thoughts? >> Sure. Just first of all to comment on the data, what we're showing there is just the data warehousing sector, but Snowflake's actual net score is that high amongst the entire universe that we follow. Their data strength is unprecedented and we have forward-looking spending intention. So this bodes very well for them. Now, what you did say very accurately is there's a difference between their spending intentions on a net revenue level compared to AWS, Microsoft. There no one's saying that this is an apples-to-apples comparison when it comes to actual revenue. So we have to be very cognizant of that. There is domination (laughs) quite frankly from AWS and from Azure. And Snowflake is a necessary component for them not only to help facilitate a multi-cloud, but look what's happening right now in the US Congress, right? We have these tech leaders being grilled on their actual dominance. And one of the main concerns they have is the amount of data that they're collecting. So I think the environment is right to have another player like this. I think Snowflake really has a lot of longevity and our data is supporting that. And the commentary that we hear from our end users, the people that take the survey are supporting that as well. >> Okay, and then let's stay on this X-Y slide for a moment. I want to just pull out a couple of other comments here, because one of the questions we're asking is Whither, the legacy EDW players. So we've got in here, IBM, Oracle, you can see Teradata and then Hortonworks and MapR. We're going to talk a little bit about Hortonworks 'cause it's now Cloudera. We're going to talk a little bit about Hadoop and some of the data lakes. So you can see there they don't have nearly the net score momentum. Oracle obviously has a huge install base and is investing quite frankly in R&D and do an Exadata and it has its own cloud. So, it's got a lock on it's customers and if it keeps investing and adding value, it's not going away. IBM with Netezza, there's really been some questions around their commitment to that base. And I know that a lot of the folks in the VENNs that we've talked to Erik have said, "Well, we're replacing Netezza." Frank Slootman has been very vocal about going after Teradata. And then we're going to talk a little bit about the Hadoop space. But, can you summarize for us your thoughts in your research and the commentary from your community, what's going on with the legacy guys? Are these guys cooked? Can they hang on? What's your take? >> Sure. We focus on this quite a bit actually. So, I'm going to talk about it from the data perspective first, and then we'll go into some of the commentary and the panel. You even joined one yesterday. You know that it was touched upon. But, first on the data side, what we're noticing and capturing is a widening bifurcation between these cloud native and the legacy on-prem. It is undeniable. There is nothing that you can really refute. The data is concrete and it is getting worse. That gap is getting wider and wider and wider. Now, the one thing I will say is, nobody's going to rip out their legacy applications tomorrow. It takes years and years. So when you look at Teradata, right? Their market cap's only 2 billion, 2.3 billion. How much revenue growth do they need to stay where they are? Not much, right? No one's expecting them to grow 20%, which is what you're seeing on the left side of that screen. So when you look at the legacy versus the cloud native, there is very clear direction of what's happening. The one thing I would note from the data perspective is if you switched from net score or adoptions and you went to flat spending, you suddenly see Oracle and Teradata move over to that left a little bit, because again what I'm trying to say is I don't think they're going to catch up. No, but also don't think they're going away tomorrow. That these have large install bases, they have relationships. Now to kind of get into what you were saying about each particular one, IBM, they shut down Netezza. They shut it down and then they brought it back to life. How does that make you feel if you're the head of data architecture or you're DevOps and you're trying to build an application for a large company? I'm not going back to that. There's absolutely no way. Teradata on the other hand is known to be incredibly stable. They are known to just not fail. If you need to kind of re-architect or you do a migration, they work. Teradata also has a lot of compliance built in. So if you're a financials, if you have a regulated business or industry, there's still some data sets that you're not going to move up to the cloud. Whether it's a PII compliance or financial reasons, some of that stuff is still going to live on-prem. So Teradata is still has a very good niche. And from what we're hearing from our panels, then this is a direct quote if you don't mind me looking off screen for one second. But this is a great one. Basically said, "Teradata is the only one from the legacy camp who is putting up a fight and not giving up." Basically from a CIO perspective, the rest of them aren't an option anymore. But Teradata is still fighting and that's great to hear. They have their own data as a service offering and listen, they're a small market cap compared to these other companies we're talking about. But, to summarize, the data is very clear. There is a widening bifurcation between the two camps. I do not think legacy will catch up. I think all net new workloads are moving to data as a service, moving to cloud native, moving to hosted, but there are still going to be some existing legacy on-prem applications that will be supported with these older databases. And of those, Oracle and Teradata are still viable options. >> I totally agree with you and my colleague David Floyd is actually quite high on Teradata Vantage because he really does believe that a key component, we're going to talk about the TAM in a minute, but a key component of the TAM he believes must include the on-premises workloads. And Frank Slootman has been very clear, "We're not doing on-prem, we're not doing this halfway house." And so that's an opportunity for companies like Teradata, certainly Oracle I would put it in that camp is putting up a fight. Vertica is another one. They're very small, but another one that's sort of battling it out from the old NPP world. But that's great. Let's go into some of the specifics. Let's bring up here some of the specific commentary that we've curated here from the roundtables. I'm going to go through these and then ask you to comment. The first one is just, I mean, people are obviously very excited about Snowflake. It's easy to use, the whole thing zero to Snowflake in 90 minutes, but Snowflake is synonymous with cloud-native data warehousing. There are no equals. We heard that a lot from your VENN panelist. >> We certainly did. There was even more euphoria around Snowflake than I expected when we started hosting these series of data warehousing panels. And this particular gentleman that said that happens to be the global head of data architecture for a fortune 100 financials company. And you mentioned earlier that we did a report alongside Eagle Alpha. And we noticed that among fortune 100 companies that are also using the big three public cloud companies, Snowflake is growing market share faster than anyone else. They are positioned in a way where even if you're aligned with Azure, even if you're aligned with AWS, if you're a large company, they are gaining share right now. So that particular gentleman's comments was very interesting. He also made a comment that said, "Snowflake is the person who championed the idea that data warehousing is not dead yet. Use that old monthly Python line and you're not dead yet." And back in the day where the Hadoop came along and the data lakes turned into a data swamp and everyone said, "We don't need warehousing anymore." Well, that turned out to be a head fake, right? Hadoop was an interesting technology, but it's a complex technology. And it ended up not really working the way people want it. I think Snowflake came in at that point at an opportune time and said, "No, data warehousing isn't dead. We just have to separate the compute from the storage layer and look at what I can do. That increases flexibility, security. It gives you that ability to run across multi-cloud." So honestly the commentary has been nothing but positive. We can get into some of the commentary about people thinking that there's competition catching up to what they do, but there is no doubt that right now Snowflake is the name when it comes to data as a service. >> The other thing we heard a lot was ETL is going to get completely disrupted, you sort of embedded ETL. You heard one panelist say, "Well, it's interesting to see that guys like Informatica are talking about how fast they can run inside a Snowflake." But Snowflake is making that easy. That data prep is sort of part of the package. And so that does not bode well for ETL vendors. >> It does not, right? So ETL is a legacy of on-prem databases and even when Hadoop came along, it still needed that extra layer to kind of work with the data. But this is really, really disrupting them. Now the Snowflake's credit, they partner well. All the ETL players are partnered with Snowflake, they're trying to play nice with them, but the writings on the wall as more and more of this application and workloads move to the cloud, you don't need the ETL layer. Now, obviously that's going to affect their talent and Informatica the most. We had a recent comment that said, this was a CIO who basically said, "The most telling thing about the ETL players right now is every time you speak to them, all they talk about is how they work in a Snowflake architecture." That's their only metric that they talk about right now. And he said, "That's very telling." That he basically used it as it's their existential identity to be part of Snowflake. If they're not, they don't exist anymore. So it was interesting to have sort of a philosophical comment brought up in one of my roundtables. But that's how important playing nice and finding a niche within this new data as a service is for ETL, but to be quite honest, they might be going the same way of, "Okay, let's figure out our niche on these still the on-prem workloads that are still there." I think over time we might see them maybe as an M&A possibility, whether it's Snowflake or one of these new up and comers, kind of bring them in and sort of take some of the technology that's useful and layer it in. But as a large market cap, solo existing niche, I just don't know how long ETL is for this world. >> Now, yeah. I mean, you're right that if it wasn't for the marketing, they're not fighting fashion. But >> No. >> really there're some challenges there. Now, there were some contrarians in the panel and they signaled some potential icebergs ahead. And I guarantee you're going to see this in Snowflake's Red Herring when we actually get it. Like we're going to see all the risks. One of the comments, I'll mention the two and then we can talk about it. "Their engineering advantage will fade over time." Essentially we're saying that people are going to copycat and we've seen that. And the other point is, "Hey, we might see some similar things that happened to Hadoop." The public cloud players giving away these offerings at zero cost. Essentially marginal cost of adding another service is near zero. So the cloud players will use their heft to compete. Your thoughts? >> Yeah, first of all one of the reasons I love doing panels, right? Because we had three gentlemen on this panel that all had nothing but wonderful things to say. But you always get one. And this particular person is a CTO of a well known online public travel agency. We'll put it that way. And he said, "I'm going to be the contrarian here. I have seven different technologies from private companies that do the same thing that I'm evaluating." So that's the pressure from behind, right? The technology, they're going to catch up. Right now Snowflake has the best engineering which interestingly enough they took a lot of that engineering from IBM and Teradata if you actually go back and look at it, which was brought up in our panel as well. He said, "However, the engineering will catch up. They always do." Now from the other side they're getting squeezed because the big cloud players just say, "Hey, we can do this too. I can bundle it with all the other services I'm giving you and I can squeeze your pay. Pretty much give it a waive at the cost." So I do think that there is a very valid concern. When you come out with a $20 billion IPO evaluation, you need to warrant that. And when you see competitive pressures from both sides, from private emerging technologies and from the more dominant public cloud players, you're going to get squeezed there a little bit. And if pricing gets squeezed, it's going to be very, very important for Snowflake to continue to innovate. That comment you brought up about possibly being the next Cloudera was certainly the best sound bite that I got. And I'm going to use it as Clickbait in future articles, because I think everyone who starts looking to buy a Snowflake stock and they see that, they're going to need to take a look. But I would take that with a grain of salt. I don't think that's happening anytime soon, but what that particular CTO was referring to was if you don't innovate, the technology itself will become commoditized. And he believes that this technology will become commoditized. So therefore Snowflake has to continue to innovate. They have to find other layers to bring in. Whether that's through their massive war chest of cash they're about to have and M&A, whether that's them buying analytics company, whether that's them buying an ETL layer, finding a way to provide more value as they move forward is going to be very important for them to justify this valuation going forward. >> And I want to comment on that. The Cloudera, Hortonworks, MapRs, Hadoop, et cetera. I mean, there are dramatic differences obviously. I mean, that whole space was so hard, very difficult to stand up. You needed science project guys and lab coats to do it. It was very services intensive. As well companies like Cloudera had to fund all these open source projects and it really squeezed their R&D. I think Snowflake is much more focused and you mentioned some of the background of their engineers, of course Oracle guys as well. However, you will see Amazon's going to trot out a ton of customers using their RA3 managed storage and their flash. I think it's the DC two piece. They have a ton of action in the marketplace because it's just so easy. It's interesting one of the comments, you asked this yesterday, was with regard to separating compute from storage, which of course it's Snowflakes they basically invented it, it was one of their climbs to fame. The comment was what AWS has done to separate compute from storage for Redshift is largely a bolt on. Which I thought that was an interesting comment. I've had some other comments. My friend George Gilbert said, "Hey, despite claims to the contrary, AWS still hasn't separated storage from compute. What they have is really primitive." We got to dig into that some more, but you're seeing some data points that suggest there's copycatting going on. May not be as functional, but at the same time, Erik, like I was saying good enough is maybe good enough in this space. >> Yeah, and especially with the enterprise, right? You see what Microsoft has done. Their technology is not as good as all the niche players, but it's good enough and I already have a Microsoft license. So, (laughs) you know why am I going to move off of it. But I want to get back to the comment you mentioned too about that particular gentleman who made that comment about RedShift, their separation is really more of a bolt on than a true offering. It's interesting because I know who these people are behind the scenes and he has a very strong relationship with AWS. So it was interesting to me that in the panel yesterday he said he switched from Redshift to Snowflake because of that and some other functionality issues. So there is no doubt from the end users that are buying this. And he's again a fortune 100 financial organization. Not the same one we mentioned. That's a different one. But again, a fortune 100 well known financials organization. He switched from AWS to Snowflake. So there is no doubt that right now they have the technological lead. And when you look at our ETR data platform, we have that adoption reasoning slide that you show. When you look at the number one reason that people are adopting Snowflake is their feature set of technological lead. They have that lead now. They have to maintain it. Now, another thing to bring up on this to think about is when you have large data sets like this, and as we're moving forward, you need to have machine learning capabilities layered into it, right? So they need to make sure that they're playing nicely with that. And now you could go open source with the Apache suite, but Google is doing so well with BigQuery and so well with their machine learning aspects. And although they don't speak enterprise well, they don't sell to the enterprise well, that's changing. I think they're somebody to really keep an eye on because their machine learning capabilities that are layered into the BigQuery are impressive. Now, of course, Microsoft Azure has Databricks. They're layering that in, but this is an area where I think you're going to see maybe what's next. You have to have machine learning capabilities out of the box if you're going to do data as a service. Right now Snowflake doesn't really have that. Some of the other ones do. So I had one of my guest panelist basically say to me, because of that, they ended up going with Google BigQuery because he was able to run a machine learning algorithm within hours of getting set up. Within hours. And he said that that kind of capability out of the box is what people are going to have to use going forward. So that's another thing we should dive into a little bit more. >> Let's get into that right now. Let's bring up the next slide which shows net score. Remember this is spending momentum across the major cloud players and plus Snowflake. So you've got Snowflake on the left, Google, AWS and Microsoft. And it's showing three survey timeframes last October, April 20, which is right in the middle of the pandemic. And then the most recent survey which has just taken place this month in July. And you can see Snowflake very, very high scores. Actually improving from the last October survey. Google, lower net scores, but still very strong. Want to come back to that and pick up on your comments. AWS dipping a little bit. I think what's happening here, we saw this yesterday with AWS's results. 30% growth. Awesome. Slight miss on the revenue side for AWS, but look, I mean massive. And they're so exposed to so many industries. So some of their industries have been pretty hard hit. Microsoft pretty interesting. A little softness there. But one of the things I wanted to pick up on Erik, when you're talking about Google and BigQuery and it's ML out of the box was what we heard from a lot of the VENN participants. There's no question about it that Google technically I would say is one of Snowflake's biggest competitors because it's cloud native. Remember >> Yep. >> AWS did a license one time. License deal with PowerShell and had a sort of refactor the thing to be cloud native. And of course we know what's happening with Microsoft. They basically were on-prem and then they put stuff in the cloud and then all the updates happen in the cloud. And then they pushed to on-prem. But they have that what Frank Slootman calls that halfway house, but BigQuery no question technically is very, very solid. But again, you see Snowflake right now anyway outpacing these guys in terms of momentum. >> Snowflake is out outpacing everyone (laughs) across our entire survey universe. It really is impressive to see. And one of the things that they have going for them is they can connect all three. It's that multi-cloud ability, right? That portability that they bring to you is such an important piece for today's modern CIO as data architects. They don't want vendor lock-in. They are afraid of vendor lock-in. And this ability to make their data portable and to do that with ease and the flexibility that they offer is a huge advantage right now. However, I think you're a hundred percent right. Google has been so focused on the engineering side and never really focusing on the enterprise sales side. That is why they're playing catch up. I think they can catch up. They're bringing in some really important enterprise salespeople with experience. They're starting to learn how to talk to enterprise, how to sell, how to support. And nobody can really doubt their engineering. How many open sources have they given us, right? They invented Kubernetes and the entire container space. No one's really going to compete with them on that side if they learn how to sell it and support it. Yeah, right now they're behind. They're a distant third. Don't get me wrong. From a pure hosted ability, AWS is number one. Microsoft is yours. Sometimes it looks like it's number one, but you have to recognize that a lot of that is because of simply they're hosted 365. It's a SAS app. It's not a true cloud type of infrastructure as a service. But Google is a distant third, but their technology is really, really great. And their ability to catch up is there. And like you said, in the panels we were hearing a lot about their machine learning capability is right out of the box. And that's where this is going. What's the point of having this huge data if you're not going to be supporting it on new application architecture. And all of those applications require machine learning. >> Awesome. So we're. And I totally agree with what you're saying about Google. They just don't have it figured out how to sell the enterprise yet. And a hundred percent AWS has the best cloud. I mean, hands down. But a very, very competitive market as we heard yesterday in front of Congress. Now we're on the point about, can Snowflake compete with the big cloud players? I want to show one more data point. So let's bring up, this is the same chart as we showed before, but it's new adoptions. And this is really telling. >> Yeah. >> You can see Snowflake with 34% in the yellow, new adoptions, down yes from previous surveys, but still significantly higher than the other players. Interesting to see Google showing momentum on new adoptions, AWS down on new adoptions. And again, exposed to a lot of industries that have been hard hit. And Microsoft actually quite low on new adoption. So this is very impressive for Snowflake. And I want to talk about the multi-cloud strategy now Erik. This came up a lot. The VENN participants who are sort of fans of Snowflake said three things: It was really the flexibility, the security which is really interesting to me. And a lot of that had to do with the flexibility. The ability to easily set up roles and not have to waste a lot of time wrangling. And then the third was multi-cloud. And that was really something that came through heavily in the VENN. Didn't it? >> It really did. And again, I think it just comes down to, I don't think you can ever overstate how afraid these guys are of vendor lock-in. They can't have it. They don't want it. And it's best practice to make sure your sensitive information is being kind of spread out a little bit. We all know that people don't trust Bezos. So if you're in certain industries, you're not going to use AWS at all, right? So yeah, this ability to have your data portability through multi-cloud is the number one reason I think people start looking at Snowflake. And to go to your point about the adoptions, it's very telling and it bodes well for them going forward. Most of the things that we're seeing right now are net new workloads. So let's go again back to the legacy side that we were talking about, the Teradatas, IBMs, Oracles. They still have the monolithic applications and the data that needs to support that, right? Like an old ERP type of thing. But anyone who's now building a new application, bringing something new to market, it's all net new workloads. There is no net new workload that is going to go to SAP or IBM. It's not going to happen. The net new workloads are going to the cloud. And that's why when you switch from net score to adoption, you see Snowflake really stand out because this is about new adoption for net new workloads. And that's really where they're driving everything. So I would just say that as this continues, as data as a service continues, I think Snowflake's only going to gain more and more share for all the reasons you stated. Now get back to your comment about security. I was shocked by that. I really was. I did not expect these guys to say, "Oh, no. Snowflake enterprise security not a concern." So two panels ago, a gentleman from a fortune 100 financials said, "Listen, it's very difficult to get us to sign off on something for security. Snowflake is past it, it is enterprise ready, and we are going full steam ahead." Once they got that go ahead, there was no turning back. We gave it to our DevOps guys, we gave it to everyone and said, "Run with it." So, when a company that's big, I believe their fortune rank is 28. (laughs) So when a company that big says, "Yeah, you've got the green light. That we were okay with the internal compliance aspect, we're okay with the security aspect, this gives us multi-cloud portability, this gives us flexibility, ease of use." Honestly there's a really long runway ahead for Snowflake. >> Yeah, so the big question I have around the multi-cloud piece and I totally and I've been on record saying, "Look, if you're going looking for an agnostic multi-cloud, you're probably not going to go with the cloud vendor." (laughs) But I've also said that I think multi-cloud to date anyway has largely been a symptom as opposed to a strategy, but that's changing. But to your point about lock-in and also I think people are maybe looking at doing things across clouds, but I think that certainly it expands Snowflake's TAM and we're going to talk about that because they support multiple clouds and they're going to be the best at that. That's a mandate for them. The question I have is how much of complex joining are you going to be doing across clouds? And is that something that is just going to be too latency intensive? Is that really Snowflake's expertise? You're really trying to build that data layer. You're probably going to maybe use some kind of Postgres database for that. >> Right. >> I don't know. I need to dig into that, but that would be an opportunity from a TAM standpoint. I just don't know how real that is. >> Yeah, unfortunately I'm going to just be honest with this one. I don't think I have great expertise there and I wouldn't want to lead anyone a wrong direction. But from what I've heard from some of my VENN interview subjects, this is happening. So the data portability needs to be agnostic to the cloud. I do think that when you're saying, are there going to be real complex kind of workloads and applications? Yes, the answer is yes. And I think a lot of that has to do with some of the container architecture as well, right? If I can just pull data from one spot, spin it up for as long as I need and then just get rid of that container, that ethereal layer of compute. It doesn't matter where the cloud lies. It really doesn't. I do think that multi-cloud is the way of the future. I know that the container workloads right now in the enterprise are still very small. I've heard people say like, "Yeah, I'm kicking the tires. We got 5%." That's going to grow. And if Snowflake can make themselves an integral part of that, then yes. I think that's one of those things where, I remember the guy said, "Snowflake has to continue to innovate. They have to find a way to grow this TAM." This is an area where they can do so. I think you're right about that, but as far as my expertise, on this one I'm going to be honest with you and say, I don't want to answer incorrectly. So you and I need to dig in a little bit on this one. >> Yeah, as it relates to question four, what's the viability of Snowflake's multi-cloud strategy? I'll say unquestionably supporting multiple clouds, very viable. Whether or not portability across clouds, multi-cloud joins, et cetera, TBD. So we'll keep digging into that. The last thing I want to focus on here is the last question, does Snowflake's TAM justify its $20 billion valuation? And you think about the data pipeline. You go from data acquisition to data prep. I mean, that really is where Snowflake shines. And then of course there's analysis. You've got to bring in EMI or AI and ML tools. That's not Snowflake's strength. And then you're obviously preparing that, serving that up to the business, visualization. So there's potential adjacencies that they could get into that they may or may not decide to. But so we put together this next chart which is kind of the TAM expansion opportunity. And I just want to briefly go through it. We published this stuff so you can go and look at all the fine print, but it's kind of starts with the data lake disruption. You called it data swamp before. The Hadoop no schema on, right? Basically the ROI of Hadoop became reduction of investment as my friend Abby Meadow would say. But so they're kind of disrupting that data lake which really was a failure. And then really going after that enterprise data warehouse which is kind of I have it here as a 10 billion. It's actually bigger than that. It's probably more like a $20 billion market. I'll update this slide. And then really what Snowflake is trying to do is be data as a service. A data layer across data stores, across clouds, really make it easy to ingest and prepare data and then serve the business with insights. And then ultimately this huge TAM around automated decision making, real-time analytics, automated business processes. I mean, that is potentially an enormous market. We got a couple of hundred billion. I mean, just huge. Your thoughts on their TAM? >> I agree. I'm not worried about their TAM and one of the reasons why as I mentioned before, they are coming out with a whole lot of cash. (laughs) This is going to be a red hot IPO. They are going to have a lot of money to spend. And look at their management team. Who is leading the way? A very successful, wise, intelligent, acquisitive type of CEO. I think there is going to be M&A activity, and I believe that M&A activity is going to be 100% for the mindset of growing their TAM. The entire world is moving to data as a service. So let's take as a backdrop. I'm going to go back to the panel we did yesterday. The first question we asked was, there was an understanding or a theory that when the virus pandemic hit, people wouldn't be taking on any sort of net new architecture. They're like, "Okay, I have Teradata, I have IBM. Let's just make sure the lights are on. Let's stick with it." Every single person I've asked, they're just now eight different experts, said to us, "Oh, no. Oh, no, no." There is the virus pandemic, the shift from work from home. Everything we're seeing right now has only accelerated and advanced our data as a service strategy in the cloud. We are building for scale, adopting cloud for data initiatives. So, across the board they have a great backdrop. So that's going to only continue, right? This is very new. We're in the early innings of this. So for their TAM, that's great because that's the core of what they do. Now on top of it you mentioned the type of things about, yeah, right now they don't have great machine learning. That could easily be acquired and built in. Right now they don't have an analytics layer. I for one would love to see these guys talk to Alteryx. Alteryx is red hot. We're seeing great data and great feedback on them. If they could do that business intelligence, that analytics layer on top of it, the entire suite as a service, I mean, come on. (laughs) Their TAM is expanding in my opinion. >> Yeah, your point about their leadership is right on. And I interviewed Frank Slootman right in the heart of the pandemic >> So impressed. >> and he said, "I'm investing in engineering almost sight unseen. More circumspect around sales." But I will caution people. That a lot of people I think see what Slootman did with ServiceNow. And he came into ServiceNow. I have to tell you. It was they didn't have their unit economics right, they didn't have their sales model and marketing model. He cleaned that up. Took it from 120 million to 1.2 billion and really did an amazing job. People are looking for a repeat here. This is a totally different situation. ServiceNow drove a truck through BMCs install base and with IT help desk and then created this brilliant TAM expansion. Let's learn and expand model. This is much different here. And Slootman also told me that he's a situational CEO. He doesn't have a playbook. And so that's what is most impressive and interesting about this. He's now up against the biggest competitors in the world: AWS, Google and Microsoft and dozens of other smaller startups that have raised a lot of money. Look at the company like Yellowbrick. They've raised I don't know $180 million. They've got a great team. Google, IBM, et cetera. So it's going to be really, really fun to watch. I'm super excited, Erik, but I'll tell you the data right now suggest they've got a great tailwind and if they can continue to execute, this is going to be really fun to watch. >> Yeah, certainly. I mean, when you come out and you are as impressive as Snowflake is, you get a target on your back. There's no doubt about it, right? So we said that they basically created the data as a service. That's going to invite competition. There's no doubt about it. And Yellowbrick is one that came up in the panel yesterday about one of our CIOs were doing a proof of concept with them. We had about seven others mentioned as well that are startups that are in this space. However, none of them despite their great valuation and their great funding are going to have the kind of money and the market lead that Slootman is going to have which Snowflake has as this comes out. And what we're seeing in Congress right now with some antitrust scrutiny around the large data that's being collected by AWS as your Google, I'm not going to bet against this guy either. Right now I think he's got a lot of opportunity, there's a lot of additional layers and because he can basically develop this as a suite service, I think there's a lot of great opportunity ahead for this company. >> Yeah, and I guarantee that he understands well that customer acquisition cost and the lifetime value of the customer, the retention rates. Those are all things that he and Mike Scarpelli, his CFO learned at ServiceNow. Not learned, perfected. (Erik laughs) Well Erik, really great conversation, awesome data. It's always a pleasure having you on. Thank you so much, my friend. I really appreciate it. >> I appreciate talking to you too. We'll do it again soon. And stay safe everyone out there. >> All right, and thank you for watching everybody this episode of "CUBE Insights" powered by ETR. This is Dave Vellante, and we'll see you next time. (soft music)

>> From the SiliconANGLE media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. >> Hello everyone, and welcome to this CUBE Conversation. You know, theCUBE has been following the trends in the so-called big data space since 2010. And one of the things that we reported on for a number of years is the complexity involved in wrangling and making sense out of data. The allure of this idea of no schema on write and very low cost platforms like Hadoop became a data magnet. And for years, organizations would shove data into a data lake. And of course the joke was it was became a data swamp. And organizations really struggled to realize the promised return on their big data investments. Now, while the cloud certainly simplified infrastructure deployment, it really introduced a much more complex data environment and data pipeline, with dozens of APIs and a mind-boggling array of services that required highly skilled data engineers to properly ingest, shape, and prepare that data, so that it could be turned into insights. This became a real time suck for data pros, who spent 70 to 80% of their time wrestling data. A number of people saw the opportunity to solve this problem and automate the heavy lift of data, and simplify the process to adjust, synchronize, transform, and really prepare data for analysis. And one of the companies that is attacking this challenge is InfoWorks. And with me to talk about the evolving data landscape is Buno Pati, CEO of InfoWorks. Buno, great to see you, thanks for coming in. >> Well thank you Dave, thanks for having me here. >> You're welcome. I love that you're in Palo Alto, you come to MetroWest in Boston to see us (Buno laughs), that's great. Well welcome. So, you heard my narrative. We're 10 years plus into this big data theme and meme. What did we learn, what are some of the failures and successes that we can now build on, from your point of view? >> All right, so Dave, I'm going to start from the top, with why big data, all right? I think this big data movement really started with the realization by companies that they need to transform their customer experience and their operations, in order to compete effectively in this increasingly digital world, right? And in that context, they also realized very quickly that data was the key asset on which this transformation would be built. So given that, you look at this and say, "What is digital transformation really about?" It is about competing with digital disruption, or fending off digital disruption. And this has become, over time, an existential imperative. You cannot survive and be relevant in this world without leveraging data to compete with others who would otherwise disrupt your business. >> You know, let's stay on that for a minute, because when we started the whole big data, covering that big data space, you didn't really hear about digital transformation. That's sort of a more recent trend. So I got to ask you, what's the difference between a business and a digital business, in your view? >> That is the foundational question behind big data. So if you look at a digital native, there are many of them that you can name. These companies start by building a foundational platform on which they build their analytics and data programs. It gives them a tremendous amount of agility and the right framework within which to build a data-first strategy. A data-first strategy where business information is persistently collected and used at every level of the organization. Furthermore, they take this and they automate this process. Because if you want to collect all your data and leverage it at every part of the business, it needs to be a highly automated system, and it needs to be able to seamlessly traverse on-premise, cloud, hybrid, and multi-cloud environments. Now, let's look at a traditional business. In a traditional enterprise, there is no foundational platform. There are things like point tools for ETL, and data integration, and you can name a whole slew of other things, that need to be stitched together and somehow made to work to deliver data to the applications that consume. The strategy is not a data-first strategy. It is use case by use case. When there is a use case, people go and find the data, they gather the data, they transform that data, and eventually feed an application. A process that can take months to years, depending on the complexity of the project that they're trying. And they don't automate this. This is heavily dependent, as you pointed out, on engineering talent, highly skilled engineering talent that is scarce. And they have not seamlessly traversed the various clouds and on-premise environments, but rather fragmented those environments, where individual teams are focused on a single environment, building different applications, using different tools, and different infrastructure. >> So you're saying the digital native company puts data at the core. They organize around that data, as opposed to maybe around a bottling plant, or around people. And then they leverage that data for competitive advantage through a platform that's kind of table stakes. And then obviously there's cultural aspects and other skills that they need to develop, right? >> Yeah, they have an ability which traditional enterprises don't. Because of this choice of a data-first strategy with a foundational platform, they have the ability to rapidly launch analytics use cases and iterate all them. That is not possible in a traditional or legacy environment. >> So their speed to market and time to value is going to be much better than their competition. This gets into the risk of disruption. Sometimes we talk about cloud native and cloud naive. You could talk about digital native and digital naive. So it's hard for incumbents to fend off the disrupters, and then ultimately become disrupters themselves. But what are you seeing in terms of some of the trends where organizations are having success there? >> One of the key trends that we're seeing, or key attributes of companies that are seeing a lot of success, is when they have organized themselves around their data. Now, what do I mean by that? This is usually a high-level mandate coming down from the top of the company, where they're forming centralized groups to manage the data and make it available for the rest of the organization to use. There are a variety of names that are being used for this. People are calling it their data fabric. They're calling it data as a service, which is pretty descriptive of what it ends up being. And those are terms that are all sort of representing the same concept of a centralized environment and, ideally, a highly automated environment that serves the rest of the business with data. And the goal, ultimately, is to get any data at any time for any application. >> So, let's talk a little bit about the cloud. I mentioned up front that the cloud really simplified infrastructure deployment, but it really didn't solve this problem of, we talked about in terms of data wrangling. So, why didn't it solve that problem? And you got companies like Amazon and Google and Microsoft, who are very adept at data. They're some of these data-first companies. Why is it that the cloud sort of in and of itself has not been able to solve this problem? >> Okay, so when you say solve this problem, it sort of begs the question, what's the goal, right? And if I were to very simply state the goal, I would call it analytics agility. It is gaining agility with analytics. Companies are going from a traditional world, where they had to generate a handful of BI and other reporting type of dashboards in a year, to where they literally need to generate thousands of these things in a year, to run the business and compete with digital disruption. So agility is the goal. >> But wait, the cloud is all about agility, is it not? >> It is, when you talk about agility of compute and storage infrastructure. So, there are three layers to this problem. The first is, what is the compute and storage infrastructure? The cloud is wonderful in that sense. It gives you the ability to rapidly add new infrastructure and spin it down when it's not in use. That is a huge blessing, when you compare it to the six to nine months, or perhaps even longer, that it takes companies to order, install, and test hardware on premise, and then find that it's only partially used. The next layer on that is what is the operating system on which my data and analytics are going to be run? This is where Hadoop comes in. Now, Hadoop is inherently complex, but operating systems are complex things. And Spark falls in that category. Databricks has taken some of the complexity out of running Spark because of their sort of manage service type of offering. But there's still a missing layer, which leverages that infrastructure and that operating system to deliver this agility where users can access data that they need anywhere in the organization, without intensely deep knowledge of what that infrastructure is and what that operating system is doing underneath. >> So, in my up front narrative, I talked about the data pipeline a little bit. But I'm inferring from your comments on platform that it's more than just this sort of narrow data pipeline. There's a macro here. I wonder if you could talk about that a little bit. >> Yeah. So, the data pipeline is one piece of the puzzle. What needs to happen? Data needs to be ingested. It needs to be brought into these environments. It has to be kept fresh, because the source data is persistently changing. It needs to be organized and cataloged, so that people know what's there. And from there, pipelines can be created that ultimately generate data in a form that's consumable by the application. But even surrounding that, you need to be able to orchestrate all of this. Typical enterprise is a multi-cloud enterprise. 80% of all enterprises have more than one cloud that they're working on, and on-premise. So if you can't orchestrate all of this activity in the pipelines, and the data across these various environments, that's not a complete solution either. There's certainly no agility in that. Then there's governance, security, lineage. All of this has to be managed. It's not simply creation of the pipeline, but all these surrounding things that need to happen in order for analytics to run at-scale within enterprises. >> So the cloud sort of solved that layer one problem. And you certainly saw this in the, not early days, but sort of mid-days of Hadoop, where the cloud really became the place where people wanted to do a lot of their Hadoop workloads. And it was kind of ironic that guys like Hortonworks, and Cloudera and MapR really didn't have a strong cloud play. But now, it's sort of flipping back where, as you point out, everybody's multi-cloud. So you have to include a lot of these on-prem systems, whether it's your Oracle database or your ETL systems or your existing data warehouse, those are data feeds into the cloud, or the digital incumbent who wants to be a digital native. They can't just throw all that stuff away, right? So you're seeing an equilibrium there. >> An equilibrium between ... ? >> Yeah, between sort of what's in the cloud and what's on-prem. Let me ask it this way: If the cloud is not a panacea, is there an approach that does really solve the problem of different datasets, the need to ingest them from different clouds, on-prem, and bring them into a platform that can be analyzed and drive insights for an organization? >> Yeah, so I'm going to stay away from the word panacea, because I don't think there ever is really a panacea to any problem. >> That's good, that means we got a good roadmap for our business then. (both laugh) >> However, there is a solution. And the solution has to be guided by three principles. Number one, automation. If you do not automate, the dependence on skill talent is never going to go away. And that talent, as we all know, is very very scarce and hard to come by. The second thing is integration. So, what's different now? All of these capabilities that we just talked about, whether it's things like ETL, or cataloging, or ingesting, or keeping data fresh, or creating pipelines, all of this needs to be integrated together as a single solution. And that's been missing. Most of what we've seen is point tools. And the third is absolutely critical. For things to work in multi-cloud and hybrid environments, you need to introduce a layer of abstraction between the complexity of the underlying systems and the user of those systems. And the way to think about this, Dave, is to think about it much like a compiler. What does a compiler do, right? You don't have to worry about what Intel processor is underneath, what version of your operating system you're running on, what memory is in the system. Ultimately, you might-- >> As much as we love assembly code. >> As much as we love assembly code. Now, so take the analogy a little bit further, there was a time when we wrote assembly code because there was no compiler. So somebody had to sit back and say, "Hey, wouldn't it be nice if we abstracted away from this?" (both laugh) >> Okay, so this sort of sets up my next question, which is, is this why you guys started InfoWorks? Maybe you could talk a little bit about your why, and kind of where you fit. >> So, let me give you the history of InfoWorks. Because the vision of InfoWorks, believe it or not, came out of a rear view mirror. Looking backwards, not forwards. And then predicting the future in a different manner. So, Amar Arsikere is the founder of InfoWorks. And when I met him, he had just left Zynga, where he was the general manager of their gaming platform. What he told me was very very simple. He said he had been at Google at a time when Google was moving off of the legacy systems of, I believe it was Netezza, and Oracle, and a variety of things. And they had just created Bigtable, and they wanted to move and create a data warehouse on Bigtable. So he was given that job. And he led that team. And that, as you might imagine, was this massive project that required a high degree of automation to make it all come together. And he built that, and then he built a very similar system at Zynga, when he was there. These foundational platforms, going back to what I was talking about before digital days. When I met him, he said, "Look, looking back, "Google may have been the only company "that needed such a platform. "But looking forward, "I believe that everyone's going to need one." And that has, you know, absolute truth in it, and that's what we're seeing today. Where, after going through this exercise of trying to write machine code, or assembly code, or whatever we'd like to call it, down at the detailed, complex level of an operating system or infrastructure, people have realized, "Hey, I need something much more holistic. "I need to look at this from a enterprise-wide perspective. "And I need to eliminate all of this dependence on," kind of like the cloud plays a role because it eliminates some of the dependence, or the bottlenecks around hardware and infrastructure. "And ultimately gain a lot more agility "than I'm able to do with legacy methodology." So you were asking early on, what are the lessons learned from that first 10 years? And lot of technology goes through these types of cycles of hype and disillusionment, and we all know the curve. I think there are two key lessons. One is, just having a place to land your data doesn't solve your problem. That's the beginning of your problems. And the second is that legacy methodologies do not transfer into the future. You have to think differently. And looking to the digital natives as guides for how to think, when you're trying to compete with them is a wonderful perspective to take. >> But those legacy technologies, if you're an incumbent, you can't just rip 'em and throw 'em out and convert. You going to use them as feeders to your digital platform. So, presumably, you guys have products. You call this space Enterprise Data Ops and Orchestration, EDO2. Presumably you have products and a portfolio to support those higher layer challenges that we talked about, right? >> Yeah, so that's a really important question. No, you don't rip and replace stuff. These enterprises have been built over years of acquisitions and business systems. These are layers, one on top of another. So think about the introduction of ERP. By the way, ERP is a good analogy of to what happened, because those were point tools that were eventually combined into a single system called ERP. Well, these are point capabilities that are being combined into a single system for EDO2, or Enterprise Data Operations and Orchestration. The old systems do not go away. And we are seeing some companies wanting to move some of their workloads from old systems to new systems. But that's not the major trend. The major trend is that new things that get done, the things that give you holistic views of the company, and then analytics based on that holistic view, are all being done on the new platforms. So it's a layer on top. It's not a rip and replace of the layers underneath. What's in place stays in place. But for the layer on top, you need to think differently. You cannot use all the legacy methodologies and just say that's going to apply to the new platform or new system. >> Okay, so how do you engage with customers? Take a customer who's got, you know, on-prem, they've got legacy infrastructure, they don't want to get disrupted. They want to be a digital native. How do you help them? You know, what do I buy from you? >> Yeah, so our product is called DataFoundry. It is a EDO2 system. It is built on the three principles, founding principles, that I mentioned earlier. It is highly automated. It is integrated in all the capabilities that surround pipelines, perhaps. And ultimately, it's also abstracting. So we're able to very easily traverse one cloud to another, or on-premise to the cloud, or even back. There are some customers that are moving some workloads back from the cloud. Now, what's the benefit here? Well first of all, we lay down the foundation for digital transformation. And we enable these companies to consolidate and organize their data in these complex hybrid, cloud, multi-cloud environments. And then generate analytics use cases 10x faster with about tenth of the resource. And I'm happy to give you some examples on how that works. >> Please do. I mean, maybe you could share some customer examples? >> Yeah, absolutely. So, let me talk about Macy's. >> Okay. >> Macy's is a customer of ours. They've been a customer for about, I think about 14 months at this point in time. And they had built a number of systems to run their analytics, but then recognized what we're seeing other companies recognize. And that is, there's a lot of complexity there. And building it isn't the end game. Maintaining it is the real challenge, right? So even if you have a lot of talent available to you, maintaining what you built is a real challenge. So they came to us. And within a period of 12 months, I'll just give you some numbers that are just mind-blowing. They are currently running 165,000 jobs a month. Now, what's a job? A job is a ingestion job, or a synchronization job, or a transformation. They have launched 431 use cases over a period of 12 months. And you know what? They're just ramping. They will get to thousands. >> Scale. >> Yeah, scale. And they have ingested a lot of data, brought in a lot of DataSources. So to do that in a period of 12 months is unheard of. It does not happen. Why is it important for them? So what problem are they trying to solve? They're a retailer. They are being digitally disruptive like (chuckles) no one else. >> They have an Amazon war room-- >> Right. >> No doubt. >> And they have had to build themselves out as a omni-channel retailer now. They are online, they are also with brick and mortar stores. So you take a look at this. And the key to competing with digital disrupters is the customer experience. What is that experience? You're online, how does that meld with your in-store experience? What happens if I buy online and return something in a store? How does all this come together into a single unified experience for the consumer? And that's what they're chasing. So that was the first application that they came to us with. They said, "Look, let us go into a customer 360. "Let us understand the entirety "of that customer's interaction "and touchpoints with our business. "And having done so, we are in a position "to deliver a better experience." >> Now that's a data problem. I mean, different DataSources, and trying to understand 360, I mean, you got data all over the place. >> All over the place. (speaking simultaneously) And there's historical data, there's stuff coming in from, you know, what's online, what's in the store. And then they progress from there. I mean, they're not restricting it to customer experience and selling. They're looking at merchandising, and inventory, and fulfillment, and store operations. Simple problem. You order something online, where do I pull this from? A store or a warehouse? >> So this is, you know, big data 2.0, just to use a sort of silly term. But it's really taking advantage of all the investment. I've often said, you know, Hadoop, for all the criticism it gets, it did lower our cost of getting data into, you know, at least one virtual place. And it got us thinking about how to get insights out of data. And so, what you're describing is the ability to operationalize your data initiatives at scale. >> Yeah, you can absolutely get your insights off of Hadoop. And I know people have different opinions of Hadoop, given their experience. But what they don't have, what these customers have not achieved yet, most of them, is that agility, right? So, how easily can you get your insights off of Hadoop? Do I need to hire a boatload of consultants who are going to write code for me, and shovel data in, and create these pipelines, and so forth? Or can I do this with a click of a button, right? And that's the difference. That is truly the difference. The level of automation that you need, and the level of abstraction that you need, away from this complexity, has not been delivered. >> We did, in, it must have been 2011, I think, the very first big data market study from anybody in the world, and put it out on, you know, Wikibon, free research. And one of the findings was (chuckles) this is a huge services business. I mean, the professional service is where all the money was going to flow because it was so complicated. And that's kind of exactly what happened. But now we're entering, really it seems like a phase where you can scale, and operationalize, and really simplify, and really focus your attention on driving business value, versus making stuff work. >> You are absolutely correct. So I'll give you the numbers. 55% of this industry is services. About 30% is software, and the rest is hardware. Break it down that way. 55%. So what's going on? People will buy a big data system. Call it Hadoop, it could be something in the cloud, it could be Databricks. And then, this is welcome to the world of SIs. Because at this point, you need these SIs to write code and perform these services in order to get any kind of value out of that. And look, we have some dismal numbers that we're staring at. According to Gardner, only 17% of those who have invested in Hadoop have anything in production. This is after how many years? And you look at surveys from, well, pick your favorite. They all look the same. People have not been able to get the value out of this, because it is too hard. It is too complex and you need too many consultants (laughs) delivering services for you to make this happen. >> Well, what I like about your story, Buno, is you're not, I mean, a lot of the data companies have pivoted to AI. Sort of like, we have a joke, ya know, same wine, new bottle. But you're not talking about, I mean sure, machine intelligence, I'm sure, fits in here, but you're talking about really taking advantage of the investments that you've made in the last decade and helping incumbents become digital natives. That sounds like it's at least a part of your mission here. >> Not become digital natives, but rather compete with them. >> Yeah, right, right. >> Effectively, right? >> Yep, okay. >> So, yeah, that is absolutely what needs to get done. So let me talk for a moment about AI, all right? Way back when, there was another wave of AI in the late 80s. I was part of that, I was doing my PhD at the time. And that obviously went nowhere, because we didn't have any data, we didn't have enough compute power or connectivity. Pretty inert. So here it is again. Very little has changed. Except for we do have the data, we have the connectivity, and we have the compute power. But do we really? So what's AI without the data? Just A, right? There's nothing there. So what's missing, even for AI and ML to be, and I believe these are going to be powerful game changers. But for them to be effective, you need to provide data to it, and you need to be able to do so in a very agile way, so that you can iterate on ideas. No one knows exactly what AI solution is going to solve your problem or enhance your business. This is a process of experimentation. This is what a company like Google can do extraordinarily well, because of this foundational platform. They have this agility to keep iterating, and experimenting, and trying ideas. Because without trying them, you will not discover what works best. >> Yeah, I mean, for 50 years, this industry has marched to the cadence of Moore's Law, and that really was the engine of innovation. And today, it's about data, applying machine intelligence to that data. And the cloud brings, as you point out, agility and scale. That's kind of the new cocktail for innovation, isn't it? >> The cloud brings agility and scale to the infrastructure. >> In low risk, as you said, right? >> Yeah. >> Experimentation, fail fast, et cetera. >> But without an EDO2 type of system, that gives you a great degree of automation, you could spend six months to run one experiment with AI. >> Yeah, because-- >> In gathering data and feeding it to it. >> 'Cause if the answer is people and throwing people at the problem, then you're not going to scale. >> You're not going to scale, and you're never going to really leverage AI and ML capabilities. You need to be able to do that not in six months, in six days, right, or less. >> So let's talk about your company a little bit. Can you give us the status, you know, where you're at? As their newly minted CEO, what your sort of goals are, milestones that we should be watching in 2020 and beyond? >> Yeah, so newly minted CEO, I came in July of last year. This has been an extraordinary company. I started my journey with this company as an investor. And it was funded by actually two funds that I was associated with, first being Nexus Venture Partners, and then Centerview Capital, where I'm still a partner. And myself and my other two partners looked at the opportunity and what the company had been able to do. And in July of last year, I joined as CEO. My partner, David Dorman, who used to be CEO of AT&T, he joined as chairman. And my third partner, Ned Hooper, joined as President and Chief Operating Officer. Ned used to be the Chief Strategy Officer of Cisco. So we pushed pause on the funding, and that's about as all-in as a fund can get. >> Yeah, so you guys were operational experts that became investors, and said, "Okay, we're going to dive back in "and actually run the business." >> And here's why. So we obviously see a lot of companies as investors, as they go out and look for funding. There are three things that come together very rarely. One is a massive market opportunity combined with the second, which is the right product to serve that opportunity. But the third is pure luck, timing. (Dave chuckles) It's timing. And timing, you know, it's a very very challenging thing to try to predict. You can get lucky and get it right, but then again, it's luck. This had all three. It was the absolute perfect time. And it's largely because of what you described, the 10 years of time that had elapsed, where people had sort of run the experiment and were not going to get fooled again by how easy this supposed to be by just getting one piece or the other. They recognized that they need to take this holistic approach and deploy something as an enterprise-wide platform. >> Yeah, I mean, you talk about a large market, I don't even know how you do a TAM, what's the TAM? It's data. (laughs) You know, it's the data universe, which is just, you know, massive. So, I have to ask you a question as an investor. I think you've raised, what 50 million, is that right? >> We've raised 50 million. The last round was led by NEA. >> Right, okay. You got great investors, hefty amount. Although, you know, in this day and age, you know, you're seeing just outrageous amounts being raised. Software obviously is a capital efficient business, but today you need to raise a lot of money for promotion, right, to get your name out there. What's your thoughts on, as a Silicon Valley investor, as this wave, I mean, get it while you can, I guess. You know, we're in the 10th year of this boom market. But your thoughts? >> You're asking me to put on my other hat. (Dave laughs) I think companies have, in general, raised too much money at too high a value too fast. And there's a penalty for that. And the down round IPO, which has become fashionable these days, is one of those penalties. It's a clear indication. Markets are very rational, public markets are very rational. And the pricing in a public market, when it's significantly below the pricing of in a private market, is telling you something. So, we are a little old-fashioned in that sense. We believe that a company has to lay down the right foundation before it adds fuel to the mix and grows. You have to have evidence that the machinery that you build, whether it's for sales, or marketing, or other go-to-market activities, or even product development, is working. And if you do not see all of those signs, you're building a very fragile company. And adding fuel in that setting is like flooding the carburetor. You don't necessarily go faster. (laughs) You just-- >> Consume more. >> You consume more. So there's a little bit of, perhaps, old-fashioned discipline that we bring to the table. And you can argue against it. You can say, "Well, why don't you just raise a lot of money, "hire a lot of sales guys, and hope for the best?" >> See what sticks? (laughs) >> Yeah. We are fully expecting to build a large institution here. And I use that word carefully. And for that to happen, you need the right foundation down first. >> Well, that resonates with us east coast people. So, Buno, thanks very much for comin' on theCUBE and sharing with us your perspectives on the marketplace. And best of luck with InfoWorks. >> Thank you, Dave. This has been a pleasure. Thank you for having me here. >> All right, we'll be watching, thank you. And thank you for watching, everybody. This is Dave Vellante for theCUBE. We'll see ya next time. (upbeat music fades out)

(upbeat music) >> Hi, and welcome to another Cube Conversation where we speak with thought leaders in depth about the topics that are most important to the overall technology community. I'm Peter Burris, your host. Every business inspires to be a digital business, which is every business, faces a significant challenge. They need to use their data in new and value creating ways. But some of that data is not lending itself to new applications, new uses because it's locked up in formats, in technologies and applications that don't lend themselves to change. That's one of the big challenges that every business faces. What can they do to help unlock, to help liberate their data from older formats and older approaches so they can create new sources of value with it. To have that conversation, we're joined by a great guest. Greg Tinker is the CTO and Founder of SereneIT. Greg, welcome back to theCube. >> Thank you Peter, very appreciate it buddy. >> It's been a long time. This is your first time here with SereneIT so why don't you tell us a little bit about SereneIT. >> Sure, so at a high level we are a technology partner. SereneIT focuses on the next generation model structures of engineering first. There's a lot of VARS, in simplest terms, I would say we're a value at a reseller, sure. But we capitalize and focus just on the VA. Anybody can bring VR. The legacy approach of just being a reseller is no longer valid in our industry. Complexities and trying to have a situation where you can liberate data, try to take it from a legacy entrenched model, process, procedure and go into a new modern IT software defined ecosystem is very complex. And our objective is to make the, enablement of IT serene or simple and that's where SereneIT comes from. >> You know, I love the name but if you go back 20 years as you said, the asset that IT was focused on and took care of was the hardware. >> That's right. >> And we bought the hardware from a reseller, they just made the installations, configurations and what not. But as you said, today we're focused on the data. That's the asset. >> That's correct. >> And just as we used to have challenges uplifting and all the things we had to do with hardware, we're having similar types of challenges when you think about how to apply data to new uses, sustain that asset feature of it but apply it in new ways to create new value. As you talk to customers, what is the problem that you find they're encountering as they try to think what to do with their high value traditional data? So there's actually, I'll call it three strategic problems. Becoming to where it to be a workload optimized model structures or your data driven intelligence, trying to pull something out of the data model, trying to pull something out of the data, make it tangible to the business. And then trying to figure out a way to make it easy to enable the users, that is the employees to do something with the data the have. Making it more of a cloud-centric approach. Everybody wants that easy button now. So at a high level, trying to make that a possibility is where we spend our time today. And give you a quick example of that would be legacy block storage. We do a lot in the storage world. And we focus on software defined storage apparatus or solutions. So a lot of our clients are kind of mired down with legacy block, via Fibre Channel basics that were great for their era. But today with cost being a big factor in trying to be able to leverage an ecosystem where I can take my data, wherever my data sits and leverage it on multiple different apparatuses, be it BlueData, be in Kubernetes, be it name your favorite Docker solution. Trying to be able to use that in an ecosystem in a software defined hyper cloud, doing that on a legacy block is very problematic. And that's where we help customers transition from that legacy mindset, legacy IT infrastructure into a more of a modern software defined data program. >> So what's talk about that. Because there's a more modern technology, but really what they're doing is they're saying, look I've got this data, using these protocols like Fibre Channel with these applications and it's doing its job. >> That's right. >> But I want to create options on how I might use that data in the future, options that aren't available to me or aren't available to my business if it stays locked inside Fibre Channel for example. >> That's correct. >> So what you're really doing, is you're giving them paths to new options with their data that can be sustained whatever the technology is. Have I got that right? >> In a nutshell, Frank I would agree with your sentiment on that, your comment is spot on. We take customers data, we look at the business as a whole. And we focus on, what is the core of the business? Be it, maybe it's a High-Performance Computing Cluster Maybe it's a Oracle, Cyrus, Informant name your favorite data base structure. Maybe it's MapR, maybe it's a Dupe. We look at the business and determine, how are we using that data? How much data do we need? What's my data working set size? Understanding that and then we actually would design a solution that will be a software defined ecosystem that we can move that data in. And nine times out of 10 we can do it on the fly. Rarely, rarely ever do we have an outage to do it. Or that might be a small few minute outage window when we do a cut over, where we keep everything in mirroring Lockstep . >> Well that's one of the beauties of software defined is that you have those kinds of flexibilities. >> That's right. >> But think of, so talk to me a little bit about the you are, the customer realizes they have a problem. They find you guys. >> Sure. >> So how do they find you? >> So we do a lot with large scale Fortune 50, Fortune 100, the large scale enterprise businesses. And we do that with our, we're known in the engineering world, big accounts, because of our backdrop in HP engineering. And so HP brings us a lot into these accounts to help them solve a big business problem. So that's how a lot of our customers are finding us today. We are reaching out with media, like theCube here to talk to clients about the fact that we do exist and that we exist to help them consume a more modern IT in footprint. To help them go from that legacy model into that more modern model. >> Okay, so the customer realizes they have a problem, HPE and others, help identify you guys, matches you together. You show up, how do you work with the customer? Is it your big brains and the customer passive? Or you're working side by side to help them accelerate their journey? >> We find it best that we do it in a cohesive manner. We sit down and have a long discussion with their, usually their Chief Executive officer, their CTO, Chief Technology Officer, we'll sit down and talk about the business constraints. And then we'll go down to the directors the guys on the front lines that see the problems on a day to day basis. And we look at where their constraints are. Is it performance, IOP driven. Nine times out of 10, those problems are no longer there. They were solved years ago. Today it's more about the legacy model of, let me log a ticket to stand up a new virtual machine to a SQL database to do this application. So I've logged the ticket, a week two later I finally get a virtual machine. And now I got to get five more teams engaged, I get it online. Total business takes about a month to get some new apparatus up. Where if we go into a software defined ecosystem where we have these playbooks and this model written for the business, we can do that in 10 minutes. Be it on Nutanix do it with SimpliVity, VMware models, we don't' differentiate that. We let the customer tell us which one they use. 'Cause everybody has their liking. Be it some are VMware shop, some are Hyper-V, some are KBM. We do all of them. >> But the point is you want to help them move form an old world that was focused on executing the tasks associate with bringing the system up to a new world that's focused on the resources being able to configure themselves, being able to bring to bring themselves up test themselves in a software defined manner introducing some of those DevOps processes. Whatever the technology is, they have the people and the process to execute the technology. >> That's exactly right, because the technology in a nutshell. If you look at just technology itself that's not the hard part. Not for us anyway, 'cause we're an engineering team that's what we do well. The data driven intelligence stuff and helping customers bring more value out of their data. We can help them with that and show them exactly how we would do it. Be it a different technologies and stuff and we'll get into that discussion later. But the biggest problem we see is the people and processes which you just mentioned. Pushing the button, achieve an objective. That is where the old way of being very ticket driven Siloed approach, really slow down the economics of business. Was a huge driving force of not achieving the ROI that you actually set out to do years ago. Where we have one client that has a little over 4,000 servers and how my team and I explain it to the clients. Come out to the Golden Gate bridge. January 1 you start painting. December 30th you're done painting and January 1 you start painting again. You never get done. It's always getting painted. Patching of these large scale enterprises is the exact same way. You can't patch all the servers on a Saturday. You can't patch three thousand machines, BIOS, firmware, the list goes on. What we do for them is we actually put in an apparatus engine, basically an automation engine and instead of an army of 10 people doing firmware or BIOS and all the stuff updates, we automated 100% of that entire process. That's what SereneIT does. Help a customer take a, could be a legacy model, bare metal machine and show them how we can automate the bare metal machine. We can do the exact same thing in any hypervisor on the planet today. >> So that it's done faster, simpler. The outcome is more predictable. The result is more measurable. >> Yes. >> That's really great stuff. Let's go back to this notion of data because we kind of started with this idea of data and having to evolve the formats increasing the flexibility of it's utilization. We talked about hypervisors and all that technology is kind of sucking it forward, bringing that data forward making it possible to do things with it, but still the data itself is a major challenge. How are you working with customers to get them to envision the new data world independent of some of these other technologies? >> Sure, okay. So yeah, we have clients right now, we have (mumbled) systems these are global file systems that have enormous amount of data in it, some of it is compiled code logics for drivers and firmware and Kernel code structure that are forthcoming technologies that aren't even released yet. We have clients that have data based structure with ascii text is very common road driven. We have customers that have flat ascii files that are just flat text files. So we help the customers grab data from that existing data footprint for new lines of business. Determine what are we touching, how are we touching and how often are we touching it and why are we touching it? Case in point, when you have a large manufacturer doing chip design and your looking at a global file system you're trying to give assertation data as to what drivers are our developers working on most frequently. In the medical community, we have a client we're working on at global scale, we're doing real time data analytics to figure out if we're doing SQL injection from a hacker. So we show them exactly how we can do this in an inline driver stack and show them how to do it with the technology reducing their actual CapEx expend. There's legacy tools out there that work great. You know one of these is like, I won't give names of product and stuff, but there's a lot of cool technologies that's been around for a long time. >> That works. >> That works. >> And it just needs a smart person, or a smart team to put it together so it can be applied. >> That's what we've been doing with our clients is trying to show them that we can take the data that you have, be it flat ascii files or binary data structures. And we can show them that we can give you data analytics and pull that back. We have another client in law industry that we manage worldwide and we do e-discovery. On trying to figure out phrases and things that are maybe concerning to them in a financial world that is the global market. And we're able to give them that data structure on their own intellectual property and we give that to them in real time. We give them a dashboard so they can log in to the dashboard and they can see real time data transparency at a moments notice, so they can tell what the market is doing in Britain or they can tell what the market is doing in Singapore or U.S. by just looking at a dashboard and we're pulling data back. And we're pulling it from outside of world data points, this could be Facebook. Real time feeds, news, media and we pull it from internal data feeds. Email transactions that are going from their financial, they have like CIO's the Chief Investment Officers. Most people think of that as an information officer, right? So we're able to pull data from that and show them that they have a great deal of intellectual property at their fingertips that honestly they've never used before and that's what we're helping customers do today. >> Greg Tinker, Founder, CTO SereneIT. Thanks so much for being on theCube. >> Thank you very much Peter. >> And once again want to thank you for listening to this Cube Conversation. Until next time. (upbeat music)

>> From the SiliconANGLE media office, in Boston, Massachusetts, it's theCUBE. Now, here's your host, Stu Miniman. >> Hi, I'm Stu Miniman, and welcome to a Cube Conversation here in our new Boston area studio, happy to welcome back to the program a VIP from our community, Patrick Osborne, who's the Vice President and General Manager for big data and secondary storage at Hewlett Packard Enterprise, Patrick, great to talk to you. >> Great to be back, thanks, Stu. >> All right, we're talking about the big thing, hundredth year of the NFL kicking off here. Maybe we're talking a little bit about the changing role of infrastructure and, we've been talking about it at the Wikibon team for a number of years, data is at the center of the universe today, when we talk about IT and businesses and what they're thinking about, and in some ways everything's changed, and in other ways it feels like I go to some of these shows and the people that have even more experience than me are like "Oh, geez, we've recreated the mainframe." So, we're fresh off of VMworld, you skipped the show this year, but I know HPE had a large presence at the show, and let me start there, I guess, we look at data centers and cloud, and the mission VMware has is how do they maintain relevant as customers are changing their applications? They just made billions of dollars of acquisitions to be more in the cloud native environment, so when you look at, HPE's very well known in the infrastructure space, had some changes as to what pieces are in the company versus partnered with the company, so when you talk to your customers and they're changing what I call the long pole in the tent of modernization, it's the applications. Where are they today, where are some of the areas that they're doing well, and where are the areas that it's challenging and struggling? >> Yeah, so I'd say from an HPE perspective, we've made a number of investments as well, over the last couple years, both inorganic and organic investments in the space, and I think that even though we've historically been known as an infrastructure company, we're very quickly pivoting towards being known as an enterprise workload company, and so for my perspective, the things that we're trying to do, especially in our division around AI and ML and analytics is being able to provide a platform for customers, especially application developers. I think when we talk about how the world is changing, the buyer personas people were selling to now have completely drastically changed, right? There's no more dedicated backup teams, there's rarely now dedicated storage teams, maybe only in very large organizations, and so now you're catering to a different set of folks, and for instance, over the last two or three years, we've seen the advent of folks like a chief data officer, the CDO, data scientists, data engineers, and so for us, we have a whole new buyer persona and user persona that we not only have to cater to in our UX design, but also present the value, which is a much different conversation than we've had in the past. >> Yeah, you know I actually had a number of conversations with customers at the VMworld show, and they talked about, organizationally they often still have hardware-defined roles, yet they live in a software-defined world. So, even groups that are like "I still have some storage headcount and some "networking headcount," but virtualization and cloud are slowly eating over pieces it, but there's still some turf battles, which I was sad to hear because, I've worked for the last couple of decades to try to eliminate silos and get people working together, so we know those organizational changes often take even longer than the long cycles of technology that we're trying to roll out here. You mentioned some of the big data pieces, and yeah, HPE's made a number of acquisitions. Most recently MapR. Wonder if you could help us connect the dots. When we covered heavily, the big data wave, and Dave Vellante would say, "Look, the people that "deploy these technologies, the end users will create "way more value than the distributions of Hadoop will." When we did our forecast, they were there, but the promise of big data was, data was going to go from that burden, how do I keep it, how do I maintain it, how do I back it up, to new value for the company, new revenue that we could have along that where, and whether or not that happened often mattered on deployment, but when you go into the AI space, like what you're doing with BlueData, is that a continuation of what we were seeing with the big data space, is there some new waves that are drastically changing the outcomes in what we're seeing, how does that all fit together? >> Yeah, so I mean I think it's definitely an extension of all these things are creative and incremental at the end of the day. I think some of the things around how people are operationalizing AI and ML are pretty unique, and so from our perspective, we made some investments around BlueData, and we've had some recent product announcements in that area around helping folks operationalize machine learning, which is, at this point it's becoming very real and people are putting it into a number of different use cases, and then to come along with that, the need to store data, right, so we talk about this often, which nobody talks about storage anymore, everyone talks about data, right? The need to store all this data that's coming in in a persistent data layer is super important, more important than it ever was, and it comes in multiple different forms and multiple different factors, and also protocols. So, to have a data platform that is very scalable, has enterprise resiliency to it, the ability to take data and manifest it in different ways, right, is important for that entire ecosystem, we felt that MapR was a great platform, they have a great data platform, that started with Hadoop, moved into supporting things like streams, Kafka and Spark, and then certainly now have been shifted into a Kubernetes and container deployment, and then mapping their file system and their database and streams to servicing AI and ML workloads, so it's kind of along the same vein and being able to live in that world that you're still separating compute and storage, and being able to scale those independently, but work together from a security perspective, I think it's really important. >> One of the boundaries that I've always been fascinated with is, some of the underlying components that we're changing, so when we rolled out virtualization, the whole storage and networking industry had to work to kind of put the pieces back together as we took advantage of that. You mentioned Kubernetes, at the KubeCon show, there's lots of that same plumbing things that need to be understand and work. But on the other hand, we've seen a massive transformation in the database market. 10 years ago, everybody had one database to rule them all, and now most companies we talk to, it's like "Oh, well I've got lots of little databases "and now pulling them together differently." But that boundary between what's happening at the infrastructure layer and what's happening at the application layer. On the one hand, they seem to be pulling apart, you know, I should just be able to use cloud or serverless and makes it easy, but on the same hot time, you're talking everybody's like "I've got the best infrastructure for your AI deployment," so can you talk a little bit about some of the hard challenges that HPE's looking at solving, what do you look to actually create, whether that be a box or a service or some offering, cause I know HPE has lots of different areas that you look at those solutions. >> Yeah, we're trying to, when we go and have a successful deployment at our customers, and we have deployments in most verticals, right, in the Fortune 500 Global 2000, whether it's financial services, automotive, manufacturing, you can name healthcare, right? I think what we've seen is that the successful deployments are the ones that bring together the application owner's line of business, even the data scientist engineers, along with the infrastructure folks, right? Think, sometimes they're at odds. And so when you can bring together a platform that at the end of the day is going to provide something, right, as a service, it's either an analytic sandbox, big data and analytics as a service, AI as a service, right, there is a set of folks that are trying to service a number of application developers and data scientists internally, that's a platform that can have a uniform data structure, where you can grab all this data and have access to it securely, and be able to deploy your workflow on top of that, in a virtualized, multitenant way, deployed in containers with the toolsets and the applications that they want to have access to, but not have to deal with the infrastructure, right? And then that can be the providence of the CIO and the data center team and the infrastructure folks working with those teams, and that's where we've seen the magic happen for successful deployments, and those are the ones that, they end up growing and scaling very quickly, and they can be deployed on-prem, they can be deployed, we have some of our pilots and POCs that start completely in the cloud and then come back on prem for different reasons, security, data locality, governance, what have you, but it provides the flexibility, but I think what we found is that, taking an outcome and a services-based approach that bring everyone to the table, that's where we see the projects really get a big business benefit for our customers. >> You know, I was having a conversation earlier today, and when we watch the adoption of virtualization, it's been almost 20 years now, since most people are doing it. When we'd reached about 10 years in, we felt that most people were doing it and were on their journey, but things like converged and hyperconverged infrastructure really helped accelerate us past that kind of early majority into the late majority because it was the simplicity of that offering. We wonder, are we reaching some of that same point when we look at cloud, and when I say cloud, not just public cloud, but what we're doing in private, where the hybrid, multicloud mixup that we have, because while cloud is definitely real and here to stay, I don't think anybody would really say that cloud, circa 2019, is easy. So, how does HPE and its partners, how do we make it even easier so that customers can move down that journey to modernize themselves even more, and get out of what we call that undifferentiated heavy lifting? >> Yeah, so definitely want to avoid the undifferentiated heavy lifting, because that's certainly a weight on many organizations, and so what we are trying to provide is a platform that increases customers' time to value, and by providing, by abstracting a lot of difficult things. I mean there's a lot of data gravity in this space, right, you're talking about, we have projects right now for autonomous cars where they ingest two, five, 10 petabytes a day, for example, and it's not, it's very difficult to migrate and move that data, right, so you want to be able to bring that data in, tap into it securely, there's a lot of networking that goes on that's very difficult from a security perspective as well as multitenancy and making sure that that model is set up correctly. So for us, it's all about providing a platform that can service multiple tenants and multiple organizations that are all using similar toolsets at the end of the day, but you can have your specific data scientists and data engineers operating on a platform that they don't have to worry about infrastructure. Right, cause at the end of the day, when we go visit those folks who own those applications, oftentimes they don't want to deal with, "I need to go request in a VM, "I need to go request a block of IP addresses, "I need some LUNs for my storage, "I need a server deployment to run bare metal," you know, some bare metal tooling. They really want to establish a service, just like we saw with virtualization, and so right now it's sort of the fight for, how can I make my infrastructure as invisible as possible and fight for the eyeballs of the developers? >> Great. Want to just give you the final word, Patrick, what's exciting you, kind of second half of the year, things you're looking forward to? >> Yeah, so the things that excite me is certainly customer acquisition, right, we've been marching along that very quickly with some of these new acquisitions and some of the net new development we've done within HPE, I think that the, we've got a lot of stuff cooking with Kubernetes in that area, and so we'll make some big announcements at KubeCon, and that's always very exciting to talk in these new ecosystems, and speaking of ecosystems, we're establishing, I think there's new ecosystems that are forming in the market, especially around AI and ML, it's still a very nascent market, and so we're bringing on new partners every week from an application development perspective, and so for me it's really exciting to talk with all these new apps, these new tool chains, new toolsets, libraries, algorithms, and I think it's really exciting to kind of move up stack and be in this very cool world of application development. >> I know when I see the market landscape of some of the AI space, you need to have a big monitor or be able to zoom in, cause there's a lot of players, there's a lot of pieces, we always worry about things like API sprawls and the like, but absolutely super exciting space, Patrick Osborne, thanks so much for giving us an update on what's happening, especially how AI is driving a lot of new innovation in the area. >> Yeah, very exciting, thanks for having me. >> All right, Patrick Osborne with HPE, and I'm Stu Miniman, thanks as always for joining theCUBE.

>> From Cambridge, Massachusetts, it's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019, brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts everybody, you're watching The Cube, the leader in tech coverage. My name is Dave Vellante here with my cohost Paul Gillin. This is day one of our two day coverage of the MIT CDOIQ conferences. CDO, Chief Data Officer, IQ, information quality. Colin Mahoney is here, he's a good friend and long time CUBE alum. I haven't seen you in awhile, >> I know >> But thank you so much for taking some time, you're like a special guest here >> Thank you, yeah it's great to be here, thank you. >> Yeah, so, this is not, you know, something that you would normally attend. I caught up with you, invited you in. This conference has started as, like back office governance, information quality, kind of wonky stuff, hidden. And then when the big data meme took off, kind of around the time we met. The Chief Data Officer role emerged, the whole Hadoop thing exploded, and then this conference kind of got bigger and bigger and bigger. Still intimate, but very high level, very senior. It's kind of come full circle as we've been saying, you know, information quality still matters. You have been in this data business forever, so I wanted to invite you in just to get your perspectives, we'll talk about what's new with what's going on in your company, but let's go back a little bit. When we first met and even before, you saw it coming, you kind of invested your whole career into data. So, take us back 10 years, I mean it was so different, remember it was Batch, it was Hadoop, but it was cool. There was a lot of cool >> It's still cool. (laughs) projects going on, and it's still cool. But, take a look back. >> Yeah, so it's changed a lot, look, I got into it a while ago, I've always loved data, I had no idea, the explosion and the three V's of data that we've seen over the last decade. But, data's really important, and it's just going to get more and more important. But as I look back I think what's really changed, and even if you just go back a decade I mean, there's an insatiable appetite for data. And that is not slowing down, it hasn't slowed down at all, and I think everybody wants that perfect solution that they can ask any question and get an immediate answers to. We went through the Hadoop boom, I'd argue that we're going through the Hadoop bust, but what people actually want is still the same. You know, they want real answers, accurate answers, they want them quickly, and they want it against all their information and all their data. And I think that Hadoop evolved a lot as well, you know, it started as one thing 10 years ago, with MapReduce and I think in the end what it's really been about is disrupting the storage market. But if you really look at what's disrupting storage right now, public clouds, S3, right? That's the new data league. So there's always a lot of hype cycles, everybody talks about you know, now it's Cloud, everything, for maybe the last 10 years it was a lot of Hadoop, but at the end of the day I think what people want to do with data is still very much the same. And a lot of companies are still struggling with it, hence the role for Chief Data Officers to really figure out how do I monetize data on the one hand and how to I protect that asset on the other hand. >> Well so, and the cool this is, so this conference is not a tech conference, really. And we love tech, we love talking about this, this is why I love having you on. We kind of have a little Vertica thread that I've created here, so Colin essentially, is the current CEO of Vertica, I know that's not your title, you're GM and Senior Vice President, but you're running Vertica. So, Michael Stonebreaker's coming on tomorrow, >> Yeah, excellent. >> Chris Lynch is coming on tomorrow, >> Oh, great, yeah. >> we've got Andy Palmer >> Awesome, yeah. >> coming up as well. >> Pretty cool. (laughs) >> So we have this connection, why is that important? It's because, you know, Vertica is a very cool company and is all about data, and it was all about disrupting, sort of the traditional relational database. It's kind of doing more with data, and if you go back to the roots of Vertica, it was like how do you do things faster? How do you really take advantage of data to really drive new business? And that's kind of what it's all about. And the tech behind it is really cool, we did your conference for many, many years. >> It's coming back by the way. >> Is it? >> Yeah, this March, so March 30th. >> Oh, wow, mark that down. >> At Boston, at the new Encore Hotel. >> Well we better have theCUBE there, bro. (laughs) >> Yeah, that's great. And yeah, you've done that conference >> Yep. >> haven't you before? So very cool customers, kind of leading edge, so I want to get to some of that, but let's talk the disruption for a minute. So you guys started with the whole architecture, MPP and so forth. And you talked about Cloud, Cloud really disrupted Hadoop. What are some of the other technology disruptions that you're seeing in the market space? >> I think, I mean, you know, it's hard not to talk about AI machine learning, and what one means versus the other, who knows right? But I think one thing that is definitely happening is people are leveraging the volumes of data and they're trying to use all the processing power and storage power that we have to do things that humans either are too expensive to do or simply can't do at the same speed and scale. And so, I think we're going through a renaissance where a lot more is being automated, certainly on the Vertica roadmap, and our path has always been initially to get the data in and then we want the platform to do a lot more for our customers, lots more analytics, lots more machine-learning in the platform. So that's definitely been a lot of the buzz around, but what's really funny is when you talk to a lot of customers they're still struggling with just some basic stuff. Forget about the predictive thing, first you've got to get to what happened in the past. Let's give accurate reporting on what's actually happening. The other big thing I think as a disruption is, I think IOT, for all the hype that it's getting it's very real. And every device is kicking off lots of information, the feedback loop of AB testing or quality testing for predictive maintenance, it's happening almost instantly. And so you're getting massive amounts of new data coming in, it's all this machine sensor type data, you got to figure out what it means really quick, and then you actually have to do something and act on it within seconds. And that's a whole new area for so many people. It's not their traditional enterprise data network warehouse and you know, back to you comment on Stonebreaker, he got a lot of this right from the beginning, you know, and I think he looked at the architectures, he took a lot of the best in class designs, we didn't necessarily invent everything, but we put a lot of that together. And then I think the other you've got to do is constantly re-invent your platform. We came out with our Eon Mode to run cloud native, we just got rated the best cloud data warehouse from a net promoter score rating perspective, so, but we got to keep going you know, we got to keep re-inventing ourselves, but leverage everything that we've done in the past as well. >> So one of the things that you said, which is kind of relevant for here, Paul, is you're still seeing a real data quality issue that customers are wrestling with, and that's a big theme here, isn't it? >> Absolutely, and the, what goes around comes around, as Dave said earlier, we're still talking about information quality 13 years after this conference began. Have the tools to improve quality improved all that much? >> I think the tools have improved, I think that's another area where machine learning, if you look at Tamr, and I know you're going to have Andy here tomorrow, they're leveraging a lot of the augmented things you can do with the processing to make it better. But I think one thing that makes the problem worse now, is it's gotten really easy to pour data in. It's gotten really easy to store data without having to have the right structure, the right quality, you know, 10 years ago, 20 years ago, everything was perfect before it got into the platform. Right, everything was, there was quality, everything was there. What's been happening over the last decade is you're pumping data into these systems, nobody knows if it's redundant data, nobody knows if the quality's any good, and the amount of data is massive. >> And it's cheap to store >> Very cheap to store. >> So people keep pumping it in. >> But I think that creates a lot of issues when it comes to data quality. So, I do think the technology's gotten better, I think there's a lot of companies that are doing a great job with it, but I think the challenge has definitely upped. >> So, go ahead. >> I'm sorry. You mentioned earlier that we're seeing the death of Hadoop, but I'd like you to elaborate on that becuase (Dave laughs) Hadoop actually came up this morning in the keynote, it's part of what GlaxoSmithKline did. Came up in a conversation I had with the CEO of Experian last week, I mean, it's still out there, why do you think it's in decline? >> I think, I mean first of all if you look at the Hadoop vendors that are out there, they've all been struggling. I mean some of them are shutting down, two of them have merged and they've got killed lately. I think there are some very successful implementations of Hadoop. I think Hadoop as a storage environment is wonderful, I think you can process a lot of data on Hadoop, but the problem with Hadoop is it became the panacea that was going to solve all things data. It was going to be the database, it was going to be the data warehouse, it was going to do everything. >> That's usually the kiss of death, isn't it? >> It's the kiss of death. And it, you know, the killer app on Hadoop, ironically, became SQL. I mean, SQL's the killer app on Hadoop. If you want to SQL engine, you don't need Hadoop. But what we did was, in the beginning Mike sort of made fun of it, Stonebreaker, and joked a lot about he's heard of MapReduce, it's called Group By, (Dave laughs) and that created a lot of tension between the early Vertica and Hadoop. I think, in the end, we embraced it. We sit next to Hadoop, we sit on top of Hadoop, we sit behind it, we sit in front of it, it's there. But I think what the reality check of the industry has been, certainly by the business folks in these companies is it has not fulfilled all the promises, it has not fulfilled a fraction on the promises that they bet on, and so they need to figure those things out. So I don't think it's going to go away completely, but I think its best success has been disrupting the storage market, and I think there's some much larger disruptions of technologies that frankly are better than HTFS to do that. >> And the Cloud was a gamechanger >> And a lot of them are in the cloud. >> Which is ironic, 'cause you know, cloud era, (Colin laughs) they didn't really have a cloud strategy, neither did Hortonworks, neither did MapR and, it just so happened Amazon had one, Google had one, and Microsoft has one, so, it's just convenient to-- >> Well, how is that affecting your business? We've seen this massive migration to the cloud (mumbles) >> It's actually been great for us, so one of the things about Vertica is we run everywhere, and we made a decision a while ago, we had our own data warehouse as a service offering. It might have been ahead of its time, never really took off, what we did instead is we pivoted and we say "you know what? "We're going to invest in that experience "so it's a SaaS-like experience, "but we're going to let our customers "have full control over the cloud. "And if they want to go to Amazon they can, "if they want to go to Google they can, "if they want to go to Azure they can." And we really invested in that and that experience. We're up on the Amazon marketplace, we have lots of customers running up on Amazon Cloud as well as Google and Azure now, and then about two years ago we went down and did this endeavor to completely re-architect our product so that we could separate compute and storage so that our customers could actually take advantage of the cloud economics as well. That's been huge for us, >> So you scale independent-- >> Scale independently, cloud native, add compute, take away compute, and for our existing customers, they're loving the hybrid aspect, they love that they can still run on Premise, they love that they can run up on a public cloud, they love that they can run in both places. So we will continue to invest a lot in that. And it is really, really important, and frankly, I think cloud has helped Vertica a lot, because being able to provision hardware quickly, being able to tie in to these public clouds, into our customers' accounts, give them control, has been great and we're going to continue on that path. >> Because Vertica's an ISV, I mean you're a software company. >> We're a software company. >> I know you were a part of HP for a while, and HP wanted to mash that in and run it on it's hardware, but software runs great in the cloud. And then to you it's another hardware platform. >> It's another hardware platform, exactly. >> So give us the update on Micro Focus, Micro Focus acquired Vertica as part of the HPE software business, how many years ago now? Two years ago? >> Less than two years ago. >> Okay, so how's that going, >> It's going great. >> Give us the update there. >> Yeah, so first of all it is great, HPE and HP were wonderful to Vertica, but it's great being part of a software company. Micro Focus is a software company. And more than just a software company it's a company that has a lot of experience bridging the old and the new. Leveraging all of the investments that you've made but also thinking about cloud and all these other things that are coming down the pike. I think for Vertica it's been really great because, as you've seen Vertica has gotten its identity back again. And that's something that Micro Focus is very good at. You can look at what Micro Focus did with SUSE, the Linux company, which actually you know, now just recently spun out of Micro Focus but, letting organizations like Vertica that have this culture, have this product, have this passion, really focus on our market and our customers and doing the right thing by them has been just really great for us and operating as a software company. The other nice thing is that we do integrate with a lot of other products, some of which came from the HPE side, some of which came from Micro Focus, security products is an example. The other really nice thing is we've been doing this insource thing at Micro Focus where we open up our source code to some of the other teams in Micro Focus and they've been contributing now in amazing ways to the product. In ways that we would just never be able to scale, but with 4,000 engineers strong in Micro Focus, we've got a much larger development organization that can actually contribute to the things that Vertica needs to do. And as we go into the cloud and as we do a lot more operational aspects, the experience that these teams have has been incredible, and security's another great example there. So overall it's been great, we've had four different owners of Vertica, our job is to continue what we do on the innovation side in the culture, but so far Micro Focus has been terrific. >> Well, I'd like to say, you're kind of getting that mojo back, because you guys as an independent company were doing your own thing, and then you did for a while inside of HP, >> We did. >> And that obviously changed, 'cause they wanted more integration, but, and Micro Focus, they know what they're doing, they know how to do acquisitions, they've been very successful. >> It's a very well run company, operationally. >> The SUSE piece was really interesting, spinning that out, because now RHEL is part of IBM, so now you've got SUSE as the lone independent. >> Yeah. >> Yeah. >> But I want to ask you, go back to a technology question, is NoSQL the next Hadoop? Are these databases, it seems to be that the hot fad now is NoSQL, it can do anything. Is the promise overblown? >> I think, I mean NoSQL has been out almost as long as Hadoop, and I, we always say not only SQL, right? Mike's said this from day one, best tool for the job. Nothing is going to do every job well, so I think that there are, whether it's key value stores or other types of NoSQL engines, document DB's, now you have some of these DB's that are running on different chips, >> Graph, yeah. >> there's always, yeah, graph DBs, there's always going to be specialty things. I think one of the things about our analytic platform is we can do, time series is a great example. Vertica's a great time series database. We can compete with specialized time series databases. But we also offer a lot of, the other things that you can do with Vertica that you wouldn't be able to do on a database like that. So, I always think there's going to be specialty products, I also think some of these can do a lot more workloads than you might think, but I don't see as much around the NoSQL movement as say I did a few years ago. >> But so, and you mentioned the cloud before as kind of, your position on it I think is a tailwind, not to put words in your mouth, >> Yeah, yeah, it's a great tailwind. >> You're in the Amazon marketplace, I mean they have products that are competitive, right? >> They do, they do. >> But, so how are you differentiating there? >> I think the way we differentiate, whether it's Redshift from Amazon, or BigQuery from Google, or even what Azure DB does is, first of all, Vertica, I think from, feature functionality and performance standpoint is ahead. Number one, I think the second thing, and we hear this from a lot of customers, especially at the C-level is they don't want to be locked into these full stacks of the clouds. Having the ability to take a product and run it across multiple clouds is a big thing, because the stack lock-in now, the full stack lock-in of these clouds is scary. It's really easy to develop in their ecosystems but you get very locked into them, and I think a lot of people are concerned about that. So that works really well for Vertica, but I think at the end of the day it's just, it's the robustness of the product, we continue to innovate, when you look at separating compute and storage, believe it or not, a lot of these cloud-native databases don't do that. And so we can actually leverage a lot of the cloud hardware better than the native cloud databases do themselves. So, like I said, we have to keep going, those guys aren't going to stop, and we actually have great relationships with those companies, we work really well with the clouds, they seem to care just as much about their cloud ecosystem as their own database products, and so I think that's going to continue as well. >> Well, Colin, congratulations on all the success >> Yeah, thank you, yeah. >> It's awesome to see you again and really appreciate you coming to >> Oh thank you, it's great, I appreciate the invite, >> MIT. >> it's great to be here. >> All right, keep it right there everybody, Paul and I will be back with our next guest from MIT, you're watching theCUBE. (electronic jingle)

>> Announcer: Live from San Diego, California, it's theCUBE, covering Cisco Live US 2019. Brought to you by Cisco and its ecosystem partners. >> Hey, welcome back to theCUBE's coverage. Day two of Cisco Live from San Diego. I'm Lisa Martin. Dave Vellante is my esteemed cohost. And we're pleased to welcome one of Cisco and Cohesity's customers from Quantium, Craig Tayler, Executive Manager at Business Technology and Platforms. Craig, welcome to theCUBE. >> Thank you. It's great to be here. >> Great seeing you. >> So, we love talking with customers. We love talking about data. Tell our audience a little bit about Quantium. I know you guys have expertise in two core domains, data science, AI, two really sexy topics that we talk about on theCUBE at every event. But give our audience a little bit of the flavor of who you guys are. >> Yeah, so Quantium's been around for 16 years, founded and headquartered in Sydney, Australia. And really, they are like you mentioned, the two main aspects of our business. So when you think of data science more as human intelligence, and then the AI side is how we can augment that with computers as much as possible. So, on the human intelligence side, we're looking at things like data curation, how can we work with a company to understand their data, perhaps monetize their data. And then on the AI side, we're more looking at things like, how do we do predictive modeling or predictive analytics, and how can we get that in front of maybe say a supply chain solution, or working with grocery stores around actually predicting how much fresh food they need. So we think of these things like, wouldn't it be great if we had a better idea of how much we needed? Less waste, less cost, everything else. So that's really how we kind of split the two sides of the company. >> You guys provide this as a service, is that right? >> Yeah, that's correct. So, with those two arms we focus on, whether it be a consulting engagement with a company, where that's a one-off, or an ongoing thing, and we have a range of products that we sell as well, with the idea that any of these companies, whether it be a bank or a retailer, can plug these tools into their existing solutions to give them some real data, some real impact, as opposed to the thoughts, or the feels, or the gut instincts, that we've been working on for so long, all right. >> So paint a picture of your environment. I mean, what does it look like? Cloud, not cloud, apps. >> Yeah. It's certainly a variety. So, if we think, on-premise is really where we do a lot of our work. And this is around, a lot of companies still feel a little bit sensitive around where their data is going, and they like that security of knowing physically where it's located. So on-premise stack we have a bit over 300 servers running a Hadoop cluster, that's where we do the majority of our AI work. And then what we augment that with is, and what we use the cloud a lot for, as we're doing work globally, we're doing a lot of work in North America, it's not feasible to bring all that data back to Sydney, process it, and send it all back, so then really, what we use the cloud for is to take our technology, take our analytics, to the data. So if we're working with a customer, West Coast, East Coast, and they're in Azure we'll deploy in Azure. If they're in GCP, we can deploy in GCP. And that's really how we use cloud is to offer our service, as much as we can, around the world. >> So you said, you got 300 servers, did I hear you right, in a Hadoop cluster, right? >> Yeah, correct. >> What's your distribution? >> We use MapR at the moment. I know there's certainly been a bit of news about them. >> I was going to ask you, well, all three of them. (Craig laughs) Well I guess Hortonworks now folded in, but-- >> Yeah, correct. Cloud has certainly shaken up that marketplace quite a bit. >> Dave: I'm sure, yeah. >> It's been something that we've been keeping a close eye on for quite a while. What's the future there? Is it another distribution? Will someone pick up MapR? Will they get through it? So it is interesting, it's certainly a challenge, but when you're playing in a more emerging space, these are some of the risks you take, but we've always felt that they're worth it. We've had many great years of that and we don't really see any reason that we're not going to get more great years out of that Hadoop environment. >> Yeah, I mean, the IP's going to survive, and it sounds like you guys were early on into it, you got a lot of value out of it. If you had to do it again, you'd probably do the same thing. >> Yeah, that's certainly true. I think, what we've built, there are cloud options on the hyperscale providers that you can use, but look, out of the box, they're not really capable of what we were trying to do. So if we had our time again, we probably would still build the same solution. We'd build it a little bit quicker, obviously, because it's a little bit more in the marketplace, it's not such an emerging technology, but I think we would do the same thing again. >> Dave: Right, and MapR was always ahead of the game with their approach. >> Correct. >> So, obvious question is, how do you protect that data? You're a Cohesity customer, but talk about the data protection aspect of that. >> Yeah, so this is where Cohesity really had a lot of synergies with us, was centralizing a whole raft of datasets into one location. And that's what we do with Hadoop. We take a lot of different datasets and we put it all there. We aggregate it there. So on the secondary data side we had the same problem. Silo datasets all over the environment. Things like, the protection aspect, the compliance aspect, it's not impossible, but it's very hard to manage. So what we really wanted to do was, what do we do with the data when we're not using it anymore? So we might still want to use it in the future, we have to hold onto it. And we needed a better solution for how we manage that. So, having Cohesity, which, to us, being a hyper-converged solution, it's very similar to how Hadoop works. It's a lot of data, a lot of compute, and that's how you deploy it. So we found that actually having all of that, the secondary kind of data that we still needed to keep, combined into one location, for us, it matched on a technology level. And then being able to have all that data in one space, you can do some analytics on it. How often are we using it? What is the data? How many copies of it do we have? So there are a lot of synergies from the data science aspect, and also the technology aspect, which has worked really well for us. >> So what was profound about Hadoop was the idea of bringing five megabytes of code to a petabyte of data, leaving the data where it is, highly distributed environment, obviously challenge protecting that. Help us understand. You're saying that Cohesity architecture is well-suited for that type of environment? >> Yeah, it certainly is. I mean, it augments it quite well, is how I'd say. So at the moment we keep the environments quite separate, but the way we manage them is very similar. So there's great audit login, great security controls that you can place on both environments. So the way that we structure Hadoop with role-based access, who can perform what action, the same thing applies in Cohesity. So now we sort of see that the way that we manage primary is the same way that we can manage secondary. So, it's easier for the staff, when we come to things like compliance or legislation, or, we value data, it's our lifeblood, so we have to be very careful with it. So if we want to do any audit reports or anything like this, we can do 'em the same way. Who has access, what they've done. >> So, Hadoop's been around a lot longer than Cohesity. So, what were you doing before Cohesity, and what were some of those challenges? >> Yeah, what we were doing was a lot. And that was really the only option we had. So we had four or five different solutions that had kind of organically grown over time, whether that was some secondary storage, multiple different backup products, throw a couple of NASes in there, just for good measure. >> Just in case. >> Yeah, just in case. And then really, what we were doing, and how we managed that, is we had close to one FTE dedicated to that environment. It's not great for that person, it's not really the funnest of jobs. And then obviously, the management of it becomes quite difficult. And so that was how we did it. We got by. But it certainly could have been a lot better. >> So that was one FTE dedicated to the backup? >> Just dedicated to the backup. >> Dedicated to data protection? >> Yeah, yeah, yeah. >> Okay. So then you bring in Cohesity, you do the business case, say oh wow, and part of that was we can free up this person to do other things, I presume, right? >> Yeah, yeah, definitely. That was actually certainly one of the key business cases. So, IT is a cost center. We certainly, we work for the business, we support the business, there's no doubt about that. But we are, at the end of the day, a cost center. So getting extra headcount or getting equipment, there has to be a really good business case behind this. And so we found that, so we freed up about 80% of time that we're spending on this, and so actually the two biggest things that we've seen as a benefit of that, staff engagement is actually a lot higher, right, because we don't have someone just dedicated to turning the screws on this old solution all the time. So they get to spend more time on newer tech, which is great, and obviously, if their time's freed-up, value-added activities. What can they be focusing on. >> So how's it work? Is it a self-service platform now? Or somebody, this individual, sets the overall policy, and then people apply it as they see fit, the application guys? >> Yeah, so we have a range. So our infrastructure team holds the overall management of it, and we have that one person who kind of, say rules it, so to speak, but the way we've done with this role-based access, we can give the service desk permission to search backups, so if someone needs a restore, or maybe legal and the compliance team want to know who was accessing what, we can give a lot more self-service to these teams. So the service desk, if they're dealing with an end-user that wants a restore, within 30 seconds, we can tell them, okay, here is the backup we have. Here are the dates that we have it. Which one do you want? Previously, that's a week-and-a-half turnaround. Escalate a ticket, spend three days doing restores and searching through it-- >> Dave: Working weekends. >> Right. Working weekends, and if you even do have the data. Typically what happens, by the time you've restored it, the customer has said, "Look, well I don't need it anymore." It's too late. >> So let's talk about some of the customer benefits. You've only deployed this about six months ago. >> Yeah, correct. >> You talked about a number of the benefits from a time perspective, allowing valuable FTEs to not only be reallocated for other projects, but also from a job satisfaction perspective-- >> Yeah definitely. >> Which is all the way up to the top end of the business. But in terms of helping customers extract more value from their data, monetizing their data, that example that you just gave of where it took too long to recover data before and the customer, the time has passed, what are some of the impacts that your customers are achieving so far? >> Yeah, so I think the biggest area of this that I think we actually look at the most, is that, like I mentioned earlier, we will do, say a piece of work with a customer, and then we'll keep that data. We might need it in the future, but there's not an ongoing engagement. What are we going to do with that? And so we tend to sort of put it aside. If a customer wants any further work done, or perhaps they want to come back with clarification, or anything like this, it then takes us quite a bit of time to find that data, get it back into production, get it back to the state that we were previously using it in. So, one of the biggest things that we've seen is actually now having all of that data always available on Cohesity, and being a hyper-converged platform, it has a lot of compute on it as well, so we can actually run some simple analytics on that data. So if a customer comes back and wants to query just a couple of small items, or perhaps we want to recheck a couple of things, super easy now for us to do that. And so we talk about time to market, or anything like this, is really big for us, and customer responsiveness. So if a customer is asking us a question and the answer is a five-minute answer, they don't want it in four days. So if we can turn that answer around a lot quicker, then obviously everyone's happier. >> And you've already been able to start achieving that? >> Yeah, we have been able to start achieving that already. Whether that be from a customer perspective, and certainly from a compliance perspective, if we have a customer that actually wants to know, where is our data, who has accessed it, everything else, we can turn that around straightaway. So obviously, when we talk about customer satisfaction, or that relationship, they feel a lot more comfortable that we're doing the right thing with their data, and that is obviously hugely invaluable for us as a business. >> And just another infrastructure question. These 300 servers, it's mostly UCS, is that right? Or a lot of UCS? >> Yeah, so we use Cisco for pretty much everything. We certainly are heavy, heavy users of UCS, and so, when we are looking at, I mean, implementing anything to the environment, you don't want it to be a lengthy process, because your return on investment is going to be hit. If you're spending three months installing something, you've already paid, you're getting no benefit out if it, it's now three months old before it's even implemented. So having this kit on Cisco UCS has been great for us, and we were having issues with our previous backup solution and we actually managed to implement the Cohesity solution on UCS and start using it before repairing our existing solution. So it's phenomenal how quickly, through UCS, we were able to bring it in. >> Dave: What kind of issues were you having? Just integration issues, or? >> Yeah, so with our previous backup solution, being a fragmented solution that we had stitched together, we had something as simple as a RAID controller failure caused a whole bunch of data corruption across multiple areas, and so, how the NAS saw the data corruption was different to how the SANDS saw it, and trying to re-index everything, we were struggling to understand what was going on. And whilst we were working through that, we actually had some other members of the team implement Cohesity and get it into the environment quicker than we could repair our existing solution. That's the power of Cisco UCS, really. >> Looking at this massive transformation that Cisco has been undergoing for a while, from a traditional network appliance vendor to now hardware, software, what are your thoughts on how that transformation, which is, in part, you could say, accelerated by DevNet, how is it going to enable businesses like yours to be able to start getting value even faster from the technology? >> Yeah, that's a very good question, and that's something, I think, a few of us in the industry, if we go back two, three, four, five years, was Cisco going to reinvent itself? What was that place? With hyperscale cloud, all these kind of things. I think quite a few people had some questions around what was going to happen in that space. They weren't always the quickest to market. They had great products, but there was a bit of speed issues there. And what we've seen as they've reinvented themselves is, Cisco has this great name for really being ahead of the curve, or leading industry, and this is, I think, what they were built on, really. And so it's been great from our perspective to see them, say, almost getting back to their roots a little bit, in this regard, and so for us, we are a technology business, we are fast-moving, our customers want things to be fast-moving, and so being able to rely on a technology partner like Cisco, and knowing that they're looking for the latest and greatest even quicker than ourselves, I think that's probably where we start to see the biggest impact. In the past, we might have a challenge that we need to solve, you talk to some vendors, and you might hear something like, oh, we're working on that. Maybe in 12 to 18 months we'll have it in the marketplace. Well we need it now. We don't need it in 18 months, it's a today problem. And that's not what we're seeing anymore with Cisco. Typically, any conversation we have with our account reps around here are some of the challenges, here are what our customers want to do, more frequently than not, our Cisco account reps will say, I think we have a solution for that. And that really, being able to partner with players like that in the industry, that makes some of the biggest differences for us as a company, because we need to partner with all these people to do what we do. >> Exactly. So, with all the momentum that you guys have achieved in just six short months, what's next? >> Yeah, Quantium is certainly a fast-moving company, like I mentioned, and what we wanted, we always like to run close to the leading edge, we're similar with Hadoop, we like to be early adopters. We like technology to grow with us. And this is what we saw in Cohesity. So, they haven't been around for long, and they're already doing everything we need. So we think, well this is a great mix. If we've got someone who's already solving everything that we need, this question of what next is great. And so as we move more towards your hyperscale cloud, being able to run Cohesity across all those environments to manage all of that data across all of it, that's certainly a big one that we're investigating. Like I mentioned, we keep pretty much all of our data, and so actually being able to use cloud as an archive solution, it sounds great, but then it's another silo to manage, it's another solution that you need to implement, but Cohesity will manage all that for us. So, the what next, I think, is we'll see the scale out of the solution as our data requirement grows, we will see it expand into the cloud environments that we're going to start building, so we really see it growing with us from that aspect. And then we see a great idea of being able to repurpose a lot of our on-premise hardware by archiving out to the cloud as well. >> What about SaaS? Do you see a need to use a Cohesity to protect your SaaS data, or are you kind of not there yet? >> Yeah, I think it certainly has a play there, it's still something that I think we're exploring a little bit more to make sure that it's a right fit. But certainly, there is an opportunity there to be explored, yeah. >> Always opportunities. Well Craig, we appreciate you stopping by theCUBE-- >> Thank you for having me. >> And sharing how Quantium is leveraging your partnerships with Cisco, with Cohesity, to drive those core business drivers of data science and AI. >> Thank you. >> Our pleasure. For Dave Vellante, I'm Lisa Martin. You're watching theCUBE Live from Cisco Live, in San Diego. (light music)