>> Narrator: Live from Las Vegas, it's theCUBE. Covering UiPath Forward Americas, 2019. Brought to you by UiPath. >> Welcome back everyone to theCUBE's live coverage of UiPath Forward here at the Bellagio in Las Vegas, Nevada. I'm your host, Rebecca Knight, co-hosting alongside of Dave Vellante. We're joined by Cathy Tornbohm, she is the distinguished VP Analyst at Gartner. Thank you so much for coming on theCUBE. >> Very welcome, nice to be here. >> And Guy Kirkwood, he is the Chief Evangelist at UiPath. Thank you so much. >> Thanks Rebecca. >> So, we're hearing so much of these mantras, these catchphrases of UiPath. "automation first", "a robot for every person", "we're re-booting work", these are the theme's that Guy was touting up on the main stage, Cathy. Beyond that, I'd like to hear from you a little bit about what you're seeing in the RPA space at the moment. What are the trends and the themes that you think are most salient? >> I think the most fascinating thing about RPA right now is that it's really highlighting the problems the organizations have. All their accidents of history are really being brought up by RPA. And then you've got these digital darlings that they're trying to compete with, the Greenfield site kind of people. And some of those don't have beautiful back offices, but let's not go there for a minute. So, it, RPA is an opportunity for companies to link their digital dreams with their existing legacy nightmares. >> And those legacy nightmares include all of the things that Guy was talking about today: the drudgery, the dreariness, those mundane tasks that take up so much of our time. >> Absolutely, and really, if you think about it, in organizations, typically less than 15% of the applications that they're using have got some sort of application programming interface. So if you don't have a way of linking them, you end up with this long turn of applications that aren't linked together, with people literally being swivel-chair integration between the applications. >> Well, why can't you just string a bunch of API's together and automate that way? >> Well, in fact, there's a guy called Ian Barkin who works for Symphony, one of their organizations, it was set up to create automations for organizations. So one of the services businesses since been acquired by Sykes. And he describes it as process sediment, and it builds up in businesses in the same way that sedimentary rock builds up over millions of years. And digging through that, so that you can actually become more efficient is very difficult to do. So doing it on API level means you got to join up all those things individually. Whereas, using RPA, if system 'A' has a user interface, and system 'B' has a user interface, you can just use RPA. >> So, Cathy, you've been following process automation as a category for a number of years. Why RPA, why is it so hot, and why now? We've heard that it's the number one software category... >> Cathy: Fastest growing, yeah. Fastest growing, from Gartner. We've seen spending data that confirms that. Why now? (sighing) >> It's the digital competition that companies are facing, and the recognition that they cannot continue to be quite as bad at some of the things that they are bad at. So it's really that business transformation story back again, business process re-engineering, the same story that we had with BPO like ten years ago, but now, with robots instead. >> Yeah, it's interesting, I was at a, we had a show last weekend, it was the CEO of Suze, Suze... How do ya say it? Anyway, Suze, she said to me, "Well, you know, digital transformation's really about business transformation." And you kind of said the same thing. I mean, thoughts on that? >> I mean, you look at the start of the outsourcing market, the BPA market, twenty years ago. The very first deals were actually IT outsourcing deals that then transformed the business using IT as the enabler. So the first deal that I got involved with ever, in the outsourcing market, was Perot Systems with a British and Asian company. And we were putting in business process re-engineering consultants who actually transformed the business using IT as the enabler for that. There is no difference now, in fact one of the, one of the partners here, one of our original customers, actually put together a plan where we did the implementation, you know, soup to nuts, so that we could find out how we fit in to that whole transformation piece. And our team put together a whole package on all the learnings that we got out of that. And I had to laugh, because they're exactly the same things that every transformation program has had for the last thirty years. >> You know, if you look at kind of the history of certain segments, and I wonder if, Cathy, if you see RPA as one of them, like if you could've figured out who was implementing ERP the best, you didn't know SAP was going to become the leader, but if you could've figured out who was adopting ERP, you could've made a lot of money in the stock market, 'cause those companies had a huge productivity boost. Kind of same thing with Big Data, nobody really made any money in Big Data, so-called 'Big Data', a dupe. But the guys who applied it probably did pretty well. Do you see RPA as similar where the practitioners are going to actually be the ones that add more value to the industry than the new, the newly minted billionaires? >> It's almost the opposite. So the more RPA a company needs, it means the worse they did at managing their ERP in the first place. >> So they're kind of a mess? >> Yeah, yes. That need to be cleaned up, yeah. >> Yes, if you've got a hundred and twenty four ERP's that don't talk to each other, and you want to close your books in any kind of reasonable time frame, you're going to be a massive adopter of RPA, which basically means the more rubbish you are and activity, the more opportunity there is to automate more of it. >> So, what are the metrics that matter when you talk to your clients? >> Well, what I try and encourage clients to do is to really focus on business outcomes. So, much as Guy probably doesn't want me to say this, I don't really care how many 'scripts', aka robots, you've built, or how many run times you've deployed. What I care about is the business impact that you've managed to achieve. So, whatever KPI's are important to you, so are you managing to collect more revenue? Are you managing to make your customers happier because you're managing to decrease average handle times? or increase right first time activities. So anything that you're doing that actually improves the good old business metrics, is just going to be fantastic. So those are the sort of metrics that, really, companies should be focusing on. Not how many scripts they've built, that's absolutely pointless. >> I mean, are they focusing on that? I mean, when you... >> Yeah, lots of people are. >> Yeah? >> Yeah. >> In terms of ROI, we hear from customers that it has had them more accurate, they're more efficient, they're cost saving on human hours of the mundane tasks. But, when you were up on the main stage talking about how we're rebooting work, we're changing this moment, is it sparking the creativity, the imagination, the time spent on strategy in the more higher-level things? Is that, I mean that seems like that's the goal of return on investment. >> It is, within those organizations that are the most mature. So, what we're seeing, is the bifurcation, really, of the market between those organizations that are just starting and scaling up what they can, internal senses of excellence. Those organizations that are using the partners behind us. Those organizations that are using external parties to help them develop that. So Delight, for instance, they are sort of a managed service business. And instead of using people, they're using automation. So, Delight, by accident, has a BPA business in Spain, but then they'll turn that into an automation-heavy business and then providing that managed service. And then, the smartest customers, including SNBC, who we heard from yesterday, are actually turning their back office cost operations into a front office of revenue generator. Now, that is radically different from what we've seen prior. >> So Cathy, I got to ask you, when I was on a plane out here, somebody texted me a picture of the latest hype cycle. And they said, they knew I was going to UiPath, they said, "RPA has entered the trough of disillusionment." I said, "Oh, awesome, Gartner's, Cathy's coming on, and I can ask her about that." Well, what's your take on that? >> I think as Guy says, some people have already sailed through the trough, they've already gone through the challenges, or some of the challenges, and they've already found these fantastic productive things. I mean, we're estimating that people will save close to a million dollars for a large company, and just not having to do re-work of getting it wrong first time with re-keying that data. So, where there's some fantastic savings available, that you know, some of the ones have gone through the trough and done that, a lot of the other ones, they kind of, they don't understand the limitations of RPA and all those other partner tools that they need to put with it. So, don't understand it, can't handle unstructured data by itself. It needs a sister tool, so, what Gartner's talking about right now is this concept of hyper automation where you look across all the different activities that you would need to, sort of replace a person. So the people that are heading into the trough as sort of this second wave of adopters that Guy talked about, that will really struggle because they didn't understand the limitations in the first place. >> Well then, you know the, sometimes, things like the Magic Quadrant, and the trough of disillusionment, they're somewhat misunderstood sometimes, people, you know they see 'em, Gartner's very clever with the way it works things, but, so how should we think about that hype cycle? It's actually, in a way it's progress, isn't it? For an industry where they start... Entering that trough. >> Its, what Gartner says, is all industries have to go through that type of growing pains. And I think that we're seeing that, UiPath's expanded massively, and that's always a challenge for companies as they grow very rapidly. And as companies try and, as they say, take these wrong metrics. So I think things like UiPath buying ProcessGold is fantastic, it's a really, really good move for them. And I expect to see a lot of other process mining companies acquired, brought in to the RPA fold, because, there's four reasons why companies are going to go into this disillusionment, right? These are the main challenges with companies trying to use RPA properly. One is, they don't know what the processes are. So ProcessGold will give you a really good indication, they don't know about the microscopic level, and they don't know about the macro level. So things like digital twins will be something else that we would expect to see very closely partnered with companies like UiPath. And they don't know how to orchestrate their resources. So, other companies, like Innate, that can help you figure out how to do that will become... So its kind of like we're sort of breaking down a lot of what happened in other software categories and re-building them all up, in the way that the business can actually adopt them, hence, the AI Fabric sort of idea. So they don't know the processes, politics, people will lie to you about what they do all day, so they can sabotage your process, and there's a lot of silos within organizations that hate each other and throw things over the wall. So that all needs streamlining, and the more you can do across silos, the more successful any automation project would be. Then you've got, when you take a person out of a process, you take their eyes, their ears, the mouth, the nose. How are you going to replace that when you're trying to take them out, because you've got the keyboard fingers thing with the RPA tool? You need all these other activities replaced, replicated, supported. And then you've got the economics of production, so actually making sure that the scripts that you've built are actually worthwhile and are going to be cost-effective. It's something that we're studying at the moment. So you've got all these, all these different barriers, from all these different angles that are really going to push this thing into the trough for a little bit. And that's why it's great that RPA companies are looking at ways to mitigate that for their customers. >> Now, remember we said, as the understandings. So RPA is really good at dealing with structured data. Rule-spaced activities, deterministic things. That's why in regulatory, highly regulated environments, it's very effective, and the regulators love this sort of stuff. Because it's deterministic. When you look at AI, then we look at it in four ways. So you've got process understanding, which is the ProcessGold acquisition, you look at conversational understanding, 'cause ultimately robots are going to be controlled by voice. So you have to understand, the system has to understand that, let's say you're sitting in a bank, and the robot doesn't understand something, you say, "Okay, robot, stick that in the Well's account." It has to understand that Well's, in this case, means Well's Fargo. It does not mean a hole in the ground, water at the bottom, or a town in Somerset, in the UK, 'cause they're well's. So getting those ontologies correct is so important. So, that's conversational understanding. Document understanding. Because, as Cathy said, companies are still wading around in paper. So, understanding what those different documents are and how to action them is going to be really important. And finally, you're looking at visual understanding. So understanding and viewing things on the screen exactly the same way that humans do. So it's getting that combination right. >> So for RPA to live up to the hype, and there's a lot of hype, and it's a good thing, it's fun to track. It's got to go presumably beyond cleaning up the crime scene, if you will, to this new vision that you and Guy just laid out. What is the distance between, I dunno, sometimes I say 'paving the cow path', which gives you a nice hit, but as you say, it's 'cause companies... Ya know, they're messed up, to this vision of this, actually the guy from Pepsi today talked about it, this fabric of automation across the organization. How big of a gap is that? >> It's very different by every different company on the planet, really, in terms of their accidents of history, what their IT application landscape looks like, and what their business landscape looks like. And when you try and put the two things together, that's where you find the opportunities for any type of automation. >> Well come on, that's such an 'it depends' answer. (laughing) At the macro, will... In your expert opinion, will RPA live up to the hype? So many trends haven't, enterprise data warehousing, Big Data, Doob, all that stuff. You think RPA has the potential to crack through that. >> You mentioned a very good point. I think the most successful companies are the ones that actually will take the person that's managing the data and analytics of how their process is performing, and doing that with their automation strategy. And there are very few companies that've actually worked that out. They've still got totally two walls and they just meet up here at the CEO. So, unless companies actually take a more active business outcomes approach, and look at their end-to-end processes of order to cash and source to pay, these problems will carry on for some time. >> Well that's a great point, I mean, so it's data, it's machine intelligence, I guess Cloud for scale, you guys made a SAS announcement today, it's "automation first", to use your buzz word. >> Cathy: You need it all to come together. >> And it's really developing those best practices in your role as Chief Evangelist in helping understand what the most successful companies do, and then making sure that's implemented. >> Well that's why I spend more of my time listening than I do talking. Because the very nature of being a Chief Evangelist is the best job and the worst job title in the world. It's the best job because I spend my entire time talking to people like Cathy who know about what's happening within the market, and then feeding it back into our organization so we can make the right bets, so we can make the right acquisitions, but develop the right things. The bad thing about the job, is that I keep getting an inordinate number of people on LinkedIn saying, "So pleased that Jesus has entered your life." And I'm not that type of evangelist. (laughing) >> It's in the title. >> You know there's always this age-old debate in the industry of best of breed versus kind of a sweet approach. You see in SAP, for instance, acquired an RPA company, In Four talks about it. And then you get the specialist, UiPath. How do you see that shaking out, as the industry gets kind of more consolidated, how do you see a company like UiPath thriving, continuing to thrive? >> Gartner's going to predict coming in our new prediction series, but... Roughly 20 to 30% of enterprise adoption of AI, machine learning activities for process-based activities, will go through the RPA market. So, and with the IBPMS market, sort of combined together, that process management, because RPA has managed, cleverly, to capture the imagination of the business person. So, actually, there's a lot of IT departments that are talking to us about, how do we, how do we enshrine this activity, foreshadow IT, that's happening in the business, and make it successful, put governance plans in place so it will actually be successful in the way that it's actually now dealing with its own crime scene... (laughing) (mumbling) Its own rubbish, in a much better way. And I think that responsibility of business to understand how it can automate things and how it can manage things will really help a lot. So, I think the RPA players are well-placed to either be acquired into that bigger set of the established, large... Software providers, all to kind of keep blazing a trail for independence of the business. I'm not so sure about this idea that everybody should be programming their own scripts, I think that's a challenge. And I think the new interfaces will help mitigate some of the problems that we've seen with that approach, that hasn't been, haven't been very well done historically, so that's another area that will probably be a bit trough of disillusionment, but, actually, well-managed RPA projects have actually got a really good chance of delivering back very interesting benefits for businesses. >> Yeah, as a discreet innovation category, it does kind of feel that way, and often times, those markets are winner take most, the winner makes a ton of dough, number two makes a little bit of money, number three kind of breaks even, and everybody else gets consolidated or goes out of business, so, you guys go big or go home. That's kind of... Your posture. >> Tomorrow morning I'm doing, I'm doing my predictions for next year, and one of them is that the challenger RPA vendors, and indeed the service organizations that are small, are going to continue to consolidate and get acquired next year. So that's the 2020 prediction for us. >> Great. Well, Guy and Cathy, thank you both so much for coming on theCUBE. It was a great conversation. >> Oh, good, thank you. >> Thank you very much, indeed. Thanks Rebecca. >> Dave: Thanks you guys. >> I'm Rebecca Knight for Dave Vellante, stay tuned for more of theCUBES live coverage of UiPath. (techno music)

>> live from London, England. It's the key covering a ws summat London twenty nineteen brought to you by Amazon Web services. >> Welcome back to London. Everybody, this is David. Continue watching the Cube. The leader in live tech coverage. We're here at the London A ws sum of twelve thousand people here for one day summit, which is typically the size of a large tech event that we cover in Las Vegas. John Lovelock is here is a vice president analyst that gardeners essentially gardeners Chief Forecaster John, Thanks for coming with huge pleasure to have you. Thanks for >> having me. It's a great show today. Great event. Happy to be here. >> You're in from Toronto. And, uh, yeah, I'm very impressed with the crowd. He obviously a developer crowd. You and I aren't ties. They see us coming. They think we're trying to sell him something. Waseem have >> ah, monopoly on all the ties and the rule. We have a very diverse group here, but they're all very enthusiastic. Could be here. It's been a great conference. >> So everywhere we go, we hear numbers. Obviously people want toe talk about the size of the market, its growth. That's your job to figure that out. I mean, I've heard numbers that it's a multi trillion dollar market now, uh, growing faster than GDP. I'd love to get your your thoughts on that. Where do we start? Top level macro. What's the pick? >> Top level macro cloud in all of its forms is the fastest growing tech the gardener is tracking. There is definitely spending in there. We're in the twenty twenty five percent growth globally. Nothing else comes close. Your overall growth rate for Total I'd spend this year is one point one percent cloud a twenty five percent. It is moving the market. The only way is doing that, of course, it's by taking money away from legacy lines of business. You know, it's about the switch and spending preference from legacy it and moving that into clouded in all of its forms. >> So it's a share shift you see going on. So you've got the total market growing below global GDP. Is that is that a fair statement? >> It's just below global TV >> usually tracks pretty closely. You would think right? I mean, it's logical that it would >> actually this almost no correlation between GDP and spend really It is one of the biggest things that we have to fight again. >> So that's a myth. >> Absolute myth here to tell you it is dead. There is a flight co relation, but there's no causation. Yl move between GDP and spending, just not there. >> So that makes your job even harder. It does. We have to >> watch what the vendors. They're selling off what they hope to spend. But most importantly, it's about what the demand side is doing. What are people doing? Why air they buying what they're buying? How much are they spending on the stuff that they have, what's get retired and what gets replaced with something new? And that's the whole big shift that we're seeing is a lot of things that are being retired out of the CEOs bag of tricks and a lot of new things coming in. So the spending shift that we're seeing it's all down to where is the CEO in their journey? Howie? How quickly are they able to move from legacy? I t to the new it How quickly is their business moving into being a digital business? >> So okay, so it's one plus percent growth on what we're talking two trillion, three trillion. I mean, what's the four trillion >> four trillion dollars by twenty twenty? >> Okay, And you said Cloud computing growing its twentieth twenty five percent. Eight of us, a thirty billion dollars run right business now growing at forty two percent. Inconstant currency. We're going in at nearly or maybe even slightly more than twice the market. That's astounding, that basically adding nine to ten million dollars a year. >> And they are right in the sweet spot for cloud growth. Do >> you think they hit the law of large numbers of people have been predicting that for years. Could get a company that size in your experience. Continue to grow at that pace? >> Absolutely there is. There is nothing stopping a ws from taking advantage of this market. We're nowhere near saturated for cloud changes. Most of software spend is still on legacy and maintenance of of software. On Prem. There's still a great deal of money being spent on servers and infrastructure and networking equipment, and all of that gets bled out into the cloud. Eventually, where they have opportunity to shift is almost limitless. You know the amount of money that is being spent by enterprises on cloud is different around the world. In the US, where cloud basically started where the infection started and it's spreading around the world. Back in twenty sixteen, there were about sixty percent of overall enterprise spend was on cloud. The rest of the world is tracking towards that. We have company countries that air close the U. K Canada one two years behind France Germany three four, most of Europe in the three to five years behind. We have some countries that are lagging a little bit further and several dinner just resisting that are not on track to get to cloud. We don't see them getting to cloud even in the ten year times, fam. But the fact that cloud spend in the U. S. Still makes up over fifty percent of global spending on cloud, but only twenty five percent of global spending on it, a lot of money still left to move over. >> That's interesting that that was the facts that's that suggest that there is a delta and cloud adoption between between United States and rest of world that the vendor narrative would not have you believe that? Am I getting that right? Is it? Is it not only slower adoption? What are they they as sophisticated in their adoption, or is there a delta there as well? >> There is a bit of it. There is a delta also in the sophistication. We know that there's a skill gap when it comes to cloud. Everywhere in the world faces the skill gap of the number of people they need with the new skills and cloud and the people they have with the skills that they have. Many companies are missing the fact that some of their Cobol programmers are the ones that should be developing their new cloud applications because it's about changing the business. And nobody knows their business better than the guys that have been writing the legacy apse that have been running the business for the last twenty years. So the training opportunity is actually with their Kobol programs with their long term programmers. We're not seeing that hitting into the market as much as we'd like. >> So your job very difficult job spent. The consolidation makes your job harder in a way, because part of a squint through companies want to tell you what they want to tell you, but you got to figure out what the truth is. When you think about Cloud, it appears relatively straightforward. It's a pure play. They now report their numbers. That must have helped you a lot. But a lot of vendors will throw everything the kitchen sink, you know, numbers for cloud. So you have to parse through that. You have to come up with common definitions across. I mean, good example. Certainly. IBM Oracle broke it out earlier, but now they sort of consolidate everything. One wonders, OK, Where they trying to hide? Not not to pick on people, but their large, established legacy companies. But they want to show their investors. Oh, we're growing at this. The Sirait. So how do you parse through that and squint through that and then come out the other end with the >> real numbers? Well, we have a lot of advantages of Gardner. We spend millions of dollars every year on surveying out globally. We get, we get responses back from CEOs from around the world. We do the largest CEO survey every single year, so we're getting feedback on where the money is being spent. We also have interviews that we do with our clients every single day. We do over two hundred fifty thousand enquiries with clients every year. So we're getting a great deal of feedback from where the money is being spent. We have to reconcile both sides of it. What the vendors air expecting to be what they're telling us that they're making and reconcile ing that with what we're being told is being spent. So we have multiple sides to get to this angle and again. When you start with a vendor, you start with their global revenue. It has to parse out from They're >> gonna match the income statement somehow. But so you've got the empirical data from your surveys. You've got the vendor data. You bottom up. You could do that. And you've got the anecdotal data from your inquiry. You know, your your corporate memory on kind of putting your job is to put all that together. >> Yeah, and we're tracking what we call our peer inside data. We're asking our clients, you know, when they're making a choice which fenders air, they choosing Which friends are they considering? Why did they make the choices? They are. We have our talent neuron database where we're scraping job postings from around the world. So we have somewhere over four billion job postings covering the last five years. So when a company is telling us that they have a large new division, we could go back and say, I don't see you ever hiring those people. So we do have multiple points of light that all really have to come together. It is a tremendously interesting job in a bit of a challenge, but it's one that keeps me up. >> Okay, I often joke. Well, well, Doctor, Uh, Oz. Sorry, Dr Watson. Replace Dr Welby and the answer comes back. Well, you won't replace Dr Oz because you still have to have that nurturing and that interaction. Do you feel as though machine intelligence Based on what? You know, Gardner analysts, You got experts? Many, I'm sure that Follow artificial intelligence machine intelligence. Do you feel like you guys can start applying? Aye, aye. Deep learning, et cetera. To identify patterns to make your job easier, more effective, more science than art. What? Your thoughts on >> that? Well, we have taken a different road. Artificial intelligence requires a lot of good bad data going into it in order to make the right decision. It is changing so quickly. It's difficult to get enough data points together to train and artificial intelligence. We do do some augmentation way. Do have tools that automates certain processes for us and feed us results from multiple millions of data points. But at the end of the day, it's not about coming up with four trillion dollars. That's interesting to anybody. It's the why is it four trillion dollars? Why is it a different four trillion dollars than last year's three point nine trillion dollars? And what's the changing environment that is going >> on >> and the story behind it? The segments, the share shifts and those other trends that you're seeing? >> Because everybody on this floor, all of these eyes start ups, they desperately want to make my number's wrong. They want to change the market in such a dramatic way that they disrupt all of the spending. I can't train in a eye to watch for that >> is your background in econometrics. You an economist? Do you have a math whiz or you're computer scientist? >> All of that, Yeah, have degrees in economics and statistics. I have forty years almost in computer programming been through this cycle for many, many times. So I did a great job from he has all of my sword skill sets coming together. >> You're obviously not a one man band. You mentioned you do, you know, spend millions of dollars on surveys. Two hundred fifty thousand enquiries, but still hurting all that data and actually making sense of it, is it is. It is a challenge. How do you How do you manage that? How are you evolving your your systems, your models? I mean what you used today The tooling is different than it was ten years ago, and you've gotta stay. Current >> are are forecasting model generically. We call the market dynamic models, and what they do is build out user behavior. Where's demand coming from? How are we fulfilling on that demand? What do we do with the investments that we've already made? The's models run from nineteen eighty through twenty thirty. It takes somewhere in the neighborhood of eight hundred thousand calculations to come up with one segment forecast for forty three countries. We have over two hundred fifty segments that we forecast, so you could see the complexity that we're getting into. There are over two hundred fifty analysts that gardener who are working on from what we call her our technology and service provider research group, to help our vendor clients know where their market is, know where it's going, and the partners that they should be looking >> towards you factor in or how do you factor in if it all your geo political trends? Um, tariffs, things of that nature. What do you say? You know what we're gonna do? A clean forecast on DH. Let the market figure that out. How do you handle it? At >> the end of the day, there's two very important pieces within a model. They break into signal and noise. The signal is the shifting buying patterns. When the demand level changes, there's a signal there when a choice pattern changes. Instead of buying license software, I'm starting to buy Cloud. That's a signal change. Those are the things that we focus on. The stuff that you were talking about the economic situations brexit, terrorists, China. Those were all noise. They're important. They have to be taken account of in the model, but they're not the most important thing. All right, Brexit right now is depressing the US air, the European spending on it. It is below that one point one percent growth rate. Because of the uncertainty. People are keeping their finger, their hands in their pockets when it comes to big changes in it. But the big shift is still happening. We're still seeing movement towards cloud. We're still seeing movement towards digital business. All those big signals air there, there dampened a little bit by the noise of the economy. >> So the rip currents obviously cloud. You mentioned that digital business, which I interpreted is data orientation toward a business a little >> bit more with you. >> But please add some color to that. And what are some of the other rip currents that you're seeing? >> Artificial intelligence is another riptide that is moving through. It is a big trend that is changing what's expected of technology at every level. Digital business is changing what's expected of customer interactions at every level. Digital business ecosystems, where companies air able to interact in a way that moves data from one organization to the other without necessarily having trust, commitment or a contract is a major change that we're seeing it reduces the friction of handoff between one business and the other speeds. The process drops the cost. >> A lot of your clients are large, established businesses, gardeners well known for advising those businesses. Many of those businesses, their data lives in silos. They have legacy infrastructure, technical debt. Call it whatever you want it, and they're getting disrupted by these. You know, the guys who were doing Cloud Native, all the guys out here that want to make your full forecast wrong. How does Gardner see just sort of anecdotally, those guys closing the gap, the traditional, the incumbents closing that gap >> into the source extent they don't have to, right? Certainly their size is going to give them longevity. Whether they make change or not, they will see their influence on the market. Chip away if they don't start to, they don't have the same urgency is the small vendors that are moving quickly. Where we see them doing things is very patiently and incrementally, they're taking different processes and moving them to the cloud. It is very common to see them take something that they're already doing are comfortably doing and moving that to a new platform and improving that small piece incremental change. The world gets better with incremental change. Where we love to see them do something is where they actually change the business model first using the technology that's going to enable that we have the company in China who has managed to get home food delivery cheaper than buying it in a restaurant because they change the business model First. They work with the places that are selling the food they're doing group on their doing direct cash, ordering they're doing guaranteed sale so that they could get food less expensively. They're using artificial intelligence to workout delivery routes and pick up so that multiple deliveries are made at the same time. In most of the world, that's not the That's not been the model. They've changed one part of delivery. We're going to make it easier for you to order food on your phone, and then we're going to charge you for the delivery, and we're going to charge you more for the food that's coming in. That's incremental. It's nice, it's helping. But when we change the model first, the outcome is so much better. >> So last course of U. S. Largest market, right? In terms >> of largest market for fifty eight percent of cloud. Spend >> little nightie spending Generally correct. Correct. China. When do you think Do you think China will overtake The U. S. Is the largest market for I spent >> china right now. Is Scott almost double the growth and cloud spending of the U. S. It is as a percentage of spends still well below. But they're the only country that is breaking the trend of following the US. They're on a much steeper incline. They could be above the US spend by twenty twenty five, even with a growth rate that the U. S. Is on. >> John. Awesome having you on. Thanks so much for having me really a pleasure having you great insights from Gardner analyst John Lovelock. And you're watching the Cube were bringing it all to you live from London this day. Volonte, we're right back right after this short break

>> Live from San Francisco, it's theCUBE covering IBM Think 2019, brought to you by IBM. >> Hey, welcome back everyone. We're here live at theCUBE in Moscone North in San Francisco, for IBM Think 2019. I'm John Furrier with Stu Miniman, talking to all the top executives, top people here at IBM, getting the scoop on cloud and AI. Our next guest, Jason Gardner, Vice President of Worldwide Sales for Hybrid Cloud at IBM, manages key product, which is part of the IBM Cloud Private, big part of the announcements, big Cloud story here. It's multi-cloud, it's hybrid. Welcome back. >> It's hybrid multi-cloud. Thank you, for having me back. >> CUBE Alumni been on as early, going back as 2012. Now, one big event. >> I can't believe it's been that long. But yeah, I'm happy to be back and I can't believe I've been on theCUBE for so long. >> Talk about your new role, and you had previous roles within IBM dealing with the kind of clients and integration. Your role now is worldwide sales. You're taking this Cloud Private offering, bringing the customers, being as the linchpin for integration. Talk about what you do and some of the engagements you have. >> Yeah, previously, I was really focused in on development and offering management on, point products and how they help clients move to the Cloud. Things such as our Pure Business, our Spare Business, and now I've actually been able to move into a much more horizontal role, where I have the portfolio across the Hybrid Cloud integration side, so everything from our Websphere family, which includes IBM Cloud Private, straight to the integration challenges that that brings as well as our digital business automation portfolio. >> Yeah, I have a personal joy. Stu knows I'm fanatic about Kubernetes, and when I heard Ginni Rometty say Kubernetes twice in a CNBC interview you know it's made it. >> Yes. >> Kubernetes is a big part of cloud native containers, really now has created the connective tissue to make cloud and multi cloud viable. This is a key part of it. I want you to talk about the context of these trends and unpack this Cloud Private offering. Because it's instrumental in seems in the news. >> It is, it is. >> What is it about? >> It is, it really creates that ubiquitous layer I think that we've all been searching for. That next generation of virtualization and connective tissue as you call it. And as you begin to unpack that it really kind of starts with the rise of microservices and the need to be able to pack them very tightly into containers. That's really the birth of Kubernetes, was the ability to orchestrate those containers. So Kubernetes becomes that ubiquitous layer in there. But, IBM Cloud Private takes that and takes it to the next level, right. And, really what it is, it's the services on top of that, the cloud services which enable those containers to work together. And, it is a lot of open source capabilities such as Helm, Prometheus, Kibana and some of those core services that those microservices require in order to be able to run efficiently. >> So, Jason, we know it's a multicloud world. Everybody out there would love to say, oh yes, there's one cloud, I can simplify it. I'd like to get to a nice scalable model that's simple. But, the reality is customers choose lots of different solutions because they have different needs. The Private Cloud piece is not really well understood. I'd love you to take us inside your users. Because they say okay, I'm using Amazon, I'm using Microsoft Business Services. There are certain data things that Google has. IBM has AI and business productivity and database offerings. That Cloud Private, what are the services, what are the use cases, what are the reasons why I'm buying this and being part of my overall portfolio. >> Yeah, Ginni called it Cloud 2.0, right. 1.0 was about lifting shift, it was about cloud native, and that really got us about 20% of the way there. It's at 80%, that's the real challenge, that's really where the complication comes into play. That's really what Private Cloud is about. Because not everybody can be able to take their applications, throw them away, build cloud native, or lift and shift them. If you think of big regulated industries like banking, insurance, healthcare, government. They really need to be able to have that level of security and assurances that they need within there. And, that's really where private cloud comes into play, is those really tough, challenging problems in the industry. >> Yeah, I love that. A trend I've heard from a number of customers, you talk about them getting to containerization and multifactor services, is, step one is, I've got to modernize the platform-- >> Absolutely. >> Then I can modernize the applications on top it. Is that the trend you're seeing? >> Yeah, definitely. We've been building on microservices and modernization, it's a journey right, and it's a journey of discovery I think for a lot of clients out there. And, we'd all love to be able to say, OK this is my platform and now I'm going to work on the applications. But really, sometimes the starting point may be one or the another, and it usually comes in a space of a digital requirement, and so they begin to out modernize the application and then realize, jeez! I need to be able to manage all of this, I need to be able to deploy it all, and that's when the platform comes into play and all the other services, I should say, that come along with it. >> Stu, I think you coined the term Private Cloud. I think wasn't it? >> The true private cloud. >> True private cloud. So the private cloud, again, it's all cloud operations, so I kind of disagree on this whole point about one cloud or multi-cloud. Because I think, yes multi-cloud, but you see people use cloud for workloads, right? So pick the right cloud for the right application. So this basically says, okay, if you want to use Amazon, use Amazon if that's what you want, but if you are going to use 365, maybe use Azure. >> Yep. >> If you are going to use G Suite, use Google. You guys kind of have the business apps nailed down. >> Right. >> So If you're going to use your business apps, maybe IBM. This is your opportunity. >> This is our opportunity. >> Talk about specifically the kinds of apps that you guys will power with your cloud, because multi-cloud certainly makes sense for you guys. It's multi-cloud, you won't that portability and interoperability, but the apps that you're going to power with IBM Cloud. Talk about what they are, how-- >> Yeah, if you look at, from a language perspective over the last, jeez it's been 23 years I think, since the rise of Java, right? And 1995, when the first app servers came out. Those app servers, that is really where ore applications really run on top of. And, it's those core Java applications, that are now needing that facelift, right? They need to be able to be injected with new forms of AI, new types of integrations, new types of personalization of that digital transformation that's driving it, and that's really the core suite, right? And if I look at that core suite in there, and then what do you do to modernize a Java application, and what kind of tools are available to you. How do you then manage, how do you distribute, and how do you scale those applications. It's very important. >> What is the adoption of the private cloud or the Cloud Private product. >> Yeah. >> Talk about some of the trends, how is it being used, be specific on how customers are using it. What are some of the use cases? >> Yeah, so the primary use case is to increase the agility, lower cost on the overall managing of them. But it's the increase in the agility, which is really hard to measure. Because clients want to be able to react very fast to it. And so as they build up microservices, microservices then become independent with one another. You can then update ones, very quickly and easily. They manage and they run independently, and they scale independently, and so Cloud Private provides you with all those services to able to run those microservices as containers, but then be able to tie them together in a much more comprehensive enterprise suite. You know, a core technology like Helm, I'm waiting for Ginni to say that one on stage. But a core technology like Helm, really provides that robust, enterprise class distribution for scalability and high availability of a microservice based application. >> Jason, can you bring us inside the organization of the customers your selling to? It used to be, it was the refresh cycle. It's like OK, my X86 refresh, or you know, the budget cycles that I had. Cloud is quite a bit different. >> It is. >> Private Cloud is kind of straddling between the old world and the new world. What are the dynamics you're seeing as to who controls the purse strings? Are they moving faster to that opex model. >> You know, there's no one person who owns the purse strings on it, but it does float between the infrastructure team, knows that they need to do something different, the developers or the application development team, and really the strategy, the Chief Strategy Officer, in that IT organization is really where it's coming together. Because one thing I think that we've all learned is that developers will find the easiest, fastest way to do something. No matter what rules or policies we put down. And this is about providing them with an environment that has guardrails, for them to be able to innovate as fast as they want, use the tools that they want, that their most comfortable with. Really, it's a grass roots kind of movement into these microservices, led by the developers. But the purse strings are still held at the CTO side. >> That's always a fascinating interest, because the developers they're going to go do it, but they're not usually the ones with the budget. >> That's right. >> But when do the ops people get involved, the business people, to make sure that IT manages it, gets rid of like stealth IT? >> And a lot of clients have learned to listen to the developers, because the early days of cloud, they didn't, and developers found ways through it, no matter what. And so that's really what it's about. It's like a game of bumper cars, right? You got to make sure they stay within the ring of what's safe. And, especially in this day and age of the security requirements that are out there, it's more important today than ever before. >> Jason, can you share some data around some observations that you've noticed on trends around industry uptake or is there any patterns in terms of the customer base? Obviously, people aren't going to going to cloud operations. Just, Ginni mentioned 60/40, 80/20, the ratios. What does that all mean? And, just share the trend data around adoption and patterns? >> Probably the biggest onE in there, is the 80/20, right? That there's still 80% of the applications left in the world are still locked behind the brick and mortar. That's probably our biggest piece of our opportunity, and providing clients with a way to lift them up and be able to modernize them. I think is where the huge opportunity is. But then looking at where do they land, it's not all going to public cloud, right. So private cloud it's a huge business. I think a lot of us underestimated how large that business really is, and depending on the industry, you'll see 50/50, 60/40, 40/60 split, depending on the regulations within that industry, that country, the geography, of where they really want to go to. And, a lot of our clients are asking us for solutions around that private side, but yet be able to have the flexibility to be able to-- >> So you're seeing friction on the public cloud, mainly that's inherent from either regulatory compliance, or just technical challenges. Is that kind of the vibe? >> That's probably the first one. I think there's still that regulatory requirements of data residency, and how do I get my data to application. I can build all the applications I want in the cloud, but how do I get my data there? How do I synchronize it? My lineage of my data. So they really challenged her on that. But, then on the other side of it, is around the cost, right. And, if you wanted to rebuild all of your applications, as true cloud native, from scratch. It will take you a very long time and be very, very expensive. And so, there's also a cost element and speed. You can modernize something much more quickly, and be able to get it to that same level of service, without having to start over. >> We had Arvind on earlier, yesterday, and I want to get your thoughts on the impact of the Red Hat acquisition news, because if you look at what Open Shift is doing with Cloud Private. Arvind was saying yesterday that, Arvind Krishna, he's like, this is really enabling a lot of the acceleration for the modernization of the new cloud stuff, and keeping the legacy stuff and/or transition out on different timetables. Your thought on that? >> Absolutely right, Open Shift is going to be a critical component for our overall hybrid strategy. I'm very excited about it and really looking forward to it. And, Cloud Private and the services that I talked about, run in Open Shift today. That was part of our partnership agreement. I think that you guys were at, that Arvind talked about at that time. But, it provides the platform, for all of those traditional applications, which we've modernized. And the interesting thing is that we've actually modernized ourselves. We've modernized our middle-ware. We've modernized some of those products that are you know, 10, 20 years old. Everything from WebSphere, to MQ, to BPM. They've all been modernized in that same fashion. >> Yeah, Jason, speaking of modernization. Bring us inside you're sales force a little bit. How do they keep up, and what's the skill set that you're looking for, on your team to sell on this. You know, they need to understand Helm and Kubernetes, and all these microservice architecture, where five years ago, it was a totally different world. >> Absolutely, you know I think that if I look at a, it's not a skill, it's passion, right? It's that never give up type of mentality, I think that we look for, in a sales force and I never give up attitude really provides you with that foundation, for never stop learning, right. If anything that you've guys have noticed here over the last ten years in your guys' journey, is that this industry just changes so repidly, all the time. And, so as a sales force, you can't just acquire skills. You don't go out and hire skills. You hire people and you hire passion, and you hire people with that never give up attitude. I've been going around. We've been doing our sales kick-offs. I've done two out of the three now, so far. I tell you they are energized. They love it. They are energized about the Red Hat Acquisition. It shows that IBM really gets it. They've been telling me, does IBM really get it? And now they're like wow, we really do get it? And, they're really energized, because all of the pieces are falling into place, around this modernization, and clients, and we're hitting the timeing. >> It's time to hit that pedal to the metal, put the gas on-- >> They always say, there's no speed limit on sales. >> (laughs) Exactly. OK, first of all great, great conversation, and thanks for waiting out our journey. Stu, I would say that the salespeople got to watch all theCube videos, because all of the best content is coming out of theCube here, and great to have you on. But, quick plug, I'll give you the last word. What's the pitch, share the pitch for the Hybrid Cloud, what your team is offering? What's the, the core pitch for your customers, when you go to them? >> I think the core pitch is around modernization. It's the journey that clients are on, from application development, to how you build your apps, and how you build the microservices. How you integrate those applications, what's your API strategy, how do you move that data around securely, and then how do you manage all of those pieces together in that new modern world. And then, really looking your overall processes, and can you modernize your overall processes, add AI capabilities into that. So, it's that modernization journey. That's really what I talk to them about, and you don't have to do everything, right? Start small, start as a pinpointed piece, and we'll help you along that journey. And it becomes a journey of self-discovery, but we're there the whole way. We're a partner, that's really what it's about. >> Jason Gardner, Vice President of Worldwide Sales with Hybrid Cloud at IBM. TheCube, bringing all the data here, from IBM Think 2019. This is day three, of four days of coverage, here in Moscone live in San Francisco. We'll be right back with more, after this short break. (upbeat music)

>> Live from New York City, It's theCUBE! Covering Levnovo Transform 2.0. Brought to you by Lenovo. (techno music) >> Welcome back to theCUBE's live coverage of Lenovo Transform here in New York City. I'm your host Rebecca Knight along with my co-host Stu Miniman. We're joined by Rod Lappin. He is the senior vice president in marketing here at Lenovo. And Roger Cox, the research vice president at Gartner. Thank you much, gentlemen for coming on theCUBE. >> Thanks for having us! Excited. >> So the big news for the day, NetApp, Lenvovo Two global powerhouses joining forces I want to hear Rod, the Lenovo speil, and then I want to hear your analysis of the deal and what you make of it. So why don't you go ahead. >> Yes, so obviously we're really really excited, Rebecca. It's a great day for us. I think it's something that we've been really planning with NetApp for obviously a long time, and to actually have it come to fruition is really exciting for all of us, right. So as you would have known, probably, our storage offering in market has been quite small up until now. We're addressing about 15% of the market. With this new deal with NetApp we're sort of in the TAM the target market. We can after up to about 92%. We've been quite good at storage. We've been growing about 2X the market average in Flash, over 100% year on year, but we haven't really had the full product range that we've needed to really address out customers' needs, and so, now having this, having this deal now with NetApp means we can go after our customers and really bring value to them the way that we have wanted and definitely the way our customers are asking us to. >> And so that's my question. Was it customer driven? Was it them saying: We just need you to able to do more or what? >> I think if you look at our core business last quarter, Gartner, obviously, ranked us as the fastest growing server business globally. We grew 68% year on year on our revenue last quarter. And so, with the momentum we've got now as a business, we're seeing our customers want us to do more. We're number one in customer satisfaction, number one in reliability, so they see value in us generally but we had sort of what I would classify as a subsegment of the storage market that we want to address. And of course our customers are saying to us: Hey, we want you to do more for us because we like the way you've performed. So it's been good for us. We're excited >> Yeah. >> So, Rodger you know a thing or two about the storage industry and NetApp specifically. So give us the customer viewpoint. You talked to a lot of them. >> Well, first of all let's say this is one of the better kept secrets because it never leaked out. So really haven't been getting customer calls on this event yet but I'm sure we will starting today be getting a lot >> Yeah, they brought a few analysts down to RTP in July and I remember, Rodger you said: What do I do when the customers call? and they're like, shh, shh. Customers aren't going to call. We're going to keep it under wraps. >> And they did a good job of it. But anyways, what it means, I think, to Lenovo is it really elevates Lenovo's standing as a provider of IT technology for the data center. So they now have, not only a very competitive server offering, which Rod's talked about quite a bit, they have what many believe to be one of the best storage offerings in the business. And now they can go compete head to head against Dell EMC, as well as with HPE, which would be the two larger competitors that they have to deal with. So it's going to be very good in terms of providing an alternative to clients for data center technology-involved storage. Good thing. We like competition. >> Absolutely, and we want to be part of it. I think up until now we probably haven't been able to. So when you look at how we're going to market, my field sales team has been planning with the NetApp field's also. We've been basically coordinating how we go to market where do we attack together, where do we have conflict, where do we not. So we actually go in and really focus in on those core competitors that Roger's just described which is where we want to go. >> In the keynote this morning it said from a channel standpoint, there's not a lot of overlap which, on the one hand, I'm saying: Well, sounds like we'll need a lot of training then. But how do you hit the ground running fast? >> So we are already ready to go. We start shipping tomorrow, so that's the good thing about this announcement. Like Roger said, we kept it under wraps, but we are ready to manufacture and go. So, I think it's a really exciting spot for us. From a go-to-market perspective in the channel, NetApp has traditionally been very much about engaging end users, fulfilling through the channel, but engaging end users. Where Lenovo's got a much stronger forte around mid-market and SMB. And we've got a much stronger forte in emerging market, so if you actually start to split geographically the world up a little bit and then you can start to split how we got to market a little you can actually find some really big parts of the market that we don't conflict at all that we can go after. >> But you see, from my point of view, the bigger challenge they have is to go to market. Now they could say there's not much overlap, but you know there's always overlap. There's going to be certain accounts where Lenovo already has a position, and maybe NetApp has a position, too. Then, who's going to do what given a time? So, the biggest challenge that Lenovo has here is also a challenge for NetApp is how they manage together the go to market motion, as well as the service and support because Lenovo's going to have level one, level two support responsibility. They're going to have that revenue to go for support. We'll see how that works out over time. >> I want to ask you what your advice would be to Lenovo leadership in terms of, this deal enables it to go after bigger players and to take over more of the market. But when it's now going head to head with Dell EMC , what do you think it should focus on? >> I think it should focus more on marketing. The products speak for themselves. The competency of these products are well-known. Besides this, it gives Lenovo the opportunity to become more cloud-friendly, too. Because they also have access to all of the software out of NetApp's cloud data services organization. So my main advice would be to Rod, because he's responsible for this, (Rod and Rebecca laugh) is put more wood behind arrow. Get Huawei to put up more money to accentuates the marketing of the product. Create more enthusiasm about the fact that you're now up at another level in terms of being an IT provider to the data center. >> It's a well kept secret as you started out by saying >> Yeah! >> That's right so we've got a business case that we've put together that's starting today obviously. Which involves us getting out and starting to hit the ground running with a lot of media. There's a lot of social media noise today on it obviously. Thanks very much to people like yourselves which is great. And I think we're going to see a lot more marketing-based initiative that run both through the channel as well as to the end users across our, what I classify as our T-1 countries to start with. To Roger's point, though, when we look at the go-to-market, we basically categorize in all the accounts into four boxes. Those accounts where NetApp's very strong and Lenovo's very strong which means Lenovo's strong from the server perspective. NetApp's very strong from the storage perspective. >> FAP would be one of those. >> That's right. That's a very good example. And in that environment, we're going to collaborate and show them we're communicating with each other and ultimately, not fight with each other. We're going to recognize that we want to continue to protect our server business. They're protecting their storage business. We don't want to touch that. In a place where Lenovo, for example, may be weak and NetApp very strong, so they're got a very good storage relationship, we want them to bring our servers into that space. Because obviously if they don't bring us in, then one of our key competitors that is also competitive in NetApp is going to have a foot in the door there somewhere. So, we're going to drive a little bit of a different strategy in that environment. Then, we've got obviously the third environment where Lenovo's very strong from a server perspective and NetApp's nowhere. In that environment, it's free fields for Lenovo to go after that with our new storage array. And then obviously, where we're both neither engaging those customers, it's in acquisition for both. We're going to play and ultimately go after them. There's some really great things that we've been able to put together with this relationship. Like for example, comp neutrality. So, the NetApp teams when we go into that third and fourth box I was just talking about, the NetApp sales force is going to paid the same whether it goes on a Lenovo hardware or goes on the NetApp hardware. So, we've got some pieces that sort of ensure that we don't have conflicts and we're all aligned to ultimately grow and compete with Dell EMC and HPE. >> So, Roger There have been some interesting server and storage partnerships. I think back a decade ago, Dell and EMC did billions of dollars together. It eventually broke up, and then what do you know, it went back together. I think five years ago, NetApp had pretty strong server partnership there. The storage market has changed a lot in the five to ten years. Tell our audience a little bit how NetApp's different, how the storage market's different, and how customers should be thinking about an arrangement like this. >> Well, the storage market's different because there's more alternatives for storage. There's the Cloud: AWS, Azure, even Google Cloud. You get over to Asia, Alibaba over in Asia and so forth. So that's had a very large impact on on-premises storage. The other one is hyper converged. Lenovo's very much a hyper converged business. They have relationships with Nutanix. They've got them with VMware. They have them with Pivot3 and some others. And so, all of these things come together to create a different alternative to the classical three-tier infrastructure: server, networking, and storage. So, all those things are going to exist. And, the upright storage market, while it may be a declining market from a revenue perspective, has a long payoff. It's going to be like mainframe, so it'll be here forever. Like Tate, here forever. It's like me. I'll be here forever. (Rod and Rebecca laugh) So right now, Stu, we're seeing a little bit of a bubble. So we are getting a bubble interns of, this is a good time by the way for Lenovo to have this partnership because there is more likelihood of increase spending for IT. Good economy in the States, good economy over in Europe. Good economy around the world for that matter. I think it's going to last another couple years. 2018, 2019, maybe in the 20's before it starts tailing off again. So the way people are talking to me now, it's kind of like a flush. Hey, we got all this money. We're going to go spend it. Refresh everything. Get more over into Flash. I think they will sell a lot of Flash, even with the entry product, what they call a DE. I think they'll sell a lot of Flash there. And of course up in the DM series, which is the equivalent of NetApp's A200, A300 which are top tier products. They'll sell a lot of Flash there, too. >> I would say as what you just mentioned, the traditional storage market is reducing, but Flash is obviously growing. NetApp last quarter was the number one Flash company in the world. 27.6% market share-- >> Where do I check that out? (Rebecca laughs) That comes from another source >> That comes from another source, yeah sorry. But they hit number one last quarter according to an unknown source. But I think that's really encouraging, right? And at that part of the market Flash is about 30-40% of the overall storage market right now and easily the fastest growing. So this product range really drives an all-Flash array type solution that we can actually take advantage of. >> Rod, we want to get your perspective China, too. That's a big piece of this announcement. Maybe you an talk a little bit about that. I think Roger's got some comments on it. >> Well, think this is a good deal for NetApp. This is the reason why I think maybe the channel conflict won't be as bad as it was for the Dell EMC guys. This is the way for NetApp. NetApp wants to go more and more towards the Cloud. You look at their strategy. It's going more and more towards the Cloud with all of the Cloud data services software that they're developing. And so they're putting more and more emphasis on that. At the same time, the relationship they have with Lenovo gives them the opportunity to get really a creative revenue that otherwise would not get. Allows them maybe to reduce the burden that they would have under manufacturing SGNE expenses and stuff like that. But the big benefit is China. They JB in China is going to give NetApp a entry into China that otherwise would not be able to get because of the laws that the People's Republic of China have. It's a big deal. >> I think we're really excited about China. Obviously that's one of the cornerstones of the deal. So it's an independent organization that's going to be set up. Lenovo will have 51% ownership. NetApp will have 49. Seven board members. Four of them will be Lenovo. Three of them will be NetApp. And ultimately we are going to have that organization just purely specializing on the Lenovo product that is designed by NetApp originally. And it's going to be doing joint IP. We'll have joint developers in there. We'll be able to leverage my existing sales force in China, that's our traditional sales force, to go and drive for everything from a tier one city all the way down to a tier six, tier seven city in PRC. But, that joint venture itself will just be a specialist organization specifically on storage. It's really exciting. >> The thing about the JB it's very very important. Whatever is developed by the JB has to stay in China. That software cannot be taken outside of China because of all the geopolitical issues that you have around the world. Big point. >> Yeah, and a challenge. >> Absolutely It's a challenge that both NetApp and Lenovo have to manage with respect to each other. >> Just for the record, I'm not totally sure. If we develop something in the joint venture, I'm not totally sure that we have to keep that in China. >> I'm not saying that legally you have to. I'm saying emotionally you should. >> Emotional we should. >> Ahhh... >> I was going to say... >> There might be some government concerns on some of that. >> I think it's always going to depend on the government. And we don't want to get into a geopolitical conversation. I think Europe for example will be a lot more liberally open to that sort of stuff >> Speak a little bit about the cultures coming together. NetApp. You've been working on this deal together. sometimes that can be the strength or the challenge. >> I think company cultures are always challenging. And when you get two companies that are, especially right now, as we've heard this morning in some of the sessions, turning the corner. They're both growing. They're both doing very well at the moment. So, there's always a level of confidence, shall we say, in both those situations that you've just got to break down. And I think what we've done very successfully this time is Wei Wei, and George and Kirk, and George and Henri Ricard and myself and Brad Anderson, you saw today who was actually up on stage with us today as well working with us as the executive sponsor on that side. We're lining up our executives globally. All of the field team for Henri Ricard's team and my field team globally have all been interlocking with each other. They're account planning. They're territory planning. We're really trying to break down any of the walls the way may have from a cultural perspective. And really drive a much more open conversation, so we don't get caught out early in the deal. There's a escalation process in the deal. So it goes to a geo level up and then ends up with Henri and myself to actually manage worldwide if something was to get really out of control. But, at the moment, day one, don't see any issues. Seems to be going okay, touch wood. >> They both, Brad and Kirk, said they're complementary companies. Is that your perspective, too? Would you agree with that? >> Well, I think they gave complementary purposes. The interesting thing here is this thing's coming together when both companies are on the uptick. It's not because, go back three years ago and look at where NetApp was three years ago versus where they are today. So it's coming together when both companies, and matter of fact, go back to look at Lenovo two years ago as well >> Absolutely. >> So it's been an uptick that happened here over the last three years, so this thing's coming together when both companies are doing quite well in that respect. >> So-- >> By the way I want to mention Gartner will be publishing a report in September on this transaction from NetApp's point of view. And we'll be publishing a report on in November on this transaction from Lenovo's point of view. >> Great, so one of the things people like Roger and I have to do is, we look at how we would say whether something's successful or not. So I want to get your point first too and then when you look out six, 12, 18 months from now, whether it is successful and the thing I have to say is Kirk and Brad said, well, our goal is to number three in China, and I said isn't that a low bar? Aren't you practically on day one? I mean you're two joint companies. It's going to be there? >> I can't count on my boss to be honest with you. >> You've got the sales team. I know you can do that. >> I would love to. I think at the moment let's just talk about the joint venture for a second. I think the point is at the moment we are only 15% addressable market with our existing range. And so at the moment, we're saying, hey we can address 15% of the market. That puts us way outside of the number 10 slot in PRC. So, to say we want to be number three, is quite ambitious. Especially because we want to try to do it in the next couple of years. So, I actually feel like he's being quite aggressive from a growth perspective, so I think that's quite balanced. Outside of that, I really want to measure us on profitable growth. We really want to diversify our share of wallet and our customer base. We've got a great customer base now from a server perspective. We need to really expand that to ensure that we're taking advantage of the customer feedback we've had. So, I think that's a pretty good spot. >> At the end of the day, the success of non-success of this program is in Rod's hands. (laughing) On the one hand, >> I love the pressure you're putting me under >> And then Laura, on the services support side. People will support this program if they get good quality service and support. So, you have to keep that up for this program. >> Absolutely, and at the moment, the services model is level one and level two is run by Lenovo internally and then level three escalation runs into the NetApp program. We believe we've got a model that runs well. >> A good note to end on. Thanks so much for coming on theCUBE Rod and Roger. >> Thank you very much. >> Thanks for having us I appreciate it very much. It's my inaugural time here. >> First of many. I'm Rebecca Knight for Stu Miniman. We will have more from theCUBE at Lenovo Transform just in a little bit. (techno music)

(upbeat techno music) >> Announcer: Live from Boston, Massachusetts, it's theCUBE, covering ZertoCON 2018. Brought to you by Zerto. >> This is theCUBE, I'mm Paul Gillin. We're here at ZertoCON 2018 in Boston, final day of ZertoCON, a beautiful May day, and the key note we heard this morning by John Morency, Gardener analyst, talking about resilience, which is something you've been doing for the last 11 years at Gardener, I understand. >> Yeah, that's right Paul. >> My career at Gardener has really been focused primarily on recovery, continuity, resilience. I've had the good fortune to have done well over 10,000 inquiries with about 3300 organizations across the world. And if nothing else, it's given me a good opportunity to see what's happening, what's not happening in that area, how services and how the technologies of all things, it's been a lot of fun. >> You said something that struck me this morning, you said that two years ago you were sort of a voice in the wilderness talking about resilience. Today it's a mainstream topic. What has changed in that time? >> I think a couple things, number one, so what's happened to resilience in the past couple years, what's changed, number one, the impact in digital business. With digital business, given that it's always on operation, that it scans both production data centers and public clouds, the trying to apply some older technologies or methodologies like disaster recovery to a digital business, and always on digital business, doesn't make a whole lot of sense. I think what happened was that we began to see both internally as well as externally, a significant rise in customer inquiries, specific to resilience. So, for example, from the calendar year 2017 to 2018, year over year, we've seen a 30, 35% increase in customer related inquiries. Actually we began to sense that something was really going on at our infrastructure and operations data center, so back in 2015 I had about 40 inquiries during that conference and resilience came up in about 75%, and it wasn't just financial services, it wasn't just healthcare, it wasn't just telecom providers, it was lots of different verticals. And so at that time, my conclusion was that something interesting is going on here, but I don't think sometimes that what's happening at a lot of individual clients, sometimes always translates or flows back what Gardener covers from a research standpoint, but I think with e-business, with the focus especially around cyber resilience, threat attack mitigation, if nothing else, cyber attack resilience is probably getting one of the most significant drivers to create the need for resilience. And I think what's happened there is that it's actually pulled through some of the operations availability, some of the data integrity management and so on. So I think without a doubt, cyber resilience has been probably the most significant driver that's really changed. >> When you think back six or eight years ago, it wasn't uncommon for Amazon to go down, or Twitter, the Fail Whale, some big services would go offline sometimes for hours. We don't hear about that anymore. And is that because it's a common place? Or are these organizations now so good at resilience that they virtually eliminate a down time? >> Down time never gets eliminated. We had an interesting discussion with Amazon a few years back, and the perspective that they shared with us was: "Look, we're getting better at sustaining "continuity and availability, "but we'll be the first to admit "that things happen, unexpected things happen, "and they can be the result of an external event "which you can't control, "it can be the result of an internal event.' What's happened is that there's a separation of duties that's interesting to note. So if you look at Amazon and Microsoft and Google, they do a great job at keeping the infrastructure, the cloud services, the infrastructure, the service, alive and humming and scalable, and elastic and so on. However, when you look at what's going on in the context of either a virtual machine, or a container or some other type of compute instance, that's where the provider's responsibilities end from an availability point of view, from a data integrity point of view. And so that's where even though the providers themselves have great service levels, so Amazon may report five nines, six nines, whatever happens to be in terms of unplanned down time, you can still have disruptions for specific customers within virtual private clouds that may be the result of, it could be an external attack, it could be a mass supply change. And so this duality in terms of unplanned downtime from the cloud providers perspective, but from the cloud customers perspective, and the two quite often are very different. >> Interesting point. Now also seeing the emergence of some new computing paradigms, containers, a huge phenomenon right now, serverless computing, microservices in which computers instances may be spun up for literally milliseconds for connections, is that going to create a resilience problem? Or does it, in fact, solve resilience problems? >> I think it could be a little of both. Certainly when you make the compute service less complex in their fewer moving parts, and you leave the orchestration of the service fulfillment function in the hands of the provider who can do a better job at that. That could certainly have an impact on improving the level of resilience. Not just from the provider's point of view, obviously, but from the provider's customer point of view. But with microservices or containers or what have you, there's still the issue of sustainable data integrity. How do I know that my data is what I expect it to be, where I expect it to be, has there been any unplanned change? Because some of the changes in data can be the result of things that have happened internally as well as externally with a given service service provider customer. And so, from that point of view, certainly the fewer moving parts are reduced complexity, the orchestration automation a provider provides no doubt that will help. I think at the same time, there's still some issues, especially around data integrity, cyber tech mitigation, data protection, that I think will still be specific issues and opportunities for cloud provider customers to focus on. >> Now we're about to see companies very excited about the inner and outer things and the possibility of getting into streaming data, really large scale data collection about to come online. What kind of new resilience challenges will that present? >> I think, getting back to what we were talking about earlier, when we look at streaming services or inner and outer things, it's the additional complexity, it's the value chain, if you will. The service deliver chain between the source and the destination, so more moving parts creates opportunity for greater complexity. There's no one entity that is responsible from the serviced assurance point of view for each and every component part. So certainly there's a huge opportunity from a new business opportunity, and a service fulfillment point of view, but from a resilience point of view, given that you have more moving parts that you have distributed entities responsible for managing that, it does create some new risk, new issues, but also new opportunities. Have we as an industry solved all those yet? Not really, I think this is very much a work in progress. >> We've got also, the tremendous focus now on information governance, particularly the new regulations coming online, companies trying to get a better handle on the data that they've got, do these disciplines merge at some point, resilience and governance? >> Very much so, very much so. It gets back to the question, one of the key questions around resilience is who is responsible and accountable for making business and operations resilience within an organization happen? And one of the things that we've seen if you look at it from a senior management point of view, really the responsibility, I think, is co-owned by both the chief risk officer and the chief information officer, and probably you could add the chief information security officer on top of that. But since resilience in many ways is top down, it's not just at the infrastructure level. It has culture implications, it has business process implications, it even has implications on what the individuals within the organization need to know about, what they need to be aware of. All of that is related to effective, top down governance. And in the key note this morning we spoke about that bank that I've worked with. They had that problem in spades in terms of different businesses, different geographies, where to start in terms of the governance model. Where to start, what services and what geographies with what business opportunities? But even with that initial focus, had the bank entirely address it's resilience challenge? Not really and that's a process that likely will take several years to complete. >> And plenty for you to talk about with your clients, those inquiries in the coming years. >> John: Absolutely, absolutely. >> No shortage of changes there. John Morency, thanks very much for joining us. >> My pleasure, Paul. >> We're here in ZertoCON 2018 in Boston, I'm Paul Gillin, this is theCUBE. (upbeat techno music)

(upbeat music) >> Narrator: Live from Washington D.C. It's the Cube. Covering .NEXT Conference. Brought to you by Nutanix. >> Welcome back to .NEXT in D.C. everybody. My name is Dave Vellante and I'm with my co-host Stewart Miniman. This is the Cube, the leader in live tech coverage. We go out to the events and extract a signal from the know as we hear it. .NEXT, Nutanix's customer event. Two days of wall to wall coverage. Julia Palmer is here. She's a research director at Gartner. My new best friend. (laughs) Great to see you again. We had a great dinner last night. I really enjoyed the conversation. Thanks for coming on the Cube. >> Oh, my pleasure. >> So, it's a good little event here. Lot of excitement. But what's your take? You are a former practitioner, now an analyist. You were in the heart of technology at GoDaddy. You really know the market, the products. What do you make of what's going on here at .NEXT? >> You know when hyper convergence first emerged it was all about saving money. It was all about going from infrastructure that was maybe too complex and too expensive to something that maybe, based on commodity will bring lower acquisition costs. But this not the story today at all. That's what, I think my IT leaders are telling me. They're not going after acquisition costs. They're not looking at things and just comparing by the capex. They're looking at the bigger picture and how will this technology will help them to enable business. So that's I think a the biggest difference now. Going from something as simple as, is it going to to be more expensive? Less expensive? To how will it move the needle to my enterprise, to my organization? >> Dave: So that's certainly the messaging that you're hearing from, from Nutanix. As a practitioner, do you buy that? Do you believe that they're more than just an infrastructure company? That they are a transformative force in the industry. >> Julia: Yeah, I hear a lot, you know. I moderated a panel today with three customers and one of them said, you know, I'm in the health care business. I'm here to save lives. I'm not here to reinvent my own hyper converge infrastructures. So, he wants to focus on what's important for his end users. And he wants to stop manage (mumbles). That's just not a focus. And I hear it over and over again from different types of customers. >> Dave: Hmm, now you were not a Nutanix customer previously, correct? >> No. But you did see a lot of different infrastructure products? >> Julia: Absolutely. >> As a practitioner what bothered you about what the vendor community did. What were your likes and dislikes? >> Julia: Everything. Everything bothered me. >> Everything bothered you. I was part of pretty large organization and when you have a big footprint you have big problems. And one of them, for example, was that we would have an outage and we reach out to the vendor and they would tell us, you know, you hit a bug and we have a fix and we will give you the fix and you will be good to go tomorrow. Nevermind the outage that you had and impacted end users. So now a lot of vendors are using predictive analytics. Cloud based analytics, >> Right. to see if there's anything in your existing environment that's susceptible to existing bugs and proactively reach out to you to provide a fix. So I was just thinking, looking back, how many outages I could have prevented if this technology was available when I was running it. >> Stewart: Yeah, Julia, I mean we know that companies for so long, you know, infrastructure, they spent so much of their time, you know, running around, patching it, fixing it, worrying about that. Hyper converge now is trying to talk about, you know, where it fits into the whole cloud picture, which is mostly about an operational model. Where do you see along those trends. Do you believe that hyper converge really fits into a cloud strategy or is it cloud washing from a bunch of infrastructure people? You know? >> I think it has a potential. I don't think it's there today. But I think it has a great potential because when I talked to Gartner end users about, like, why hyper converge? And I actually did some total cost of ownership research, what they all told me that looking back they realized how much OpEx it saved them. And they say it was very difficult. You kind of had to take our chance on it because upfront you can't predict the outcome. Is it really going to be more simple? What does simple mean? What's key performance indicator and simple you can put. So, but looking back, the guys that implemented, they all told me that 60 percent of OpEx they saved. Meaning they didn't last with infrastructure (mumbles). How do they do this? They stop manage components. They start managing VM's. So next step is stop manage VM's, start managing applications and that's what cloud management is all about. Getting out of infrastructure management all together and deliver a business what they want. And usually, they want support for their applications. >> Dave: So, my understanding is that Gartner has analysts that service the vendor community, the executive community, and the practitioner community. You are a direct practitioner, >> Yes. Advisor. >> I deal with IT leaders. Okay, your peeps. (laughs) I think you mentioned to me last night that you've had hundreds of conversations and you've only been at Gartner, what, six months? >> Two years. >> Oh, two years, sorry. I apologize for that. Okay, so in the two years, hundreds of conversations. Is that fair? What kinds of conversations are you having with clients around infrastructure? What are the challenges that they're having? And what are you advising them? I know there are many, many, but maybe you can summarize the top ones. >> That's a very good question. I actually want to write research about it. Top five questions about hyper converse people asking so I've been thinking about it for a while. So, different types of customers, new customers are asking questions about, is it ready? Should I go for it? Why would I go for it? Why can't I keep my (mumbles) infrastructure design? What should I look for as a new key performance indicators? It's not the same way, how would you judge it here. Then existing hyper converge customer are looking for what's next step in hyper convergence. Is it ready for prime time? Is it ready for mission critical applications? Because they're looking at the boxes and they look at the commodity hardware and they still feel uncertain. Can it really run something that they're a proprietary hardware used to run. So we explore the advantages of software defined, software defined storage. Value is in the software. You know, being backed up by software defined storage, my favorite subject, is a, is a, you know abstracting and distributing data that you don't worry about us anymore. So scale out storage replacing proprietary architecture can provide you same level of uptime and performance especially with new, you know, flash options. So that's a popular question. Number three is just the, you know, we leave it to in the age of a compressed differentiation I believe my colleague Dave Russell calls it, and there's a small differences between the vendors and end users are not aware of this. And they can be critical for particular use case. So they always ask strengths, weaknesses, opportunities, threats on each and every one them. Because we have a lot of solutions on hyper converge now. A lot of vendors, prominent vendors now join the market. So end users are a little bit confused. How do I navigate through this ocean of different hyper converge solutions. >> Stewart: Yeah, so Julia, Nutanix helped really drive a lot of this awareness for the hyper converge market. Now, every company, you know, all the big players have at least one, if not multiple solutions out there. How do you see Nutanix? Are they differentiating themselves? Are they, I know they're trying move beyond kind of the hyper converge label, ya know. What are the doing good? What would you like to see them do more? >> Julia: Yeah, Nutanix is a, you know, was one of the leaders from the very beginning. And, you know, remains the leader. They obviously succeed in at least in a lot a features. And a very fast release cycle of new features. It's easy when you have one focus, you know. Other companies have so many different areas they need to focus or protect and Nutanix doesn't have this problem. And also being able to mix different hardware, I think it's an advantage, you know. Being able, the customer needs to make a choice, you know. I think the structure of the future is going to be all about choice. It's less about, ya know, this is a lock in. I want to pick my hyper visor. I want to pick my hardware and move on. >> Stewart: So one of the things I think Nutanix does best when they're not positioning themselves as a storage solution, however, cause the storage market is tremendously competitive and there's always the, you know, there's the next technology, the next wave. There's so many competitors out there. I mean, do you think things like NVMe over Fabric are going to just, you know, have the potential to disrupt everything that Nutanix is doing? You know, what are some of the big threats to, ya know, their current position? >> Actually, I just wrote a research about how NVMe and NVMe over Fabrics is going to disrupt and improve integrated and hyper converge systems. I think those technologies and it's like NVMe without NVMe over Fabric. It's like, I call it, it's like barbecue without barbecue sauce, right? So the NVMe and NVMe over Fabric has potential to boost performance of hyper converge systems on par with what a solid state, erase today do. So I think a, and it's commodity hardware, right? We're not talking about anything proprietary. So when a we going to move towards this territory when NVMe and NVME over Fabrics become mainstream maybe two years from now, three maybe years from now. I think everybody can enjoy shared distributed storage performance. And, but honestly, your question about storage, like do you need to position yourself as a storage company or not, the major difference about different hyper converge products, in my opinion, is how they do storage. Other than this, it's the same flavors of hyper visor, it's the same commodity hardware. So what do we have different? The ways you did data services. The ways you position your storage. You, you deliver the storage services. >> Stewart: So, you know what, I'm curious. When I read Wall Street stuff about Nutanix they seem to overreact to every bit of news so, you know, the Dell relationship, ya know, is challenging there for that to head win. Oh wait, the Google announcement seems to be a great tailwind, ya know, the big bump in the stock today. Do you see those partnerships as critically important or is it the vision and execution of Nutanix and what they're doing with their customers? >> I think so. I think we live in the age when a ecosystem support is everything, ya know. People not necessarily today go to the public cloud to save money. They go for ecosystem support. To expand their services and their capabilities. That's why, ya know, embracing the cloud and not trying to position yourself against is the right way to go. I think we all need to embrace cloud and find the way that will benefit the end users. >> Dave: Um hmm, so you were sharing with, you spend a fair amount of time, all Gartner analysts who do these things do on magic quadrants. They, we put a lot of effort into them. A lot of people criticize magic quadrants. I think they're unfairly criticized. I know how much work goes into them. >> Thank you. And they are fact based opinions if I could categorize them like that, right? Is that fair? So, do you do one on hyper converged infrastructure or converged? Do you separate converged from hyper converged? How do you look at the market? >> Julia: So last year magic quadrant was integrated systems, which is converged and hyper converged. But what Gartner does is actually, every year we look at the market and we adjust our inclusion criteria. We adjust market definition. So, I don't think it's a big secret that hyper conversion is leading this market right now. And, honestly, in conversion infrastructure, if you look at conversion infrastructure, it's very similar. The only difference in conversion infrastructure is how you do storage. Which storage area you are using. So it becomes less strategic to even analyze conversion infrastructure. So you will see this year, I cannot break all of the news here, but much more emphasis on software driven, hyper converged infrastructure. Not services. Not the appliances, but more software. >> Stewart: I love to hear that cause at Wikimon when we called the category "server sand" so like VM ware, major player both as a partner in Nutanix. A competitor in Nutanix. Ya know, I know there like, they don't show up on the Gartner magic quadrant because they don't fit into that environment. Also the lines between converge, hyper converge, and software defined storage seem to be blurring a lot. I mean, in some ways they're just different ways of packaging. Some of the others, they, hyper converged is a, ya know, delivery option for what they're doing, so. >> Julia: Exactly. >> Where do you see it going, ya know, it's, ya know, obviously beyond the appliance but, ya know. Say there's the Google announcement today. Where do you see, ya know, a company like Nutanix fitting into this hybrid or multi-cloud world? >> Differentiating on software, this is the name of the game, right? So, if you can have a portable software you can run on any hardware, you obviously can continue and run on any cloud as well. And this is an idea. You said it absolutely right. Like software defines storage. It's not a technology. It's a delivery option. So customer needs to be in charge of their options. Do I want to deploy on premises? Do I want to go on cloud? Do I want to have an appliance? Do I want to buy a software, bring your own hardware? All of those choices need to be given to the end user. They need to decide which way they want to go. >> Dave: So, we're going to have Chad Saccage on tomorrow and it's obviously interesting, we see Nutanix selling through Dell. We were there two years ago when that announcement was made. Great, ya know, business. Terrific. But as you were saying, converged and hyper converged and software defined, they're all coming together now. What do you expect is going to happen with EMC and Nutanix? Do you have any... I don't want to use the prediction, but any scenarios that you can see developing there? >> I think, you know I hate to speculate, but I think both of those companies are extremely user oriented. So, if there will be demand for Nutanix that will continue to support Nutanix because they will do it right by the customers. And same with Nutanix, ya know, they never want to turn someone down saying it's not their problem. Both support them in parallel as long as demand is there. >> Dave: So let me ask the question differently, cause I agree with you. EMC, customer centric. Michael Dell, there's nobody more customer centric on the planet. Clearly Nutanix is customer focused. Having said that, if the three of us were advising Dell, EMC on what to do, we would say keep doing what the customers want. Great, check. But from a product roadmap standpoint, I don't know about you Stew, but I know I would push them to look at doing more of a hyper converge, software defined, like roadmap, as opposed to kind of bolted on V-blocks. Which got it all started. Would you agree with that? Or, do you think that's a waste of R&D? Just outsource it or OEM it? >> Software defined storage is hard to do. It's hard to do it from the ground up, ya know. Products need to mature, ya know, VMware, VSEN. It's a mature product. It's a good foundation for software defined storage and for hyper converged. Building something from the ground up, just to separated from VMware, it will be very difficult. >> Dave: Okay, well okay, right. Well then double down on VMware maybe is the advice there. Or maybe they're not really inquisitive right now because they have the debt service but over time maybe bring in startups to innovate there. Or maybe not because when you look at the Dell EMC deal from previous generations, there's a very successful deal. One of the most, probably the most successful storage deal in the history >> Stewart: Talking about the partnership? >> of storage. The partnership. >> Sure. Before Dell bought Compellent, then remember, Dell buys Compellent. I would look back on that and say Dell probably would have been better off just staying with EMC. Reselling EMC. I mean you were there during those days. I don't know. Was Compellent and EqualLogic, >> EqualLogic were those successful acquisitions in your view? In retrospect. >> Stewart: In retrospect they did pretty well but you're right Dave, the EMC partnership was way more money. I think by the time Dell bought EMC the internal Dell storage, ya know, revenue had grown to almost, or a, ya know, order of magnitude, the same size of EMC and they had to put a lot more emphasis into it. So, you know, better margins, ya know, just if they continue to partner. >> Dave: So maybe it's better for Dell to continue to partner is kind of your point. >> Stewart: Yeah. >> Julia: Absolutely. >> Uh huh, okay. Very diplomatic. (laughs) >> Julia: Would you expect anything else? (laughs) >> Julia, thanks so much for coming on the Cube >> Oh, thank you guys it was a pleasure having you. >> it was my pleasure >> Julia: Thank you for having me. >> You're welcome. Alright, keep it right there everybody. We'll be back to wrap right after this short break. This is the Cube. We're live from D.C. at Nutanix .NEXT. Be right back. (electronic music) >> Narrator: Robert Hershev.

>> We just started reselling Veeam We now have a combination of a very strong technology portfolio, deep integration, and a commitment to good market partnership. The combination, we think, will be very exciting for HP, Nimble, and Veeam customers in the years to come. (relaxed electronic music) >> Announcer: Live from New Orleans it's theCUBE covering Veeam On 2017. Brought to you by Veeam. >> Welcome back to New Orleans, everybody. This is theCUBE, the leader in live tech coverage. We go out to the events and we extract the signal from the noise. I'm Dave Vellante with Stu Miniman. Dave Russell is here. He's a vice-president and distinguished analyst at Gartner. David, good to see you. Thanks for coming on. >> Hey, good to see you guys. Nice to see you again. >> So, we were talking off camera. I mean, you are probably the number one known backup, data protection analyst in the business and have been for quite some time. You've seen it all. Give us the state of backup, recovery, data protection, availability, whatever you want to call it. But where are we today? >> You know, in some regards, I don't know if we're any different than we were 28 years ago when I got into the business. The interesting thing is, my wife actually got into this before I did. We were both mainframe developers of backup at IBM and I didn't really want to get a real job. Maybe you could argue I still don't have a real job but what I wanted to do is to stay in grad school forever and I started doing backup there in grad school for undergraduate computing lab. And about six years ago, I showed my wife some of the polls that we do at Gartner events. We can do realtime feedback, what's your greatest challenge, what are your issues with backup? And then she said that was kind of interesting. Two years ago, she came to an event we did in Las Vegas and afterwards she came up and I was hoping she was going to say, "Hey, you did a good job." She said, "What in the heck have you been doing? "These are the same problems when I left the industry 20 years ago to be a mom." Everybody still has too much data, too little backup window, the cost is too high, the complexity is too great. So a lot of infrastructure changes but not a lot of the same pain points have shifted dramatically. What has shifted, though, is cost is even more important than it ever was. Obviously, we could talk about volume of data but now we maybe want to have multiple copies of even our backup data. We want faster access to that backup data. 'Cause we want, now, backup to be a high-availability replication solution not just the tape in the vault somewhere. So there's now speed requirements on our backup. So, I could keep going forever but I'll just let it out to say that as an industry, we still have many of the same challenges that we've always had, arguably for decades and decades. Now, the challenge is the cat's out of the bag, meaning the rest of the business sometimes is aware of just how costly this is. Just how difficult this is from an op-ecs perspective. We can't go hire five, 10 smart people to do this. >> And the backup window, is it correct to say it's essentially disappeared? >> Yeah, there's some organizations that really feel like we don't have a backup window 'cause if we just take a step back, what is really backup, nevermind how you could use it for other use cases like DevOps. Backup, if you state it in the most unappealing terms, it's how much data are you willing to lose, how much time are you willing to take to go get that aged copy of data. And, of course, the rhetorical answer would be well, I don't want any of those bad things to happen, right. But at the end of the day, that's really our frustration. >> David: And I want it back instantly. >> Yeah. >> Okay, so that's obviously putting great pressure on the businesses. So when you look at Veeam's ascendancy, I've been saying it all day and I'd like to test this with you, it sort of coincided, obviously, with VMware and when people had to sort of rethink their VMware backups. You just did a webinar entitled "Backup: Fix it or Ditch it." I feel like a lot of people went through that, answering that question in early VMware days. So, give us, what was the conclusion of that webinar? >> Yeah, well, the number one thing is frustration. And we've done a lot of drill down on what are you frustrated on. Number one is cost, number two is complexity and we could even break this up by large enterprise, mid-size, and smaller enterprise but there's a lot of similarities. So now, where do you come out on fix it or ditch it? The answer for many organizations, is a little bit of both. And what I mean by that, this is kind of mind-boggling, I think, is that backup space used to be sweep the floor. If you were in an incumbent vendor, you wanted to kick out any other solution, if you were an organization, you wanted to collapse from three, five backup products to one backup product, and if you were an emerging vendor, what do you want to do? Go kick out the incumbent vendor. But now, an organization says, "You know, maybe we'd like "to completely change, but we can't. "So we're going to try and fix what we've got." And that's usually what I recommend, at least try and get the value out of what you've already bought and deployed But we're going to implement something else, too. So, there's probably 15 years or more of trying to collapse the number of solutions. Now an organization says, not 'cause I want five solutions but because through pain, basically, not getting my needs met, I'm going to continue running two solutions or expand to two solutions. And you could argue Veeam invented that. They came in on the virtual end, exactly to your point, and then it was a land and expand. We see this happening, though, in the industry overall. >> Dave, I have to think that just the current state of cloud is compounding what you're talking about. Customers have their own data centers, they have virtualized environments. I think Veeam said this morning the average customer they have is only 75% virtualized so they've got 25 physical. Everybody's got SASS, everybody's using some public cloud, at least for some test data. Veeam says that they can now go everywhere but most customers are probably doing piecemeal deployments. Everything in IT is additive. What do you see, how does cloud impact that space in general? >> Well, my biggest fear on the cloud aspect, whether it's software as a service or public cloud, someone's going to rent you infrastructure, is that we're going to learn some lessons the hard way. Again, meaning that most organizations typically think well, if we went to software as a service, they'll take care of it. We have no responsibility anymore or didn't we "get rid of that problem" meaning backup or DR. And the answer is no. You're still the owner of the data. And where it gets shades of gray is that SASS provider's going to give you some level of protection, some level of backup. Chances are they're not going to give you everything you had when you had that email system on premise. So my fear is that organizations are going to suffer an outage and realize there is still a need for additional protection. Right now, many organizations, they're running a bit exposed or don't even realize that they're running a bit exposed. >> Yeah, what is the state of those SASS providers and public cloud providers? Is Veeam still best of breed to go in those environments or are we starting to see them all offer their own native pieces? >> Well, I think we're in a transition period because there's a number of third party solutions that can be good at handling this and you'd have to believe that ... So, take Microsoft for example. They're in the unique position of having had on premise applications and now having public cloud and so eventually, someone's going to say well, here's all the things we did for exchange on premise. Why can't we get all that availability beyond 60, 90 day retention if we go to SharePoint Online or exchange in Azure. There's a tension that's taking place right now. Right now, at this point in time, though, I think if an organization really wants to protect their data like they have and they're used to having been doing on prem, they're going to need a third party solution, whether it's Veeam or someone else. >> David, I want to ask you about your magic corner on data center backup and recovery software. It struck me that ... I don't want to overdo it. I know you guys are very sensitive about each quadrant and how customers should interpret that but we all do the same thing. We go right to the leader. People fight to be in the upper right. And it struck me that Veeam was the only relatively smaller company that sort of knows their way in there. And they're known for SMB but in the magic quadrant you were saying this is really the upper end of M and larger organizations. So what is it that sort of sets leadership apart and how is it that Veeam was able to get in there with those established, much larger players? >> Yeah, that's a great question because exactly what you said, the competitive response would have been isn't Veeam just deployed in small environments? And collectively, we take about two and a half thousand end user inquiry calls a year in backup. So we started seeing a number of trends a couple of years earlier that hey, Fortune 500 companies are deploying Veeam and it's not in the plant in Mexico City or in a small, little area. It's in the Detroit Motor City in the data center and we're seeing a bid for six figures or higher, in some cases. So that's when we started realizing, hey wait a minute. The point of being cast an enterprise supplier is to actually be in the enterprise. They're already in the enterprise. So that's what we started to notice and finally we said another issue we have with putting some of the leaders in quadrants, are they really leading the market or pushing the market? And we really felt that Veeam had kind of crossed over the point last year when we issued the quadrant in June that they were causing the market to shift, whether it was having better virtualization capability, changing to socket-level pricing, addressing ease of use. They were doing things and give sort of "extra credit" for a provider that can not only sense what the market is looking for but kind of push the market. >> Can you explain the socket-based pricing a little bit and how that affected the market? 'Cause I know a number of vendors have made some pricing changes. IBM in particular sort of said everybody can buy anything and use credits there and that was, I felt, a move to keep the install base where it is. Veeama interpreting was different with the socket-based pricing. What was that, did it have an effect on the market in any other way? >> Yeah, the short answer is it absolutely effected the market because you look at the number of heterogeneous backup vendors that have come out and now offer socket-based pricing. So they're doing this in response to Veeam. And what we see now is the organization, depending on who the buyer is, they have no idea what terabytes are. I know what server deployment we have, meaning how much socket we've got so it was just speaking to that constituency in a buying motion that they understood. >> Stu: Something they could quantify. >> Exactly. >> Veeam made a number of announcements this morning and some prior to the show. Anything jump out at you? CDP's one of the ones we've been talking the most. Maybe you could give us your quick competitive analysis of how that looks. >> Well, CDP was near and dear to my heart. In 2005, it was September 2005, almost the same day Microsoft came out with their data protection manager for CDP, Backup Exec came out with CDP. >> Stu: I was trying to remember when Kosha came out because I was at the company that acquired Kosha. >> Yeah, sure. So Kosha, Topio, you know, it can go on. And CDP, around 2005 and 6 was really a lot of buzz, going to change everything. The problem was it was difficult to do because thee infrastructure didn't facilitate it. So, back then you had to split the volume manager and have multiple rights. Now, today's announcement on CDP where you don't have to have a lot of extra infrastructure but it's the hypervisor that's splintering this off for you. IL filtering that's making this easier, making this actually achievable. I think that's going to be really compelling. Most people here I've been talking to say this is going to be great for critical applications. There were some shops I spoke with in the mid-2000s, you know, five, six, seven years, that said we use CDP even on general file systems and why? It's because if I keep making a delete and I call up the help desk and it's like, oh, Dave hit confirm to delete again. He called up to say can you get me my file back and it's the fifth time I've called this week. Well, data protection would allow us to go let him self-service perhaps, but definitely use less data. >> So, for Veeam to get that CDP granularity, if I could talk about that for a second. It's got to obviously rely on VMware APIs. Are you, I'm sure you're tracking this, but are you concerned about Dell EMC gaming the system? Historically, what have you seen there? Difficulty getting hands on SDKs? Trying to put the incumbent in an advantage. What are your thoughts on that? >> Well, you're right. Historically, especially at the storage rate perspective, proprietary APIs or sort of supporting SMIS but having quote "extensions" which are basically proprietary off to the side, were an issue. Here is a case where I think it's in the hypervisor's best interest, and soon it'll be in Microsoft's best interest with Hyper-V and you could go on and on about the other platforms to offer the capability as well. So there is a danger but I don't see how the sort of storage oligarchs are going to be able to fence that off in this case. >> Yeah, I call them the cartel. Is Veeam now, because of its ascendancy, part of that oligarchy? >> Well, I think you have to say approaching half a billion dollars in revenue, it's sort of like the enterprise question. How many enterprises do you have to get in before you enterprise? Well, how many hundreds of millions of dollars do you have to make before you're one of the big ones? >> What do you make of this messaging of Veeam, companies like Veeam, don't want to talk about backup anymore. Backups kind of past ... You see some start-ups like Datos the other day said no, no, we're not a backup company. Okay, and then there's shifting to this notion of availability. Does that resonate with customers? Is that the way customers are thinking about this or is it just sort of good marketing? >> It resonates with some customers. Now, personally, I like it 'cause to me availability is an umbrella. We can put backup and we can put disaster recovery and high availability under there. And maybe you can sort of find a way that DevOps and copy data kind of plays under availability. It doesn't actually work in all geographies. So, I was in Tokyo at a Gartner data center conference three weeks ago, I guess, almost. And they don't really, availability doesn't sound good and disaster recovery sounds worse because that meant you had disaster. So how much disaster recovery do you want to buy? Well, none because I don't want any disasters. So availability is a little regionalized. There are definitely some shops that just say look, I have a backup budget and that's what I need to go and do better. I have a backup pain point, etc. I think, though, whether it's replication and instant VM mounting and the notion of DevOps, we're seeing more and more organizations get their head around ... Whether they want to call it availability or something else but it's beyond backup. >> Well, what's come through loud and clear, however, is your point about cost. I mean, it seems like customers are still insanely focused on cost and that's because backup generally is insurance. So cost and complexity have to be minimized and a lot of the backup platforms that are out there are expensive and they're anything but simple. >> Yeah, and you look at the economics. We've seen negative pricing pressure on dollars per terabyte of backup software now for three years running. Now, list price and obviously, no one really pays list, but list price starting with just a small number of terabytes, some vendors were 10,000 dollars, some vendors were 14 and a half thousand dollars a terabyte and you and I go down to whatever shop and we go buy a terabyte drive, if you can find a one terabyte drive, for a couple hundred dollars. >> David: Four terabytes now. >> And obviously, the data written on it is where the real value is but you see the mismatch of I'm spending list price 14,000 dollars terabytes to protect 140 dollars worth of equipment. There's a problem here. So, whether you're the VP of infrastructure, the purchasing department, or just the backup admin that says I have a problem because I can't go buy now the agent for the database that I'm trying to buy 'cause we've already spent all this money on just the base backup platform. >> Yeah, there's really this 10 year pressure on all infrastructure pricing. Cloud, open source, is really putting pressure on that. So, David, thanks very much for coming on theCUBE. We really appreciate your insights and keep up the great work. >> It was great to see you guys. Thanks for having me. >> You're welcome. Alright, keep it right there everybody. We'll be back with our next guest. It's theCUBE, we're live from New Orleans, Veeam On 2017. (relaxed electronic music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Spark became a top level Apache project in 2014, and then shortly thereafter, burst onto the big data scene. Spark, along with the cloud, transformed and in many ways, disrupted the big data market. Databricks optimized its tech stack for Spark and took advantage of the cloud to really cleverly deliver a managed service that has become a leading AI and data platform among data scientists and data engineers. However, emerging customer data requirements are shifting into a direction that will cause modern data platform players generally and Databricks, specifically, we think, to make some key directional decisions and perhaps even reinvent themselves. Hello and welcome to this week's wikibon theCUBE Insights, powered by ETR. In this Breaking Analysis, we're going to do a deep dive into Databricks. We'll explore its current impressive market momentum. We're going to use some ETR survey data to show that, and then we'll lay out how customer data requirements are changing and what the ideal data platform will look like in the midterm future. We'll then evaluate core elements of the Databricks portfolio against that vision, and then we'll close with some strategic decisions that we think the company faces. And to do so, we welcome in our good friend, George Gilbert, former equities analyst, market analyst, and current Principal at TechAlpha Partners. George, good to see you. Thanks for coming on. >> Good to see you, Dave. >> All right, let me set this up. We're going to start by taking a look at where Databricks sits in the market in terms of how customers perceive the company and what it's momentum looks like. And this chart that we're showing here is data from ETS, the emerging technology survey of private companies. The N is 1,421. What we did is we cut the data on three sectors, analytics, database-data warehouse, and AI/ML. The vertical axis is a measure of customer sentiment, which evaluates an IT decision maker's awareness of the firm and the likelihood of engaging and/or purchase intent. The horizontal axis shows mindshare in the dataset, and we've highlighted Databricks, which has been a consistent high performer in this survey over the last several quarters. And as we, by the way, just as aside as we previously reported, OpenAI, which burst onto the scene this past quarter, leads all names, but Databricks is still prominent. You can see that the ETR shows some open source tools for reference, but as far as firms go, Databricks is very impressively positioned. Now, let's see how they stack up to some mainstream cohorts in the data space, against some bigger companies and sometimes public companies. This chart shows net score on the vertical axis, which is a measure of spending momentum and pervasiveness in the data set is on the horizontal axis. You can see that chart insert in the upper right, that informs how the dots are plotted, and net score against shared N. And that red dotted line at 40% indicates a highly elevated net score, anything above that we think is really, really impressive. And here we're just comparing Databricks with Snowflake, Cloudera, and Oracle. And that squiggly line leading to Databricks shows their path since 2021 by quarter. And you can see it's performing extremely well, maintaining an elevated net score and net range. Now it's comparable in the vertical axis to Snowflake, and it consistently is moving to the right and gaining share. Now, why did we choose to show Cloudera and Oracle? The reason is that Cloudera got the whole big data era started and was disrupted by Spark. And of course the cloud, Spark and Databricks and Oracle in many ways, was the target of early big data players like Cloudera. Take a listen to Cloudera CEO at the time, Mike Olson. This is back in 2010, first year of theCUBE, play the clip. >> Look, back in the day, if you had a data problem, if you needed to run business analytics, you wrote the biggest check you could to Sun Microsystems, and you bought a great big, single box, central server, and any money that was left over, you handed to Oracle for a database licenses and you installed that database on that box, and that was where you went for data. That was your temple of information. >> Okay? So Mike Olson implied that monolithic model was too expensive and inflexible, and Cloudera set out to fix that. But the best laid plans, as they say, George, what do you make of the data that we just shared? >> So where Databricks has really come up out of sort of Cloudera's tailpipe was they took big data processing, made it coherent, made it a managed service so it could run in the cloud. So it relieved customers of the operational burden. Where they're really strong and where their traditional meat and potatoes or bread and butter is the predictive and prescriptive analytics that building and training and serving machine learning models. They've tried to move into traditional business intelligence, the more traditional descriptive and diagnostic analytics, but they're less mature there. So what that means is, the reason you see Databricks and Snowflake kind of side by side is there are many, many accounts that have both Snowflake for business intelligence, Databricks for AI machine learning, where Snowflake, I'm sorry, where Databricks also did really well was in core data engineering, refining the data, the old ETL process, which kind of turned into ELT, where you loaded into the analytic repository in raw form and refine it. And so people have really used both, and each is trying to get into the other. >> Yeah, absolutely. We've reported on this quite a bit. Snowflake, kind of moving into the domain of Databricks and vice versa. And the last bit of ETR evidence that we want to share in terms of the company's momentum comes from ETR's Round Tables. They're run by Erik Bradley, and now former Gartner analyst and George, your colleague back at Gartner, Daren Brabham. And what we're going to show here is some direct quotes of IT pros in those Round Tables. There's a data science head and a CIO as well. Just make a few call outs here, we won't spend too much time on it, but starting at the top, like all of us, we can't talk about Databricks without mentioning Snowflake. Those two get us excited. Second comment zeros in on the flexibility and the robustness of Databricks from a data warehouse perspective. And then the last point is, despite competition from cloud players, Databricks has reinvented itself a couple of times over the year. And George, we're going to lay out today a scenario that perhaps calls for Databricks to do that once again. >> Their big opportunity and their big challenge for every tech company, it's managing a technology transition. The transition that we're talking about is something that's been bubbling up, but it's really epical. First time in 60 years, we're moving from an application-centric view of the world to a data-centric view, because decisions are becoming more important than automating processes. So let me let you sort of develop. >> Yeah, so let's talk about that here. We going to put up some bullets on precisely that point and the changing sort of customer environment. So you got IT stacks are shifting is George just said, from application centric silos to data centric stacks where the priority is shifting from automating processes to automating decision. You know how look at RPA and there's still a lot of automation going on, but from the focus of that application centricity and the data locked into those apps, that's changing. Data has historically been on the outskirts in silos, but organizations, you think of Amazon, think Uber, Airbnb, they're putting data at the core, and logic is increasingly being embedded in the data instead of the reverse. In other words, today, the data's locked inside the app, which is why you need to extract that data is sticking it to a data warehouse. The point, George, is we're putting forth this new vision for how data is going to be used. And you've used this Uber example to underscore the future state. Please explain? >> Okay, so this is hopefully an example everyone can relate to. The idea is first, you're automating things that are happening in the real world and decisions that make those things happen autonomously without humans in the loop all the time. So to use the Uber example on your phone, you call a car, you call a driver. Automatically, the Uber app then looks at what drivers are in the vicinity, what drivers are free, matches one, calculates an ETA to you, calculates a price, calculates an ETA to your destination, and then directs the driver once they're there. The point of this is that that cannot happen in an application-centric world very easily because all these little apps, the drivers, the riders, the routes, the fares, those call on data locked up in many different apps, but they have to sit on a layer that makes it all coherent. >> But George, so if Uber's doing this, doesn't this tech already exist? Isn't there a tech platform that does this already? >> Yes, and the mission of the entire tech industry is to build services that make it possible to compose and operate similar platforms and tools, but with the skills of mainstream developers in mainstream corporations, not the rocket scientists at Uber and Amazon. >> Okay, so we're talking about horizontally scaling across the industry, and actually giving a lot more organizations access to this technology. So by way of review, let's summarize the trend that's going on today in terms of the modern data stack that is propelling the likes of Databricks and Snowflake, which we just showed you in the ETR data and is really is a tailwind form. So the trend is toward this common repository for analytic data, that could be multiple virtual data warehouses inside of Snowflake, but you're in that Snowflake environment or Lakehouses from Databricks or multiple data lakes. And we've talked about what JP Morgan Chase is doing with the data mesh and gluing data lakes together, you've got various public clouds playing in this game, and then the data is annotated to have a common meaning. In other words, there's a semantic layer that enables applications to talk to the data elements and know that they have common and coherent meaning. So George, the good news is this approach is more effective than the legacy monolithic models that Mike Olson was talking about, so what's the problem with this in your view? >> So today's data platforms added immense value 'cause they connected the data that was previously locked up in these monolithic apps or on all these different microservices, and that supported traditional BI and AI/ML use cases. But now if we want to build apps like Uber or Amazon.com, where they've got essentially an autonomously running supply chain and e-commerce app where humans only care and feed it. But the thing is figuring out what to buy, when to buy, where to deploy it, when to ship it. We needed a semantic layer on top of the data. So that, as you were saying, the data that's coming from all those apps, the different apps that's integrated, not just connected, but it means the same. And the issue is whenever you add a new layer to a stack to support new applications, there are implications for the already existing layers, like can they support the new layer and its use cases? So for instance, if you add a semantic layer that embeds app logic with the data rather than vice versa, which we been talking about and that's been the case for 60 years, then the new data layer faces challenges that the way you manage that data, the way you analyze that data, is not supported by today's tools. >> Okay, so actually Alex, bring me up that last slide if you would, I mean, you're basically saying at the bottom here, today's repositories don't really do joins at scale. The future is you're talking about hundreds or thousands or millions of data connections, and today's systems, we're talking about, I don't know, 6, 8, 10 joins and that is the fundamental problem you're saying, is a new data error coming and existing systems won't be able to handle it? >> Yeah, one way of thinking about it is that even though we call them relational databases, when we actually want to do lots of joins or when we want to analyze data from lots of different tables, we created a whole new industry for analytic databases where you sort of mung the data together into fewer tables. So you didn't have to do as many joins because the joins are difficult and slow. And when you're going to arbitrarily join thousands, hundreds of thousands or across millions of elements, you need a new type of database. We have them, they're called graph databases, but to query them, you go back to the prerelational era in terms of their usability. >> Okay, so we're going to come back to that and talk about how you get around that problem. But let's first lay out what the ideal data platform of the future we think looks like. And again, we're going to come back to use this Uber example. In this graphic that George put together, awesome. We got three layers. The application layer is where the data products reside. The example here is drivers, rides, maps, routes, ETA, et cetera. The digital version of what we were talking about in the previous slide, people, places and things. The next layer is the data layer, that breaks down the silos and connects the data elements through semantics and everything is coherent. And then the bottom layers, the legacy operational systems feed that data layer. George, explain what's different here, the graph database element, you talk about the relational query capabilities, and why can't I just throw memory at solving this problem? >> Some of the graph databases do throw memory at the problem and maybe without naming names, some of them live entirely in memory. And what you're dealing with is a prerelational in-memory database system where you navigate between elements, and the issue with that is we've had SQL for 50 years, so we don't have to navigate, we can say what we want without how to get it. That's the core of the problem. >> Okay. So if I may, I just want to drill into this a little bit. So you're talking about the expressiveness of a graph. Alex, if you'd bring that back out, the fourth bullet, expressiveness of a graph database with the relational ease of query. Can you explain what you mean by that? >> Yeah, so graphs are great because when you can describe anything with a graph, that's why they're becoming so popular. Expressive means you can represent anything easily. They're conducive to, you might say, in a world where we now want like the metaverse, like with a 3D world, and I don't mean the Facebook metaverse, I mean like the business metaverse when we want to capture data about everything, but we want it in context, we want to build a set of digital twins that represent everything going on in the world. And Uber is a tiny example of that. Uber built a graph to represent all the drivers and riders and maps and routes. But what you need out of a database isn't just a way to store stuff and update stuff. You need to be able to ask questions of it, you need to be able to query it. And if you go back to prerelational days, you had to know how to find your way to the data. It's sort of like when you give directions to someone and they didn't have a GPS system and a mapping system, you had to give them turn by turn directions. Whereas when you have a GPS and a mapping system, which is like the relational thing, you just say where you want to go, and it spits out the turn by turn directions, which let's say, the car might follow or whoever you're directing would follow. But the point is, it's much easier in a relational database to say, "I just want to get these results. You figure out how to get it." The graph database, they have not taken over the world because in some ways, it's taking a 50 year leap backwards. >> Alright, got it. Okay. Let's take a look at how the current Databricks offerings map to that ideal state that we just laid out. So to do that, we put together this chart that looks at the key elements of the Databricks portfolio, the core capability, the weakness, and the threat that may loom. Start with the Delta Lake, that's the storage layer, which is great for files and tables. It's got true separation of compute and storage, I want you to double click on that George, as independent elements, but it's weaker for the type of low latency ingest that we see coming in the future. And some of the threats highlighted here. AWS could add transactional tables to S3, Iceberg adoption is picking up and could accelerate, that could disrupt Databricks. George, add some color here please? >> Okay, so this is the sort of a classic competitive forces where you want to look at, so what are customers demanding? What's competitive pressure? What are substitutes? Even what your suppliers might be pushing. Here, Delta Lake is at its core, a set of transactional tables that sit on an object store. So think of it in a database system, this is the storage engine. So since S3 has been getting stronger for 15 years, you could see a scenario where they add transactional tables. We have an open source alternative in Iceberg, which Snowflake and others support. But at the same time, Databricks has built an ecosystem out of tools, their own and others, that read and write to Delta tables, that's what makes the Delta Lake and ecosystem. So they have a catalog, the whole machine learning tool chain talks directly to the data here. That was their great advantage because in the past with Snowflake, you had to pull all the data out of the database before the machine learning tools could work with it, that was a major shortcoming. They fixed that. But the point here is that even before we get to the semantic layer, the core foundation is under threat. >> Yep. Got it. Okay. We got a lot of ground to cover. So we're going to take a look at the Spark Execution Engine next. Think of that as the refinery that runs really efficient batch processing. That's kind of what disrupted the DOOp in a large way, but it's not Python friendly and that's an issue because the data science and the data engineering crowd are moving in that direction, and/or they're using DBT. George, we had Tristan Handy on at Supercloud, really interesting discussion that you and I did. Explain why this is an issue for Databricks? >> So once the data lake was in place, what people did was they refined their data batch, and Spark has always had streaming support and it's gotten better. The underlying storage as we've talked about is an issue. But basically they took raw data, then they refined it into tables that were like customers and products and partners. And then they refined that again into what was like gold artifacts, which might be business intelligence metrics or dashboards, which were collections of metrics. But they were running it on the Spark Execution Engine, which it's a Java-based engine or it's running on a Java-based virtual machine, which means all the data scientists and the data engineers who want to work with Python are really working in sort of oil and water. Like if you get an error in Python, you can't tell whether the problems in Python or where it's in Spark. There's just an impedance mismatch between the two. And then at the same time, the whole world is now gravitating towards DBT because it's a very nice and simple way to compose these data processing pipelines, and people are using either SQL in DBT or Python in DBT, and that kind of is a substitute for doing it all in Spark. So it's under threat even before we get to that semantic layer, it so happens that DBT itself is becoming the authoring environment for the semantic layer with business intelligent metrics. But that's again, this is the second element that's under direct substitution and competitive threat. >> Okay, let's now move down to the third element, which is the Photon. Photon is Databricks' BI Lakehouse, which has integration with the Databricks tooling, which is very rich, it's newer. And it's also not well suited for high concurrency and low latency use cases, which we think are going to increasingly become the norm over time. George, the call out threat here is customers want to connect everything to a semantic layer. Explain your thinking here and why this is a potential threat to Databricks? >> Okay, so two issues here. What you were touching on, which is the high concurrency, low latency, when people are running like thousands of dashboards and data is streaming in, that's a problem because SQL data warehouse, the query engine, something like that matures over five to 10 years. It's one of these things, the joke that Andy Jassy makes just in general, he's really talking about Azure, but there's no compression algorithm for experience. The Snowflake guy started more than five years earlier, and for a bunch of reasons, that lead is not something that Databricks can shrink. They'll always be behind. So that's why Snowflake has transactional tables now and we can get into that in another show. But the key point is, so near term, it's struggling to keep up with the use cases that are core to business intelligence, which is highly concurrent, lots of users doing interactive query. But then when you get to a semantic layer, that's when you need to be able to query data that might have thousands or tens of thousands or hundreds of thousands of joins. And that's a SQL query engine, traditional SQL query engine is just not built for that. That's the core problem of traditional relational databases. >> Now this is a quick aside. We always talk about Snowflake and Databricks in sort of the same context. We're not necessarily saying that Snowflake is in a position to tackle all these problems. We'll deal with that separately. So we don't mean to imply that, but we're just sort of laying out some of the things that Snowflake or rather Databricks customers we think, need to be thinking about and having conversations with Databricks about and we hope to have them as well. We'll come back to that in terms of sort of strategic options. But finally, when come back to the table, we have Databricks' AI/ML Tool Chain, which has been an awesome capability for the data science crowd. It's comprehensive, it's a one-stop shop solution, but the kicker here is that it's optimized for supervised model building. And the concern is that foundational models like GPT could cannibalize the current Databricks tooling, but George, can't Databricks, like other software companies, integrate foundation model capabilities into its platform? >> Okay, so the sound bite answer to that is sure, IBM 3270 terminals could call out to a graphical user interface when they're running on the XT terminal, but they're not exactly good citizens in that world. The core issue is Databricks has this wonderful end-to-end tool chain for training, deploying, monitoring, running inference on supervised models. But the paradigm there is the customer builds and trains and deploys each model for each feature or application. In a world of foundation models which are pre-trained and unsupervised, the entire tool chain is different. So it's not like Databricks can junk everything they've done and start over with all their engineers. They have to keep maintaining what they've done in the old world, but they have to build something new that's optimized for the new world. It's a classic technology transition and their mentality appears to be, "Oh, we'll support the new stuff from our old stuff." Which is suboptimal, and as we'll talk about, their biggest patron and the company that put them on the map, Microsoft, really stopped working on their old stuff three years ago so that they could build a new tool chain optimized for this new world. >> Yeah, and so let's sort of close with what we think the options are and decisions that Databricks has for its future architecture. They're smart people. I mean we've had Ali Ghodsi on many times, super impressive. I think they've got to be keenly aware of the limitations, what's going on with foundation models. But at any rate, here in this chart, we lay out sort of three scenarios. One is re-architect the platform by incrementally adopting new technologies. And example might be to layer a graph query engine on top of its stack. They could license key technologies like graph database, they could get aggressive on M&A and buy-in, relational knowledge graphs, semantic technologies, vector database technologies. George, as David Floyer always says, "A lot of ways to skin a cat." We've seen companies like, even think about EMC maintained its relevance through M&A for many, many years. George, give us your thought on each of these strategic options? >> Okay, I find this question the most challenging 'cause remember, I used to be an equity research analyst. I worked for Frank Quattrone, we were one of the top tech shops in the banking industry, although this is 20 years ago. But the M&A team was the top team in the industry and everyone wanted them on their side. And I remember going to meetings with these CEOs, where Frank and the bankers would say, "You want us for your M&A work because we can do better." And they really could do better. But in software, it's not like with EMC in hardware because with hardware, it's easier to connect different boxes. With software, the whole point of a software company is to integrate and architect the components so they fit together and reinforce each other, and that makes M&A harder. You can do it, but it takes a long time to fit the pieces together. Let me give you examples. If they put a graph query engine, let's say something like TinkerPop, on top of, I don't even know if it's possible, but let's say they put it on top of Delta Lake, then you have this graph query engine talking to their storage layer, Delta Lake. But if you want to do analysis, you got to put the data in Photon, which is not really ideal for highly connected data. If you license a graph database, then most of your data is in the Delta Lake and how do you sync it with the graph database? If you do sync it, you've got data in two places, which kind of defeats the purpose of having a unified repository. I find this semantic layer option in number three actually more promising, because that's something that you can layer on top of the storage layer that you have already. You just have to figure out then how to have your query engines talk to that. What I'm trying to highlight is, it's easy as an analyst to say, "You can buy this company or license that technology." But the really hard work is making it all work together and that is where the challenge is. >> Yeah, and well look, I thank you for laying that out. We've seen it, certainly Microsoft and Oracle. I guess you might argue that well, Microsoft had a monopoly in its desktop software and was able to throw off cash for a decade plus while it's stock was going sideways. Oracle had won the database wars and had amazing margins and cash flow to be able to do that. Databricks isn't even gone public yet, but I want to close with some of the players to watch. Alex, if you'd bring that back up, number four here. AWS, we talked about some of their options with S3 and it's not just AWS, it's blob storage, object storage. Microsoft, as you sort of alluded to, was an early go-to market channel for Databricks. We didn't address that really. So maybe in the closing comments we can. Google obviously, Snowflake of course, we're going to dissect their options in future Breaking Analysis. Dbt labs, where do they fit? Bob Muglia's company, Relational.ai, why are these players to watch George, in your opinion? >> So everyone is trying to assemble and integrate the pieces that would make building data applications, data products easy. And the critical part isn't just assembling a bunch of pieces, which is traditionally what AWS did. It's a Unix ethos, which is we give you the tools, you put 'em together, 'cause you then have the maximum choice and maximum power. So what the hyperscalers are doing is they're taking their key value stores, in the case of ASW it's DynamoDB, in the case of Azure it's Cosmos DB, and each are putting a graph query engine on top of those. So they have a unified storage and graph database engine, like all the data would be collected in the key value store. Then you have a graph database, that's how they're going to be presenting a foundation for building these data apps. Dbt labs is putting a semantic layer on top of data lakes and data warehouses and as we'll talk about, I'm sure in the future, that makes it easier to swap out the underlying data platform or swap in new ones for specialized use cases. Snowflake, what they're doing, they're so strong in data management and with their transactional tables, what they're trying to do is take in the operational data that used to be in the province of many state stores like MongoDB and say, "If you manage that data with us, it'll be connected to your analytic data without having to send it through a pipeline." And that's hugely valuable. Relational.ai is the wildcard, 'cause what they're trying to do, it's almost like a holy grail where you're trying to take the expressiveness of connecting all your data in a graph but making it as easy to query as you've always had it in a SQL database or I should say, in a relational database. And if they do that, it's sort of like, it'll be as easy to program these data apps as a spreadsheet was compared to procedural languages, like BASIC or Pascal. That's the implications of Relational.ai. >> Yeah, and again, we talked before, why can't you just throw this all in memory? We're talking in that example of really getting down to differences in how you lay the data out on disk in really, new database architecture, correct? >> Yes. And that's why it's not clear that you could take a data lake or even a Snowflake and why you can't put a relational knowledge graph on those. You could potentially put a graph database, but it'll be compromised because to really do what Relational.ai has done, which is the ease of Relational on top of the power of graph, you actually need to change how you're storing your data on disk or even in memory. So you can't, in other words, it's not like, oh we can add graph support to Snowflake, 'cause if you did that, you'd have to change, or in your data lake, you'd have to change how the data is physically laid out. And then that would break all the tools that talk to that currently. >> What in your estimation, is the timeframe where this becomes critical for a Databricks and potentially Snowflake and others? I mentioned earlier midterm, are we talking three to five years here? Are we talking end of decade? What's your radar say? >> I think something surprising is going on that's going to sort of come up the tailpipe and take everyone by storm. All the hype around business intelligence metrics, which is what we used to put in our dashboards where bookings, billings, revenue, customer, those things, those were the key artifacts that used to live in definitions in your BI tools, and DBT has basically created a standard for defining those so they live in your data pipeline or they're defined in their data pipeline and executed in the data warehouse or data lake in a shared way, so that all tools can use them. This sounds like a digression, it's not. All this stuff about data mesh, data fabric, all that's going on is we need a semantic layer and the business intelligence metrics are defining common semantics for your data. And I think we're going to find by the end of this year, that metrics are how we annotate all our analytic data to start adding common semantics to it. And we're going to find this semantic layer, it's not three to five years off, it's going to be staring us in the face by the end of this year. >> Interesting. And of course SVB today was shut down. We're seeing serious tech headwinds, and oftentimes in these sort of downturns or flat turns, which feels like this could be going on for a while, we emerge with a lot of new players and a lot of new technology. George, we got to leave it there. Thank you to George Gilbert for excellent insights and input for today's episode. I want to thank Alex Myerson who's on production and manages the podcast, of course Ken Schiffman as well. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our EIC over at Siliconangle.com, he does some great editing. Remember all these episodes, they're available as podcasts. Wherever you listen, all you got to do is search Breaking Analysis Podcast, we publish each week on wikibon.com and siliconangle.com, or you can email me at David.Vellante@siliconangle.com, or DM me @DVellante. Comment on our LinkedIn post, and please do check out ETR.ai, great survey data, enterprise tech focus, phenomenal. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, and we'll see you next time on Breaking Analysis.

(bright music) >> Hi, everybody, this is Dave Vellante, welcome back to Supercloud 2. Last August, at the first Supercloud event, we invited the broader community to help further define Supercloud, we assessed its viability, and identified the critical elements and deployment models of the concept. The objectives here at Supercloud too are, first of all, to continue to tighten and test the concept, the second is, we want to get real world input from practitioners on the problems that they're facing and the viability of Supercloud in terms of applying it to their business. So on the program, we got companies like Walmart, Sachs, Western Union, Ionis Pharmaceuticals, NASDAQ, and others. And the third thing that we want to do is we want to drill into the intersection of cloud and data to project what the future looks like in the context of Supercloud. So in this segment, we want to explore the concept of data architectures and what's going to be required for Supercloud. And I'm pleased to welcome one of our Supercloud sponsors, ChaosSearch, Ed Walsh is the CEO of the company, with Thomas Hazel, who's the Founder, CTO, and Chief Scientist. Guys, good to see you again, thanks for coming into our Marlborough studio. >> Always great. >> Great to be here. >> Okay, so there's a little debate, I'm going to put you right in the spot. (Ed chuckling) A little debate going on in the community started by Bob Muglia, a former CEO of Snowflake, and he was at Microsoft for a long time, and he looked at the Supercloud definition, said, "I think you need to tighten it up a little bit." So, here's what he came up with. He said, "A Supercloud is a platform that provides a programmatically consistent set of services hosted on heterogeneous cloud providers." So he's calling it a platform, not an architecture, which was kind of interesting. And so presumably the platform owner is going to be responsible for the architecture, but Dr. Nelu Mihai, who's a computer scientist behind the Cloud of Clouds Project, he chimed in and responded with the following. He said, "Cloud is a programming paradigm supporting the entire lifecycle of applications with data and logic natively distributed. Supercloud is an open architecture that integrates heterogeneous clouds in an agnostic manner." So, Ed, words matter. Is this an architecture or is it a platform? >> Put us on the spot. So, I'm sure you have concepts, I would say it's an architectural or design principle. Listen, I look at Supercloud as a mega trend, just like cloud, just like data analytics. And some companies are using the principle, design principles, to literally get dramatically ahead of everyone else. I mean, things you couldn't possibly do if you didn't use cloud principles, right? So I think it's a Supercloud effect, you're able to do things you're not able to. So I think it's more a design principle, but if you do it right, you get dramatic effect as far as customer value. >> So the conversation that we were having with Muglia, and Tristan Handy of dbt Labs, was, I'll set it up as the following, and, Thomas, would love to get your thoughts, if you have a CRM, think about applications today, it's all about forms and codifying business processes, you type a bunch of stuff into Salesforce, and all the salespeople do it, and this machine generates a forecast. What if you have this new type of data app that pulls data from the transaction system, the e-commerce, the supply chain, the partner ecosystem, et cetera, and then, without humans, actually comes up with a plan. That's their vision. And Muglia was saying, in order to do that, you need to rethink data architectures and database architectures specifically, you need to get down to the level of how the data is stored on the disc. What are your thoughts on that? Well, first of all, I'm going to cop out, I think it's actually both. I do think it's a design principle, I think it's not open technology, but open APIs, open access, and you can build a platform on that design principle architecture. Now, I'm a database person, I love solving the database problems. >> I'm waited for you to launch into this. >> Yeah, so I mean, you know, Snowflake is a database, right? It's a distributed database. And we wanted to crack those codes, because, multi-region, multi-cloud, customers wanted access to their data, and their data is in a variety of forms, all these services that you're talked about. And so what I saw as a core principle was cloud object storage, everyone streams their data to cloud object storage. From there we said, well, how about we rethink database architecture, rethink file format, so that we can take each one of these services and bring them together, whether distributively or centrally, such that customers can access and get answers, whether it's operational data, whether it's business data, AKA search, or SQL, complex distributed joins. But we had to rethink the architecture. I like to say we're not a first generation, or a second, we're a third generation distributed database on pure, pure cloud storage, no caching, no SSDs. Why? Because all that availability, the cost of time, is a struggle, and cloud object storage, we think, is the answer. >> So when you're saying no caching, so when I think about how companies are solving some, you know, pretty hairy problems, take MySQL Heatwave, everybody thought Oracle was going to just forget about MySQL, well, they come out with Heatwave. And the way they solve problems, and you see their benchmarks against Amazon, "Oh, we crush everybody," is they put it all in memory. So you said no caching? You're not getting performance through caching? How is that true, and how are you getting performance? >> Well, so five, six years ago, right? When you realize that cloud object storage is going to be everywhere, and it's going to be a core foundational, if you will, fabric, what would you do? Well, a lot of times the second generation say, "We'll take it out of cloud storage, put in SSDs or something, and put into cache." And that adds a lot of time, adds a lot of costs. But I said, what if, what if we could actually make the first read hot, the first read distributed joins and searching? And so what we went out to do was said, we can't cache, because that's adds time, that adds cost. We have to make cloud object storage high performance, like it feels like a caching SSD. That's where our patents are, that's where our technology is, and we've spent many years working towards this. So, to me, if you can crack that code, a lot of these issues we're talking about, multi-region, multicloud, different services, everybody wants to send their data to the data lake, but then they move it out, we said, "Keep it right there." >> You nailed it, the data gravity. So, Bob's right, the data's coming in, and you need to get the data from everywhere, but you need an environment that you can deal with all that different schema, all the different type of technology, but also at scale. Bob's right, you cannot use memory or SSDs to cache that, that doesn't scale, it doesn't scale cost effectively. But if you could, and what you did, is you made object storage, S3 first, but object storage, the only persistence by doing that. And then we get performance, we should talk about it, it's literally, you know, hundreds of terabytes of queries, and it's done in seconds, it's done without memory caching. We have concepts of caching, but the only caching, the only persistence, is actually when we're doing caching, we're just keeping another side-eye track of things on the S3 itself. So we're using, actually, the object storage to be a database, which is kind of where Bob was saying, we agree, but that's what you started at, people thought you were crazy. >> And maybe make it live. Don't think of it as archival or temporary space, make it live, real time streaming, operational data. What we do is make it smart, we see the data coming in, we uniquely index it such that you can get your use cases, that are search, observability, security, or backend operational. But we don't have to have this, I dunno, static, fixed, siloed type of architecture technologies that were traditionally built prior to Supercloud thinking. >> And you don't have to move everything, essentially, you can do it wherever the data lands, whatever cloud across the globe, you're able to bring it together, you get the cost effectiveness, because the only persistence is the cheapest storage persistent layer you can buy. But the key thing is you cracked the code. >> We had to crack the code, right? That was the key thing. >> That's where the plans are. >> And then once you do that, then everything else gets easier to scale, your architecture, across regions, across cloud. >> Now, it's a general purpose database, as Bob was saying, but we use that database to solve a particular issue, which is around operational data, right? So, we agree with Bob's. >> Interesting. So this brings me to this concept of data, Jimata Gan is one of our speakers, you know, we talk about data fabric, which is a NetApp, originally NetApp concept, Gartner's kind of co-opted it. But so, the basic concept is, data lives everywhere, whether it's an S3 bucket, or a SQL database, or a data lake, it's just a node on the data mesh. So in your view, how does this fit in with Supercloud? Ed, you've said that you've built, essentially, an enabler for that, for the data mesh, I think you're an enabler for the Supercloud-like principles. This is a big, chewy opportunity, and it requires, you know, a team approach. There's got to be an ecosystem, there's not going to be one Supercloud to rule them all, so where does the ecosystem fit into the discussion, and where do you fit into the ecosystem? >> Right, so we agree completely, there's not one Supercloud in effect, but we use Supercloud principles to build our platform, and then, you know, the ecosystem's going to be built on leveraging what everyone else's secret powers are, right? So our power, our superpower, based upon what we built is, we deal with, if you're having any scale, or cost effective scale issues, with data, machine generated data, like business observability or security data, we are your force multiplier, we will take that in singularly, just let it, simply put it in your object storage wherever it sits, and we give you uniformity access to that using OpenAPI access, SQL, or you know, Elasticsearch API. So, that's what we do, that's our superpower. So I'll play it into data mesh, that's a perfect, we are a node on a data mesh, but I'll play it in the soup about how, the ecosystem, we see it kind of playing, and we talked about it in just in the last couple days, how we see this kind of possibly. Short term, our superpowers, we deal with this data that's coming at these environments, people, customers, building out observability or security environments, or vendors that are selling their own Supercloud, I do observability, the Datadogs of the world, dot dot dot, the Splunks of the world, dot dot dot, and security. So what we do is we fit in naturally. What we do is a cost effective scale, just land it anywhere in the world, we deal with ingest, and it's a cost effective, an order of magnitude, or two or three order magnitudes more cost effective. Allows them, their customers are asking them to do the impossible, "Give me fast monitoring alerting. I want it snappy, but I want it to keep two years of data, (laughs) and I want it cost effective." It doesn't work. They're good at the fast monitoring alerting, we're good at the long-term retention. And yet there's some gray area between those two, but one to one is actually cheaper, so we would partner. So the first ecosystem plays, who wants to have the ability to, really, all the data's in those same environments, the security observability players, they can literally, just through API, drag our data into their point to grab. We can make it seamless for customers. Right now, we make it helpful to customers. Your Datadog, we make a button, easy go from Datadog to us for logs, save you money. Same thing with Grafana. But you can also look at ecosystem, those same vendors, it used to be a year ago it was, you know, its all about how can you grow, like it's growth at all costs, now it's about cogs. So literally we can go an environment, you supply what your customer wants, but we can help with cogs. And one-on one in a partnership is better than you trying to build on your own. >> Thomas, you were saying you make the first read fast, so you think about Snowflake. Everybody wants to talk about Snowflake and Databricks. So, Snowflake, great, but you got to get the data in there. All right, so that's, can you help with that problem? >> I mean we want simple in, right? And if you have to have structure in, you're not simple. So the idea that you have a simple in, data lake, schema read type philosophy, but schema right type performance. And so what I wanted to do, what we have done, is have that simple lake, and stream that data real time, and those access points of Search or SQL, to go after whatever business case you need, security observability, warehouse integration. But the key thing is, how do I make that click, click, click answer, and do it quickly? And so what we want to do is, that first read has to be fast. Why? 'Cause then you're going to do all this siloing, layers, complexity. If your first read's not fast, you're at a disadvantage, particularly in cost. And nobody says I want less data, but everyone has to, whether they say we're going to shorten the window, we're going to use AI to choose, but in a security moment, when you don't have that answer, you're in trouble. And that's why we are this service, this Supercloud service, if you will, providing access, well-known search, well-known SQL type access, that if you just have one access point, you're at a disadvantage. >> We actually talked about Snowflake and BigQuery, and a different platform, Data Bricks. That's kind of where we see the phase two of ecosystem. One is easy, the low-hanging fruit is observability and security firms. But the next one is, what we do, our super power is dealing with this messy data that schema is changing like night and day. Pipelines are tough, and it's changing all the time, but you want these things fast, and it's big data around the world. That's the next point, just use us alongside, or inside, one of their platforms, and now we get the best of both worlds. Our superpower is keeping this messy data as a streaming, okay, not a batch thing, allow you to do that. So, that's the second one. And then to be honest, the third one, which plays you to Supercloud, it also plays perfectly in the data mesh, is if you really go to the ultimate thing, what we have done is made object storage, S3, GCS, and blob storage, we made it a database. Put, get, complex query with big joins. You know, so back to your original thing, and Muglia teed it up perfectly, we've done that. Now imagine if that's an ecosystem, who would want that? If it's, again, it's uniform available across all the regions, across all the clouds, and it's right next to where you are building a service, or a client's trying, that's where the ecosystem, I think people are going to use Superclouds for their superpowers. We're really good at this, allows that short term. I think the Snowflakes and the Data Bricks are the medium term, you know? And then I think eventually gets to, hey, listen if you can make object storage fast, you can just go after it with simple SQL queries, or elastic. Who would want that? I think that's where people are going to leverage it. It's not going to be one Supercloud, and we leverage the super clouds. >> Our viewpoint is smart object storage can be programmable, and so we agree with Bob, but we're not saying do it here, do it here. This core, fundamental layer across regions, across clouds, that everyone has? Simple in. Right now, it's hard to get data in for access for analysis. So we said, simply, we'll automate the entire process, give you API access across regions, across clouds. And again, how do you do a distributed join that's fast? How do you do a distributed join that doesn't cost you an arm or a leg? And how do you do it at scale? And that's where we've been focused. >> So prior, the cloud object store was a niche. >> Yeah. >> S3 obviously changed that. How standard is, essentially, object store across the different cloud platforms? Is that a problem for you? Is that an easy thing to solve? >> Well, let's talk about it. I mean we've fundamentally, yeah we've extracted it, but fundamentally, cloud object storage, put, get, and list. That's why it's so scalable, 'cause it doesn't have all these other components. That complexity is where we have moved up, and provide direct analytical API access. So because of its simplicity, and costs, and security, and reliability, it can scale naturally. I mean, really, distributed object storage is easy, it's put-get anywhere, now what we've done is we put a layer of intelligence, you know, call it smart object storage, where access is simple. So whether it's multi-region, do a query across, or multicloud, do a query across, or hunting, searching. >> We've had clients doing Amazon and Google, we have some Azure, but we see Amazon and Google more, and it's a consistent service across all of them. Just literally put your data in the bucket of choice, or folder of choice, click a couple buttons, literally click that to say "that's hot," and after that, it's hot, you can see it. But we're not moving data, the data gravity issue, that's the other. That it's already natively flowing to these pools of object storage across different regions and clouds. We don't move it, we index it right there, we're spinning up stateless compute, back to the Supercloud concept. But now that allows us to do all these other things, right? >> And it's no longer just cheap and deep object storage. Right? >> Yeah, we make it the same, like you have an analytic platform regardless of where you're at, you don't have to worry about that. Yeah, we deal with that, we deal with a stateless compute coming up -- >> And make it programmable. Be able to say, "I want this bucket to provide these answers." Right, that's really the hope, the vision. And the complexity to build the entire stack, and then connect them together, we said, the fabric is cloud storage, we just provide the intelligence on top. >> Let's bring it back to the customers, and one of the things we're exploring in Supercloud too is, you know, is Supercloud a solution looking for a problem? Is a multicloud really a problem? I mean, you hear, you know, a lot of the vendor marketing says, "Oh, it's a disaster, because it's all different across the clouds." And I talked to a lot of customers even as part of Supercloud too, they're like, "Well, I solved that problem by just going mono cloud." Well, but then you're not able to take advantage of a lot of the capabilities and the primitives that, you know, like Google's data, or you like Microsoft's simplicity, their RPA, whatever it is. So what are customers telling you, what are their near term problems that they're trying to solve today, and how are they thinking about the future? >> Listen, it's a real problem. I think it started, I think this is a a mega trend, just like cloud. Just, cloud data, and I always add, analytics, are the mega trends. If you're looking at those, if you're not considering using the Supercloud principles, in other words, leveraging what I have, abstracting it out, and getting the most out of that, and then build value on top, I think you're not going to be able to keep up, In fact, no way you're going to keep up with this data volume. It's a geometric challenge, and you're trying to do linear things. So clients aren't necessarily asking, hey, for Supercloud, but they're really saying, I need to have a better mechanism to simplify this and get value across it, and how do you abstract that out to do that? And that's where they're obviously, our conversations are more amazed what we're able to do, and what they're able to do with our platform, because if you think of what we've done, the S3, or GCS, or object storage, is they can't imagine the ingest, they can't imagine how easy, time to glass, one minute, no matter where it lands in the world, querying this in seconds for hundreds of terabytes squared. People are amazed, but that's kind of, so they're not asking for that, but they are amazed. And then when you start talking on it, if you're an enterprise person, you're building a big cloud data platform, or doing data or analytics, if you're not trying to leverage the public clouds, and somehow leverage all of them, and then build on top, then I think you're missing it. So they might not be asking for it, but they're doing it. >> And they're looking for a lens, you mentioned all these different services, how do I bring those together quickly? You know, our viewpoint, our service, is I have all these streams of data, create a lens where they want to go after it via search, go after via SQL, bring them together instantly, no e-tailing out, no define this table, put into this database. We said, let's have a service that creates a lens across all these streams, and then make those connections. I want to take my CRM with my Google AdWords, and maybe my Salesforce, how do I do analysis? Maybe I want to hunt first, maybe I want to join, maybe I want to add another stream to it. And so our viewpoint is, it's so natural to get into these lake platforms and then provide lenses to get that access. >> And they don't want it separate, they don't want something different here, and different there. They want it basically -- >> So this is our industry, right? If something new comes out, remember virtualization came out, "Oh my God, this is so great, it's going to solve all these problems." And all of a sudden it just got to be this big, more complex thing. Same thing with cloud, you know? It started out with S3, and then EC2, and now hundreds and hundreds of different services. So, it's a complex matter for a lot of people, and this creates problems for customers, especially when you got divisions that are using different clouds, and you're saying that the solution, or a solution for the part of the problem, is to really allow the data to stay in place on S3, use that standard, super simple, but then give it what, Ed, you've called superpower a couple of times, to make it fast, make it inexpensive, and allow you to do that across clouds. >> Yeah, yeah. >> I'll give you guys the last word on that. >> No, listen, I think, we think Supercloud allows you to do a lot more. And for us, data, everyone says more data, more problems, more budget issue, everyone knows more data is better, and we show you how to do it cost effectively at scale. And we couldn't have done it without the design principles of we're leveraging the Supercloud to get capabilities, and because we use super, just the object storage, we're able to get these capabilities of ingest, scale, cost effectiveness, and then we built on top of this. In the end, a database is a data platform that allows you to go after everything distributed, and to get one platform for analytics, no matter where it lands, that's where we think the Supercloud concepts are perfect, that's where our clients are seeing it, and we're kind of excited about it. >> Yeah a third generation database, Supercloud database, however we want to phrase it, and make it simple, but provide the value, and make it instant. >> Guys, thanks so much for coming into the studio today, I really thank you for your support of theCUBE, and theCUBE community, it allows us to provide events like this and free content. I really appreciate it. >> Oh, thank you. >> Thank you. >> All right, this is Dave Vellante for John Furrier in theCUBE community, thanks for being with us today. You're watching Supercloud 2, keep it right there for more thought provoking discussions around the future of cloud and data. (bright music)

(upbeat electronic music) >> Okay, welcome back to Supercloud 2, live here in Palo Alto. I'm John Furrier, with Dave Vellante. Again, all day wall-to-wall coverage, just had a great interview with Walmart, we've got a Next interview coming up, you're going to hear from Bob Muglia and Tristan Handy, two experts, both experienced entrepreneurs, executives in technology. We're here to break down what just happened with Walmart, and what's coming up with George Gilbert, former colleague, Wikibon analyst, Gartner Analyst, and now independent investor and expert. George, great to see you, I know you're following this space. Like you read about it, remember the first days when Dataverse came out, we were talking about them coming out of Berkeley? >> Dave: Snowflake. >> John: Snowflake. >> Dave: Snowflake In the early days. >> We, collectively, have been chronicling the data movement since 2010, you were part of our team, now you've got your nose to the grindstone, you're seeing the next wave. What's this all about? Walmart building their own super cloud, we got Bob Muglia talking about how these next wave of apps are coming. What are the super apps? What's the super cloud to you? >> Well, this key's off Dave's really interesting questions to Walmart, which was like, how are they building their supercloud? 'Cause it makes a concrete example. But what was most interesting about his description of the Walmart WCMP, I forgot what it stood for. >> Dave: Walmart Cloud Native Platform. >> Walmart, okay. He was describing where the logic could run in these stateless containers, and maybe eventually serverless functions. But that's just it, and that's the paradigm of microservices, where the logic is in this stateless thing, where you can shoot it, or it fails, and you can spin up another one, and you've lost nothing. >> That was their triplet model. >> Yeah, in fact, and that was what they were trying to move to, where these things move fluidly between data centers. >> But there's a but, right? Which is they're all stateless apps in the cloud. >> George: Yeah. >> And all their stateful apps are on-prem and VMs. >> Or the stateful part of the apps are in VMs. >> Okay. >> And so if they really want to lift their super cloud layer off of this different provider's infrastructure, they're going to need a much more advanced software platform that manages data. And that goes to the -- >> Muglia and Handy, that you and I did, that's coming up next. So the big takeaway there, George, was, I'll set it up and you can chime in, a new breed of data apps is emerging, and this highly decentralized infrastructure. And Tristan Handy of DBT Labs has a sort of a solution to begin the journey today, Muglia is working on something that's way out there, describe what you learned from it. >> Okay. So to talk about what the new data apps are, and then the platform to run them, I go back to the using what will probably be seen as one of the first data app examples, was Uber, where you're describing entities in the real world, riders, drivers, routes, city, like a city plan, these are all defined by data. And the data is described in a structure called a knowledge graph, for lack of a, no one's come up with a better term. But that means the tough, the stuff that Jack built, which was all stateless and sits above cloud vendors' infrastructure, it needs an entirely different type of software that's much, much harder to build. And the way Bob described it is, you're going to need an entirely new data management infrastructure to handle this. But where, you know, we had this really colorful interview where it was like Rock 'Em Sock 'Em, but they weren't really that much in opposition to each other, because Tristan is going to define this layer, starting with like business intelligence metrics, where you're defining things like bookings, billings, and revenue, in business terms, not in SQL terms -- >> Well, business terms, if I can interrupt, he said the one thing we haven't figured out how to APIify is KPIs that sit inside of a data warehouse, and that's essentially what he's doing. >> George: That's what he's doing, yes. >> Right. And so then you can now expose those APIs, those KPIs, that sit inside of a data warehouse, or a data lake, a data store, whatever, through APIs. >> George: And the difference -- >> So what does that do for you? >> Okay, so all of a sudden, instead of working at technical data terms, where you're dealing with tables and columns and rows, you're dealing instead with business entities, using the Uber example of drivers, riders, routes, you know, ETA prices. But you can define, DBT will be able to define those progressively in richer terms, today they're just doing things like bookings, billings, and revenue. But Bob's point was, today, the data warehouse that actually runs that stuff, whereas DBT defines it, the data warehouse that runs it, you can't do it with relational technology >> Dave: Relational totality, cashing architecture. >> SQL, you can't -- >> SQL caching architectures in memory, you can't do it, you've got to rethink down to the way the data lake is laid out on the disk or cache. Which by the way, Thomas Hazel, who's speaking later, he's the chief scientist and founder at Chaos Search, he says, "I've actually done this," basically leave it in an S3 bucket, and I'm going to query it, you know, with no caching. >> All right, so what I hear you saying then, tell me if I got this right, there are some some things that are inadequate in today's world, that's not compatible with the Supercloud wave. >> Yeah. >> Specifically how you're using storage, and data, and stateful. >> Yes. >> And then the software that makes it run, is that what you're saying? >> George: Yeah. >> There's one other thing you mentioned to me, it's like, when you're using a CRM system, a human is inputting data. >> George: Nothing happens till the human does something. >> Right, nothing happens until that data entry occurs. What you're talking about is a world that self forms, polling data from the transaction system, or the ERP system, and then builds a plan without human intervention. >> Yeah. Something in the real world happens, where the user says, "I want a ride." And then the software goes out and says, "Okay, we got to match a driver to the rider, we got to calculate how long it takes to get there, how long to deliver 'em." That's not driven by a form, other than the first person hitting a button and saying, "I want a ride." All the other stuff happens autonomously, driven by data and analytics. >> But my question was different, Dave, so I want to get specific, because this is where the startups are going to come in, this is the disruption. Snowflake is a data warehouse that's in the cloud, they call it a data cloud, they refactored it, they did it differently, the success, we all know it looks like. These areas where it's inadequate for the future are areas that'll probably be either disrupted, or refactored. What is that? >> That's what Muglia's contention is, that the DBT can start adding that layer where you define these business entities, they're like mini digital twins, you can define them, but the data warehouse isn't strong enough to actually manage and run them. And Muglia is behind a company that is rethinking the database, really in a fundamental way that hasn't been done in 40 or 50 years. It's the first, in his contention, the first real rethink of database technology in a fundamental way since the rise of the relational database 50 years ago. >> And I think you admit it's a real Hail Mary, I mean it's quite a long shot right? >> George: Yes. >> Huge potential. >> But they're pretty far along. >> Well, we've been talking on theCUBE for 12 years, and what, 10 years going to AWS Reinvent, Dave, that no one database will rule the world, Amazon kind of showed that with them. What's different, is it databases are changing, or you can have multiple databases, or? >> It's a good question. And the reason we've had multiple different types of databases, each one specialized for a different type of workload, but actually what Muglia is behind is a new engine that would essentially, you'll never get rid of the data warehouse, or the equivalent engine in like a Databricks datalake house, but it's a new engine that manages the thing that describes all the data and holds it together, and that's the new application platform. >> George, we have one minute left, I want to get real quick thought, you're an investor, and we know your history, and the folks watching, George's got a deep pedigree in investment data, and we can testify against that. If you're going to invest in a company right now, if you're a customer, I got to make a bet, what does success look like for me, what do I want walking through my door, and what do I want to send out? What companies do I want to look at? What's the kind of of vendor do I want to evaluate? Which ones do I want to send home? >> Well, the first thing a customer really has to do when they're thinking about next gen applications, all the people have told you guys, "we got to get our data in order," getting that data in order means building an integrated view of all your data landscape, which is data coming out of all your applications. It starts with the data model, so, today, you basically extract data from all your operational systems, put it in this one giant, central place, like a warehouse or lake house, but eventually you want this, whether you call it a fabric or a mesh, it's all the data that describes how everything hangs together as in one big knowledge graph. There's different ways to implement that. And that's the most critical thing, 'cause that describes your Uber landscape, your Uber platform. >> That's going to power the digital transformation, which will power the business transformation, which powers the business model, which allows the builders to build -- >> Yes. >> Coders to code. That's Supercloud application. >> Yeah. >> George, great stuff. Next interview you're going to see right here is Bob Muglia and Tristan Handy, they're going to unpack this new wave. Great segment, really worth unpacking and reading between the lines with George, and Dave Vellante, and those two great guests. And then we'll come back here for the studio for more of the live coverage of Supercloud 2. Thanks for watching. (upbeat electronic music)

(gentle music playing) >> For centuries, we've been captivated by the concept of machines doing the job of humans. And over the past decade or so, we've really focused on AI and the possibility of intelligent machines that can perform cognitive tasks. Now in the past few years, with the popularity of machine learning models ranging from recent ChatGPT to Bert, we're starting to see how AI is changing the way we interact with the world. How is AI transforming the way we do business? And what does the future hold for us there. At theCube, we've covered Oracle's AI and ML strategy for years, which has really been used to drive automation into Oracle's autonomous database. We've talked a lot about MySQL HeatWave in database machine learning, and AI pushed into Oracle's business apps. Oracle, it tends to lead in AI, but not competing as a direct AI player per se, but rather embedding AI and machine learning into its portfolio to enhance its existing products, and bring new services and offerings to the market. Now, last October at Cloud World in Las Vegas, Oracle partnered with Nvidia, which is the go-to AI silicon provider for vendors. And they announced an investment, a pretty significant investment to deploy tens of thousands more Nvidia GPUs to OCI, the Oracle Cloud Infrastructure and build out Oracle's infrastructure for enterprise scale AI. Now, Oracle CEO, Safra Catz said something to the effect of this alliance is going to help customers across industries from healthcare, manufacturing, telecoms, and financial services to overcome the multitude of challenges they face. Presumably she was talking about just driving more automation and more productivity. Now, to learn more about Oracle's plans for AI, we'd like to welcome in Elad Ziklik, who's the vice president of AI services at Oracle. Elad, great to see you. Welcome to the show. >> Thank you. Thanks for having me. >> You're very welcome. So first let's talk about Oracle's path to AI. I mean, it's the hottest topic going for years you've been incorporating machine learning into your products and services, you know, could you tell us what you've been working on, how you got here? >> So great question. So as you mentioned, I think most of the original four-way into AI was on embedding AI and using AI to make our applications, and databases better. So inside mySQL HeatWave, inside our autonomous database in power, we've been driving AI, all of course are SaaS apps. So Fusion, our large enterprise business suite for HR applications and CRM and ELP, and whatnot has built in AI inside it. Most recently, NetSuite, our small medium business SaaS suite started using AI for things like automated invoice processing and whatnot. And most recently, over the last, I would say two years, we've started exposing and bringing these capabilities into the broader OCI Oracle Cloud infrastructure. So the developers, and ISVs and customers can start using our AI capabilities to make their apps better and their experiences and business workflow better, and not just consume these as embedded inside Oracle. And this recent partnership that you mentioned with Nvidia is another step in bringing the best AI infrastructure capabilities into this platform so you can actually build any type of machine learning workflow or AI model that you want on Oracle Cloud. >> So when I look at the market, I see companies out there like DataRobot or C3 AI, there's maybe a half dozen that sort of pop up on my radar anyway. And my premise has always been that most customers, they don't want to become AI experts, they want to buy applications and have AI embedded or they want AI to manage their infrastructure. So my question to you is, how does Oracle help its OCI customers support their business with AI? >> So it's a great question. So I think what most customers want is business AI. They want AI that works for the business. They want AI that works for the enterprise. I call it the last mile of AI. And they want this thing to work. The majority of them don't want to hire a large and expensive data science teams to go and build everything from scratch. They just want the business problem solved by applying AI to it. My best analogy is Lego. So if you think of Lego, Lego has these millions Lego blocks that you can use to build anything that you want. But the majority of people like me or like my kids, they want the Lego death style kit or the Lego Eiffel Tower thing. They want a thing that just works, and it's very easy to use. And still Lego blocks, you still need to build some things together, which just works for the scenario that you're looking for. So that's our focus. Our focus is making it easy for customers to apply AI where they need to, in the right business context. So whether it's embedding it inside the business applications, like adding forecasting capabilities to your supply chain management or financial planning software, whether it's adding chat bots into the line of business applications, integrating these things into your analytics dashboard, even all the way to, we have a new platform piece we call ML applications that allows you to take a machine learning model, and scale it for the thousands of tenants that you would be. 'Cause this is a big problem for most of the ML use cases. It's very easy to build something for a proof of concept or a pilot or a demo. But then if you need to take this and then deploy it across your thousands of customers or your thousands of regions or facilities, then it becomes messy. So this is where we spend our time making it easy to take these things into production in the context of your business application or your business use case that you're interested in right now. >> So you mentioned chat bots, and I want to talk about ChatGPT, but my question here is different, we'll talk about that in a minute. So when you think about these chat bots, the ones that are conversational, my experience anyway is they're just meh, they're not that great. But the ones that actually work pretty well, they have a conditioned response. Now they're limited, but they say, which of the following is your problem? And then if that's one of the following is your problem, you can maybe solve your problem. But this is clearly a trend and it helps the line of business. How does Oracle think about these use cases for your customers? >> Yeah, so I think the key here is exactly what you said. It's about task completion. The general purpose bots are interesting, but as you said, like are still limited. They're getting much better, I'm sure we'll talk about ChatGPT. But I think what most enterprises want is around task completion. I want to automate my expense report processing. So today inside Oracle we have a chat bot where I submit my expenses the bot ask a couple of question, I answer them, and then I'm done. Like I don't need to go to our fancy application, and manually submit an expense report. I do this via Slack. And the key is around managing the right expectations of what this thing is capable of doing. Like, I have a story from I think five, six years ago when technology was much inferior than it is today. Well, one of the telco providers I was working with wanted to roll a chat bot that does realtime translation. So it was for a support center for of the call centers. And what they wanted do is, Hey, we have English speaking employees, whatever, 24/7, if somebody's calling, and the native tongue is different like Hebrew in my case, or Chinese or whatnot, then we'll give them a chat bot that they will interact with and will translate this on the fly and everything would work. And when they rolled it out, the feedback from customers was horrendous. Customers said, the technology sucks. It's not good. I hate it, I hate your company, I hate your support. And what they've done is they've changed the narrative. Instead of, you go to a support center, and you assume you're going to talk to a human, and instead you get a crappy chat bot, they're like, Hey, if you want to talk to a Hebrew speaking person, there's a four hour wait, please leave your phone and we'll call you back. Or you can try a new amazing Hebrew speaking AI powered bot and it may help your use case. Do you want to try it out? And some people said, yeah, let's try it out. Plus one to try it out. And the feedback, even though it was the exact same technology was amazing. People were like, oh my God, this is so innovative, this is great. Even though it was the exact same experience that they hated a few weeks earlier on. So I think the key lesson that I picked from this experience is it's all about setting the right expectations, and working around the right use case. If you are replacing a human, the level is different than if you are just helping or augmenting something that otherwise would take a lot of time. And I think this is the focus that we are doing, picking up the tasks that people want to accomplish or that enterprise want to accomplish for the customers, for the employees. And using chat bots to make those specific ones better rather than, hey, this is going to replace all humans everywhere, and just be better than that. >> Yeah, I mean, to the point you mentioned expense reports. I'm in a Twitter thread and one guy says, my favorite part of business travel is filling out expense reports. It's an hour of excitement to figure out which receipts won't scan. We can all relate to that. It's just the worst. When you think about companies that are building custom AI driven apps, what can they do on OCI? What are the best options for them? Do they need to hire an army of machine intelligence experts and AI specialists? Help us understand your point of view there. >> So over the last, I would say the two or three years we've developed a full suite of machine learning and AI services for, I would say probably much every use case that you would expect right now from applying natural language processing to understanding customer support tickets or social media, or whatnot to computer vision platforms or computer vision services that can understand and detect objects, and count objects on shelves or detect cracks in the pipe or defecting parts, all the way to speech services. It can actually transcribe human speech. And most recently we've launched a new document AI service. That can actually look at unstructured documents like receipts or invoices or government IDs or even proprietary documents, loan application, student application forms, patient ingestion and whatnot and completely automate them using AI. So if you want to do one of the things that are, I would say common bread and butter for any industry, whether it's financial services or healthcare or manufacturing, we have a suite of services that any developer can go, and use easily customized with their own data. You don't need to be an expert in deep learning or large language models. You could just use our automobile capabilities, and build your own version of the models. Just go ahead and use them. And if you do have proprietary complex scenarios that you need customer from scratch, we actually have the most cost effective platform for that. So we have the OCI data science as well as built-in machine learning platform inside the databases inside the Oracle database, and mySQL HeatWave that allow data scientists, python welding people that actually like to build and tweak and control and improve, have everything that they need to go and build the machine learning models from scratch, deploy them, monitor and manage them at scale in production environment. And most of it is brand new. So we did not have these technologies four or five years ago and we've started building them and they're now at enterprise scale over the last couple of years. >> So what are some of the state-of-the-art tools, that AI specialists and data scientists need if they're going to go out and develop these new models? >> So I think it's on three layers. I think there's an infrastructure layer where the Nvidia's of the world come into play. For some of these things, you want massively efficient, massively scaled infrastructure place. So we are the most cost effective and performant large scale GPU training environment today. We're going to be first to onboard the new Nvidia H100s. These are the new super powerful GPU's for large language model training. So we have that covered for you in case you need this 'cause you want to build these ginormous things. You need a data science platform, a platform where you can open a Python notebook, and just use all these fancy open source frameworks and create the models that you want, and then click on a button and deploy it. And it infinitely scales wherever you need it. And in many cases you just need the, what I call the applied AI services. You need the Lego sets, the Lego death style, Lego Eiffel Tower. So we have a suite of these sets for typical scenarios, whether it's cognitive services of like, again, understanding images, or documents all the way to solving particular business problems. So an anomaly detection service, demand focusing service that will be the equivalent of these Lego sets. So if this is the business problem that you're looking to solve, we have services out there where we can bring your data, call an API, train a model, get the model and use it in your production environment. So wherever you want to play, all the way into embedding this thing, inside this applications, obviously, wherever you want to play, we have the tools for you to go and engage from infrastructure to SaaS at the top, and everything in the middle. >> So when you think about the data pipeline, and the data life cycle, and the specialized roles that came out of kind of the (indistinct) era if you will. I want to focus on two developers and data scientists. So the developers, they hate dealing with infrastructure and they got to deal with infrastructure. Now they're being asked to secure the infrastructure, they just want to write code. And a data scientist, they're spending all their time trying to figure out, okay, what's the data quality? And they're wrangling data and they don't spend enough time doing what they want to do. So there's been a lack of collaboration. Have you seen that change, are these approaches allowing collaboration between data scientists and developers on a single platform? Can you talk about that a little bit? >> Yeah, that is a great question. One of the biggest set of scars that I have on my back from for building these platforms in other companies is exactly that. Every persona had a set of tools, and these tools didn't talk to each other and the handoff was painful. And most of the machine learning things evaporate or die on the floor because of this problem. It's very rarely that they are unsuccessful because the algorithm wasn't good enough. In most cases it's somebody builds something, and then you can't take it to production, you can't integrate it into your business application. You can't take the data out, train, create an endpoint and integrate it back like it's too painful. So the way we are approaching this is focused on this problem exactly. We have a single set of tools that if you publish a model as a data scientist and developers, and even business analysts that are seeing a inside of business application could be able to consume it. We have a single model store, a single feature store, a single management experience across the various personas that need to play in this. And we spend a lot of time building, and borrowing a word that cellular folks used, and I really liked it, building inside highways to make it easier to bring these insights into where you need them inside applications, both inside our applications, inside our SaaS applications, but also inside custom third party and even first party applications. And this is where a lot of our focus goes to just because we have dealt with so much pain doing this inside our own SaaS that we now have built the tools, and we're making them available for others to make this process of building a machine learning outcome driven insight in your app easier. And it's not just the model development, and it's not just the deployment, it's the entire journey of taking the data, building the model, training it, deploying it, looking at the real data that comes from the app, and creating this feedback loop in a more efficient way. And that's our focus area. Exactly this problem. >> Well thank you for that. So, last week we had our super cloud two event, and I had Juan Loza on and he spent a lot of time talking about how open Oracle is in its philosophy, and I got a lot of feedback. They were like, Oracle open, I don't really think, but the truth is if you think about database Oracle database, it never met a hardware platform that it didn't like. So in that sense it's open. So, but my point is, a big part of of machine learning and AI is driven by open source tools, frameworks, what's your open source strategy? What do you support from an open source standpoint? >> So I'm a strong believer that you don't actually know, nobody knows where the next slip fog or the next industry shifting innovation in AI is going to come from. If you look six months ago, nobody foreseen Dali, the magical text to image generation and the exploding brought into just art and design type of experiences. If you look six weeks ago, I don't think anybody's seen ChatGPT, and what it can do for a whole bunch of industries. So to me, assuming that a customer or partner or developer would want to lock themselves into only the tools that a specific vendor can produce is ridiculous. 'Cause nobody knows, if anybody claims that they know where the innovation is going to come from in a year or two, let alone in five or 10, they're just wrong or lying. So our strategy for Oracle is to, I call this the Netflix of AI. So if you think about Netflix, they produced a bunch of high quality shows on their own. A few years ago it was House of Cards. Last month my wife and I binge watched Ginny and Georgie, but they also curated a lot of shows that they found around the world and bought them to their customers. So it started with things like Seinfeld or Friends and most recently it was Squid games and those are famous Israeli TV series called Founder that Netflix bought in, and they bought it as is and they gave it the Netflix value. So you have captioning and you have the ability to speed the movie and you have it inside your app, and you can download it and watch it offline and everything, but nobody Netflix was involved in the production of these first seasons. Now if these things hunt and they're great, then the third season or the fourth season will get the full Netflix production value, high value budget, high value location shooting or whatever. But you as a customer, you don't care whether the producer and director, and screenplay writing is a Netflix employee or is somebody else's employee. It is fulfilled by Netflix. I believe that we will become, or we are looking to become the Netflix of AI. We are building a bunch of AI in a bunch of places where we think it's important and we have some competitive advantage like healthcare with Acellular partnership or whatnot. But I want to bring the best AI software and hardware to OCI and do a fulfillment by Oracle on that. So you'll get the Oracle security and identity and single bill and everything you'd expect from a company like Oracle. But we don't have to be building the data science, and the models for everything. So this means both open source recently announced a partnership with Anaconda, the leading provider of Python distribution in the data science ecosystem where we are are doing a joint strategic partnership of bringing all the goodness into Oracle customers as well as in the process of doing the same with Nvidia, and all those software libraries, not just the Hubble, both for other stuff like Triton, but also for healthcare specific stuff as well as other ISVs, other AI leading ISVs that we are in the process of partnering with to get their stuff into OCI and into Oracle so that you can truly consume the best AI hardware, and the best AI software in the world on Oracle. 'Cause that is what I believe our customers would want the ability to choose from any open source engine, and honestly from any ISV type of solution that is AI powered and they want to use it in their experiences. >> So you mentioned ChatGPT, I want to talk about some of the innovations that are coming. As an AI expert, you see ChatGPT on the one hand, I'm sure you weren't surprised. On the other hand, maybe the reaction in the market, and the hype is somewhat surprising. You know, they say that we tend to under or over-hype things in the early stages and under hype them long term, you kind of use the internet as example. What's your take on that premise? >> So. I think that this type of technology is going to be an inflection point in how software is being developed. I truly believe this. I think this is an internet style moment, and the way software interfaces, software applications are being developed will dramatically change over the next year two or three because of this type of technologies. I think there will be industries that will be shifted. I think education is a good example. I saw this thing opened on my son's laptop. So I think education is going to be transformed. Design industry like images or whatever, it's already been transformed. But I think that for mass adoption, like beyond the hype, beyond the peak of inflected expectations, if I'm using Gartner terminology, I think certain things need to go and happen. One is this thing needs to become more reliable. So right now it is a complete black box that sometimes produce magic, and sometimes produce just nonsense. And it needs to have better explainability and better lineage to, how did you get to this answer? 'Cause I think enterprises are going to really care about the things that they surface with the customers or use internally. So I think that is one thing that's going to come out. And the other thing that's going to come out is I think it's going to come industry specific large language models or industry specific ChatGPTs. Something like how OpenAI did co-pilot for writing code. I think we will start seeing this type of apps solving for specific business problems, understanding contracts, understanding healthcare, writing doctor's notes on behalf of doctors so they don't have to spend time manually recording and analyzing conversations. And I think that would become the sweet spot of this thing. There will be companies, whether it's OpenAI or Microsoft or Google or hopefully Oracle that will use this type of technology to solve for specific very high value business needs. And I think this will change how interfaces happen. So going back to your expense report, the world of, I'm going to go into an app, and I'm going to click on seven buttons in order to get some job done like this world is gone. Like I'm going to say, hey, please do this and that. And I expect an answer to come out. I've seen a recent demo about, marketing in sales. So a customer sends an email that is interested in something and then a ChatGPT powered thing just produces the answer. I think this is how the world is going to evolve. Like yes, there's a ton of hype, yes, it looks like magic and right now it is magic, but it's not yet productive for most enterprise scenarios. But in the next 6, 12, 24 months, this will start getting more dependable, and it's going to change how these industries are being managed. Like I think it's an internet level revolution. That's my take. >> It's very interesting. And it's going to change the way in which we have. Instead of accessing the data center through APIs, we're going to access it through natural language processing and that opens up technology to a huge audience. Last question, is a two part question. And the first part is what you guys are working on from the futures, but the second part of the question is, we got data scientists and developers in our audience. They love the new shiny toy. So give us a little glimpse of what you're working on in the future, and what would you say to them to persuade them to check out Oracle's AI services? >> Yep. So I think there's two main things that we're doing, one is around healthcare. With a new recent acquisition, we are spending a significant effort around revolutionizing healthcare with AI. Of course many scenarios from patient care using computer vision and cameras through automating, and making better insurance claims to research and pharma. We are making the best models from leading organizations, and internal available for hospitals and researchers, and insurance providers everywhere. And we truly are looking to become the leader in AI for healthcare. So I think that's a huge focus area. And the second part is, again, going back to the enterprise AI angle. Like we want to, if you have a business problem that you want to apply here to solve, we want to be your platform. Like you could use others if you want to build everything complicated and whatnot. We have a platform for that as well. But like, if you want to apply AI to solve a business problem, we want to be your platform. We want to be the, again, the Netflix of AI kind of a thing where we are the place for the greatest AI innovations accessible to any developer, any business analyst, any user, any data scientist on Oracle Cloud. And we're making a significant effort on these two fronts as well as developing a lot of the missing pieces, and building blocks that we see are needed in this space to make truly like a great experience for developers and data scientists. And what would I recommend? Get started, try it out. We actually have a shameless sales plug here. We have a free deal for all of our AI services. So it typically cost you nothing. I would highly recommend to just go, and try these things out. Go play with it. If you are a python welding developer, and you want to try a little bit of auto mail, go down that path. If you're not even there and you're just like, hey, I have these customer feedback things and I want to try out, if I can understand them and apply AI and visualize, and do some cool stuff, we have services for that. My recommendation is, and I think ChatGPT got us 'cause I see people that have nothing to do with AI, and can't even spell AI going and trying it out. I think this is the time. Go play with these things, go play with these technologies and find what AI can do to you or for you. And I think Oracle is a great place to start playing with these things. >> Elad, thank you. Appreciate you sharing your vision of making Oracle the Netflix of AI. Love that and really appreciate your time. >> Awesome. Thank you. Thank you for having me. >> Okay. Thanks for watching this Cube conversation. This is Dave Vellante. We'll see you next time. (gentle music playing)

Hello and welcome back to Supercloud 2 where we examine the intersection of cloud and data in the 2020s. My name is Dave Vellante. Our Supercloud panel, our power panel is back. Maribel Lopez is the founder and principal analyst at Lopez Research. Sanjeev Mohan is former Gartner analyst and principal at Sanjeev Mohan. And Keith Townsend is the CTO advisor. Folks, welcome back and thanks for your participation today. Good to see you. >> Okay, great. >> Great to see you. >> Thanks. Let me start, Maribel, with you. Bob Muglia, we had a conversation as part of Supercloud the other day. And he said, "Dave, I like the work, you got to simplify this a little bit." So he said, quote, "A Supercloud is a platform." He said, "Think of it as a platform that provides programmatically consistent services hosted on heterogeneous cloud providers." And then Nelu Mihai said, "Well, wait a minute. This is just going to create more stove pipes. We need more standards in an architecture," which is kind of what Berkeley Sky Computing initiative is all about. So there's a sort of a debate going on. Is supercloud an architecture, a platform? Or maybe it's just another buzzword. Maribel, do you have a thought on this? >> Well, the easy answer would be to say it's just a buzzword. And then we could just kill the conversation and be done with it. But I think the term, it's more than that, right? The term actually isn't new. You can go back to at least 2016 and find references to supercloud in Cornell University or assist in other documents. So, having said this, I think we've been talking about Supercloud for a while, so I assume it's more than just a fancy buzzword. But I think it really speaks to that undeniable trend of moving towards an abstraction layer to deal with the chaos of what we consider managing multiple public and private clouds today, right? So one definition of the technology platform speaks to a set of services that allows companies to build and run that technology smoothly without worrying about the underlying infrastructure, which really gets back to something that Bob said. And some of the question is where that lives. And you could call that an abstraction layer. You could call it cross-cloud services, hybrid cloud management. So I see momentum there, like legitimate momentum with enterprise IT buyers that are trying to deal with the fact that they have multiple clouds now. So where I think we're moving is trying to define what are the specific attributes and frameworks of that that would make it so that it could be consistent across clouds. What is that layer? And maybe that's what the supercloud is. But one of the things I struggle with with supercloud is. What are we really trying to do here? Are we trying to create differentiated services in the supercloud layer? Is a supercloud just another variant of what AWS, GCP, or others do? You spoken to Walmart about its cloud native platform, and that's an example of somebody deciding to do it themselves because they need to deal with this today and not wait for some big standards thing to happen. So whatever it is, I do think it's something. I think we're trying to maybe create an architecture out of it would be a better way of saying it so that it does get to those set of principles, but it also needs to be edge aware. I think whenever we talk about supercloud, we're always talking about like the big centralized cloud. And I think we need to think about all the distributed clouds that we're looking at in edge as well. So that might be one of the ways that supercloud evolves. >> So thank you, Maribel. Keith, Brian Gracely, Gracely's law, things kind of repeat themselves. We've seen it all before. And so what Muglia brought to the forefront is this idea of a platform where the platform provider is really responsible for the architecture. Of course, the drawback is then you get a a bunch of stove pipes architectures. But practically speaking, that's kind of the way the industry has always evolved, right? >> So if we look at this from the practitioner's perspective and we talk about platforms, traditionally vendors have provided the platforms for us, whether it's distribution of lineage managed by or provided by Red Hat, Windows, servers, .NET, databases, Oracle. We think of those as platforms, things that are fundamental we can build on top. Supercloud isn't today that. It is a framework or idea, kind of a visionary goal to get to a point that we can have a platform or a framework. But what we're seeing repeated throughout the industry in customers, whether it's the Walmarts that's kind of supersized the idea of supercloud, or if it's regular end user organizations that are coming out with platform groups, groups who normalize cloud native infrastructure, AWS multi-cloud, VMware resources to look like one thing internally to their developers. We're seeing this trend that there's a desire for a platform that provides the capabilities of a supercloud. >> Thank you for that. Sanjeev, we often use Snowflake as a supercloud example, and now would presumably would be a platform with an architecture that's determined by the vendor. Maybe Databricks is pushing for a more open architecture, maybe more of that nirvana that we were talking about before to solve for supercloud. But regardless, the practitioner discussions show. At least currently, there's not a lot of cross-cloud data sharing. I think it could be a killer use case, egress charges or a barrier. But how do you see it? Will that change? Will we hide that underlying complexity and start sharing data across cloud? Is that something that you think Snowflake or others will be able to achieve? >> So I think we are already starting to see some of that happen. Snowflake is definitely one example that gets cited a lot. But even we don't talk about MongoDB in this like, but you could have a MongoDB cluster, for instance, with nodes sitting in different cloud providers. So there are companies that are starting to do it. The advantage that these companies have, let's take Snowflake as an example, it's a centralized proprietary platform. And they are building the capabilities that are needed for supercloud. So they're building things like you can push down your data transformations. They have the entire security and privacy suite. Data ops, they're adding those capabilities. And if I'm not mistaken, it'll be very soon, we will see them offer data observability. So it's all works great as long as you are in one platform. And if you want resilience, then Snowflake, Supercloud, great example. But if your primary goal is to choose the most cost-effective service irrespective of which cloud it sits in, then things start falling sideways. For example, I may be a very big Snowflake user. And I like Snowflake's resilience. I can move from one cloud to another cloud. Snowflake does it for me. But what if I want to train a very large model? Maybe Databricks is a better platform for that. So how do I do move my workload from one platform to another platform? That tooling does not exist. So we need server hybrid, cross-cloud, data ops platform. Walmart has done a great job, but they built it by themselves. Not every company is Walmart. Like Maribel and Keith said, we need standards, we need reference architectures, we need some sort of a cost control. I was just reading recently, Accenture has been public about their AWS bill. Every time they get the bill is tens of millions of lines, tens of millions 'cause there are over thousand teams using AWS. If we have not been able to corral a usage of a single cloud, now we're talking about supercloud, we've got multiple clouds, and hybrid, on-prem, and edge. So till we've got some cross-platform tooling in place, I think this will still take quite some time for it to take shape. >> It's interesting. Maribel, Walmart would tell you that their on-prem infrastructure is cheaper to run than the stuff in the cloud. but at the same time, they want the flexibility and the resiliency of their three-legged stool model. So the point as Sanjeev was making about hybrid. It's an interesting balance, isn't it, between getting your lowest cost and at the same time having best of breed and scale? >> It's basically what you're trying to optimize for, as you said, right? And by the way, to the earlier point, not everybody is at Walmart's scale, so it's not actually cheaper for everybody to have the purchasing power to make the cloud cheaper to have it on-prem. But I think what you see almost every company, large or small, moving towards is this concept of like, where do I find the agility? And is the agility in building the infrastructure for me? And typically, the thing that gives you outside advantage as an organization is not how you constructed your cloud computing infrastructure. It might be how you structured your data analytics as an example, which cloud is related to that. But how do you marry those two things? And getting back to sort of Sanjeev's point. We're in a real struggle now where one hand we want to have best of breed services and on the other hand we want it to be really easy to manage, secure, do data governance. And those two things are really at odds with each other right now. So if you want all the knobs and switches of a service like geospatial analytics and big query, you're going to have to use Google tools, right? Whereas if you want visibility across all the clouds for your application of state and understand the security and governance of that, you're kind of looking for something that's more cross-cloud tooling at that point. But whenever you talk to somebody about cross-cloud tooling, they look at you like that's not really possible. So it's a very interesting time in the market. Now, we're kind of layering this concept of supercloud on it. And some people think supercloud's about basically multi-cloud tooling, and some people think it's about a whole new architectural stack. So we're just not there yet. But it's not all about cost. I mean, cloud has not been about cost for a very, very long time. Cloud has been about how do you really make the most of your data. And this gets back to cross-cloud services like Snowflake. Why did they even exist? They existed because we had data everywhere, but we need to treat data as a unified object so that we can analyze it and get insight from it. And so that's where some of the benefit of these cross-cloud services are moving today. Still a long way to go, though, Dave. >> Keith, I reached out to my friends at ETR given the macro headwinds, And you're right, Maribel, cloud hasn't really been about just about cost savings. But I reached out to the ETR, guys, what's your data show in terms of how customers are dealing with the economic headwinds? And they said, by far, their number one strategy to cut cost is consolidating redundant vendors. And a distant second, but still notable was optimizing cloud costs. Maybe using reserve instances, or using more volume buying. Nowhere in there. And I asked them to, "Could you go look and see if you can find it?" Do we see repatriation? And you hear this a lot. You hear people whispering as analysts, "You better look into that repatriation trend." It's pretty big. You can't find it. But some of the Walmarts in the world, maybe even not repatriating, but they maybe have better cost structure on-prem. Keith, what are you seeing from the practitioners that you talk to in terms of how they're dealing with these headwinds? >> Yeah, I just got into a conversation about this just this morning with (indistinct) who is an analyst over at GigaHome. He's reading the same headlines. Repatriation is happening at large scale. I think this is kind of, we have these quiet terms now. We have quiet quitting, we have quiet hiring. I think we have quiet repatriation. Most people haven't done away with their data centers. They're still there. Whether they're completely on-premises data centers, and they own assets, or they're partnerships with QTX, Equinix, et cetera, they have these private cloud resources. What I'm seeing practically is a rebalancing of workloads. Do I really need to pay AWS for this instance of SAP that's on 24 hours a day versus just having it on-prem, moving it back to my data center? I've talked to quite a few customers who were early on to moving their static SAP workloads onto the public cloud, and they simply moved them back. Surprising, I was at VMware Explore. And we can talk about this a little bit later on. But our customers, net new, not a lot that were born in the cloud. And they get to this point where their workloads are static. And they look at something like a Kubernetes, or a OpenShift, or VMware Tanzu. And they ask the question, "Do I need the scalability of cloud?" I might consider being a net new VMware customer to deliver this base capability. So are we seeing repatriation as the number one reason? No, I think internal IT operations are just naturally come to this realization. Hey, I have these resources on premises. The private cloud technologies have moved far along enough that I can just simply move this workload back. I'm not calling it repatriation, I'm calling it rightsizing for the operating model that I have. >> Makes sense. Yeah. >> Go ahead. >> If I missed something, Dave, why we are on this topic of repatriation. I'm actually surprised that we are talking about repatriation as a very big thing. I think repatriation is happening, no doubt, but it's such a small percentage of cloud migration that to me it's a rounding error in my opinion. I think there's a bigger problem. The problem is that people don't know where the cost is. If they knew where the cost was being wasted in the cloud, they could do something about it. But if you don't know, then the easy answer is cloud costs a lot and moving it back to on-premises. I mean, take like Capital One as an example. They got rid of all the data centers. Where are they going to repatriate to? They're all in the cloud at this point. So I think my point is that data observability is one of the places that has seen a lot of traction is because of cost. Data observability, when it first came into existence, it was all about data quality. Then it was all about data pipeline reliability. And now, the number one killer use case is FinOps. >> Maribel, you had a comment? >> Yeah, I'm kind of in violent agreement with both Sanjeev and Keith. So what are we seeing here? So the first thing that we see is that many people wildly overspent in the big public cloud. They had stranded cloud credits, so to speak. The second thing is, some of them still had infrastructure that was useful. So why not use it if you find the right workloads to what Keith was talking about, if they were more static workloads, if it was already there? So there is a balancing that's going on. And then I think fundamentally, from a trend standpoint, these things aren't binary. Everybody, for a while, everything was going to go to the public cloud and then people are like, "Oh, it's kind of expensive." Then they're like, "Oh no, they're going to bring it all on-prem 'cause it's really expensive." And it's like, "Well, that doesn't necessarily get me some of the new features and functionalities I might want for some of my new workloads." So I'm going to put the workloads that have a certain set of characteristics that require cloud in the cloud. And if I have enough capability on-prem and enough IT resources to manage certain things on site, then I'm going to do that there 'cause that's a more cost-effective thing for me to do. It's not binary. That's why we went to hybrid. And then we went to multi just to describe the fact that people added multiple public clouds. And now we're talking about super, right? So I don't look at it as a one-size-fits-all for any of this. >> A a number of practitioners leading up to Supercloud2 have told us that they're solving their cloud complexity by going in monocloud. So they're putting on the blinders. Even though across the organization, there's other groups using other clouds. You're like, "In my group, we use AWS, or my group, we use Azure. And those guys over there, they use Google. We just kind of keep it separate." Are you guys hearing this in your view? Is that risky? Are they missing out on some potential to tap best of breed? What do you guys think about that? >> Everybody thinks they're monocloud. Is anybody really monocloud? It's like a group is monocloud, right? >> Right. >> This genie is out of the bottle. We're not putting the genie back in the bottle. You might think your monocloud and you go like three doors down and figure out the guy or gal is on a fundamentally different cloud, running some analytics workload that you didn't know about. So, to Sanjeev's earlier point, they don't even know where their cloud spend is. So I think the concept of monocloud, how that's actually really realized by practitioners is primary and then secondary sources. So they have a primary cloud that they run most of their stuff on, and that they try to optimize. And we still have forked workloads. Somebody decides, "Okay, this SAP runs really well on this, or these analytics workloads run really well on that cloud." And maybe that's how they parse it. But if you really looked at it, there's very few companies, if you really peaked under the hood and did an analysis that you could find an actual monocloud structure. They just want to pull it back in and make it more manageable. And I respect that. You want to do what you can to try to streamline the complexity of that. >> Yeah, we're- >> Sorry, go ahead, Keith. >> Yeah, we're doing this thing where we review AWS service every day. Just in your inbox, learn about a new AWS service cursory. There's 238 AWS products just on the AWS cloud itself. Some of them are redundant, but you get the idea. So the concept of monocloud, I'm in filing agreement with Maribel on this that, yes, a group might say I want a primary cloud. And that primary cloud may be the AWS. But have you tried the licensed Oracle database on AWS? It is really tempting to license Oracle on Oracle Cloud, Microsoft on Microsoft. And I can't get RDS anywhere but Amazon. So while I'm driven to desire the simplicity, the reality is whether be it M&A, licensing, data sovereignty. I am forced into a multi-cloud management style. But I do agree most people kind of do this one, this primary cloud, secondary cloud. And I guarantee you're going to have a third cloud or a fourth cloud whether you want to or not via shadow IT, latency, technical reasons, et cetera. >> Thank you. Sanjeev, you had a comment? >> Yeah, so I just wanted to mention, as an organization, I'm complete agreement, no organization is monocloud, at least if it's a large organization. Large organizations use all kinds of combinations of cloud providers. But when you talk about a single workload, that's where the program arises. As Keith said, the 238 services in AWS. How in the world am I going to be an expert in AWS, but then say let me bring GCP or Azure into a single workload? And that's where I think we probably will still see monocloud as being predominant because the team has developed its expertise on a particular cloud provider, and they just don't have the time of the day to go learn yet another stack. However, there are some interesting things that are happening. For example, if you look at a multi-cloud example where Oracle and Microsoft Azure have that interconnect, so that's a beautiful thing that they've done because now in the newest iteration, it's literally a few clicks. And then behind the scene, your .NET application and your Oracle database in OCI will be configured, the identities in active directory are federated. And you can just start using a database in one cloud, which is OCI, and an application, your .NET in Azure. So till we see this kind of a solution coming out of the providers, I think it's is unrealistic to expect the end users to be able to figure out multiple clouds. >> Well, I have to share with you. I can't remember if he said this on camera or if it was off camera so I'll hold off. I won't tell you who it is, but this individual was sort of complaining a little bit saying, "With AWS, I can take their best AI tools like SageMaker and I can run them on my Snowflake." He said, "I can't do that in Google. Google forces me to go to BigQuery if I want their excellent AI tools." So he was sort of pushing, kind of tweaking a little bit. Some of the vendor talked that, "Oh yeah, we're so customer-focused." Not to pick on Google, but I mean everybody will say that. And then you say, "If you're so customer-focused, why wouldn't you do a ABC?" So it's going to be interesting to see who leads that integration and how broadly it's applied. But I digress. Keith, at our first supercloud event, that was on August 9th. And it was only a few months after Broadcom announced the VMware acquisition. A lot of people, myself included said, "All right, cuts are coming." Generally, Tanzu is probably going to be under the radar, but it's Supercloud 22 and presumably VMware Explore, the company really... Well, certainly the US touted its Tanzu capabilities. I wasn't at VMware Explore Europe, but I bet you heard similar things. Hawk Tan has been blogging and very vocal about cross-cloud services and multi-cloud, which doesn't happen without Tanzu. So what did you hear, Keith, in Europe? What's your latest thinking on VMware's prospects in cross-cloud services/supercloud? >> So I think our friend and Cube, along host still be even more offended at this statement than he was when I sat in the Cube. This was maybe five years ago. There's no company better suited to help industries or companies, cross-cloud chasm than VMware. That's not a compliment. That's a reality of the industry. This is a very difficult, almost intractable problem. What I heard that VMware Europe were customers serious about this problem, even more so than the US data sovereignty is a real problem in the EU. Try being a company in Switzerland and having the Swiss data solvency issues. And there's no local cloud presence there large enough to accommodate your data needs. They had very serious questions about this. I talked to open source project leaders. Open source project leaders were asking me, why should I use the public cloud to host Kubernetes-based workloads, my projects that are building around Kubernetes, and the CNCF infrastructure? Why should I use AWS, Google, or even Azure to host these projects when that's undifferentiated? I know how to run Kubernetes, so why not run it on-premises? I don't want to deal with the hardware problems. So again, really great questions. And then there was always the specter of the problem, I think, we all had with the acquisition of VMware by Broadcom potentially. 4.5 billion in increased profitability in three years is a unbelievable amount of money when you look at the size of the problem. So a lot of the conversation in Europe was about industry at large. How do we do what regulators are asking us to do in a practical way from a true technology sense? Is VMware cross-cloud great? >> Yeah. So, VMware, obviously, to your point. OpenStack is another way of it. Actually, OpenStack, uptake is still alive and well, especially in those regions where there may not be a public cloud, or there's public policy dictating that. Walmart's using OpenStack. As you know in IT, some things never die. Question for Sanjeev. And it relates to this new breed of data apps. And Bob Muglia and Tristan Handy from DBT Labs who are participating in this program really got us thinking about this. You got data that resides in different clouds, it maybe even on-prem. And the machine polls data from different systems. No humans involved, e-commerce, ERP, et cetera. It creates a plan, outcomes. No human involvement. Today, you're on a CRM system, you're inputting, you're doing forms, you're, you're automating processes. We're talking about a new breed of apps. What are your thoughts on this? Is it real? Is it just way off in the distance? How does machine intelligence fit in? And how does supercloud fit? >> So great point. In fact, the data apps that you're talking about, I call them data products. Data products first came into limelight in the last couple of years when Jamal Duggan started talking about data mesh. I am taking data products out of the data mesh concept because data mesh, whether data mesh happens or not is analogous to data products. Data products, basically, are taking a product management view of bringing data from different sources based on what the consumer needs. We were talking earlier today about maybe it's my vacation rentals, or it may be a retail data product, it may be an investment data product. So it's a pre-packaged extraction of data from different sources. But now I have a product that has a whole lifecycle. I can version it. I have new features that get added. And it's a very business data consumer centric. It uses machine learning. For instance, I may be able to tell whether this data product has stale data. Who is using that data? Based on the usage of the data, I may have a new data products that get allocated. I may even have the ability to take existing data products, mash them up into something that I need. So if I'm going to have that kind of power to create a data product, then having a common substrate underneath, it can be very useful. And that could be supercloud where I am making API calls. I don't care where the ERP, the CRM, the survey data, the pricing engine where they sit. For me, there's a logical abstraction. And then I'm building my data product on top of that. So I see a new breed of data products coming out. To answer your question, how early we are or is this even possible? My prediction is that in 2023, we will start seeing more of data products. And then it'll take maybe two to three years for data products to become mainstream. But it's starting this year. >> A subprime mortgages were a data product, definitely were humans involved. All right, let's talk about some of the supercloud, multi-cloud players and what their future looks like. You can kind of pick your favorites. VMware, Snowflake, Databricks, Red Hat, Cisco, Dell, HP, Hashi, IBM, CloudFlare. There's many others. cohesive rubric. Keith, I wanted to start with CloudFlare because they actually use the term supercloud. and just simplifying what they said. They look at it as taking serverless to the max. You write your code and then you can deploy it in seconds worldwide, of course, across the CloudFlare infrastructure. You don't have to spin up containers, you don't go to provision instances. CloudFlare worries about all that infrastructure. What are your thoughts on CloudFlare this approach and their chances to disrupt the current cloud landscape? >> As Larry Ellison said famously once before, the network is the computer, right? I thought that was Scott McNeley. >> It wasn't Scott McNeley. I knew it was on Oracle Align. >> Oracle owns that now, owns that line. >> By purpose or acquisition. >> They should have just called it cloud. >> Yeah, they should have just called it cloud. >> Easier. >> Get ahead. >> But if you think about the CloudFlare capability, CloudFlare in its own right is becoming a decent sized cloud provider. If you have compute out at the edge, when we talk about edge in the sense of CloudFlare and points of presence, literally across the globe, you have all of this excess computer, what do you do with it? First offering, let's disrupt data in the cloud. We can't start the conversation talking about data. When they say we're going to give you object-oriented or object storage in the cloud without egress charges, that's disruptive. That we can start to think about supercloud capability of having compute EC2 run in AWS, pushing and pulling data from CloudFlare. And now, I've disrupted this roach motel data structure, and that I'm freely giving away bandwidth, basically. Well, the next layer is not that much more difficult. And I think part of CloudFlare's serverless approach or supercloud approaches so that they don't have to commit to a certain type of compute. It is advantageous. It is a feature for me to be able to go to EC2 and pick a memory heavy model, or a compute heavy model, or a network heavy model, CloudFlare is taken away those knobs. and I'm just giving code and allowing that to run. CloudFlare has a massive network. If I can put the code closest using the CloudFlare workers, if I can put that code closest to where the data is at or residing, super compelling observation. The question is, does it scale? I don't get the 238 services. While Server List is great, I have to know what I'm going to build. I don't have a Cognito, or RDS, or all these other services that make AWS, GCP, and Azure appealing from a builder's perspective. So it is a very interesting nascent start. It's great because now they can hide compute. If they don't have the capacity, they can outsource that maybe at a cost to one of the other cloud providers, but kind of hiding the compute behind the surplus architecture is a really unique approach. >> Yeah. And they're dipping their toe in the water. And they've announced an object store and a database platform and more to come. We got to wrap. So I wonder, Sanjeev and Maribel, if you could maybe pick some of your favorites from a competitive standpoint. Sanjeev, I felt like just watching Snowflake, I said, okay, in my opinion, they had the right strategy, which was to run on all the clouds, and then try to create that abstraction layer and data sharing across clouds. Even though, let's face it, most of it might be happening across regions if it's happening, but certainly outside of an individual account. But I felt like just observing them that anybody who's traditional on-prem player moving into the clouds or anybody who's a cloud native, it just makes total sense to write to the various clouds. And to the extent that you can simplify that for users, it seems to be a logical strategy. Maybe as I said before, what multi-cloud should have been. But are there companies that you're watching that you think are ahead in the game , or ones that you think are a good model for the future? >> Yes, Snowflake, definitely. In fact, one of the things we have not touched upon very much, and Keith mentioned a little bit, was data sovereignty. Data residency rules can require that certain data should be written into certain region of a certain cloud. And if my cloud provider can abstract that or my database provider, then that's perfect for me. So right now, I see Snowflake is way ahead of this pack. I would not put MongoDB too far behind. They don't really talk about this thing. They are in a different space, but now they have a lakehouse, and they've got all of these other SQL access and new capabilities that they're announcing. So I think they would be quite good with that. Oracle is always a dark forest. Oracle seems to have revived its Cloud Mojo to some extent. And it's doing some interesting stuff. Databricks is the other one. I have not seen Databricks. They've been very focused on lakehouse, unity, data catalog, and some of those pieces. But they would be the obvious challenger. And if they come into this space of supercloud, then they may bring some open source technologies that others can rely on like Delta Lake as a table format. >> Yeah. One of these infrastructure players, Dell, HPE, Cisco, even IBM. I mean, I would be making my infrastructure as programmable and cloud friendly as possible. That seems like table stakes. But Maribel, any companies that stand out to you that we should be paying attention to? >> Well, we already mentioned a bunch of them, so maybe I'll go a slightly different route. I'm watching two companies pretty closely to see what kind of traction they get in their established companies. One we already talked about, which is VMware. And the thing that's interesting about VMware is they're everywhere. And they also have the benefit of having a foot in both camps. If you want to do it the old way, the way you've always done it with VMware, they got all that going on. If you want to try to do a more cross-cloud, multi-cloud native style thing, they're really trying to build tools for that. So I think they have really good access to buyers. And that's one of the reasons why I'm interested in them to see how they progress. The other thing, I think, could be a sleeping horse oddly enough is Google Cloud. They've spent a lot of work and time on Anthos. They really need to create a certain set of differentiators. Well, it's not necessarily in their best interest to be the best multi-cloud player. If they decide that they want to differentiate on a different layer of the stack, let's say they want to be like the person that is really transformative, they talk about transformation cloud with analytics workloads, then maybe they do spend a good deal of time trying to help people abstract all of the other underlying infrastructure and make sure that they get the sexiest, most meaningful workloads into their cloud. So those are two people that you might not have expected me to go with, but I think it's interesting to see not just on the things that might be considered, either startups or more established independent companies, but how some of the traditional providers are trying to reinvent themselves as well. >> I'm glad you brought that up because if you think about what Google's done with Kubernetes. I mean, would Google even be relevant in the cloud without Kubernetes? I could argue both sides of that. But it was quite a gift to the industry. And there's a motivation there to do something unique and different from maybe the other cloud providers. And I'd throw in Red Hat as well. They're obviously a key player and Kubernetes. And Hashi Corp seems to be becoming the standard for application deployment, and terraform, or cross-clouds, and there are many, many others. I know we're leaving lots out, but we're out of time. Folks, I got to thank you so much for your insights and your participation in Supercloud2. Really appreciate it. >> Thank you. >> Thank you. >> Thank you. >> This is Dave Vellante for John Furrier and the entire Cube community. Keep it right there for more content from Supercloud2.

(upbeat music) >> Hello, everybody. This is Dave Vellante. Welcome back to Supercloud2, where we're exploring the intersection of data analytics and the future of cloud. In this segment, we're going to look at how the Supercloud will support a new class of applications, not just work that runs on multiple clouds, but rather a new breed of apps that can orchestrate things in the real world. Think Uber for many types of businesses. These applications, they're not about codifying forms or business processes. They're about orchestrating people, places, and things in a business ecosystem. And I'm pleased to welcome my colleague and friend, George Gilbert, former Gartner Analyst, Wiki Bond market analyst, former equities analyst as my co-host. And we're thrilled to have Tristan Handy, who's the founder and CEO of DBT Labs and Bob Muglia, who's the former President of Microsoft's Enterprise business and former CEO of Snowflake. Welcome all, gentlemen. Thank you for coming on the program. >> Good to be here. >> Thanks for having us. >> Hey, look, I'm going to start actually with the SuperCloud because both Tristan and Bob, you've read the definition. Thank you for doing that. And Bob, you have some really good input, some thoughts on maybe some of the drawbacks and how we can advance this. So what are your thoughts in reading that definition around SuperCloud? >> Well, I thought first of all that you did a very good job of laying out all of the characteristics of it and helping to define it overall. But I do think it can be tightened a bit, and I think it's helpful to do it in as short a way as possible. And so in the last day I've spent a little time thinking about how to take it and write a crisp definition. And here's my go at it. This is one day old, so gimme a break if it's going to change. And of course we have to follow the industry, and so that, and whatever the industry decides, but let's give this a try. So in the way I think you're defining it, what I would say is a SuperCloud is a platform that provides programmatically consistent services hosted on heterogeneous cloud providers. >> Boom. Nice. Okay, great. I'm going to go back and read the script on that one and tighten that up a bit. Thank you for spending the time thinking about that. Tristan, would you add anything to that or what are your thoughts on the whole SuperCloud concept? >> So as I read through this, I fully realize that we need a word for this thing because I have experienced the inability to talk about it as well. But for many of us who have been living in the Confluence, Snowflake, you know, this world of like new infrastructure, this seems fairly uncontroversial. Like I read through this, and I'm just like, yeah, this is like the world I've been living in for years now. And I noticed that you called out Snowflake for being an example of this, but I think that there are like many folks, myself included, for whom this world like fully exists today. >> Yeah, I think that's a fair, I dunno if it's criticism, but people observe, well, what's the big deal here? It's just kind of what we're living in today. It reminds me of, you know, Tim Burns Lee saying, well, this is what the internet was supposed to be. It was supposed to be Web 2.0, so maybe this is what multi-cloud was supposed to be. Let's turn our attention to apps. Bob first and then go to Tristan. Bob, what are data apps to you? When people talk about data products, is that what they mean? Are we talking about something more, different? What are data apps to you? >> Well, to understand data apps, it's useful to contrast them to something, and I just use the simple term people apps. I know that's a little bit awkward, but it's clear. And almost everything we work with, almost every application that we're familiar with, be it email or Salesforce or any consumer app, those are applications that are targeted at responding to people. You know, in contrast, a data application reacts to changes in data and uses some set of analytic services to autonomously take action. So where applications that we're familiar with respond to people, data apps respond to changes in data. And they both do something, but they do it for different reasons. >> Got it. You know, George, you and I were talking about, you know, it comes back to SuperCloud, broad definition, narrow definition. Tristan, how do you see it? Do you see it the same way? Do you have a different take on data apps? >> Oh, geez. This is like a conversation that I don't know has an end. It's like been, I write a substack, and there's like this little community of people who all write substack. We argue with each other about these kinds of things. Like, you know, as many different takes on this question as you can find, but the way that I think about it is that data products are atomic units of functionality that are fundamentally data driven in nature. So a data product can be as simple as an interactive dashboard that is like actually had design thinking put into it and serves a particular user group and has like actually gone through kind of a product development life cycle. And then a data app or data application is a kind of cohesive end-to-end experience that often encompasses like many different data products. So from my perspective there, this is very, very related to the way that these things are produced, the kinds of experiences that they're provided, that like data innovates every product that we've been building in, you know, software engineering for, you know, as long as there have been computers. >> You know, Jamak Dagani oftentimes uses the, you know, she doesn't name Spotify, but I think it's Spotify as that kind of example she uses. But I wonder if we can maybe try to take some examples. If you take, like George, if you take a CRM system today, you're inputting leads, you got opportunities, it's driven by humans, they're really inputting the data, and then you got this system that kind of orchestrates the business process, like runs a forecast. But in this data driven future, are we talking about the app itself pulling data in and automatically looking at data from the transaction systems, the call center, the supply chain and then actually building a plan? George, is that how you see it? >> I go back to the example of Uber, may not be the most sophisticated data app that we build now, but it was like one of the first where you do have users interacting with their devices as riders trying to call a car or driver. But the app then looks at the location of all the drivers in proximity, and it matches a driver to a rider. It calculates an ETA to the rider. It calculates an ETA then to the destination, and it calculates a price. Those are all activities that are done sort of autonomously that don't require a human to type something into a form. The application is using changes in data to calculate an analytic product and then to operationalize that, to assign the driver to, you know, calculate a price. Those are, that's an example of what I would think of as a data app. And my question then I guess for Tristan is if we don't have all the pieces in place for sort of mainstream companies to build those sorts of apps easily yet, like how would we get started? What's the role of a semantic layer in making that easier for mainstream companies to build? And how do we get started, you know, say with metrics? How does that, how does that take us down that path? >> So what we've seen in the past, I dunno, decade or so, is that one of the most successful business models in infrastructure is taking hard things and rolling 'em up behind APIs. You take messaging, you take payments, and you all of a sudden increase the capability of kind of your median application developer. And you say, you know, previously you were spending all your time being focused on how do you accept credit cards, how do you send SMS payments, and now you can focus on your business logic, and just create the thing. One of, interestingly, one of the things that we still don't know how to API-ify is concepts that live inside of your data warehouse, inside of your data lake. These are core concepts that, you know, you would imagine that the business would be able to create applications around very easily, but in fact that's not the case. It's actually quite challenging to, and involves a lot of data engineering pipeline and all this work to make these available. And so if you really want to make it very easy to create some of these data experiences for users, you need to have an ability to describe these metrics and then to turn them into APIs to make them accessible to application developers who have literally no idea how they're calculated behind the scenes, and they don't need to. >> So how rich can that API layer grow if you start with metric definitions that you've defined? And DBT has, you know, the metric, the dimensions, the time grain, things like that, that's a well scoped sort of API that people can work within. How much can you extend that to say non-calculated business rules or governance information like data reliability rules, things like that, or even, you know, features for an AIML feature store. In other words, it starts, you started pragmatically, but how far can you grow? >> Bob is waiting with bated breath to answer this question. I'm, just really quickly, I think that we as a company and DBT as a product tend to be very pragmatic. We try to release the simplest possible version of a thing, get it out there, and see if people use it. But the idea that, the concept of a metric is really just a first landing pad. The really, there is a physical manifestation of the data and then there's a logical manifestation of the data. And what we're trying to do here is make it very easy to access the logical manifestation of the data, and metric is a way to look at that. Maybe an entity, a customer, a user is another way to look at that. And I'm sure that there will be more kind of logical structures as well. >> So, Bob, chime in on this. You know, what's your thoughts on the right architecture behind this, and how do we get there? >> Yeah, well first of all, I think one of the ways we get there is by what companies like DBT Labs and Tristan is doing, which is incrementally taking and building on the modern data stack and extending that to add a semantic layer that describes the data. Now the way I tend to think about this is a fairly major shift in the way we think about writing applications, which is today a code first approach to moving to a world that is model driven. And I think that's what the big change will be is that where today we think about data, we think about writing code, and we use that to produce APIs as Tristan said, which encapsulates those things together in some form of services that are useful for organizations. And that idea of that encapsulation is never going to go away. It's very, that concept of an API is incredibly useful and will exist well into the future. But what I think will happen is that in the next 10 years, we're going to move to a world where organizations are defining models first of their data, but then ultimately of their business process, their entire business process. Now the concept of a model driven world is a very old concept. I mean, I first started thinking about this and playing around with some early model driven tools, probably before Tristan was born in the early 1980s. And those tools didn't work because the semantics associated with executing the model were too complex to be written in anything other than a procedural language. We're now reaching a time where that is changing, and you see it everywhere. You see it first of all in the world of machine learning and machine learning models, which are taking over more and more of what applications are doing. And I think that's an incredibly important step. And learned models are an important part of what people will do. But if you look at the world today, I will claim that we've always been modeling. Modeling has existed in computers since there have been integrated circuits and any form of computers. But what we do is what I would call implicit modeling, which means that it's the model is written on a whiteboard. It's in a bunch of Slack messages. It's on a set of napkins in conversations that happen and during Zoom. That's where the model gets defined today. It's implicit. There is one in the system. It is hard coded inside application logic that exists across many applications with humans being the glue that connects those models together. And really there is no central place you can go to understand the full attributes of the business, all of the business rules, all of the business logic, the business data. That's going to change in the next 10 years. And we'll start to have a world where we can define models about what we're doing. Now in the short run, the most important models to build are data models and to describe all of the attributes of the data and their relationships. And that's work that DBT Labs is doing. A number of other companies are doing that. We're taking steps along that way with catalogs. People are trying to build more complete ontologies associated with that. The underlying infrastructure is still super, super nascent. But what I think we'll see is this infrastructure that exists today that's building learned models in the form of machine learning programs. You know, some of these incredible machine learning programs in foundation models like GPT and DALL-E and all of the things that are happening in these global scale models, but also all of that needs to get applied to the domains that are appropriate for a business. And I think we'll see the infrastructure developing for that, that can take this concept of learned models and put it together with more explicitly defined models. And this is where the concept of knowledge graphs come in and then the technology that underlies that to actually implement and execute that, which I believe are relational knowledge graphs. >> Oh, oh wow. There's a lot to unpack there. So let me ask the Colombo question, Tristan, we've been making fun of your youth. We're just, we're just jealous. Colombo, I'll explain it offline maybe. >> I watch Colombo. >> Okay. All right, good. So but today if you think about the application stack and the data stack, which is largely an analytics pipeline. They're separate. Do they, those worlds, do they have to come together in order to achieve Bob's vision? When I talk to practitioners about that, they're like, well, I don't want to complexify the application stack cause the data stack today is so, you know, hard to manage. But but do those worlds have to come together? And you know, through that model, I guess abstraction or translation that Bob was just describing, how do you guys think about that? Who wants to take that? >> I think it's inevitable that data and AI are going to become closer together? I think that the infrastructure there has been moving in that direction for a long time. Whether you want to use the Lakehouse portmanteau or not. There's also, there's a next generation of data tech that is still in the like early stage of being developed. There's a company that I love that is essentially Cross Cloud Lambda, and it's just a wonderful abstraction for computing. So I think that, you know, people have been predicting that these worlds are going to come together for awhile. A16Z wrote a great post on this back in I think 2020, predicting this, and I've been predicting this since since 2020. But what's not clear is the timeline, but I think that this is still just as inevitable as it's been. >> Who's that that does Cross Cloud? >> Let me follow up on. >> Who's that, Tristan, that does Cross Cloud Lambda? Can you name names? >> Oh, they're called Modal Labs. >> Modal Labs, yeah, of course. All right, go ahead, George. >> Let me ask about this vision of trying to put the semantics or the code that represents the business with the data. It gets us to a world that's sort of more data centric, where data's not locked inside or behind the APIs of different applications so that we don't have silos. But at the same time, Bob, I've heard you talk about building the semantics gradually on top of, into a knowledge graph that maybe grows out of a data catalog. And the vision of getting to that point, essentially the enterprise's metadata and then the semantics you're going to add onto it are really stored in something that's separate from the underlying operational and analytic data. So at the same time then why couldn't we gradually build semantics beyond the metric definitions that DBT has today? In other words, you build more and more of the semantics in some layer that DBT defines and that sits above the data management layer, but any requests for data have to go through the DBT layer. Is that a workable alternative? Or where, what type of limitations would you face? >> Well, I think that it is the way the world will evolve is to start with the modern data stack and, you know, which is operational applications going through a data pipeline into some form of data lake, data warehouse, the Lakehouse, whatever you want to call it. And then, you know, this wide variety of analytics services that are built together. To the point that Tristan made about machine learning and data coming together, you see that in every major data cloud provider. Snowflake certainly now supports Python and Java. Databricks is of course building their data warehouse. Certainly Google, Microsoft and Amazon are doing very, very similar things in terms of building complete solutions that bring together an analytics stack that typically supports languages like Python together with the data stack and the data warehouse. I mean, all of those things are going to evolve, and they're not going to go away because that infrastructure is relatively new. It's just being deployed by companies, and it solves the problem of working with petabytes of data if you need to work with petabytes of data, and nothing will do that for a long time. What's missing is a layer that understands and can model the semantics of all of this. And if you need to, if you want to model all, if you want to talk about all the semantics of even data, you need to think about all of the relationships. You need to think about how these things connect together. And unfortunately, there really is no platform today. None of our existing platforms are ultimately sufficient for this. It was interesting, I was just talking to a customer yesterday, you know, a large financial organization that is building out these semantic layers. They're further along than many companies are. And you know, I asked what they're building it on, and you know, it's not surprising they're using a, they're using combinations of some form of search together with, you know, textual based search together with a document oriented database. In this case it was Cosmos. And that really is kind of the state of the art right now. And yet those products were not built for this. They don't really, they can't manage the complicated relationships that are required. They can't issue the queries that are required. And so a new generation of database needs to be developed. And fortunately, you know, that is happening. The world is developing a new set of relational algorithms that will be able to work with hundreds of different relations. If you look at a SQL database like Snowflake or a big query, you know, you get tens of different joins coming together, and that query is going to take a really long time. Well, fortunately, technology is evolving, and it's possible with new join algorithms, worst case, optimal join algorithms they're called, where you can join hundreds of different relations together and run semantic queries that you simply couldn't run. Now that technology is nascent, but it's really important, and I think that will be a requirement to have this semantically reach its full potential. In the meantime, Tristan can do a lot of great things by building up on what he's got today and solve some problems that are very real. But in the long run I think we'll see a new set of databases to support these models. >> So Tristan, you got to respond to that, right? You got to, so take the example of Snowflake. We know it doesn't deal well with complex joins, but they're, they've got big aspirations. They're building an ecosystem to really solve some of these problems. Tristan, you guys are part of that ecosystem, and others, but please, your thoughts on what Bob just shared. >> Bob, I'm curious if, I would have no idea what you were talking about except that you introduced me to somebody who gave me a demo of a thing and do you not want to go there right now? >> No, I can talk about it. I mean, we can talk about it. Look, the company I've been working with is Relational AI, and they're doing this work to actually first of all work across the industry with academics and research, you know, across many, many different, over 20 different research institutions across the world to develop this new set of algorithms. They're all fully published, just like SQL, the underlying algorithms that are used by SQL databases are. If you look today, every single SQL database uses a similar set of relational algorithms underneath that. And those algorithms actually go back to system R and what IBM developed in the 1970s. We're just, there's an opportunity for us to build something new that allows you to take, for example, instead of taking data and grouping it together in tables, treat all data as individual relations, you know, a key and a set of values and then be able to perform purely relational operations on it. If you go back to what, to Codd, and what he wrote, he defined two things. He defined a relational calculus and relational algebra. And essentially SQL is a query language that is translated by the query processor into relational algebra. But however, the calculus of SQL is not even close to the full semantics of the relational mathematics. And it's possible to have systems that can do everything and that can store all of the attributes of the data model or ultimately the business model in a form that is much more natural to work with. >> So here's like my short answer to this. I think that we're dealing in different time scales. I think that there is actually a tremendous amount of work to do in the semantic layer using the kind of technology that we have on the ground today. And I think that there's, I don't know, let's say five years of like really solid work that there is to do for the entire industry, if not more. But the wonderful thing about DBT is that it's independent of what the compute substrate is beneath it. And so if we develop new platforms, new capabilities to describe semantic models in more fine grain detail, more procedural, then we're going to support that too. And so I'm excited about all of it. >> Yeah, so interpreting that short answer, you're basically saying, cause Bob was just kind of pointing to you as incremental, but you're saying, yeah, okay, we're applying it for incremental use cases today, but we can accommodate a much broader set of examples in the future. Is that correct, Tristan? >> I think you're using the word incremental as if it's not good, but I think that incremental is great. We have always been about applying incremental improvement on top of what exists today, but allowing practitioners to like use different workflows to actually make use of that technology. So yeah, yeah, we are a very incremental company. We're going to continue being that way. >> Well, I think Bob was using incremental as a pejorative. I mean, I, but to your point, a lot. >> No, I don't think so. I want to stop that. No, I don't think it's pejorative at all. I think incremental, incremental is usually the most successful path. >> Yes, of course. >> In my experience. >> We agree, we agree on that. >> Having tried many, many moonshot things in my Microsoft days, I can tell you that being incremental is a good thing. And I'm a very big believer that that's the way the world's going to go. I just think that there is a need for us to build something new and that ultimately that will be the solution. Now you can argue whether it's two years, three years, five years, or 10 years, but I'd be shocked if it didn't happen in 10 years. >> Yeah, so we all agree that incremental is less disruptive. Boom, but Tristan, you're, I think I'm inferring that you believe you have the architecture to accommodate Bob's vision, and then Bob, and I'm inferring from Bob's comments that maybe you don't think that's the case, but please. >> No, no, no. I think that, so Bob, let me put words into your mouth and you tell me if you disagree, DBT is completely useless in a world where a large scale cloud data warehouse doesn't exist. We were not able to bring the power of Python to our users until these platforms started supporting Python. Like DBT is a layer on top of large scale computing platforms. And to the extent that those platforms extend their functionality to bring more capabilities, we will also service those capabilities. >> Let me try and bridge the two. >> Yeah, yeah, so Bob, Bob, Bob, do you concur with what Tristan just said? >> Absolutely, I mean there's nothing to argue with in what Tristan just said. >> I wanted. >> And it's what he's doing. It'll continue to, I believe he'll continue to do it, and I think it's a very good thing for the industry. You know, I'm just simply saying that on top of that, I would like to provide Tristan and all of those who are following similar paths to him with a new type of database that can actually solve these problems in a much more architected way. And when I talk about Cosmos with something like Mongo or Cosmos together with Elastic, you're using Elastic as the join engine, okay. That's the purpose of it. It becomes a poor man's join engine. And I kind of go, I know there's a better answer than that. I know there is, but that's kind of where we are state of the art right now. >> George, we got to wrap it. So give us the last word here. Go ahead, George. >> Okay, I just, I think there's a way to tie together what Tristan and Bob are both talking about, and I want them to validate it, which is for five years we're going to be adding or some number of years more and more semantics to the operational and analytic data that we have, starting with metric definitions. My question is for Bob, as DBT accumulates more and more of those semantics for different enterprises, can that layer not run on top of a relational knowledge graph? And what would we lose by not having, by having the knowledge graph store sort of the joins, all the complex relationships among the data, but having the semantics in the DBT layer? >> Well, I think this, okay, I think first of all that DBT will be an environment where many of these semantics are defined. The question we're asking is how are they stored and how are they processed? And what I predict will happen is that over time, as companies like DBT begin to build more and more richness into their semantic layer, they will begin to experience challenges that customers want to run queries, they want to ask questions, they want to use this for things where the underlying infrastructure becomes an obstacle. I mean, this has happened in always in the history, right? I mean, you see major advances in computer science when the data model changes. And I think we're on the verge of a very significant change in the way data is stored and structured, or at least metadata is stored and structured. Again, I'm not saying that anytime in the next 10 years, SQL is going to go away. In fact, more SQL will be written in the future than has been written in the past. And those platforms will mature to become the engines, the slicer dicers of data. I mean that's what they are today. They're incredibly powerful at working with large amounts of data, and that infrastructure is maturing very rapidly. What is not maturing is the infrastructure to handle all of the metadata and the semantics that that requires. And that's where I say knowledge graphs are what I believe will be the solution to that. >> But Tristan, bring us home here. It sounds like, let me put pause at this, is that whatever happens in the future, we're going to leverage the vast system that has become cloud that we're talking about a supercloud, sort of where data lives irrespective of physical location. We're going to have to tap that data. It's not necessarily going to be in one place, but give us your final thoughts, please. >> 100% agree. I think that the data is going to live everywhere. It is the responsibility for both the metadata systems and the data processing engines themselves to make sure that we can join data across cloud providers, that we can join data across different physical regions and that we as practitioners are going to kind of start forgetting about details like that. And we're going to start thinking more about how we want to arrange our teams, how does the tooling that we use support our team structures? And that's when data mesh I think really starts to get very, very critical as a concept. >> Guys, great conversation. It was really awesome to have you. I can't thank you enough for spending time with us. Really appreciate it. >> Thanks a lot. >> All right. This is Dave Vellante for George Gilbert, John Furrier, and the entire Cube community. Keep it right there for more content. You're watching SuperCloud2. (upbeat music)

>>From the Cube Studios in Palo Alto in Boston, bringing you data-driven insights from the cube and E T R. This is breaking analysis with Dave Valante. >>Making technology predictions in 2022 was tricky business, especially if you were projecting the performance of markets or identifying I P O prospects and making binary forecast on data AI and the macro spending climate and other related topics in enterprise tech 2022, of course was characterized by a seesaw economy where central banks were restructuring their balance sheets. The war on Ukraine fueled inflation supply chains were a mess. And the unintended consequences of of forced march to digital and the acceleration still being sorted out. Hello and welcome to this week's weekly on Cube Insights powered by E T R. In this breaking analysis, we continue our annual tradition of transparently grading last year's enterprise tech predictions. And you may or may not agree with our self grading system, but look, we're gonna give you the data and you can draw your own conclusions and tell you what, tell us what you think. >>All right, let's get right to it. So our first prediction was tech spending increases by 8% in 2022. And as we exited 2021 CIOs, they were optimistic about their digital transformation plans. You know, they rushed to make changes to their business and were eager to sharpen their focus and continue to iterate on their digital business models and plug the holes that they, the, in the learnings that they had. And so we predicted that 8% rise in enterprise tech spending, which looked pretty good until Ukraine and the Fed decided that, you know, had to rush and make up for lost time. We kind of nailed the momentum in the energy sector, but we can't give ourselves too much credit for that layup. And as of October, Gartner had it spending growing at just over 5%. I think it was 5.1%. So we're gonna take a C plus on this one and, and move on. >>Our next prediction was basically kind of a slow ground ball. The second base, if I have to be honest, but we felt it was important to highlight that security would remain front and center as the number one priority for organizations in 2022. As is our tradition, you know, we try to up the degree of difficulty by specifically identifying companies that are gonna benefit from these trends. So we highlighted some possible I P O candidates, which of course didn't pan out. S NQ was on our radar. The company had just had to do another raise and they recently took a valuation hit and it was a down round. They raised 196 million. So good chunk of cash, but, but not the i p O that we had predicted Aqua Securities focus on containers and cloud native. That was a trendy call and we thought maybe an M SS P or multiple managed security service providers like Arctic Wolf would I p o, but no way that was happening in the crummy market. >>Nonetheless, we think these types of companies, they're still faring well as the talent shortage in security remains really acute, particularly in the sort of mid-size and small businesses that often don't have a sock Lacework laid off 20% of its workforce in 2022. And CO C e o Dave Hatfield left the company. So that I p o didn't, didn't happen. It was probably too early for Lacework. Anyway, meanwhile you got Netscope, which we've cited as strong in the E T R data as particularly in the emerging technology survey. And then, you know, I lumia holding its own, you know, we never liked that 7 billion price tag that Okta paid for auth zero, but we loved the TAM expansion strategy to target developers beyond sort of Okta's enterprise strength. But we gotta take some points off of the failure thus far of, of Okta to really nail the integration and the go to market model with azero and build, you know, bring that into the, the, the core Okta. >>So the focus on endpoint security that was a winner in 2022 is CrowdStrike led that charge with others holding their own, not the least of which was Palo Alto Networks as it continued to expand beyond its core network security and firewall business, you know, through acquisition. So overall we're gonna give ourselves an A minus for this relatively easy call, but again, we had some specifics associated with it to make it a little tougher. And of course we're watching ve very closely this this coming year in 2023. The vendor consolidation trend. You know, according to a recent Palo Alto network survey with 1300 SecOps pros on average organizations have more than 30 tools to manage security tools. So this is a logical way to optimize cost consolidating vendors and consolidating redundant vendors. The E T R data shows that's clearly a trend that's on the upswing. >>Now moving on, a big theme of 2020 and 2021 of course was remote work and hybrid work and new ways to work and return to work. So we predicted in 2022 that hybrid work models would become the dominant protocol, which clearly is the case. We predicted that about 33% of the workforce would come back to the office in 2022 in September. The E T R data showed that figure was at 29%, but organizations expected that 32% would be in the office, you know, pretty much full-time by year end. That hasn't quite happened, but we were pretty close with the projection, so we're gonna take an A minus on this one. Now, supply chain disruption was another big theme that we felt would carry through 2022. And sure that sounds like another easy one, but as is our tradition, again we try to put some binary metrics around our predictions to put some meat in the bone, so to speak, and and allow us than you to say, okay, did it come true or not? >>So we had some data that we presented last year and supply chain issues impacting hardware spend. We said at the time, you can see this on the left hand side of this chart, the PC laptop demand would remain above pre covid levels, which would reverse a decade of year on year declines, which I think started in around 2011, 2012. Now, while demand is down this year pretty substantially relative to 2021, I D C has worldwide unit shipments for PCs at just over 300 million for 22. If you go back to 2019 and you're looking at around let's say 260 million units shipped globally, you know, roughly, so, you know, pretty good call there. Definitely much higher than pre covid levels. But so what you might be asking why the B, well, we projected that 30% of customers would replace security appliances with cloud-based services and that more than a third would replace their internal data center server and storage hardware with cloud services like 30 and 40% respectively. >>And we don't have explicit survey data on exactly these metrics, but anecdotally we see this happening in earnest. And we do have some data that we're showing here on cloud adoption from ET R'S October survey where the midpoint of workloads running in the cloud is around 34% and forecast, as you can see, to grow steadily over the next three years. So this, well look, this is not, we understand it's not a one-to-one correlation with our prediction, but it's a pretty good bet that we were right, but we gotta take some points off, we think for the lack of unequivocal proof. Cause again, we always strive to make our predictions in ways that can be measured as accurate or not. Is it binary? Did it happen, did it not? Kind of like an O K R and you know, we strive to provide data as proof and in this case it's a bit fuzzy. >>We have to admit that although we're pretty comfortable that the prediction was accurate. And look, when you make an hard forecast, sometimes you gotta pay the price. All right, next, we said in 2022 that the big four cloud players would generate 167 billion in IS and PaaS revenue combining for 38% market growth. And our current forecasts are shown here with a comparison to our January, 2022 figures. So coming into this year now where we are today, so currently we expect 162 billion in total revenue and a 33% growth rate. Still very healthy, but not on our mark. So we think a w s is gonna miss our predictions by about a billion dollars, not, you know, not bad for an 80 billion company. So they're not gonna hit that expectation though of getting really close to a hundred billion run rate. We thought they'd exit the year, you know, closer to, you know, 25 billion a quarter and we don't think they're gonna get there. >>Look, we pretty much nailed Azure even though our prediction W was was correct about g Google Cloud platform surpassing Alibaba, Alibaba, we way overestimated the performance of both of those companies. So we're gonna give ourselves a C plus here and we think, yeah, you might think it's a little bit harsh, we could argue for a B minus to the professor, but the misses on GCP and Alibaba we think warrant a a self penalty on this one. All right, let's move on to our prediction about Supercloud. We said it becomes a thing in 2022 and we think by many accounts it has, despite the naysayers, we're seeing clear evidence that the concept of a layer of value add that sits above and across clouds is taking shape. And on this slide we showed just some of the pickup in the industry. I mean one of the most interesting is CloudFlare, the biggest supercloud antagonist. >>Charles Fitzgerald even predicted that no vendor would ever use the term in their marketing. And that would be proof if that happened that Supercloud was a thing and he said it would never happen. Well CloudFlare has, and they launched their version of Supercloud at their developer week. Chris Miller of the register put out a Supercloud block diagram, something else that Charles Fitzgerald was, it was was pushing us for, which is rightly so, it was a good call on his part. And Chris Miller actually came up with one that's pretty good at David Linthicum also has produced a a a A block diagram, kind of similar, David uses the term metacloud and he uses the term supercloud kind of interchangeably to describe that trend. And so we we're aligned on that front. Brian Gracely has covered the concept on the popular cloud podcast. Berkeley launched the Sky computing initiative. >>You read through that white paper and many of the concepts highlighted in the Supercloud 3.0 community developed definition align with that. Walmart launched a platform with many of the supercloud salient attributes. So did Goldman Sachs, so did Capital One, so did nasdaq. So you know, sorry you can hate the term, but very clearly the evidence is gathering for the super cloud storm. We're gonna take an a plus on this one. Sorry, haters. Alright, let's talk about data mesh in our 21 predictions posts. We said that in the 2020s, 75% of large organizations are gonna re-architect their big data platforms. So kind of a decade long prediction. We don't like to do that always, but sometimes it's warranted. And because it was a longer term prediction, we, at the time in, in coming into 22 when we were evaluating our 21 predictions, we took a grade of incomplete because the sort of decade long or majority of the decade better part of the decade prediction. >>So last year, earlier this year, we said our number seven prediction was data mesh gains momentum in 22. But it's largely confined and narrow data problems with limited scope as you can see here with some of the key bullets. So there's a lot of discussion in the data community about data mesh and while there are an increasing number of examples, JP Morgan Chase, Intuit, H S P C, HelloFresh, and others that are completely rearchitecting parts of their data platform completely rearchitecting entire data platforms is non-trivial. There are organizational challenges, there're data, data ownership, debates, technical considerations, and in particular two of the four fundamental data mesh principles that the, the need for a self-service infrastructure and federated computational governance are challenging. Look, democratizing data and facilitating data sharing creates conflicts with regulatory requirements around data privacy. As such many organizations are being really selective with their data mesh implementations and hence our prediction of narrowing the scope of data mesh initiatives. >>I think that was right on J P M C is a good example of this, where you got a single group within a, within a division narrowly implementing the data mesh architecture. They're using a w s, they're using data lakes, they're using Amazon Glue, creating a catalog and a variety of other techniques to meet their objectives. They kind of automating data quality and it was pretty well thought out and interesting approach and I think it's gonna be made easier by some of the announcements that Amazon made at the recent, you know, reinvent, particularly trying to eliminate ET t l, better connections between Aurora and Redshift and, and, and better data sharing the data clean room. So a lot of that is gonna help. Of course, snowflake has been on this for a while now. Many other companies are facing, you know, limitations as we said here and this slide with their Hadoop data platforms. They need to do new, some new thinking around that to scale. HelloFresh is a really good example of this. Look, the bottom line is that organizations want to get more value from data and having a centralized, highly specialized teams that own the data problem, it's been a barrier and a blocker to success. The data mesh starts with organizational considerations as described in great detail by Ash Nair of Warner Brothers. So take a listen to this clip. >>Yeah, so when people think of Warner Brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of H B O, you think of t n t, you think of C N N. We have 30 plus brands in our portfolio and each have their own needs. So the, the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against, as an example, HBO if Game of Thrones is going on. >>So it's often the case that data mesh is in the eyes of the implementer. And while a company's implementation may not strictly adhere to Jamma Dani's vision of data mesh, and that's okay, the goal is to use data more effectively. And despite Gartner's attempts to deposition data mesh in favor of the somewhat confusing or frankly far more confusing data fabric concept that they stole from NetApp data mesh is taking hold in organizations globally today. So we're gonna take a B on this one. The prediction is shaping up the way we envision, but as we previously reported, it's gonna take some time. The better part of a decade in our view, new standards have to emerge to make this vision become reality and they'll come in the form of both open and de facto approaches. Okay, our eighth prediction last year focused on the face off between Snowflake and Databricks. >>And we realized this popular topic, and maybe one that's getting a little overplayed, but these are two companies that initially, you know, looked like they were shaping up as partners and they, by the way, they are still partnering in the field. But you go back a couple years ago, the idea of using an AW w s infrastructure, Databricks machine intelligence and applying that on top of Snowflake as a facile data warehouse, still very viable. But both of these companies, they have much larger ambitions. They got big total available markets to chase and large valuations that they have to justify. So what's happening is, as we've previously reported, each of these companies is moving toward the other firm's core domain and they're building out an ecosystem that'll be critical for their future. So as part of that effort, we said each is gonna become aggressive investors and maybe start doing some m and a and they have in various companies. >>And on this chart that we produced last year, we studied some of the companies that were targets and we've added some recent investments of both Snowflake and Databricks. As you can see, they've both, for example, invested in elation snowflake's, put money into Lacework, the Secur security firm, ThoughtSpot, which is trying to democratize data with ai. Collibra is a governance platform and you can see Databricks investments in data transformation with D B T labs, Matillion doing simplified business intelligence hunters. So that's, you know, they're security investment and so forth. So other than our thought that we'd see Databricks I p o last year, this prediction been pretty spot on. So we'll give ourselves an A on that one. Now observability has been a hot topic and we've been covering it for a while with our friends at E T R, particularly Eric Bradley. Our number nine prediction last year was basically that if you're not cloud native and observability, you are gonna be in big trouble. >>So everything guys gotta go cloud native. And that's clearly been the case. Splunk, the big player in the space has been transitioning to the cloud, hasn't always been pretty, as we reported, Datadog real momentum, the elk stack, that's open source model. You got new entrants that we've cited before, like observe, honeycomb, chaos search and others that we've, we've reported on, they're all born in the cloud. So we're gonna take another a on this one, admittedly, yeah, it's a re reasonably easy call, but you gotta have a few of those in the mix. Okay, our last prediction, our number 10 was around events. Something the cube knows a little bit about. We said that a new category of events would emerge as hybrid and that for the most part is happened. So that's gonna be the mainstay is what we said. That pure play virtual events are gonna give way to hi hybrid. >>And the narrative is that virtual only events are, you know, they're good for quick hits, but lousy replacements for in-person events. And you know that said, organizations of all shapes and sizes, they learn how to create better virtual content and support remote audiences during the pandemic. So when we set at pure play is gonna give way to hybrid, we said we, we i we implied or specific or specified that the physical event that v i p experience is going defined. That overall experience and those v i p events would create a little fomo, fear of, of missing out in a virtual component would overlay that serves an audience 10 x the size of the physical. We saw that really two really good examples. Red Hat Summit in Boston, small event, couple thousand people served tens of thousands, you know, online. Second was Google Cloud next v i p event in, in New York City. >>Everything else was, was, was, was virtual. You know, even examples of our prediction of metaverse like immersion have popped up and, and and, and you know, other companies are doing roadshow as we predicted like a lot of companies are doing it. You're seeing that as a major trend where organizations are going with their sales teams out into the regions and doing a little belly to belly action as opposed to the big giant event. That's a definitely a, a trend that we're seeing. So in reviewing this prediction, the grade we gave ourselves is, you know, maybe a bit unfair, it should be, you could argue for a higher grade, but the, but the organization still haven't figured it out. They have hybrid experiences but they generally do a really poor job of leveraging the afterglow and of event of an event. It still tends to be one and done, let's move on to the next event or the next city. >>Let the sales team pick up the pieces if they were paying attention. So because of that, we're only taking a B plus on this one. Okay, so that's the review of last year's predictions. You know, overall if you average out our grade on the 10 predictions that come out to a b plus, I dunno why we can't seem to get that elusive a, but we're gonna keep trying our friends at E T R and we are starting to look at the data for 2023 from the surveys and all the work that we've done on the cube and our, our analysis and we're gonna put together our predictions. We've had literally hundreds of inbounds from PR pros pitching us. We've got this huge thick folder that we've started to review with our yellow highlighter. And our plan is to review it this month, take a look at all the data, get some ideas from the inbounds and then the e t R of January surveys in the field. >>It's probably got a little over a thousand responses right now. You know, they'll get up to, you know, 1400 or so. And once we've digested all that, we're gonna go back and publish our predictions for 2023 sometime in January. So stay tuned for that. All right, we're gonna leave it there for today. You wanna thank Alex Myerson who's on production and he manages the podcast, Ken Schiffman as well out of our, our Boston studio. I gotta really heartfelt thank you to Kristen Martin and Cheryl Knight and their team. They helped get the word out on social and in our newsletters. Rob Ho is our editor in chief over at Silicon Angle who does some great editing for us. Thank you all. Remember all these podcasts are available or all these episodes are available is podcasts. Wherever you listen, just all you do Search Breaking analysis podcast, really getting some great traction there. Appreciate you guys subscribing. I published each week on wikibon.com, silicon angle.com or you can email me directly at david dot valante silicon angle.com or dm me Dante, or you can comment on my LinkedIn post. And please check out ETR AI for the very best survey data in the enterprise tech business. Some awesome stuff in there. This is Dante for the Cube Insights powered by etr. Thanks for watching and we'll see you next time on breaking analysis.

>> Narrator: "TheCUBE" presents Ignite 22. Brought to you by Palo Alto Networks. >> Hey everyone. Welcome back to "TheCUBE's" live coverage of Palo Alto Network's Ignite 22 from the MGM Grand in beautiful Las Vegas. I am Lisa Martin here with Dave Vellante. Dave, we just had a great conversa- First of all, we got to hear the keynote, most of it. We also just had a great conversation with the CEO and chairman of Palo Alto Networks, Nikesh Arora. You know, this is a company that was founded back in 2005, he's been there four years, a lot has happened. A lot of growth, a lot of momentum in his tenure. You were saying in your breaking analysis, that they are on track to nearly double revenues from FY 20 to 23. Lots of momentum in this cloud security company. >> Yeah, I'd never met him before. I mean, I've been following a little bit. It's interesting, he came in as, sort of, a security outsider. You know, he joked today that he, the host, I forget the guy's name on the stage, what was his name? Hassan. Hassan, he said "He's the only guy in the room that knows less about security than I do." Because, normally, this is an industry that's steeped in deep expertise. He came in and I think is given a good compliment to the hardcore techies at Palo Alto Network. The company, it's really interesting. The company started out building their own data centers, they called it. Now they look back and call it cloud, but it was their own data centers, kind of like Salesforce did, it's kind of like ServiceNow. Because at the time, you really couldn't do it in the public cloud. The public cloud was a little too unknown. And so they needed that type of control. But Palo Alto's been amazing story since 2020, we wrote about this during the pandemic. So what they did, is they began to pivot to the the true cloud native public cloud, which is kind of immature still. They don't tell you that, but it's kind of still a little bit immature, but it's working. And when they were pivoting, it was around the same time, at Fortinet, who's a competitor there's like, I call 'em a poor man's Palo Alto, and Fortinet probably hates that, but it's kind of true. It's like a value play on a comprehensive platform, and you know Fortinet a little bit. And so, but what was happening is Fortinet was executing on its cloud strategy better than Palo Alto. And there was a real divergence in the valuations of these stocks. And we said at the time, we felt like Palo Alto, being the gold standard, would get through it. And they did. And what's happened is interesting, I wrote about this two weeks ago. If you go back to the pandemic, peak of the pandemic, or just before the peak, kind of in that tech bubble, if you will. Splunk's down 44% from that peak, Okta's down, sorry, not down 44%. 44% of the peak. Okta's 22% of their peak. CrowdStrike, 41%, Zscaler, 36%, Fortinet, 71%. Not so bad. Palo Altos maintained 93% of its peak value, right? So it's a combination of two things. One is, they didn't run up as much during the pandemic, and they're executing through their cloud strategy. And that's provided a sort of softer landing. And I think it's going to be interesting to see where they go from here. And you heard Nikesh, we're going to double, and then double again. So that's 7 billion, 14 billion, heading to 30 billion. >> Lisa: Yeah, yeah. He also talked about one of the things that he's done in his tenure here, as really a workforce transformation. And we talk all the time, it's not just technology and processes, it's people. They've also seemed to have done a pretty good job from a cultural transformation perspective, which is benefiting their customers. And they're also growing- The ecosystem, we talked a little bit about the ecosystem with Nikesh. We've got Google Cloud on, we've got AWS on the program today alone, talking about the partnerships. The ecosystem is expanding, as well. >> Have you ever met Nir Zuk? >> I have not, not yet. >> He's the founder and CTO. I haven't, we've never been on "theCUBE." He was supposed to come on one day down in New York City. Stu and I were going to interview him, and he cut out of the conference early, so we didn't interview him. But he's a very opinionated dude. And you're going to see, he's basically going to come on, and I mean, I hope he is as opinionated on "TheCUBE," but he'll talk about how the industry has screwed it up. And Nikesh sort of talked about that, it's a shiny new toy strategy. Oh, there's another one, here's another one. It's the best in that category. Okay, let's get, and that's how we've gotten to this point. I always use that Optive graphic, which shows the taxonomy, and shows hundreds and hundreds of suppliers in the industry. And again, it's true. Customers have 20, 30, sometimes 40 different tool sets. And so now it's going to be interesting to see. So I guess my point is, it starts at the top. The founder, he's an outspoken, smart, tough Israeli, who's like, "We're going to take this on." We're not afraid to be ambitious. And so, so to your point about people and the culture, it starts there. >> Absolutely. You know, one of the things that you've written about in your breaking analysis over the weekend, Nikesh talked about it, they want to be the consolidator. You see this as they're building out the security supercloud. Talk to me about that. What do you think? What is a security supercloud in your opinion? >> Yeah, so let me start with the consolidator. So Palo Alto obviously is executing on that strategy. CrowdStrike as well, wants to be a consolidator. I would say Zscaler wants to be a consolidator. I would say that Microsoft wants to be a consolidator, so does Cisco. So they're all coming at it from different angles. Cisco coming at it from network security, which is Palo Alto's wheelhouse, with their next gen firewalls, network security. What Palo Alto did was interesting, was they started out with kind of a hardware based firewall, but they didn't try to shove everything into it. They put the other function in there, their cloud. Zscaler. Zscaler is the one running around saying you don't need firewalls anymore. Just run everything through our cloud, our security cloud. I would think that as Zscaler expands its TAM, it's going to start to acquire, and do similar types of things. We'll see how that integrates. CrowdStrike is clearly executing on a similar portfolio strategy, but they're coming at it from endpoint, okay? They have to partner for network security. Cisco is this big and legacy, but they've done a really good job of acquiring and using services to hide some of that complexity. Microsoft is, you know, they probably hate me saying this, but it's the just good enough strategy. And that may have hurt CrowdStrike last quarter, because the SMB was a soft, we'll see. But to specifically answer your question, the opportunity, we think, is to build the security supercloud. What does that mean? That means to have a common security platform across all clouds. So irrespective of whether you're running an Amazon, whether you're running an on-prem, Google, or Azure, the security policies, and the edicts, and the way you secure your enterprise, look the same. There's a PaaS layer, super PaaS layer for developers, so that that the developers can secure their code in a common framework across cloud. So that essentially, Nikesh sort of balked at it, said, "No, no, no, we're not, we're not really building a super cloud." But essentially they kind of are headed in that direction, I think. Although, what I don't know, like CrowdStrike and Microsoft are big competitors. He mentioned AWS and Google. We run on AWS, Google, and in their own data centers. That sounds like they don't currently run a Microsoft. 'Cause Microsoft is much more competitive with the security ecosystem. They got Identity, so they compete with Okta. They got Endpoint, so they compete with CrowdStrike, and Palo Alto. So Microsoft's at war with everybody. So can you build a super cloud on top of the clouds, the hyperscalers, and not do Microsoft? I would say no. >> Right. >> But there's nothing stopping Palo Alto from running in the Microsoft cloud. I don't know if that's a strategy, we should ask them. >> Yeah. They've done a great job in our last few minutes, of really expanding their TAM in the last few years, particularly under Nikesh's leadership. What are some of the things that you heard this morning that you think, really they've done a great job of expanding that TAM. He talked a little bit about, I didn't write the number down, but he talked a little bit about the market opportunity there. What do you see them doing as being best of breed for organizations that have 30 to 50 tools and need to consolidate that? >> Well the market opportunity's enormous. >> Lisa: It is. >> I mean, we're talking about, well north of a hundred billion dollars, I mean 150, 180, depending on whose numerator you use. Gartner, IDC. Dave's, whatever, it's big. Okay, and they've got... Okay, they're headed towards 7 billion out of 180 billion, whatever, again, number you use. So they started with network security, they put most of the network function in the cloud. They moved to Endpoint, Sassy for the edge. They've done acquisitions, the Cortex acquisition, to really bring automated threat intelligence. They just bought Cider Security, which is sort of the shift left, code security, developer, assistance, if you will. That whole shift left, protect right. And so I think a lot of opportunities to continue to acquire best of breed. I liked what Nikesh said. Keep the founders on board, sell them on the mission. Let them help with that integration and putting forth the cultural aspects. And then, sort of, integrate in. So big opportunities, do they get into Endpoint and compete with Okta? I think Okta's probably the one sort of outlier. They want to be the consolidator of identity, right? And they'll probably partner with Okta, just like Okta partners with CrowdStrike. So I think that's part of the challenge of being the consolidator. You're probably not going to be the consolidator for everything, but maybe someday you'll see some kind of mega merger of these companies. CrowdStrike and Okta, or Palo Alto and Okta, or to take on Microsoft, which would be kind of cool to watch. >> That would be. We have a great lineup, Dave. Today and tomorrow, full days, two full days of cube coverage. You mentioned Nir Zuk, we already had the CEO on, founder and CTO. We've got the chief product officer coming on next. We've got chief transformation officer of customers, partners. We're going to have great conversations, and really understand how this organization is helping customers ultimately achieve their SecOps transformation, their digital transformation. And really moved the needle forward to becoming secure data companies. So I'm looking forward to the next two days. >> Yeah, and Wendy Whitmore is coming on. She heads Unit 42, which is, from what I could tell, it's pretty much the competitor to Mandiant, which Google just bought. We had Kevin Mandia on at September at the CrowdStrike event. So that's interesting. That's who I was poking Nikesh a little bit on industry collaboration. You're tight with Google, and then he had an interesting answer. He said "Hey, you start sharing data, you don't know where it's going to go." I think Snowflake could help with that problem, actually. >> Interesting. >> Yeah, little Snowflake and some of the announcements ar Reinvent with the data clean rooms. Data sharing, you know, trusted data. That's one of the other things we didn't talk about, is the real tension in between security and regulation. So the regulators in public policy saying you can't move the data out of the country. And you have to prove to me that you have a chain of custody. That when you say you deleted something, you have to show me that you not only deleted the file, then the data, but also the metadata. That's a really hard problem. So to my point, something that Palo Alto might be able to solve. >> It might be. It'll be an interesting conversation with Unit 42. And like we said, we have a great lineup of guests today and tomorrow with you, so stick around. Lisa Martin and Dave Vellante are covering Palo Alto Networks Ignite 22 for you. We look forward to seeing you in our next segment. Stick around. (light music)