>> from Cambridge, Massachusetts. It's three Cube covering M. I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to M I. T. CDO I Q everybody, you're watching the cube we got. We go out to the events we extract the signal from the noise is day one of this conference. Chief Data Officer event. I'm Dave, along with my co host, Paul Gillen. Stuart Bond is here is a research director of International Data Corporation I DC Stewart. Welcome to the Cube. Thanks for coming on. Thank you for having me. You're very welcome. So your space data intelligence tell us about your swim lane? Sure. >> So my role it I. D. C is a ZAY. Follow the data integration and data intelligence software market. So I follow all the different vendors in the market. I look at what kinds of solutions they're bringing to market, what kinds of problems. They're solving both business and technical for their clients. And so I can then report on the trends and market sizes, forecasts and such, And within that part of what I what I cover is everything from data integration which is more than traditionally E T l change data capture data movements, data, virtualization types of technologies as well as what we call date integrity of one. And I'm calling data intelligence, which is all of the Tell the metadata about the data. It's the data catalogs meditating management's data lineage. It's the data quality data profiling, master data intelligence. It's all of the data about the data and understanding really answering what I call a entering the five W's and h of data. It's the who, what, where, when, why and how. Data. So that's the market that I'm covering and following, and that's why I'm >> here. Were you here this morning for Mark Ramsey's Yes, I talk. So he kind of went to you. Heard it started with the D W kind of through E T L under the bus. Well, MGM, then the Enterprise data model said all that failed. But that stuff's not going away, and I'm sure they're black. So still using, you know, all those all that tooling today. So what was your reaction to that you were not in your head and yeah, it's true or saying, Well, maybe there's a little we'll have what we've been saying. The mainframe is gonna go away for years and >> still around, so I think they're obviously there's still those technologies out there and they're still being used. You can look at any of the major dtl vendors and there's new ones coming to the market, so that's still alive and well. There's no doubt that it's out there and its biggest segment of the market that I followed. So there's no source tooling, right? Yes, >> there's no doubt that it's still >> there. But Mark's vision of where things are going, where things are heading with, with data intelligence really being at the Cory talk about those spiders talked about that central depository of information about knowledge of the data. That's where things are heading to, whether you call it a data hub, whether you call it a date, a platform, not really a one big, huge data pop for one big, huge data depository, but one a place where you can go to get the information but natives you can find out where the data is. You could find out what it means, both the business context as well as the technical information you find out who's using that data. You can find out when it's being used, Why it's being used in. Why do we even have it and how it should >> be used? So it's being used >> appropriately. So you would say that his vision, actually what he implemented was visionary skating. They skated to the puck, so to speak, and that's we're going >> to see more of that. Where are seeing more of that? That's why we've seen such a jump in the number of vendors that air providing data catalogue solutions. I did, Uh, I d. C has this work product calling market glance. I did that >> beginning of 2018. >> I just did it again. In the middle of this year, the number of vendors that offer data catalogue solutions has significantly interest 240% increase in the number of vendors that offer that now itself of a small base. These air, not exhaustive studies. It may be that I didn't know about all those data catalogue vendors a year and 1/2 ago, but may also be that people are now saying that we've got a data catalogue, >> but you've really got a >> peel back the layers a little bit. Understand what these different data catalysts are and what they're doing because not all of them are crediting. >> We'll hear Radar. You don't know about it. 99% of the world mark talked this morning about some interesting new technologies. They were using Spider Ring to find the data bots to classify the data tools wrangle the data. I mean, there's a lot of new technology being applied to this area. What? Which of those technologies do you think has the greatest promise right now? And how? How how automated can this process become? >> It's the spider ring, and it's the cataloging of the data. It's understanding what you've got out there that is growing crazy. Just started to track that it's growing a lot that has the most promised. And as I said, I think that's going to be the data platform in the future. Is the intelligence knowing about where your data is? You men go on, get it. You know it's not a matter of all. The data is one place anymore. Data's everywhere Date is in hybrid cloud. It's in on premise. It's in private. Cloud isn't hosted. It's everywhere. I just did a survey. I got the results back in June 2019 just a month ago, and the data is all over the place. So really having that knowledge having that intelligence about where your data is, that has the most promise. As faras, the automation is concerned. Next step there. It's not just about collecting the information about where your data is, but it's actually applying the analytics, the machine learning and the artificial intelligence to that metadata collection that you've got so that you can then start to create those bots to create those pipelines to start to automate those tasks. We're starting to see some vendors move in that area, moving that direction. There's a lot of promise there >> you guys, at least when I remember. You see, the software is pretty robust taxonomy. I'm sure it's evolved over the years. So how do you sort of define your space? I'm interested in How big is that space, you know, in terms of market size and is a growing and where do you see it going? >> Right. So my my coverage of data integration and data intelligence is fairly small. It's a small, little marketed. I D. C. I'm part of a larger team that looks a data management, the analytics and information management. So we've got people on our team like a damn vessel. Who covers the analytics? Advanced Analytics show Nautical Palo Carlson. He's been on the cable covers, innovative technologies, those I apologize. I don't have that number off the top. >> Okay, No, But your space, my space is it. That's that Software market is so fragmented. And what I d. C has always done well, as you put people on those fragments and you know, deep in there. So So how you've been ableto not make your eyes bleed when you do that, challenging so the data and put it all together. >> It's important. Integration markets about 66 and 1/2 1,000,000,000 >> dollars. Substantial size. Yeah, but again, a lot of vendors Growing number of events in the markets growing, >> the market continues to grow as the data is becoming more distributed, more dispersed. There's no need to continue to integrate that data. There's also that need that growing >> need for that date intelligence. It's not >> just, you know, we've had a lot of enquiries lately about data being fed into machine learning artificial intelligence and people realizing our data isn't clean. We have to clean up our data because we're garbage in garbage. Out is probably more important now than ever before because you don't have someone saying, I don't think that day is right. You've got machines were looking at data instead. The technology that's out there and the problem with data quality. It's on a new problem. It's the same problem we've had for years. All of the technology is there to clean that data up, and that's a part of what I saw. I look at the data quality vendors experience here, sink sort in all of the other data quality capabilities that you get from in from Attica, from Tahoe or from a click podium. Metal is there, and so that part is growing. And there's a lot of more interest in that data quality and that data intelligence side again so the right data can be used. Good data can be used to trust in that data. Can the increase we used for the right reasons as well That's adding that context. Understand that Samantha having all that metadata that goes around that data so that could be used. Most of >> it is one of those markets that you may be relatively small. It's not 100,000,000,000 but it it enables a lot of larger markets. So okay, so it's 66 and 1/2 1,000,000,000 it's growing. It is a growing single digits, double digits. It's growing. It's hovering around the double dip double. It is okay, it's 10%. And then and then who were the, You know, big players who was driving the shares there? Is there a dominant player there? Bunch of >> so infirm. Atticus Number one in the market. Okay, followed by IBM. And I say peas right up there. Sass is there. Tell End is making a good Uh, okay, they're making a nice with Yeah, but there there's a number of different players. There's There's a lot of different players in that market. >> And in the leading market share player has what, 10%? 15%? 50%? Is it like a dominant divine spot? That's tough to say. You got a big It's over 1,000,000,000,000,000,000 right? So they've got maybe 1/6 of the market. Okay, so but it's not like Cisco as 2/3 of the networking market or anything like that. And what about the cloud guys? A participating in this guy's deal with >> the cloud guys? Yeah, the ClA got so there are some pure cloud solutions. There's a relative, for example. Pure cloud MBM mastered a management there. There's I'd say there's less pure cloud than there used to be. But, you know, but someone like an infra matic is really pushing that clouds presence in that cloud >> running these tools, this tooling in in the cloud But the cloud guys directly or not competing at this >> point. So Amazon Google? Yes, Those cloud guys. Yes. Okay, there, there. Google announced data flow back in our data. Sorry. Data fusion back. Google. >> Yeah, that's right. >> And so there they've got an e t l two on the cloud now. Ah, Amazon has blue yet which is both a catalog and an e t l tool. Microsoft course has data factory in azure. >> So those guys are coming on. I'm guessing if you talk to in dramatic and they said, Well, they're not as robust as we are. And we got a big install base and we go multi cloud is that kind of posturing of the incumbents or yeah, that's posturing. And maybe that's I don't mean it is a pejorative. If I were, those guys would be doing the same thing. You know, we were talking earlier about how the cloud guys essentially killed the Duke. All right, do you Do you see the same thing happening here, or is it well, the will the tool vendors be able to stay ahead in your view, >> depends on how they execute. If they're there and they're available in the cloud along with along with those clapper viers, they're able to provide solutions in the same same way the same elasticity, the same type of consumption based pricing models that pod vendors air offering. They can compete with that. They still have a better solution. Easton What >> in multi cloud in hybrid is a big part of their value problems that the cloud guys aren't really going hard after. I mean, this sort of dangling your toe in the water, some of them some of the >> cloud guys they have. They have the hybrid capabilities because they've got some of what they're what they built comes from on premises, worlds as well. So they've got that ability. Microsoft in particular >> on Google, >> Google that the data fusion came out of >> You're saying, But it's part of the Antos initiative. Er, >> um, I apologize. Folks are watching, >> but soup of acronyms notices We're starting a little bit. What tools have you seen or technology? Have you seen making governance of unstructured data? That looks promising? Uh, so I don't really cover >> the instructor data space that much. What I can say is Justus in the structure data world. It's about the metadata. It's about having the proper tags about that unstructured data. It's about getting the information of that unstructured data so that it can then be governed appropriately, making structure out of that, that is, I can't really say, because I don't cover that market explicitly. But I think again it comes back to the same type of data intelligence having that intelligence about that data by understanding what's in there. >> What advice are you giving to, you know, the buyers in your community and the sellers in your community, >> So the buyer's within the market. I talk a lot about that. The need for that data intelligence, so data governance to me is not a technology you can't go by data governance data governance is an organizational disappoint. Technology is a part of that. To me, the data intelligence technology is a part of that. So, really, organizations, if they really want a good handle, get a good handle on what data they have, how to use that, how to be enabled by that data. They need to have that date intelligence into go look for solutions that can help him pull that data intelligence out. But the other part of that is measurement. It's critical to measure because you can't improve what you're not measuring. So you know that type of approach to it is critical Eve, and you've got to be able to have people in the organization. You've got to be able to have cooperation collaboration across the business. I t. The the gifted office chief Officer office. You've gotta have that collaboration. You've gotta have accountability and for in order for that, to really be successful. For the vendors in the space hybrid is the new reality. In my survey data, it shows clearly that hybrid is where things are. It's not just cloud, it's not just on promise Tiebreak. That's where the future is. They've got to be able to have solutions that work in that environment. Working that hybrid cloud ability has got to be able to have solutions that can be purchased and used again in the same sort of elastic type of method that they're able to get consumers able to get. Service is from other vendors in that same >> height, so we gotta run. Thank you so much for sharing your insights and your data. And I know we were fired. I was firing a lot of questions. Did pretty well, not having the report in front of me. I know what that's like. So thank you for sharing and good luck with your challenges in the future. You got You got a lot of a lot of data to collect and a lot of fast moving markets. So come back any time. Share with you right now, Okay? And thank you for watching Paul and I will be back with our next guest right after this short break from M I t cdo. Right back

>> Announcer: Live, from Las Vegas, it's theCUBE covering Dell Technologies World 2018. Brought to you by Dell EMC and its ecosystem partners. >> Welcome back. We are live here in Las Vegas at the Sands, along with Stu Miniman. I'm John Walls. You're watching theCUBE, of course, Dell Technologies World 2018. It's now a pleasure to welcome to the set, we have Bryan Bond, director of IT Infrastructure at Siemens eMeter. Bryan, thank you for being with us. >> Thank you for having me. >> John: And Andre Leibovici, who is the Vice President of Solutions and Alliances at Datrium. Andre, good afternoon to you. Good to see you. >> Great to see you. >> Alright, Bryan, tell us about Siemens eMeter, first, just for viewers who might not be familiar with the company and your mission. >> eMeter, basically, is a software development company. We do enterprise-level software for utilities, so gas, power, water, just about anything that has a meter. We do not make meters, but we deal with all the data that comes from those meters. So, data acquisition, meter data management, loss prevention, all those types of things that come from that data that's leaving your house or your business. We deal with that for the utilities. So, back-in billing systems, longterm data analytics, all of those types of things, that's what we do. >> Yeah, so, Bryan, most companies I talk to, it's like your industry's changing so fast, digital transformation, software, everything. Utilities are considered by most to be one of the slower moving pieces, so what's the reality in your world? >> It's like selling to a rock. (Stu laughs) A rock, right? It's tough, historically, it is very tough. Especially in the United States, with PUC regulations, with the way you can charge customers and can't, it makes it very hard. And I wish I was a real expert at that type of stuff, but... It's a slow-moving process. The good news is most countries in the planet have decided that they need to go full-on smart grid and they need to do it fast. So, in a lot of countries in Europe, there's an edict out, we're going to do this and that has helped move this along. So it's very helpful to us, as a business. I also think it's very helpful to us in general, you know, on the planet, being able to manage grids better and more efficiently. >> Okay, so we're not going to be talking about power grids and all the things on the utility. You're an IT guy. And that's what we love talking about on theCUBE here. So, give us a thumbnail sketch of your environment, your purview. What's going on? >> All right. So, like I said, so we're a software development house. It's all developers: dev test QA, sales, support, you know, all that type of stuff. I'm fortunate to be part of a very large company, so I don't have to worry about e-mail, SharePoint sites, or any of that stuff. I get to deal with the real fun stuff, which is our product, how it's deployed, how it's developed and tested. We're a pretty much a 100% virtualized. VMware shop. We use VMware-based cloud services for the appropriate things for that. And we do all of that work ourself with our own team. So we have a small team in the U.S., we have a small team in India, and we handle all of that ourselves, we don't really outsource any of that. >> Alright, so Andre, I want to pull you in here. You're software development in VMware environment. Brings me back; I remember early days of VMware was always only for test dev. Today, I hear developers, I hear this stuff, and it's like, "Oh, isn't that kind of public cloud "and some of those things?" So, give us your viewpoint on customers like Bryan and what kind of things Datrium brings to that environment, obviously virtualized and all that. >> Yeah, no, that's a good point. So... All types of customers know suddenly looking at how they can leverage private cloud, but also public cloud. Create the ideal, hybrid cloud. What does that mean, right? So we have Fortune 100 companies like Siemens who are leveraging our technology to deploy the private cloud, run the VMware infrastructure on us. At the same time, create, you know, DR strategies to their secondary sites. But there is also those customers who are looking to, "How can I actually push workloads to the cloud? "How can I create a strategy around disaster recovery "to the cloud?" And I believe that, as part of our journey as a company, embracing private data centers, we got to embrace, also, the cloud. And this is the next big thing for us at Datrium. Where are we going to help customers on the journey to take their workloads running on-premise to the cloud, but at the same time enabling them to use as as DR and also move back when needed. I may as well just spill the beans here. I'm not sure if I'm getting trouble with marketing or not. >> John: I'm sure you're not. >> So we actually releasing very soon a fully orchestrated DR from our platform to the VMware cloud, to VMC. Fully orchestrated and enables you to fire over environment to the cloud and back, once your DR site or your primary site is actually back. There's a lot of promise on this market. There's a lot of companies doing, saying that they would do, but, you know, I see that's something that customers are really excited... >> You know, how does it work when you're dealing with a customer who is dealing with a customer, who's dealing with customers who... You know, privacy's essential, right? And there's a lot of concern... They have to be the customer of a utility. So how do you treat them, you know, because they have very unique needs, I would assume and that's a major consideration, because of their position with their customer. I mean, that's got to create a new dynamic, or an interesting dynamic, for both of you to handle. >> Yeah, it does. You know, from a development standpoint, you know, you may not be actually dealing with that particular customer's data, but you're helping that customer deal with that data. So, we're having to go through and make sure that our software doesn't have any holes in it and it's patchable, and that it follows, you know, simple guidelines. But, at the same time, we make recommendations to customers all the time, you know. "Well, how are you guys doing X, Y, Z in-house, "because you seem to be doing okay." And we say, "Well, we're using this particular platform." And, their encryption is probably the best there is right now out there. De-duped encryption, it's just fantastic. And across different storage arrays. And being able to that to the cloud and be encrypted there, and not have to worry about that is a big bonus. And that's definitely something that we look at. Obviously, we don't encrypt all of our data, because a lot of it's just nonsense. But, we do have stuff that we do that with. And we do it both for testing purposes and to prove that this meets the requirements of the customer. Because those requirements are different, not just in different countries, but in every state you go to. So, being able to provide that level of assurance of yeah you can meet your requirements with our software regardless of what platform you're running on. >> Bryan, you mentioned a couple of features there. But I wonder if you could back us up a second. You've got a virtualized environment. There's, you know, so many options that you can choose on there. Walk us a little bit through the problems that you were having, the decision process, and ultimately what led to Datrium. >> So... The set of primary goals for us was the typical thing you see in IT is you're doing the same thing for a long period of time. You're buying the same stuff, you buy more of it, you renew, and then they tell you that the price is going to go way up on support. So you buy a new one and start over again, right? The hockey stick approach. And so that's the time I like to actually stop and say, "Hey, am I doing this right, still?" Because what I did five years ago may not be right, you know, going forward, knowing what the changes are in the business. We were looking for great cost to capacity. Right? And ease of management and overall cost of the deployment. And when we started looking at all the different players in the space... For us, the big thing was going to NFS. So, single file system for management. Prior to that, we were either fibre channel on or iSCSCI. So, mini management points. Hundreds of LUNs. Hundreds of LUNs. We're managing storage, right? A small group of people, three, four guys? You're spending 20 hours a week managing storage? That's nuts, right? So, day one, we put these guys in in a POC. And my guys are like, "This stuff's never leaving." Because now I'm down to one management point, right? Six months, seven months later, I'm down six hundred LUNs from where I was with three management points. I don't manage storage anymore. None of my guys manage storage anymore. That's a hidden cost, you know? And I'm not suggesting reduction in FTE or anything like that. I'm saying, "Oh, now those guys can go work "on operating system patching." You know, the other paying points that you've got in the business, rather than managing, you know, that platform. So, all of those things rolled in together. And when we tried to compare them to other vendors, we couldn't get an apples to apples comparison. We had to go with multiple vendors to get the same performance, to get the same capacity, and we could never get the pricing. The best-case scenario we got for capacity and performance was three times the cost. Best-case scenario. And I still had to manage LUNs. >> Yeah, Andre, I used to always joke simplicity in the enterprise was an oxymoron, because there's so much happening. You hear, "Okay, get rid of one thing, I got to patch the other thing." There's no such thing as eliminating bottlenecks, you just move them. But, you know, sounds like some common problems we've been hearing out there. What's typical about his environment? What are you hearing from customers in general that Datrium's helping? >> So, I think the first point is simplicity. And it's something that I know we've been evolving, it's a journey not only for Datrium, but the whole data center industry, right? Went through ACI and now it's open conversions. So the whole simplification of the data center and make sure that most of the task can be automated. So some of the things that we do, that we simplify from a management perspective: we have no knobs, you don't decide if it's compression, the de-duplication enable, the erasure codings. Everything is owned by default and that's the way it's going to be because it doesn't make sense for an organization with thousands of virtual machines and applications to start tweaking every single knob to make sure they're going to get the best possible performance. Across the board, once we've actually verified, you might get like one or 2% CPU back. So, simplicity's a big point. Also, the other point that we mitigate in the organization, especially compared to ACI's solutions, is the data resiliency. So we actually offer enterprise-grade data resiliency that for ACI... And when talking about evolution with data center, you know, taking like putting SSDs into the servers, ACI clusters, and moving forward. So we actually make all the management of this SSDs much simpler. I forgot the line, where I was going to, but I... (laughs) I think the message is simplicity, skill ability, back data resiliency. Making sure you get enterprise-greater data resiliency in the data center. And you don't compromise on that. You get capacity, data resiliency, simplicity at the same time. >> Keep it simple, make it work. >> Andre: Exactly. >> Right. Faster. Gentleman, thanks for joining us. We appreciate the time. Thanks for telling the Siemens eMeter story. We look forward to seeing you down the road. And good luck, continue success at Datrium, as well. Thanks, Andre. >> Yeah, thank you. >> Alright, thanks for having us. >> Back with more. You're watching Dell Technologies World 2018 right here on theCUBE. (techno music)

(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 ETR. This is Breaking Analysis with Dave Vellante. >> A better than expected earnings report in late August got people excited about Snowflake again, but the negative sentiment in the market is weighed heavily on virtually all growth tech stocks and Snowflake is no exception. As we've stressed many times the company's management is on a long term mission to dramatically simplify the way organizations use data. Snowflake is tapping into a multi hundred billion dollar total available market and continues to grow at a rapid pace. In our view, Snowflake is embarking on its third major wave of innovation data apps, while its first and second waves are still bearing significant fruit. Now for short term traders focused on the next 90 or 180 days, that probably doesn't matter. But those taking a longer view are asking, "Should we still be optimistic about the future of this high flyer or is it just another over hyped tech play?" Hello and welcome to this week's Wiki Bond Cube Insights powered by ETR. Snowflake's Quarter just ended. And in this breaking analysis we take a look at the most recent survey data from ETR to see what clues and nuggets we can extract to predict the near term future in the long term outlook for Snowflake which is going to announce its earnings at the end of this month. Okay, so you know the story. If you've been investor in Snowflake this year, it's been painful. We said at IPO, "If you really want to own this stock on day one, just hold your nose and buy it." But like most IPOs we said there will be likely a better entry point in the future, and not surprisingly that's been the case. Snowflake IPOed a price of 120, which you couldn't touch on day one unless you got into a friends and family Delio. And if you did, you're still up 5% or so. So congratulations. But at one point last year you were up well over 200%. That's been the nature of this volatile stock, and I certainly can't help you with the timing of the market. But longer term Snowflake is targeting 10 billion in revenue for fiscal year 2028. A big number. Is it achievable? Is it big enough? Tell you what, let's come back to that. Now shorter term, our expert trader and breaking analysis contributor Chip Simonton said he got out of the stock a while ago after having taken a shot at what turned out to be a bear market rally. He pointed out that the stock had been bouncing around the 150 level for the last few months and broke that to the downside last Friday. So he'd expect 150 is where the stock is going to find resistance on the way back up, but there's no sign of support right now. He said maybe at 120, which was the July low and of course the IPO price that we just talked about. Now, perhaps earnings will be a catalyst, when Snowflake announces on November 30th, but until the mentality toward growth tech changes, nothing's likely to change dramatically according to Simonton. So now that we have that out of the way, let's take a look at the spending data for Snowflake in the ETR survey. Here's a chart that shows the time series breakdown of snowflake's net score going back to the October, 2021 survey. Now at that time, Snowflake's net score stood at a robust 77%. And remember, net score is a measure of spending velocity. It's a proprietary network, and ETR derives it from a quarterly survey of IT buyers and asks the respondents, "Are you adopting the platform new? Are you spending 6% or more? Is you're spending flat? Is you're spending down 6% or worse? Or are you leaving the platform decommissioning?" You subtract the percent of customers that are spending less or churning from those that are spending more and adopting or adopting and you get a net score. And that's expressed as a percentage of customers responding. In this chart we show Snowflake's in out of the total survey which ranges... The total survey ranges between 1,200 and 1,400 each quarter. And the very last column... Oh sorry, very last row, we show the number of Snowflake respondents that are coming in the survey from the Fortune 500 and the Global 2000. Those are two very important Snowflake constituencies. Now what this data tells us is that Snowflake exited 2021 with very strong momentum in a net score of 82%, which is off the charts and it was actually accelerating from the previous survey. Now by April that sentiment had flipped and Snowflake came down to earth with a 68% net score. Still highly elevated relative to its peers, but meaningfully down. Why was that? Because we saw a drop in new ads and an increase in flat spend. Then into the July and most recent October surveys, you saw a significant drop in the percentage of customers that were spending more. Now, notably, the percentage of customers who are contemplating adding the platform is actually staying pretty strong, but it is off a bit this past survey. And combined with a slight uptick in planned churn, net score is now down to 60%. That uptick from 0% and 1% and then 3%, it's still small, but that net score at 60% is still 20 percentage points higher than our highly elevated benchmark of 40% as you recall from listening to earlier breaking analysis. That 40% range is we consider a milestone. Anything above that is actually quite strong. But again, Snowflake is down and coming back to churn, while 3% churn is very low, in previous quarters we've seen Snowflake 0% or 1% decommissions. Now the last thing to note in this chart is the meaningful uptick in survey respondents that are citing, they're using the Snowflake platform. That's up to 212 in the survey. So look, it's hard to imagine that Snowflake doesn't feel the softening in the market like everyone else. Snowflake is guiding for around 60% growth in product revenue against the tough compare from a year ago with a 2% operating margin. So like every company, the reaction of the street is going to come down to how accurate or conservative the guide is from their CFO. Now, earlier this year, Snowflake acquired a company called Streamlit for around $800 million. Streamlit is an open source Python library and it makes it easier to build data apps with machine learning, obviously a huge trend. And like Snowflake, generally its focus is on simplifying the complex, in this case making data science easier to integrate into data apps that business people can use. So we were excited this summer in the July ETR survey to see that they added some nice data and pick on Streamlit, which we're showing here in comparison to Snowflake's core business on the left hand side. That's the data warehousing, the Streamlit pieces on the right hand side. And we show again net score over time from the previous survey for Snowflake's core database and data warehouse offering again on the left as compared to a Streamlit on the right. Snowflake's core product had 194 responses in the October, 22 survey, Streamlit had an end of 73, which is up from 52 in the July survey. So significant uptick of people responding that they're doing business in adopting Streamlit. That was pretty impressive to us. And it's hard to see, but the net scores stayed pretty constant for Streamlit at 51%. It was 52% I think in the previous quarter, well over that magic 40% mark. But when you blend it with Snowflake, it does sort of bring things down a little bit. Now there are two key points here. One is that the acquisition seems to have gained exposure right out of the gate as evidenced by the large number of responses. And two, the spending momentum. Again while it's lower than Snowflake overall, and when you blend it with Snowflake it does pull it down, it's very healthy and steady. Now let's do a little pure comparison with some of our favorite names in this space. This chart shows net score or spending velocity in the Y-axis, an overlap or presence, pervasiveness if you will, in the data set on the X-axis. That red dotted line again is that 40% highly elevated net score that we like to talk about. And that table inserted informs us as to how the companies are plotted, where the dots set up, the net score, the ins. And we're comparing a number of database players, although just a caution, Oracle includes all of Oracle including its apps. But we just put it in there for reference because it is the leader in database. Right off the bat, Snowflake jumps out with a net score of 64%. The 60% from the earlier chart, again included Streamlit. So you can see its core database, data warehouse business actually is higher than the total company average that we showed you before 'cause the Streamlit is blended in. So when you separate it out, Streamlit is right on top of data bricks. Isn't that ironic? Only Snowflake and Databricks in this selection of names are above the 40% level. You see Mongo and Couchbase, they know they're solid and Teradata cloud actually showing pretty well compared to some of the earlier survey results. Now let's isolate on the database data platform sector and see how that shapes up. And for this analysis, same XY dimensions, we've added the big giants, AWS and Microsoft and Google. And notice that those three plus Snowflake are just at or above the 40% line. Snowflake continues to lead by a significant margin in spending momentum and it keeps creeping to the right. That's that end that we talked about earlier. Now here's an interesting tidbit. Snowflake is often asked, and I've asked them myself many times, "How are you faring relative to AWS, Microsoft and Google, these big whales with Redshift and Synapse and Big Query?" And Snowflake has been telling folks that 80% of its business comes from AWS. And when Microsoft heard that, they said, "Whoa, wait a minute, Snowflake, let's partner up." 'Cause Microsoft is smart, and they understand that the market is enormous. And if they could do better with Snowflake, one, they may steal some business from AWS. And two, even if Snowflake is winning against some of the Microsoft database products, if it wins on Azure, Microsoft is going to sell more compute and more storage, more AI tools, more other stuff to these customers. Now AWS is really aggressive from a partnering standpoint with Snowflake. They're openly negotiating, not openly, but they're negotiating better prices. They're realizing that when it comes to data, the cheaper that you make the offering, the more people are going to consume. At scale economies and operating leverage are really powerful things at volume that kick in. Now Microsoft, they're coming along, they obviously get it, but Google is seemingly resistant to that type of go to market partnership. Rather than lean into Snowflake as a great partner Google's field force is kind of fighting fashion. Google itself at Cloud next heavily messaged what they call the open data cloud, which is a direct rip off of Snowflake. So what can we say about Google? They continue to be kind of behind the curve when it comes to go to market. Now just a brief aside on the competitive posture. I've seen Slootman, Frank Slootman, CEO of Snowflake in action with his prior companies and how he depositioned the competition. At Data Domain, he eviscerated a company called Avamar with their, what he called their expensive and slow post process architecture. I think he actually called it garbage, if I recall at one conference I heard him speak at. And that sort of destroyed BMC when he was at ServiceNow, kind of positioning them as the equivalent of the department of motor vehicles. And so it's interesting to hear how Snowflake openly talks about the data platforms of AWS, Microsoft, Google, and data bricks. I'll give you this sort of short bumper sticker. Redshift is just an on-prem database that AWS morphed to the cloud, which by the way is kind of true. They actually did a brilliant job of it, but it's basically a fact. Microsoft Excel, a collection of legacy databases, which also kind of morphed to run in the cloud. And even Big Query, which is considered cloud native by many if not most, is being positioned by Snowflake as originally an on-prem database to support Google's ad business, maybe. And data bricks is for those people smart enough to get it to Berkeley that love complexity. And now Snowflake doesn't, they don't mention Berkeley as far as I know. That's my addition. But you get the point. And the interesting thing about Databricks and Snowflake is a while ago in the cube I said that there was a new workload type emerging around data where you have AWS cloud, Snowflake obviously for the cloud database and Databricks data for the data science and EML, you bring those things together and there's this new workload emerging that's going to be very powerful in the future. And it's interesting to see now the aspirations of all three of these platforms are colliding. That's quite a dynamic, especially when you see both Snowflake and Databricks putting venture money and getting their hooks into the loyalties of the same companies like DBT labs and Calibra. Anyway, Snowflake's posture is that we are the pioneer in cloud native data warehouse, data sharing and now data apps. And our platform is designed for business people that want simplicity. The other guys, yes, they're formidable, but we Snowflake have an architectural lead and of course we run in multiple clouds. So it's pretty strong positioning or depositioning, you have to admit. Now I'm not sure I agree with the big query knockoffs completely. I think that's a bit of a stretch, but snowflake, as we see in the ETR survey data is winning. So in thinking about the longer term future, let's talk about what's different with Snowflake, where it's headed and what the opportunities are for the company. Snowflake put itself on the map by focusing on simplifying data analytics. What's interesting about that is the company's founders are as you probably know from Oracle. And rather than focusing on transactional data, which is Oracle's sweet spot, the stuff they worked on when they were at Oracle, the founder said, "We're going to go somewhere else. We're going to attack the data warehousing problem and the data analytics problem." And they completely re-imagined the database and how it could be applied to solve those challenges and reimagine what was possible if you had virtually unlimited compute and storage capacity. And of course Snowflake became famous for separating the compute from storage and being able to completely shut down compute so you didn't have to pay for it when you're not using it. And the ability to have multiple clusters hit the same data without making endless copies and a consumption/cloud pricing model. And then of course everyone on the planet realized, "Wow, that's a pretty good idea." Every venture capitalist in Silicon Valley has been funding companies to copy that move. And that today has pretty much become mainstream in table stakes. But I would argue that Snowflake not only had the lead, but when you look at how others are approaching this problem, it's not necessarily as clean and as elegant. Some of the startups, the early startups I think get it and maybe had an advantage of starting later, which can be a disadvantage too. But AWS is a good example of what I'm saying here. Is its version of separating compute from storage was an afterthought and it's good, it's... Given what they had it was actually quite clever and customers like it, but it's more of a, "Okay, we're going to tier to storage to lower cost, we're going to sort of dial down the compute not completely, we're not going to shut it off, we're going to minimize the compute required." It's really not true as separation is like for instance Snowflake has. But having said that, we're talking about competitors with lots of resources and cohort offerings. And so I don't want to make this necessarily all about the product, but all things being equal architecture matters, okay? So that's the cloud S-curve, the first one we're showing. Snowflake's still on that S-curve, and in and of itself it's got legs, but it's not what's going to power the company to 10 billion. The next S-curve we denote is the multi-cloud in the middle. And now while 80% of Snowflake's revenue is AWS, Microsoft is ramping up and Google, well, we'll see. But the interesting part of that curve is data sharing, and this idea of data clean rooms. I mean it really should be called the data sharing curve, but I have my reasons for calling it multi-cloud. And this is all about network effects and data gravity, and you're seeing this play out today, especially in industries like financial services and healthcare and government that are highly regulated verticals where folks are super paranoid about compliance. There not going to share data if they're going to get sued for it, if they're going to be in the front page of the Wall Street Journal for some kind of privacy breach. And what Snowflake has done is said, "Put all the data in our cloud." Now, of course now that triggers a lot of people because it's a walled garden, okay? It is. That's the trade off. It's not the Wild West, it's not Windows, it's Mac, it's more controlled. But the idea is that as different parts of the organization or even partners begin to share data that they need, it's got to be governed, it's got to be secure, it's got to be compliant, it's got to be trusted. So Snowflake introduced the idea of, they call these things stable edges. I think that's the term that they use. And they track a metric around stable edges. And so a stable edge, or think of it as a persistent edge is an ongoing relationship between two parties that last for some period of time, more than a month. It's not just a one shot deal, one a done type of, "Oh guys shared it for a day, done." It sent you an FTP, it's done. No, it's got to have trajectory over time. Four weeks or six weeks or some period of time that's meaningful. And that metric is growing. Now I think sort of a different metric that they track. I think around 20% of Snowflake customers are actively sharing data today and then they track the number of those edge relationships that exist. So that's something that's unique. Because again, most data sharing is all about making copies of data. That's great for storage companies, it's bad for auditors, and it's bad for compliance officers. And that trend is just starting out, that middle S-curve, it's going to kind of hit the base of that steep part of the S-curve and it's going to have legs through this decade we think. And then finally the third wave that we show here is what we call super cloud. That's why I called it multi-cloud before, so it could invoke super cloud. The idea that you've built a PAS layer that is purpose built for a specific objective, and in this case it's building data apps that are cloud native, shareable and governed. And is a long-term trend that's going to take some time to develop. I mean, application development platforms can take five to 10 years to mature and gain significant adoption, but this one's unique. This is a critical play for Snowflake. If it's going to compete with the big cloud players, it has to have an app development framework like Snowpark. It has to accommodate new data types like transactional data. That's why it announced this thing called UniStore last June, Snowflake a summit. And the pattern that's forming here is Snowflake is building layer upon layer with its architecture at the core. It's not currently anyway, it's not going out and saying, "All right, we're going to buy a company that's got to another billion dollars in revenue and that's how we're going to get to 10 billion." So it's not buying its way into new markets through revenue. It's actually buying smaller companies that can complement Snowflake and that it can turn into revenue for growth that fit in to the data cloud. Now as to the 10 billion by fiscal year 28, is that achievable? That's the question. Yeah, I think so. Would the momentum resources go to market product and management prowess that Snowflake has? Yes, it's definitely achievable. And one could argue to $10 billion is too conservative. Indeed, Snowflake CFO, Mike Scarpelli will fully admit his forecaster built on existing offerings. He's not including revenue as I understand it from all the new stuff that's in the pipeline because he doesn't know what it's going to look like. He doesn't know what the adoption is going to look like. He doesn't have data on that adoption, not just yet anyway. And now of course things can change quite dramatically. It's possible that is forecast for existing businesses don't materialize or competition picks them off or a company like Databricks actually is able in the longer term replicate the functionality of Snowflake with open source technologies, which would be a very competitive source of innovation. But in our view, there's plenty of room for growth, the market is enormous and the real key is, can and will Snowflake deliver on the promises of simplifying data? Of course we've heard this before from data warehouse, the data mars and data legs and master data management and ETLs and data movers and data copiers and Hadoop and a raft of technologies that have not lived up to expectations. And we've also, by the way, seen some tremendous successes in the software business with the likes of ServiceNow and Salesforce. So will Snowflake be the next great software name and hit that 10 billion magic mark? I think so. Let's reconnect in 2028 and see. Okay, we'll leave it there today. I want to thank Chip Simonton for his input to today's episode. Thanks to Alex Myerson who's on production and manages the podcast. Ken Schiffman as well. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hove is our Editor in Chief over at Silicon Angle. He does some great editing for us. Check it out for all the news. Remember all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me to get in touch David.vallante@siliconangle.com. DM me @dvellante or comment on our LinkedIn post. And please do check out etr.ai, they've got the best survey data in the enterprise tech business. This is Dave Vellante for the CUBE Insights, powered by ETR. Thanks for watching, thanks for listening and we'll see you next time on breaking analysis. (upbeat music)

>>From the Cube Studios in Palo Alto in Boston, bringing you data driven insights from the cube and etr. This is breaking analysis with Dave Ante. >>Have you ever been driving on the highway and traffic suddenly slows way down and then after a little while it picks up again and you're cruising along and you're thinking, Okay, hey, that was weird. But it's clear sailing now. Off we go, only to find out in a bit that the traffic is building up ahead again, forcing you to pump the brakes as the traffic pattern ebbs and flows well. Welcome to the Seesaw economy. The fed induced fire that prompted an unprecedented rally in tech is being purposefully extinguished now by that same fed. And virtually every sector of the tech industry is having to reset its expectations, including the cloud segment. Hello and welcome to this week's Wikibon Cube Insights powered by etr. In this breaking analysis will review the implications of the earnings announcements from the big three cloud players, Amazon, Microsoft, and Google who announced this week. >>And we'll update you on our quarterly IAS forecast and share the latest from ETR with a focus on cloud computing. Now, before we get into the new data, we wanna review something we shared with you on October 14th, just a couple weeks back, this is sort of a, we told you it was coming slide. It's an XY graph that shows ET R'S proprietary net score methodology on the vertical axis. That's a measure of spending momentum, spending velocity, and an overlap or presence in the dataset that's on the X axis. That's really a measure of pervasiveness. In the survey, the table, you see that table insert there that shows Wiki Bond's Q2 estimates of IAS revenue for the big four hyperscalers with their year on year growth rates. Now we told you at the time, this is data from the July TW 22 ETR survey and the ETR hadn't released its October survey results at that time. >>This was just a couple weeks ago. And while we couldn't share the specific data from the October survey, we were able to get a glimpse and we depicted the slowdown that we saw in the October data with those dotted arrows kind of down into the right, we said at the time that we were seeing and across the board slowdown even for the big three cloud vendors. Now, fast forward to this past week and we saw earnings releases from Alphabet, Microsoft, and just last night Amazon. Now you may be thinking, okay, big deal. The ETR survey data didn't really tell us anything we didn't already know. But judging from the negative reaction in the stock market to these earnings announcements, the degree of softness surprised a lot of investors. Now, at the time we didn't update our forecast, it doesn't make sense for us to do that when we're that close to earning season. >>And now that all the big three ha with all the big four with the exception of Alibaba have announced we've, we've updated. And so here's that data. This chart lays out our view of the IS and PAs worldwide revenue. Basically it's cloud infrastructure with an attempt to exclude any SaaS revenue so we can make an apples to apples comparison across all the clouds. Now the reason that actual is in quotes is because Microsoft and Google don't report IAS revenue, but they do give us clues and kind of directional commentary, which we then triangulate with other data that we have from the channel and ETR surveys and just our own intelligence. Now the second column there after the vendor name shows our previous estimates for q3, and then next to that we show our actuals. Same with the growth rates. And then we round out the chart with that lighter blue color highlights, the full year estimates for revenue and growth. >>So the key takeaways are that we shaved about $4 billion in revenue and roughly 300 basis points of growth off of our full year estimates. AWS had a strong July but exited Q3 in the mid 20% growth rate year over year. So we're using that guidance, you know, for our Q4 estimates. Azure came in below our earlier estimates, but Google actually exceeded our expectations. Now the compression in the numbers is in our view of function of the macro demand climate, we've made every attempt to adjust for constant currency. So FX should not be a factor in this data, but it's sure you know that that ma the the, the currency effects are weighing on those companies income statements. And so look, this is the fundamental dynamic of a cloud model where you can dial down consumption when you need to and dial it up when you need to. >>Now you may be thinking that many big cloud customers have a committed level of spending in order to get better discounts. And that's true. But what's happening we think is they'll reallocate that spend toward, let's say for example, lower cost storage tiers or they may take advantage of better price performance processors like Graviton for example. That is a clear trend that we're seeing and smaller companies that were perhaps paying by the drink just on demand, they're moving to reserve instance models to lower their monthly bill. So instead of taking the easy way out and just spending more companies are reallocating their reserve capacity toward lower cost. So those sort of lower cost services, so they're spending time and effort optimizing to get more for, for less whereas, or get more for the same is really how we should, should, should phrase it. Whereas during the pandemic, many companies were, you know, they perhaps were not as focused on doing that because business was booming and they had a response. >>So they just, you know, spend more dial it up. So in general, as they say, customers are are doing more with, with the same. Now let's look at the growth dynamic and spend some time on that. I think this is important. This data shows worldwide quarterly revenue growth rates back to Q1 2019 for the big four. So a couple of interesting things. The data tells us during the pandemic, you saw both AWS and Azure, but the law of large numbers and actually accelerate growth. AWS especially saw progressively increasing growth rates throughout 2021 for each quarter. Now that trend, as you can see is reversed in 2022 for aws. Now we saw Azure come down a bit, but it's still in the low forties in terms of percentage growth. While Google actually saw an uptick in growth this last quarter for GCP by our estimates as GCP is becoming an increasingly large portion of Google's overall cloud business. >>Now, unfortunately Google Cloud continues to lose north of 850 million per quarter, whereas AWS and Azure are profitable cloud businesses even though Alibaba is suffering its woes from China. And we'll see how they come in when they report in mid-November. The overall hyperscale market grew at 32% in Q3 in terms of worldwide revenue. So the slowdown isn't due to the repatriation or competition from on-prem vendors in our view, it's a macro related trend. And cloud will continue to significantly outperform other sectors despite its massive size. You know, on the repatriation point, it just still doesn't show up in the data. The A 16 Z article from Sarah Wong and Martin Martin Kasa claiming that repatriation was inevitable as a means to lower cost of good sold for SaaS companies. You know, while that was thought provoking, it hasn't shown up in the numbers. And if you read the financial statements of both AWS and its partners like Snowflake and you dig into the, to the, to the quarterly reports, you'll see little notes and comments with their ongoing negotiations to lower cloud costs for customers. >>AWS and no doubt execs at Azure and GCP understand that the lifetime value of a customer is worth much more than near term gross margin. And you can expect the cloud vendors to strike a balance between profitability, near term profitability anyway and customer attention. Now, even though Google Cloud platform saw accelerated growth, we need to put that in context for you. So GCP, by our estimate, has now crossed over the $3 billion for quarter market actually did so last quarter, but its growth rate accelerated to 42% this quarter. And so that's a good sign in our view. But let's do a quick little comparison with when AWS and Azure crossed the $3 billion mark and compare their growth rates at the time. So if you go back to to Q2 2016, as we're showing in this chart, that's around the time that AWS hit 3 billion per quarter and at the same time was growing at 58%. >>Azure by our estimates crossed that mark in Q4 2018 and at that time was growing at 67%. Again, compare that to Google's 42%. So one would expect Google's growth rate would be higher than its competitors at this point in the MO in the maturity of its cloud, which it's, you know, it's really not when you compared to to Azure. I mean they're kind of con, you know, comparable now but today, but, but you'll go back, you know, to that $3 billion mark. But more so looking at history, you'd like to see its growth rate at this point of a maturity model at least over 50%, which we don't believe it is. And one other point on this topic, you know, my business friend Matt Baker from Dell often says it's not a zero sum game, meaning there's plenty of opportunity exists to build value on top of hyperscalers. >>And I would totally agree it's not a dollar for dollar swap if you can continue to innovate. But history will show that the first company in makes the most money. Number two can do really well and number three tends to break even. Now maybe cloud is different because you have Microsoft software estate and the power behind that and that's driving its IAS business and Google ads are funding technology buildouts for, for for Google and gcp. So you know, we'll see how that plays out. But right now by this one measurement, Google is four years behind Microsoft in six years behind aws. Now to the point that cloud will continue to outpace other markets, let's, let's break this down a bit in spending terms and see why this claim holds water. This is data from ET r's latest October survey that shows the granularity of its net score or spending velocity metric. >>The lime green is new adoptions, so they're adding the platform, the forest green is spending more 6% or more. The gray bars spending is flat plus or minus, you know, 5%. The pinkish colors represent spending less down 6% or worse. And the bright red shows defections or churn of the platform. You subtract the reds from the greens and you get what's called net score, which is that blue dot that you can see on each of the bars. So what you see in the table insert is that all three have net scores above 40%, which is a highly elevated measure. Microsoft's net scores above 60% AWS well into the fifties and GCP in the mid forties. So all good. Now what's happening with all three is more customers are keep keeping their spending flat. So a higher percentage of customers are saying, our spending is now flat than it was in previous quarters and that's what's accounting for the compression. >>But the churn of all three, even gcp, which we reported, you know, last quarter from last quarter survey was was five x. The other two is actually very low in the single digits. So that might have been an anomaly. So that's a very good sign in our view. You know, again, customers aren't repatriating in droves, it's just not a trend that we would bet on, maybe makes for a FUD or you know, good marketing head, but it's just not a big deal. And you can't help but be impressed with both Microsoft and AWS's performance in the survey. And as we mentioned before, these companies aren't going to give up customers to try and preserve a little bit of gross margin. They'll do what it takes to keep people on their platforms cuz they'll make up for it over time with added services and improved offerings. >>Now, once these companies acquire a customer, they'll be very aggressive about keeping them. So customers take note, you have negotiating leverage, so use it. Okay, let's look at another cut at the cloud market from the ETR data set. Here's the two dimensional view, again, it's back, it's one of our favorites. Net score or spending momentum plotted against presence. And the data set, that's the x axis net score on the, on the vertical axis, this is a view of et r's cloud computing sector sector. You can see we put that magic 40% dotted red line in the table showing and, and then that the table inserts shows how the data are plotted with net score against presence. I e n in the survey, notably only the big three are above the 40% line of the names that we're showing here. The oth there, there are others. >>I mean if you put Snowflake on there, it'd be higher than any of these names, but we'll dig into that name in a later breaking analysis episode. Now this is just another way of quantifying the dominance of AWS and Azure, not only relative to Google, but the other cloud platforms out there. So we've, we've taken the opportunity here to plot IBM and Oracle, which both own a public cloud. Their performance is largely a reflection of them migrating their install bases to their respective public clouds and or hybrid clouds. And you know, that's fine, they're in the game. That's a point that we've made, you know, a number of times they're able to make it through the cloud, not whole and they at least have one, but they simply don't have the business momentum of AWS and Azure, which is actually quite impressive because AWS and Azure are now as large or larger than IBM and Oracle. >>And to show this type of continued growth that that that Azure and AWS show at their size is quite remarkable and customers are starting to recognize the viability of on-prem hi, you know, hybrid clouds like HPE GreenLake and Dell's apex. You know, you may say, well that's not cloud, but if the customer thinks it is and it was reporting in the survey that it is, we're gonna continue to report this view. You know, I don't know what's happening with H P E, They had a big down tick this quarter and I, and I don't read too much into that because their end is still pretty small at 53. So big fluctuations are not uncommon with those types of smaller ends, but it's over 50. So, you know, we did notice a a a negative within a giant public and private sector, which is often a, a bellwether giant public private is big public companies and large private companies like, like a Mars for example. >>So it, you know, it looks like for HPE it could be an outlier. We saw within the Fortune 1000 HPE E'S cloud looked actually really good and it had good spending momentum in that sector. When you di dig into the industry data within ETR dataset, obviously we're not showing that here, but we'll continue to monitor that. Okay, so where's this Leave us. Well look, this is really a tactical story of currency and macro headwinds as you can see. You know, we've laid out some of the points on this slide. The action in the stock market today, which is Friday after some of the soft earnings reports is really robust. You know, we'll see how it ends up in the day. So maybe this is a sign that the worst is over, but we don't think so. The visibility from tech companies is murky right now as most are guiding down, which indicates that their conservative outlook last quarter was still too optimistic. >>But as it relates to cloud, that platform is not going anywhere anytime soon. Sure, there are potential disruptors on the horizon, especially at the edge, but we're still a long ways off from, from the possibility that a new economic model emerges from the edge to disrupt the cloud and the opportunities in the cloud remain strong. I mean, what other path is there? Really private cloud. It was kind of a bandaid until the on-prem guys could get their a as a service models rolled out, which is just now happening. The hybrid thing is real, but it's, you know, defensive for the incumbents until they can get their super cloud investments going. Super cloud implying, capturing value above the hyperscaler CapEx, you know, call it what you want multi what multi-cloud should have been, the metacloud, the Uber cloud, whatever you like. But there are opportunities to play offense and that's clearly happening in the cloud ecosystem with the likes of Snowflake, Mongo, Hashi Corp. >>Hammer Spaces is a startup in this area. Aviatrix, CrowdStrike, Zeke Scaler, Okta, many, many more. And even the projects we see coming out of enterprise players like Dell, like with Project Alpine and what Pure Storage is doing along with a number of other of the backup vendors. So Q4 should be really interesting, but the real story is the investments that that companies are making now to leverage the cloud for digital transformations will be paying off down the road. This is not 1999. We had, you know, May might have had some good ideas and admittedly at a lot of bad ones too, but you didn't have the infrastructure to service customers at a low enough cost like you do today. The cloud is that infrastructure and so far it's been transformative, but it's likely the best is yet to come. Okay, let's call this a rap. >>Many thanks to Alex Morrison who does production and manages the podcast. Also Can Schiffman is our newest edition to the Boston Studio. Kristin Martin and Cheryl Knight helped get the word out on social media and in our newsletters. And Rob Ho is our editor in chief over@siliconangle.com, who does some wonderful editing for us. Thank you. Remember, all these episodes are available as podcasts. Wherever you listen, just search breaking analysis podcast. I publish each week on wiki bond.com at silicon angle.com. And you can email me at David dot valante@siliconangle.com or DM me at Dante or comment on my LinkedIn posts. And please do checkout etr.ai. They got the best survey data in the enterprise tech business. This is Dave Valante for the Cube Insights powered by etr. Thanks for watching and we'll see you next time on breaking analysis.

>>Good afternoon, friends. My name is Savannah Peterson here in the Cube Studios live from Detroit, Michigan, where we're at Cuban and Cloud Native Foundation, Cloud Native Con all week. Our last interview of the day served me a real treat and one that I wasn't expecting. It turns out that I am in the presence of two caddies. It's a literal episode of Caddy Shack up here on Cube. John Furrier. I don't think the audience knows that you were a caddy. Tell us about your caddy days. >>I used to caddy when I was a kid at the local country club every weekend. This is amazing. Double loops every weekend. Make some bang, two bags on each shoulder. Caddying for the members where you're going. Now I'm >>On show. Just, just really impressive >>Now. Now I'm caddying for the cube where I caddy all this great content out to the audience. >>He's carrying the story of emerging brands and established companies on their cloud journey. I love it. John, well played. I don't wanna waste any more of this really wonderful individual's time, but since we now have a new trend of talking about everyone's Twitter handle here on the cube, this may be my favorite one of the day, if not Q4 so far. Drew, not reply. AKA Drew ne Drew Nielsen, excuse me, there is here with us from Teleport. Drew, thanks so much for being here. >>Oh, thanks for having me. It's great to be here. >>And so you were a caddy on a whole different level. Can you tell us >>About that? Yeah, so I was in university and I got tired after two years and didn't have a car in LA and met a pro golfer at a golf course and took two years off and traveled around caddying for him and tried to get 'em through Q School. >>This is, this is fantastic. So if you're in school and your parents are telling you to continue going to school, know that you can drop out and be a caddy and still be a very successful television personality. Like both of the gentlemen at some point. >>Well, I never said my parents like >>That decision, but we'll keep our day jobs. Yeah, exactly. And one of them is Cloud Native Security. The hottest topic here at the show. Yep. I want to get into it. You guys are doing some really cool things. Are we? We hear Zero Trust, you know, ransomware and we even, I even talked with the CEO of Dockets morning about container security issues. Sure. There's a lot going on. So you guys are in the middle of teleport. You guys have a unique solution. Tell us what you guys got going on. What do you guys do? What's the solution and what's the problem you solve? >>So Teleport is the first and only identity native infrastructure access solution in the market. So breaking that down, what that really means is identity native being the combination of secret list, getting rid of passwords, Pam Vaults, Key Vaults, Yeah. Passwords written down. Basically the number one source of breach. And 50 to 80% of breaches, depending on whose numbers you want to believe are how organizations get hacked. >>But it's not password 1 23 isn't protecting >>Cisco >>Right >>Now. Well, if you think about when you're securing infrastructure and the second component being zero trust, which assumes the network is completely insecure, right? But everything is validated. Resource to resource security is validated, You know, it assumes work from anywhere. It assumes the security comes back to that resource. And we take the combination of those two into identity, native access where we cryptographically ev, validate identity, but more importantly, we make an absolutely frictionless experience. So engineers can access infrastructure from anywhere at any time. >>I'm just flashing on my roommates, checking their little code, changing Bob login, you know, dongle essentially, and how frustrating that always was. I mean, talk about interrupting workflow was something that's obviously necessary, but >>Well, I mean, talk about frustration if I'm an engineer. Yeah, absolutely. You know, back in the day when you had these three tier monolithic applications, it was kind of simple. But now as you've got modern application development environments Yeah, multi-cloud, hybrid cloud, whatever marketing term around how you talk about this, expanding sort of disparate infrastructure. Engineers are sitting there going from system to system to machine to database to application. I mean, not even a conversation on Kubernetes yet. Yeah. And it's just, you know, every time you pull an engineer or a developer to go to a vault to pull something out, you're pulling them out for 10 minutes. Now, applications today have hundreds of systems, hundreds of microservices. I mean 30 of these a day and nine minutes, 270 minutes times 60. And they also >>Do the math. Well, there's not only that, there's also the breach from manual error. I forgot to change the password. What is that password? I left it open, I left it on >>Cognitive load. >>I mean, it's the manual piece. But even think about it, TR security has to be transparent and engineers are really smart people. And I've talked to a number of organizations who are like, yeah, we've tried to implement security solutions and they fail. Why? They're too disruptive. They're not transparent. And engineers will work their way around them. They'll write it down, they'll do a workaround, they'll backdoor it something. >>All right. So talk about how it works. But I, I mean, I'm getting the big picture here. I love this. Breaking down the silos, making engineers lives easier, more productive. Clearly the theme, everyone they want, they be gonna need. Whoever does that will win it all. How's it work? I mean, you deploying something, is it code, is it in line? It's, >>It's two binaries that you download and really it starts with the core being the identity native access proxy. Okay. So that proxy, I mean, if you look at like the zero trust principles, it all starts with a proxy. Everything connects into that proxy where all the access is gated, it's validated. And you know, from there we have an authorization engine. So we will be the single source of truth for all access across your entire infrastructure. So we bring machines, engineers, databases, applications, Kubernetes, Linux, Windows, we don't care. And we basically take that into a single architecture and single access platform that essentially secures your entire infrastructure. But more importantly, you can do audit. So for all of the organizations that are dealing with FedRAMP, pci, hipaa, we have a complete audit trail down to a YouTube style playback. >>Oh, interesting. We're we're California and ccpa. >>Oh, gdpr. >>Yeah, exactly. It, it, it's, it's a whole shebang. So I, I love, and John, maybe you've heard this term a lot more than I have, but identity native is relatively new to me as as a term. And I suspect you have a very distinct way of defining identity. How do you guys define identity internally? >>So identity is something that is cryptographically validated. It is something you have. So it's not enough. If you look at, you know, credentials today, everyone's like, Oh, I log into my computer, but that's my identity. No, it's not. Right. Those are attributes. Those are something that is secret for a period of time until you write it down. But I can't change my fingerprint. Right. And now I >>Was just >>Thinking of, well no, perfect case in point with touch ID on your meth there. Yeah. It's like when we deliver that cryptographically validated identity, we use these secure modules in like modern laptops or servers. Yeah. To store that identity so that even if you're sitting in front of your computer, you can't get to it. But more importantly, if somebody were to take that and try to be you and try to log in with your fingerprint, it's >>Not, I'm not gonna lie, I love the apple finger thing, you know, it's like, you know, space recognition, like it's really awesome. >>It save me a lot of time. I mean, even when you go through customs and they do the face scan now it actually knows who you are, which is pretty wild in the last time you wanna provide ones. But it just shifted over like maybe three months ago. Well, >>As long as no one chops your finger off like they do in the James Bond movies. >>I mean, we try and keep it a light and fluffy here on the queue, but you know, do a finger teams, we can talk about that >>Too. >>Gabby, I was thinking more minority report, >>But you >>Knows that's exactly what I, what I think of >>Hit that one outta bounds. So I gotta ask, because you said you're targeting engineers, not IT departments. What's, is that, because I in your mind it is now the engineers or what's the, is always the solution more >>Targeted? Well, if you really look at who's dealing with infrastructure on a day-to-day basis, those are DevOps individuals. Those are infrastructure teams, Those are site reliability engineering. And when it, they're the ones who are not only managing the infrastructure, but they're also dealing with the code on it and everything else. And for us, that is who is our primary customer and that's who's doing >>It. What's the biggest problem that you're solving in this use case? Because you guys are nailing it. What's the problem that your identity native solution solves? >>You know, right out of the backs we remove the number one source of breach. And that is taking passwords, secrets and, and keys off the board. That deals with most of the problem right there. But there are really two problems that organizations face. One is scaling. So as you scale, you get more secrets, you get more keys, you get all these things that is all increasing your attack vector in real time. Oh >>Yeah. Across teams locations. I can't even >>Take your pick. Yeah, it's across clouds, right? Any of it >>On-prem doesn't. >>Yeah. Any of it. We, and we allow you to scale, but do it securely and the security is transparent and your engineers will absolutely love it. What's the most important thing about this product Engineers. Absolutely. >>What are they saying? What are some of those examples? Anecdotally, pull boats out from engineering. >>You're too, we should have invent, we should have invented this ourselves. Or you know, we have run into a lot of customers who have tried to home brew this and they're like, you know, we spend an in nor not of hours on it >>And IT or they got legacy from like Microsoft or other solutions. >>Sure, yeah. Any, but a lot of 'em is just like, I wish I had done it myself. Or you know, this is what security should be. >>It makes so much sense and it gives that the team such a peace of mind. I mean, you never know when a breach is gonna come, especially >>It's peace of mind. But I think for engineers, a lot of times it deals with the security problem. Yeah. Takes it off the table so they can do their jobs. Yeah. With zero friction. Yeah. And you know, it's all about speed. It's all about velocity. You know, go fast, go fast, go fast. And that's what we enable >>Some of the benefits to them is they get to save time, focus more on, on task that they need to work on. >>Exactly. >>And get the >>Job done. And on top of it, they answer the audit and compliance mail every time it comes. >>Yeah. Why are people huge? Honestly, why are people doing this? Because, I mean, identity is just such an hard nut to crack. Everyone's got their silos, Vendors having clouds have 'em. Identity is the most fragmented thing on >>The planet. And it has been fragmented ever since my first RSA conference. >>I know. So will we ever get this do over? Is there a driver? Is there a market force? Is this the time? >>I think the move to modern applications and to multi-cloud is driving this because as those application stacks get more verticalized, you just, you cannot deal with the productivity >>Here. And of course the next big thing is super cloud and that's coming fast. Savannah, you know, You know that's Rocket. >>John is gonna be the thought leader and keyword leader of the word super cloud. >>Super Cloud is enabling super services as the cloud cast. Brian Gracely pointed out on his Sunday podcast of which if that happens, Super Cloud will enable super apps in a new architectural >>List. Please don't, and it'll be super, just don't. >>Okay. Right. So what are you guys up to next? What's the big hot spot for the company? What are you guys doing? What are you guys, What's the idea guys hiring? You put the plug in. >>You know, right now we are focused on delivering the best identity, native access platform that we can. And we will continue to support our customers that want to use Kubernetes, that want to use any different type of infrastructure. Whether that's Linux, Windows applications or databases. Wherever they are. >>Are, are your customers all of a similar DNA or are you >>No, they're all over the map. They range everything from tech companies to financial services to, you know, fractional property. >>You seem like someone everyone would need. >>Absolutely. >>And I'm not just saying that to be a really clean endorsement from the Cube, but >>If you were doing DevOps Yeah. And any type of forward-leaning shift, left engineering, you need us because we are basically making security as code a reality across your entire infrastructure. >>Love this. What about the team dna? Are you in a scale growth stage right now? What's going on? Absolutely. Sounds I was gonna say, but I feel like you would have >>To be. Yeah, we're doing, we're, we have a very positive outlook and you know, even though the economic time is what it is, we're doing very well meeting. >>How's the location? Where's the location of the headquarters now? With remote work is pretty much virtual. >>Probably. We're based in downtown Oakland, California. >>Woohoo. Bay area representing on this stage right now. >>Nice. Yeah, we have a beautiful office right in downtown Oakland and yeah, it's been great. Awesome. >>Love that. And are you hiring right now? I bet people might be. I feel like some of our cube watchers are here waiting to figure out their next big play. So love to hear that. Absolutely love to hear that. Besides Drew, not reply, if people want to join your team or say hello to you and tell you how brilliant you looked up here, or ask about your caddy days and maybe venture a guest to who that golfer may have been that you were CAD Inc. For, what are the best ways for them to get in touch with you? >>You can find me on LinkedIn. >>Great. Fantastic. John, anything else >>From you? Yeah, I mean, I just think security is paramount. This is just another example of where the innovation has to kind of break through without good identity, everything could cripple. Then you start getting into the silos and you can start getting into, you know, tracking it. You got error user errors, you got, you know, one of the biggest security risks. People just leave systems open, they don't even know it's there. So like, I mean this is just, just identity is the critical linchpin to, to solve for in security to me. And that's totally >>Agree. We even have a lot of customers who use us just to access basic cloud consoles. Yeah. >>So I was actually just gonna drive there a little bit because I think that, I'm curious, it feels like a solution for obviously complex systems and stacks, but given the utility and what sounds like an extreme ease of use, I would imagine people use this for day-to-day stuff within their, >>We have customers who use it to access their AWS consoles. We have customers who use it to access Grafana dashboards. You know, for, since we're sitting here at coupon accessing a Lens Rancher, all of the amazing DevOps tools that are out there. >>Well, I mean true. I mean, you think about all the reasons why people don't adopt this new federated approach or is because the IT guys did it and the world we're moving into, the developers are in charge. And so we're seeing the trend where developers are taking the DevOps and the data and the security teams are now starting to reset the guardrails. What's your >>Reaction to that? Well, you know, I would say that >>Over the top, >>Well I would say that you know, your DevOps teams and your infrastructure teams and your engineers, they are the new king makers. Yeah. Straight up. Full stop. >>You heard it first folks. >>And that's >>A headline right >>There. That is a headline. I mean, they are the new king makers and, but they are being forced to do it as securely as possible. And our job is really to make that as easy and as frictionless as possible. >>Awesome. >>And it sounds like you're absolutely nailing it. Drew, thank you so much for being on the show. Thanks for having today. This has been an absolute pleasure, John, as usual a joy. And thank all of you for tuning in to the Cube Live here at CU Con from Detroit, Michigan. We look forward to catching you for day two tomorrow.

>>Oracle continues to enhance my sequel Heatwave at a very rapid pace. The company is now in its fourth major release since the original announcement in December 2020. 1 of the main criticisms of my sequel, Heatwave, is that it only runs on O. C I. Oracle Cloud Infrastructure and as a lock in to Oracle's Cloud. Oracle recently announced that heat wave is now going to be available in AWS Cloud and it announced its intent to bring my sequel Heatwave to Azure. So my secret heatwave on AWS is a significant TAM expansion move for Oracle because of the momentum AWS Cloud continues to show. And evidently the Heatwave Engineering team has taken the development effort from O. C I. And is bringing that to A W S with a number of enhancements that we're gonna dig into today is senior vice president. My sequel Heatwave at Oracle is back with me on a cube conversation to discuss the latest heatwave news, and we're eager to hear any benchmarks relative to a W S or any others. Nippon has been leading the Heatwave engineering team for over 10 years and there's over 100 and 85 patents and database technology. Welcome back to the show and good to see you. >>Thank you. Very happy to be back. >>Now for those who might not have kept up with the news, uh, to kick things off, give us an overview of my sequel, Heatwave and its evolution. So far, >>so my sequel, Heat Wave, is a fully managed my secret database service offering from Oracle. Traditionally, my secret has been designed and optimised for transaction processing. So customers of my sequel then they had to run analytics or when they had to run machine learning, they would extract the data out of my sequel into some other database for doing. Unlike processing or machine learning processing my sequel, Heat provides all these capabilities built in to a single database service, which is my sequel. He'd fake So customers of my sequel don't need to move the data out with the same database. They can run transaction processing and predicts mixed workloads, machine learning, all with a very, very good performance in very good price performance. Furthermore, one of the design points of heat wave is is a scale out architecture, so the system continues to scale and performed very well, even when customers have very large late assignments. >>So we've seen some interesting moves by Oracle lately. The collaboration with Azure we've we've covered that pretty extensively. What was the impetus here for bringing my sequel Heatwave onto the AWS cloud? What were the drivers that you considered? >>So one of the observations is that a very large percentage of users of my sequel Heatwave, our AWS users who are migrating of Aurora or so already we see that a good percentage of my secret history of customers are migrating from GWS. However, there are some AWS customers who are still not able to migrate the O. C. I to my secret heat wave. And the reason is because of, um, exorbitant cost, which was charges. So in order to migrate the workload from AWS to go see, I digress. Charges are very high fees which becomes prohibitive for the customer or the second example we have seen is that the latency of practising a database which is outside of AWS is very high. So there's a class of customers who would like to get the benefits of my secret heatwave but were unable to do so and with this support of my secret trip inside of AWS, these customers can now get all the grease of the benefits of my secret he trip without having to pay the high fees or without having to suffer with the poorly agency, which is because of the ws architecture. >>Okay, so you're basically meeting the customer's where they are. So was this a straightforward lifted shift from from Oracle Cloud Infrastructure to AWS? >>No, it is not because one of the design girls we have with my sequel, Heatwave is that we want to provide our customers with the best price performance regardless of the cloud. So when we decided to offer my sequel, he headed west. Um, we have optimised my sequel Heatwave on it as well. So one of the things to point out is that this is a service with the data plane control plane and the console are natively running on AWS. And the benefits of doing so is that now we can optimise my sequel Heatwave for the E. W s architecture. In addition to that, we have also announced a bunch of new capabilities as a part of the service which will also be available to the my secret history of customers and our CI, But we just announced them and we're offering them as a part of my secret history of offering on AWS. >>So I just want to make sure I understand that it's not like you just wrapped your stack in a container and stuck it into a W s to be hosted. You're saying you're actually taking advantage of the capabilities of the AWS cloud natively? And I think you've made some other enhancements as well that you're alluding to. Can you maybe, uh, elucidate on those? Sure. >>So for status, um, we have taken the mind sequel Heatwave code and we have optimised for the It was infrastructure with its computer network. And as a result, customers get very good performance and price performance. Uh, with my secret he trade in AWS. That's one performance. Second thing is, we have designed new interactive counsel for the service, which means that customers can now provision there instances with the council. But in addition, they can also manage their schemas. They can. Then court is directly from the council. Autopilot is integrated. The council we have introduced performance monitoring, so a lot of capabilities which we have introduced as a part of the new counsel. The third thing is that we have added a bunch of new security features, uh, expose some of the security features which were part of the My Secret Enterprise edition as a part of the service, which gives customers now a choice of using these features to build more secure applications. And finally, we have extended my secret autopilot for a number of old gpus cases. In the past, my secret autopilot had a lot of capabilities for Benedict, and now we have augmented my secret autopilot to offer capabilities for elderly people. Includes as well. >>But there was something in your press release called Auto thread. Pooling says it provides higher and sustained throughput. High concerns concerns concurrency by determining Apple number of transactions, which should be executed. Uh, what is that all about? The auto thread pool? It seems pretty interesting. How does it affect performance? Can you help us understand that? >>Yes, and this is one of the capabilities of alluding to which we have added in my secret autopilot for transaction processing. So here is the basic idea. If you have a system where there's a large number of old EP transactions coming into it at a high degrees of concurrency in many of the existing systems of my sequel based systems, it can lead to a state where there are few transactions executing, but a bunch of them can get blocked with or a pilot tried pulling. What we basically do is we do workload aware admission control and what this does is it figures out, what's the right scheduling or all of these algorithms, so that either the transactions are executing or as soon as something frees up, they can start executing, so there's no transaction which is blocked. The advantage to the customer of this capability is twofold. A get significantly better throughput compared to service like Aurora at high levels of concurrency. So at high concurrency, for instance, uh, my secret because of this capability Uh oh, thread pulling offers up to 10 times higher compared to Aurora, that's one first benefit better throughput. The second advantage is that the true part of the system never drops, even at high levels of concurrency, whereas in the case of Aurora, the trooper goes up, but then, at high concurrency is, let's say, starting, uh, level of 500 or something. It depends upon the underlying shit they're using the troopers just dropping where it's with my secret heatwave. The truth will never drops. Now, the ramification for the customer is that if the truth is not gonna drop, the user can start off with a small shape, get the performance and be a show that even the workload increases. They will never get a performance, which is worse than what they're getting with lower levels of concurrency. So this let's leads to customers provisioning a shape which is just right for them. And if they need, they can, uh, go with the largest shape. But they don't like, you know, over pay. So those are the two benefits. Better performance and sustain, uh, regardless of the level of concurrency. >>So how do we quantify that? I know you've got some benchmarks. How can you share comparisons with other cloud databases especially interested in in Amazon's own databases are obviously very popular, and and are you publishing those again and get hub, as you have done in the past? Take us through the benchmarks. >>Sure, So benchmarks are important because that gives customers a sense of what performance to expect and what price performance to expect. So we have run a number of benchmarks. And yes, all these benchmarks are available on guitar for customers to take a look at. So we have performance results on all the three castle workloads, ol DB Analytics and Machine Learning. So let's start with the Rdp for Rdp and primarily because of the auto thread pulling feature. We show that for the IPCC for attended dataset at high levels of concurrency, heatwave offers up to 10 times better throughput and this performance is sustained, whereas in the case of Aurora, the performance really drops. So that's the first thing that, uh, tend to alibi. Sorry, 10 gigabytes. B B C c. I can come and see the performance are the throughput is 10 times better than Aurora for analytics. We have done a comparison of my secret heatwave in AWS and compared with Red Ship Snowflake Googled inquiry, we find that the price performance of my secret heatwave compared to read ship is seven times better. So my sequel, Heat Wave in AWS, provides seven times better price performance than red ship. That's a very, uh, interesting results to us. Which means that customers of Red Shift are really going to take the service seriously because they're gonna get seven times better price performance. And this is all running in a W s so compared. >>Okay, carry on. >>And then I was gonna say, compared to like, Snowflake, uh, in AWS offers 10 times better price performance. And compared to Google, ubiquity offers 12 times better price performance. And this is based on a four terabyte p PCH workload. Results are available on guitar, and then the third category is machine learning and for machine learning, uh, for training, the performance of my secret heatwave is 25 times faster compared to that shit. So all the three workloads we have benchmark's results, and all of these scripts are available on YouTube. >>Okay, so you're comparing, uh, my sequel Heatwave on AWS to Red Shift and snowflake on AWS. And you're comparing my sequel Heatwave on a W s too big query. Obviously running on on Google. Um, you know, one of the things Oracle is done in the past when you get the price performance and I've always tried to call fouls you're, like, double your price for running the oracle database. Uh, not Heatwave, but Oracle Database on a W s. And then you'll show how it's it's so much cheaper on on Oracle will be like Okay, come on. But they're not doing that here. You're basically taking my sequel Heatwave on a W s. I presume you're using the same pricing for whatever you see to whatever else you're using. Storage, um, reserved instances. That's apples to apples on A W s. And you have to obviously do some kind of mapping for for Google, for big query. Can you just verify that for me, >>we are being more than fair on two dimensions. The first thing is, when I'm talking about the price performance for analytics, right for, uh, with my secret heat rape, the cost I'm talking about from my secret heat rape is the cost of running transaction processing, analytics and machine learning. So it's a fully loaded cost for the case of my secret heatwave. There has been I'm talking about red ship when I'm talking about Snowflake. I'm just talking about the cost of these databases for running, and it's only it's not, including the source database, which may be more or some other database, right? So that's the first aspect that far, uh, trip. It's the cost for running all three kinds of workloads, whereas for the competition, it's only for running analytics. The second thing is that for these are those services whether it's like shit or snowflakes, That's right. We're talking about one year, fully paid up front cost, right? So that's what most of the customers would pay for. Many of the customers would pay that they will sign a one year contract and pay all the costs ahead of time because they get a discount. So we're using that price and the case of Snowflake. The costs were using is their standard edition of price, not the Enterprise edition price. So yes, uh, more than in this competitive. >>Yeah, I think that's an important point. I saw an analysis by Marx Tamer on Wiki Bond, where he was doing the TCO comparisons. And I mean, if you have to use two separate databases in two separate licences and you have to do et yelling and all the labour associated with that, that that's that's a big deal and you're not even including that aspect in in your comparison. So that's pretty impressive. To what do you attribute that? You know, given that unlike, oh, ci within the AWS cloud, you don't have as much control over the underlying hardware. >>So look hard, but is one aspect. Okay, so there are three things which give us this advantage. The first thing is, uh, we have designed hateful foreign scale out architecture. So we came up with new algorithms we have come up with, like, uh, one of the design points for heat wave is a massively partitioned architecture, which leads to a very high degree of parallelism. So that's a lot of hype. Each were built, So that's the first part. The second thing is that although we don't have control over the hardware, but the second design point for heat wave is that it is optimised for commodity cloud and the commodity infrastructure so we can have another guys, what to say? The computer we get, how much network bandwidth do we get? How much of, like objects to a brand that we get in here? W s. And we have tuned heat for that. That's the second point And the third thing is my secret autopilot, which provides machine learning based automation. So what it does is that has the users workload is running. It learns from it, it improves, uh, various premieres in the system. So the system keeps getting better as you learn more and more questions. And this is the third thing, uh, as a result of which we get a significant edge over the competition. >>Interesting. I mean, look, any I SV can go on any cloud and take advantage of it. And that's, uh I love it. We live in a new world. How about machine learning workloads? What? What did you see there in terms of performance and benchmarks? >>Right. So machine learning. We offer three capabilities training, which is fully automated, running in France and explanations. So one of the things which many of our customers told us coming from the enterprise is that explanations are very important to them because, uh, customers want to know that. Why did the the system, uh, choose a certain prediction? So we offer explanations for all models which have been derailed by. That's the first thing. Now, one of the interesting things about training is that training is usually the most expensive phase of machine learning. So we have spent a lot of time improving the performance of training. So we have a bunch of techniques which we have developed inside of Oracle to improve the training process. For instance, we have, uh, metal and proxy models, which really give us an advantage. We use adaptive sampling. We have, uh, invented in techniques for paralysing the hyper parameter search. So as a result of a lot of this work, our training is about 25 times faster than that ship them health and all the data is, uh, inside the database. All this processing is being done inside the database, so it's much faster. It is inside the database. And I want to point out that there is no additional charge for the history of customers because we're using the same cluster. You're not working in your service. So all of these machine learning capabilities are being offered at no additional charge inside the database and as a performance, which is significantly faster than that, >>are you taking advantage of or is there any, uh, need not need, but any advantage that you can get if two by exploiting things like gravity. John, we've talked about that a little bit in the past. Or trainee. Um, you just mentioned training so custom silicon that AWS is doing, you're taking advantage of that. Do you need to? Can you give us some insight >>there? So there are two things, right? We're always evaluating What are the choices we have from hybrid perspective? Obviously, for us to leverage is right and like all the things you mention about like we have considered them. But there are two things to consider. One is he is a memory system. So he favours a big is the dominant cost. The processor is a person of the cost, but memory is the dominant cost. So what we have evaluated and found is that the current shape which we are using is going to provide our customers with the best price performance. That's the first thing. The second thing is that there are opportunities at times when we can use a specialised processor for vaccinating the world for a bit. But then it becomes a matter of the cost of the customer. Advantage of our current architecture is on the same hardware. Customers are getting very good performance. Very good, energetic performance in a very good machine learning performance. If you will go with the specialised processor, it may. Actually, it's a machine learning, but then it's an additional cost with the customers we need to pay. So we are very sensitive to the customer's request, which is usually to provide very good performance at a very low cost. And we feel is that the current design we have as providing customers very good performance and very good price performance. >>So part of that is architectural. The memory intensive nature of of heat wave. The other is A W s pricing. If AWS pricing were to flip, it might make more sense for you to take advantage of something like like cranium. Okay, great. Thank you. And welcome back to the benchmarks benchmarks. Sometimes they're artificial right there. A car can go from 0 to 60 in two seconds. But I might not be able to experience that level of performance. Do you? Do you have any real world numbers from customers that have used my sequel Heatwave on A W s. And how they look at performance? >>Yes, absolutely so the my Secret service on the AWS. This has been in Vera for, like, since November, right? So we have a lot of customers who have tried the service. And what actually we have found is that many of these customers, um, planning to migrate from Aurora to my secret heat rape. And what they find is that the performance difference is actually much more pronounced than what I was talking about. Because with Aurora, the performance is actually much poorer compared to uh, like what I've talked about. So in some of these cases, the customers found improvement from 60 times, 240 times, right? So he travels 100 for 240 times faster. It was much less expensive. And the third thing, which is you know, a noteworthy is that customers don't need to change their applications. So if you ask the top three reasons why customers are migrating, it's because of this. No change to the application much faster, and it is cheaper. So in some cases, like Johnny Bites, what they found is that the performance of their applications for the complex storeys was about 60 to 90 times faster. Then we had 60 technologies. What they found is that the performance of heat we have compared to Aurora was 100 and 39 times faster. So, yes, we do have many such examples from real workloads from customers who have tried it. And all across what we find is if it offers better performance, lower cost and a single database such that it is compatible with all existing by sequel based applications and workloads. >>Really impressive. The analysts I talked to, they're all gaga over heatwave, and I can see why. Okay, last question. Maybe maybe two and one. Uh, what's next? In terms of new capabilities that customers are going to be able to leverage and any other clouds that you're thinking about? We talked about that upfront, but >>so in terms of the capabilities you have seen, like they have been, you know, non stop attending to the feedback from the customers in reacting to it. And also, we have been in a wedding like organically. So that's something which is gonna continue. So, yes, you can fully expect that people not dressed and continue to in a way and with respect to the other clouds. Yes, we are planning to support my sequel. He tripped on a show, and this is something that will be announced in the near future. Great. >>All right, Thank you. Really appreciate the the overview. Congratulations on the work. Really exciting news that you're moving my sequel Heatwave into other clouds. It's something that we've been expecting for some time. So it's great to see you guys, uh, making that move, and as always, great to have you on the Cube. >>Thank you for the opportunity. >>All right. And thank you for watching this special cube conversation. I'm Dave Volonte, and we'll see you next time.

>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> After a two-year epic run in tech, 2022 has been an epically bad year. Through yesterday, The NASDAQ composite is down 30%. The S$P 500 is off 21%. And the Dow Jones Industrial average 16% down. And the poor holders at Bitcoin have had to endure a nearly 60% decline year to date. But judging by the attendance and enthusiasm, in major in-person tech events this spring. You'd never know that tech was in the tank. Moreover, walking around the streets of Las Vegas, where most tech conferences are held these days. One can't help but notice that the good folks of Main Street, don't seem the least bit concerned that the economy is headed for a recession. Hello, and welcome to this weeks Wiki Bond Cube Insights powered by ETR. In this Breaking Analysis we'll share our main takeaways from the first half of 2022. And talk about the outlook for tech going forward, and why despite some pretty concerning headwinds we remain sanguine about tech generally, but especially enterprise tech. Look, here's the bumper sticker on why many folks are really bearish at the moment. Of course, inflation is high, other than last year, the previous inflation high this century was in July of 2008, it was 5.6%. Inflation has proven to be very, very hard to tame. You got gas at $7 dollars a gallon. Energy prices they're not going to suddenly drop. Interest rates are climbing, which will eventually damage housing. Going to have that ripple effect, no doubt. We're seeing layoffs at companies like Tesla and the crypto names are also trimming staff. Workers, however are still in short supply. So wages are going up. Companies in retail are really struggling with the right inventory, and they can't even accurately guide on their earnings. We've seen a version of this movie before. Now, as it pertains to tech, Crawford Del Prete, who's the CEO of IDC explained this on theCUBE this very week. And I thought he did a really good job. He said the following, >> Matt, you have a great statistic that 80% of companies used COVID as their point to pivot into digital transformation. And to invest in a different way. And so what we saw now is that tech is now where I think companies need to focus. They need to invest in tech. They need to make people more productive with tech and it played out in the numbers. Now so this year what's fascinating is we're looking at two vastly different markets. We got gasoline at $7 a gallon. We've got that affecting food prices. Interesting fun fact recently it now costs over $1,000 to fill an 18 wheeler. All right, based on, I mean, this just kind of can't continue. So you think about it. >> Don't put the boat in the water. >> Yeah, yeah, yeah. Good luck if ya, yeah exactly. So a family has kind of this bag of money, and that bag of money goes up by maybe three, 4% every year, depending upon earnings. So that is sort of sloshing around. So if food and fuel and rent is taking up more, gadgets and consumer tech are not, you're going to use that iPhone a little longer. You're going to use that Android phone a little longer. You're going to use that TV a little longer. So consumer tech is getting crushed, really it's very, very, and you saw it immediately in ad spending. You've seen it in Meta, you've seen it in Facebook. Consumer tech is doing very, very, it is tough. Enterprise tech, we haven't been in the office for two and a half years. We haven't upgraded whether that be campus wifi, whether that be servers, whether that be commercial PCs as much as we would have. So enterprise tech, we're seeing double digit order rates. We're seeing strong, strong demand. We have combined that with a component shortage, and you're seeing some enterprise companies with a quarter of backlog, I mean that's really unheard of. >> And higher prices, which also profit. >> And therefore that drives up the prices. >> And this is a theme that we've heard this year at major tech events, they've really come roaring back. Last year, theCUBE had a huge presence at AWS Reinvent. The first Reinvent since 2019, it was really well attended. Now this was before the effects of the omicron variant, before they were really well understood. And in the first quarter of 2022, things were pretty quiet as far as tech events go But theCUBE'a been really busy this spring and early into the summer. We did 12 physical events as we're showing here in the slide. Coupa, did Women in Data Science at Stanford, Coupa Inspire was in Las Vegas. Now these are both smaller events, but they were well attended and beat expectations. San Francisco Summit, the AWS San Francisco Summit was a bit off, frankly 'cause of the COVID concerns. They were on the rise, then we hit Dell Tech World which was packed, it had probably around 7,000 attendees. Now Dockercon was virtual, but we decided to include it here because it was a huge global event with watch parties and many, many tens of thousands of people attending. Now the Red Hat Summit was really interesting. The choice that Red Hat made this year. It was purposefully scaled down and turned into a smaller VIP event in Boston at the Western, a couple thousand people only. It was very intimate with a much larger virtual presence. VeeamON was very well attended, not as large as previous VeeamON events, but again beat expectations. KubeCon and Cloud Native Con was really successful in Spain, Valencia, Spain. PagerDuty Summit was again a smaller intimate event in San Francisco. And then MongoDB World was at the new Javits Center and really well attended over the three day period. There were lots of developers there, lots of business people, lots of ecosystem partners. And then the Snowflake summit in Las Vegas, it was the most vibrant from the standpoint of the ecosystem with nearly 10,000 attendees. And I'll come back to that in a moment. Amazon re:Mars is the Amazon AI robotic event, it's smaller but very, very cool, a lot of innovation. And just last week we were at HPE Discover. They had around 8,000 people attending which was really good. Now I've been to over a dozen HPE or HPE Discover events, within Europe and the United States over the past decade. And this was by far the most vibrant, lot of action. HPE had a little spring in its step because the company's much more focused now but people was really well attended and people were excited to be there, not only to be back at physical events, but also to hear about some of the new innovations that are coming and HPE has a long way to go in terms of building out that ecosystem, but it's starting to form. So we saw that last week. So tech events are back, but they are smaller. And of course now a virtual overlay, they're hybrid. And just to give you some context, theCUBE did, as I said 12 physical events in the first half of 2022. Just to compare that in 2019, through June of that year we had done 35 physical events. Yeah, 35. And what's perhaps more interesting is we had our largest first half ever in our 12 year history because we're doing so much hybrid and virtual to compliment the physical. So that's the new format is CUBE plus digital or sometimes just digital but that's really what's happening in our business. So I think it's a reflection of what's happening in the broader tech community. So everyone's still trying to figure that out but it's clear that events are back and there's no replacing face to face. Or as I like to say, belly to belly, because deals are done at physical events. All these events we've been to, the sales people are so excited. They're saying we're closing business. Pipelines coming out of these events are much stronger, than they are out of the virtual events but the post virtual event continues to deliver that long tail effect. So that's not going to go away. The bottom line is hybrid is the new model. Okay let's look at some of the big themes that we've taken away from the first half of 2022. Now of course, this is all happening under the umbrella of digital transformation. I'm not going to talk about that too much, you've had plenty of DX Kool-Aid injected into your veins over the last 27 months. But one of the first observations I'll share is that the so-called big data ecosystem that was forming during the hoop and around, the hadoop infrastructure days and years. then remember it dispersed, right when the cloud came in and kind of you know, not wiped out but definitely dampened the hadoop enthusiasm for on-prem, the ecosystem dispersed, but now it's reforming. There are large pockets that are obviously seen in the various clouds. And we definitely see a ecosystem forming around MongoDB and the open source community gathering in the data bricks ecosystem. But the most notable momentum is within the Snowflake ecosystem. Snowflake is moving fast to win the day in the data ecosystem. They're providing a single platform that's bringing different data types together. Live data from systems of record, systems of engagement together with so-called systems of insight. These are converging and while others notably, Oracle are architecting for this new reality, Snowflake is leading with the ecosystem momentum and a new stack is emerging that comprises cloud infrastructure at the bottom layer. Data PaaS layer for app dev and is enabling an ecosystem of partners to build data products and data services that can be monetized. That's the key, that's the top of the stack. So let's dig into that further in a moment but you're seeing machine intelligence and data being driven into applications and the data and application stacks they're coming together to support the acceleration of physical into digital. It's happening right before our eyes in every industry. We're also seeing the evolution of cloud. It started with the SaaS-ification of the enterprise where organizations realized that they didn't have to run their own software on-prem and it made sense to move to SaaS for CRM or HR, certainly email and collaboration and certain parts of ERP and early IS was really about getting out of the data center infrastructure management business called that cloud 1.0, and then 2.0 was really about changing the operating model. And now we're seeing that operating model spill into on-prem workloads finally. We're talking about here about initiatives like HPE's Green Lake, which we heard a lot about last week at Discover and Dell's Apex, which we heard about in May, in Las Vegas. John Furrier had a really interesting observation that basically this is HPE's and Dell's version of outposts. And I found that interesting because outpost was kind of a wake up call in 2018 and a shot across the bow at the legacy enterprise infrastructure players. And they initially responded with these flexible financial schemes, but finally we're seeing real platforms emerge. Again, we saw this at Discover and at Dell Tech World, early implementations of the cloud operating model on-prem. I mean, honestly, you're seeing things like consoles and billing, similar to AWS circa 2014, but players like Dell and HPE they have a distinct advantage with respect to their customer bases, their service organizations, their very large portfolios, especially in the case of Dell and the fact that they have more mature stacks and knowhow to run mission critical enterprise applications on-prem. So John's comment was quite interesting that these firms are basically building their own version of outposts. Outposts obviously came into their wheelhouse and now they've finally responded. And this is setting up cloud 3.0 or Supercloud, as we like to call it, an abstraction layer, that sits above the clouds that serves as a unifying experience across a continuum of on-prem across clouds, whether it's AWS, Azure, or Google. And out to both the near and far edge, near edge being a Lowes or a Home Depot, but far edge could be space. And that edge again is fragmented. You've got the examples like the retail stores at the near edge. Outer space maybe is the far edge and IOT devices is perhaps the tiny edge. No one really knows how the tiny edge is going to play out but it's pretty clear that it's not going to comprise traditional X86 systems with a cool name tossed out to the edge. Rather, it's likely going to require a new low cost, low power, high performance architecture, most likely RM based that will enable things like realtime AI inferencing at that edge. Now we've talked about this a lot on Breaking Analysis, so I'm not going to double click on it. But suffice to say that it's very possible that new innovations are going to emerge from the tiny edge that could really disrupt the enterprise in terms of price performance. Okay, two other quick observations. One is that data protection is becoming a much closer cohort to the security stack where data immutability and air gaps and fast recovery are increasingly becoming a fundamental component of the security strategy to combat ransomware and recover from other potential hacks or disasters. And I got to say from our observation, Veeam is leading the pack here. It's now claiming the number one revenue spot in a statistical dead heat with the Dell's data protection business. That's according to Veeam, according to IDC. And so that space continues to be of interest. And finally, Broadcom's acquisition of Dell. It's going to have ripple effects throughout the enterprise technology business. And there of course, there are a lot of questions that remain, but the one other thing that John Furrier and I were discussing last night John looked at me and said, "Dave imagine if VMware runs better on Broadcom components and OEMs that use Broadcom run VMware better, maybe Broadcom doesn't even have to raise prices on on VMware licenses. Maybe they'll just raise prices on the OEMs and let them raise prices to the end customer." Interesting thought, I think because Broadcom is so P&L focused that it's probably not going to be the prevailing model but we'll see what happens to some of the strategic projects rather like Monterey and Capitola and Thunder. We've talked a lot about project Monterey, the others we'll see if they can make the cut. That's one of the big concerns because it's how OEMs like the ones that are building their versions of outposts are going to compete with the cloud vendors, namely AWS in the future. I want to come back to the comment on the data stack for a moment that we were talking about earlier, we talked about how the big data ecosystem that was once coalescing around hadoop dispersed. Well, the data value chain is reforming and we think it looks something like this picture, where cloud infrastructure lives at the bottom. We've said many times the cloud is expanding and evolving. And if companies like Dell and HPE can truly build a super cloud infrastructure experience then they will be in a position to capture more of the data value. If not, then it's going to go to the cloud players. And there's a live data layer that is increasingly being converged into platforms that not only simplify the movement in ELTing of data but also allow organizations to compress the time to value. Now there's a layer above that, we sometimes call it the super PaaS layer if you will, that must comprise open source tooling, partners are going to write applications and leverage platform APIs and build data products and services that can be monetized at the top of the stack. So when you observe the battle for the data future it's unlikely that any one company is going to be able to do this all on their own, which is why I often joke that the 2020s version of a sweaty Steve Bomber running around the stage, screaming, developers, developers developers, and getting the whole audience into it is now about ecosystem ecosystem ecosystem. Because when you need to fill gaps and accelerate features and provide optionality a list of capabilities on the left hand side of this chart, that's going to come from a variety of different companies and places, we're talking about catalogs and AI tools and data science capabilities, data quality, governance tools and it should be of no surprise to followers of Breaking Analysis that on the right hand side of this chart we're including the four principles of data mesh, which of course were popularized by Zhamak Dehghani. So decentralized data ownership, data as products, self-serve platform and automated or computational governance. Now whether this vision becomes a reality via a proprietary platform like Snowflake or somehow is replicated by an open source remains to be seen but history generally shows that a defacto standard for more complex problems like this is often going to emerge prior to an open source alternative. And that would be where I would place my bets. Although even that proprietary platform has to include open source optionality. But it's not a winner take all market. It's plenty of room for multiple players and ecosystem innovators, but winner will definitely take more in my opinion. Okay, let's close with some ETR data that looks at some of those major platform plays who talk a lot about digital transformation and world changing impactful missions. And they have the resources really to compete. This is an XY graphic. It's a view that we often show, it's got net score on the vertical access. That's a measure of spending momentum, and overlap or presence in the ETR survey. That red, that's the horizontal access. The red dotted line at 40% indicates that the platform is among the highest in terms of spending velocity. Which is why I always point out how impressive that makes AWS and Azure because not only are they large on the horizontal axis, the spending momentum on those two platforms rivals even that of Snowflake which continues to lead all on the vertical access. Now, while Google has momentum, given its goals and resources, it's well behind the two leaders. We've added Service Now and Salesforce, two platform names that have become the next great software companies. Joining likes of Oracle, which we show here and SAP not shown along with IBM, you can see them on this chart. We've also plotted MongoDB, which we think has real momentum as a company generally but also with Atlas, it's managed cloud database as a service specifically and Red Hat with trying to become the standard for app dev in Kubernetes environments, which is the hottest trend right now in application development and application modernization. Everybody's doing something with Kubernetes and of course, Red Hat with OpenShift wants to make that a better experience than do it yourself. The DYI brings a lot more complexity. And finally, we've got HPE and Dell both of which we've talked about pretty extensively here and VMware and Cisco. Now Cisco is executing on its portfolio strategy. It's got a lot of diverse components to its company. And it's coming at the cloud of course from a networking and security perspective. And that's their position of strength. And VMware is a staple of the enterprise. Yes, there's some uncertainty with regards to the Broadcom acquisition, but one thing is clear vSphere isn't going anywhere. It's entrenched and will continue to run lots of IT for years to come because it's the best platform on the planet. Now, of course, these are just some of the players in the mix. We expect that numerous non-traditional technology companies this is important to emerge as new cloud players. We've put a lot of emphasis on the data ecosystem because to us that's really going to be the main spring of digital, i.e., a digital company is a data company and that means an ecosystem of data partners that can advance outcomes like better healthcare, faster drug discovery, less fraud, cleaner energy, autonomous vehicles that are safer, smarter, more efficient grids and factories, better government and virtually endless litany of societal improvements that can be addressed. And these companies will be building innovations on top of cloud platforms creating their own super clouds, if you will. And they'll come from non-traditional places, industries, finance that take their data, their software, their tooling bring them to their customers and run them on various clouds. Okay, that's it for today. Thanks to Alex Myerson, who is on production and does the podcast for Breaking Analysis, Kristin Martin and Cheryl Knight, they help get the word out. And Rob Hoofe is our editor and chief over at Silicon Angle who helps edit our posts. Remember all these episodes are available as podcasts wherever you listen. All you got to do is search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. You can email me directly at david.vellante@siliconangle.com or DM me at dvellante, or comment on my LinkedIn posts. And please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE's Insights powered by ETR. Thanks for watching be well. And we'll see you next time on Breaking Analysis. (upbeat music)

>>Hi, this is Dave Volonte. We're winding down Day two of the Cubes coverage of Vim on 2022. We're here at the area in Las Vegas. Myself and Dave Nicholson had been going for two days. Everybody's excited about the VM on party tonight. It's It's always epic, and, uh, it's a great show in terms of its energy. Danny Allen is here. He's cto of in his back. He gave the keynote this morning. I say, Danny, you know, you look pretty good up there with two hours of sleep. I >>had three. >>Look, don't look that good, but your energy was very high. And I got to tell you the story you told was amazing. It was one of the best keynotes I've ever seen. Even even the technology pieces were outstanding. But you weaving in that story was incredible. I'm hoping that people will go back and and watch it. We probably don't have time to go into it, but wow. Um, can you give us the the one minute version of that >>long story? >>Sure. Yeah. I read a book back in 2013 about a ship that sank off Portsmouth, Maine, and I >>thought, I'm gonna go find that >>ship. And so it's a long, >>complicated process. Five >>years in the making. But we used data, and the data that found the ship was actually from 15 years earlier. >>And in 20 >>18, we found the bow of the ship. We found the stern of the ship, but what we were really trying to answer was torpedoed. Or did the boilers explode? Because >>the navy said the boilers exploded >>and two survivors said, No, it was torpedoed or there was a German U boat there. >>And so >>our goal was fine. The ship find the boiler. >>So in 20 >>19, Sorry, Uh, it was 2018. We found the bow and the stern. And then in 2019, we found both boilers perfectly intact. And in fact, the rear end of that torpedo wasn't much left >>of it, of course, but >>data found that wreck. And so it, um, it exonerated essentially any implication that somebody screwed >>up in >>the boiler system and the survivors or the Children of the survivors obviously appreciated >>that. I'm sure. Yes, Several >>outcomes to it. So the >>chief engineer was one >>of the 13 survivors, >>and he lived with the weight of this for 75 years. 49 sailors dead because of myself. But I had the opportunity of meeting some of the Children of the victims and also attending ceremonies. The families of those victims received purple hearts because they were killed due to enemy action. And then you actually knew how to do this. I wasn't aware you had experience finding Rex. You've >>discovered several of >>them prior to this one. But >>the interesting connection >>the reason why this keynote was so powerful as we're a >>team, it's a data conference. >>You connected that to data because you you went out and bought a How do you say this? Magnanimous magnetometer. Magnetometer, Magnetometer. I don't know what that >>is. And a side >>scan Sonar, Right? I got that right. That was >>easy. But >>then you know what this stuff is. And then you >>built the model >>tensorflow. You took all the data and you found anomalies. And then you went right to that spot. Found the >>wreck with 12 >>£1000 of dynamite, >>which made your heart >>beat. But >>then you found >>the boilers. That's incredible. And >>but the point was, >>this is data >>uh, let's see, >>a lot of years after, >>right? >>Yeah. Two sets of data were used. One was the original set of side scan sonar >>data by the historian >>who discovered there was a U boat in the area that was 15 years old. >>And then we used, of >>course, the wind and weather and wave pattern data that was 75 years old to figure out where the boiler should be because they knew that the ship had continued to float for eight minutes. And so you had to go back and determine the models of where should the boilers >>be if it exploded and the boilers >>dropped out and it floated along >>for eight minutes and then sank? Where was >>that data? >>It was was a scanned was an electronic was a paper. How did you get that data? So the original side scan sonar data was just hard >>drive >>data by the historian. >>I wish I could say he used them to >>back it up. But I don't know that I can say that. But he still had >>the data. 15 years later, the >>weather and >>wind and wave data, That was all public information, and we actually used that extensively. We find other wrecks. A lot of wrecks off Boston Sunken World War Two. So we were We were used to that model of tracking what happened. Wow. So, yes, imagine if that data weren't available >>and it >>probably shouldn't have been right by all rights. So now fast forward to 2022. We've got Let's talk about just a cloud >>data. I think you said a >>couple of 100 >>petabytes in the >>cloud 2019. 500 in, Uh, >>no. Yeah. In >>20 2200 and 42. Petabytes in 20 2500 Petabytes last year. And we've already done the same as 2020. So >>240 petabytes >>in Q one. I expect >>this year to move an exhibit of >>data into the public cloud. >>Okay, so you got all that data. Who knows what's in there, right? And if it's not protected, who's going to know in 50 60 7100 years? Right. So that was your tie in? Yes. To the to the importance of data protection, which was just really, really well done. Congratulations. Honestly, one of the best keynotes I've ever seen keynotes often really boring, But you did a great job again on two hours. Sleep. So much to unpack here. The other thing that really is. I mean, we can talk about the demos. We can talk about the announcements. Um, so? Well, yeah, Let's see. Salesforce. Uh, data protection is now public. I almost spilled the beans yesterday in the cube. Caught myself the version 12. Obviously, you guys gave a great demo showing the island >>cloud with I think it >>was just four minutes. It was super fast. Recovery in four minutes of data loss was so glad you didn't say zero minutes because that would have been a live demos which, Okay, which I appreciate and also think is crazy. So some really cool demos, Um, and some really cool features. So I have so much impact, but the the insights that you can provide through them it's VM one, uh, was actually something that I hadn't heard you talk about extensively in the past. That maybe I just missed it. But I wonder if you could talk about that layer and why it's critical differentiator for Wien. It's >>the hidden gem >>within the Wien portfolio because it knows about absolutely >>everything. >>And what determines the actions >>that we take is the >>context in which >>data is surviving. So in the context of security, which we are showing, we look for CPU utilisation, memory utilisation, data change rate. If you encrypt all of the data in a file server, it's going to blow up overnight. And so we're leveraging heuristics in their reporting. But even more than that, one of the things in Wien one people don't realise we have this concept of the intelligent diagnostics. It's machine learning, which we drive on our end and we push out as packages intervene one. There's up to 200 signatures, but it helps our customers find issues before they become issues. Okay, so I want to get into because I often time times, don't geek out with you. And don't take advantage of your your technical knowledge. And you've you've triggered a couple of things, >>especially when the >>analysts call you said it again today that >>modern >>data protection has meaning to you. We talked a little bit about this yesterday, but back in >>the days of >>virtualisation, you shunned agents >>and took a different >>approach because you were going for what was then >>modern. Then you >>went to bare metal cloud hybrid >>cloud containers. Super Cloud. Using the analyst meeting. You didn't use the table. Come on, say Super Cloud and then we'll talk about the edge. So I would like to know specifically if we can go back to Virtualised >>because I didn't know >>this exactly how you guys >>defined modern >>back then >>and then. Let's take that to modern today. >>So what do you >>do back then? And then we'll get into cloud and sure, So if you go back to and being started, everyone who's using agents, you'd instal something in the operating system. It would take 10% 15% of your CPU because it was collecting all the data and sending it outside of the machine when we went through a virtual environment. If you put an agent inside that machine, what happens is you would have 100 operating systems all on the same >>server, consuming >>resources from that hyper visor. And so he said, there's a better way of capturing the data instead of capturing the data inside the operating system. And by the way, managing thousands of agents is no fun. So What we did is we captured a snapshot of the image at the hyper visor level. And then over time, we just leverage changed block >>tracking from the hyper >>visor to determine what >>had changed. And so that was modern. Because no more >>managing agents >>there was no impact >>on the operating system, >>and it was a far more >>efficient way to store >>data. You leverage CBT through the A P. Is that correct? Yeah. We used the VCR API >>for data protection. >>Okay, so I said this to Michael earlier. Fast forward to today. Your your your data protection competitors aren't as fat, dumb and happy as they used to be, so they can do things in containers, containers. And we talked about that. So now let's talk about Cloud. What's different about cloud data protection? What defines modern data protection? And where are the innovations that you're providing? >>Let me do one step in >>between those because one of the things that happened between hypervisors and Cloud was >>offline. The capture of the data >>to the storage system because >>even better than doing it >>at the hyper visor clusters >>do it on the storage >>array because that can capture the >>data instantly. Right? So as we go to the cloud, we want to do the same thing. Except we don't have access to either the hyper visor or the storage system. But what they do provide is an API. So we can use the API to capture all of the blocks, all of the data, all of the changes on that particular operating system. Now, here's where we've kind of gone full circle on a hyper >>visor. You can use the V >>sphere agent to reach into the operating system to do >>things like application consistency. What we've done modern data protection is create specific cloud agents that say Forget >>about the block changes. Make sure that I have application consistency inside that cloud operating >>system. Even though you don't have access to the hyper visor of the storage, >>you're still getting the >>operating system consistency >>while getting the really >>fast capture of data. So that gets into you talking on stage about how synapse don't equal data protection. I think you just explained it, but explain why, but let me highlight something that VM does that is important. We manage both snapshots and back up because if you can recover from your storage array >>snapshot. That is the best >>possible thing to recover from right, But we don't. So we manage both the snapshots and we converted >>into the VM portable >>data format. And here's where the super cloud comes into play because if I can convert it into the VM portable data format, I can move >>that OS >>anywhere. I can move it from >>physical to virtual to cloud >>to another cloud back to virtual. I can put it back on physical if I want to. It actually abstracts >>the cloud >>layer. There are things >>that we do when we go >>between clouds. Some use bio, >>some use, um, fee. >>But we have the data in backup format, not snapshot format. That's theirs. But we have been in backup format that we can move >>around and abstract >>workloads across. All of the infrastructure in your >>catalogue is control >>of that. Is that Is >>that right? That is about >>that 100%. And you know what's interesting about our catalogue? Dave. The catalogue is inside the backup, and so historically, one of the problems with backup is that you had a separate catalogue and if it ever got corrupted. All of your >>data is meaningless >>because the catalogue is inside >>the backup >>for that unique VM or that unique instance, you can move it anywhere and power it on. That's why people said were >>so reliable. As long >>as you have the backup file, you can delete our >>software. You can >>still get the data back, so I love this fast paced so that >>enables >>what I call Super Cloud we now call Super Cloud >>because now >>take that to the edge. >>If I want to go to the edge, I presume you can extend that. And I also presume the containers play a role there. Yes, so here's what's interesting about the edge to things on the edge. You don't want to have any state if you can help it, >>and so >>containers help with that. You can have stateless environment, some >>persistent data storage, >>but we not only >>provide the portability >>in operating systems. We also do this for containers, >>and that's >>true if you go to the cloud and you're using SE CKs >>with relational >>database service is already >>asked for the persistent data. >>Later, we can pick that up and move it to G K E or move it to open shift >>on premises. And >>so that's why I call this the super cloud. We have all of this data. Actually, I think you termed the term super thank you for I'm looking for confirmation from a technologist that it's technically feasible. It >>is technically feasible, >>and you can do it today and that's a I think it's a winning strategy. Personally, Will there be >>such a thing as edge Native? You know, there's cloud native. Will there be edge native new architectures, new ways of doing things, new workloads use cases? We talk about hardware, new hardware, architectures, arm based stuff that are going to change everything to edge Native Yes and no. There's going to be small tweaks that make it better for the edge. You're gonna see a lot of iron at the edge, obviously for power consumption purposes, and you're also going to see different constructs for networking. We're not going to use the traditional networking, probably a lot more software to find stuff. Same thing on the storage. They're going to try and >>minimise the persistent >>storage to the smallest footprint possible. But ultimately I think we're gonna see containers >>will lead >>the edge. We're seeing this now. We have a I probably can't name them, but we have a large retail organisation that is running containers in every single store with a small, persistent footprint of the point of sale and local data, but that what >>is running the actual >>system is containers, and it's completely ephemeral. So we were >>at Red Hat, I was saying >>earlier last week, and I'd say half 40 50% of the conversation was edge open shift, obviously >>playing a big role there. I >>know doing work with Rancher and Town Zoo. And so there's a lot of options there. >>But obviously, open shift has >>strong momentum in the >>marketplace. >>I've been dominating. You want to chime in? No, I'm just No, >>I yeah, I know. Sometimes >>I'll sit here like a sponge, which isn't my job absorbing stuff. I'm just fascinated by the whole concept of of a >>of a portable format for data that encapsulates virtual machines and or instances that can live in the containerised world. And once you once you create that common denominator, that's really that's >>That's the secret sauce >>for what you're talking about is a super club and what's been fascinating to watch because I've been paying attention since the beginning. You go from simply V. M. F s and here it is. And by the way, the pitch to E. M. C. About buying VM ware. It was all about reducing servers to files that can be stored on storage arrays. All of a sudden, the light bulbs went off. We can store those things, and it just began. It became it became a marriage afterwards. But to watch that progression that you guys have gone from from that fundamental to all of the other areas where now you've created this common denominator layer has has been amazing. So my question is, What's the singer? What doesn't work? Where the holes. You don't want to look at it from a from a glass half empty perspective. What's the next opportunity? We've talked about edge, but what are the things that you need to fill in to make this truly ubiquitous? Well, there's a lot of services out there that we're not protecting. To be fair, right, we do. Microsoft 3 65. We announced sales for us, but there's a dozen other paths services that >>people are moving data >>into. And until >>we had data protection >>for the assassin path services, you know >>you have to figure out how >>to protect them. Now here's the kicker about >>those services. >>Most of them have the >>ability to dump date >>out. The trick is, do they have the A >>P? I is needed to put data >>back into it right, >>which is which is a >>gap. As an industry, we need to address this. I actually think we need a common >>framework for >>how to manage the >>export of data, but also the import of data not at a at a system level, but at an atomic level of the elements within those applications. >>So there are gaps >>there at the industry, but we'll fill them >>if you look on the >>infrastructure side. We've done a lot with containers and kubernetes. I think there's a next wave around server list. There's still servers and these micro services, but we're making things smaller and smaller and smaller, and there's going to be an essential need to protect those services as well. So modern data protection is something that's going to we're gonna need modern data protection five years from now, the modern will just be different. Do you ever see the day, Danny, where governance becomes an >>adjacency opportunity for >>you guys? It's clearly an opportunity even now if you look, we spent a lot of time talking about security and what you find is when organisations go, for example, of ransomware insurance or for compliance, they need to be able to prove that they have certifications or they have security or they have governance. We just saw transatlantic privacy >>packed only >>to be able to prove what type of data they're collecting. Where are they storing it? Where are they allowed to recovered? And yes, those are very much adjacency is for our customers. They're trying to manage that data. >>So given that I mean, >>am I correct that architecturally you are, are you location agnostic? Right. We are a location agnostic, and you can actually tag data to allowable location. So the big trend that I think is happening is going to happen in in this >>this this decade. >>I think we're >>scratching the surface. Is this idea >>that, you know, leave data where it is, >>whether it's an S three >>bucket, it could be in an Oracle >>database. It could be in a snowflake database. It can be a data lake that's, you know, data, >>bricks or whatever, >>and it stays where >>it is. And it's just a note on the on the call of the data >>mesh. Not my term. Jim >>Octagon coined that term. The >>problem with that, and it puts data in the hands of closer to the domain experts. The problem with that >>scenario >>is you need self service infrastructure, which really doesn't exist today anyway. But it's coming, and the big problem is Federated >>computational >>governance. How do I automate that governance so that the people who should have access to that it can have access to that data? That, to me, seems to be an adjacency. It doesn't exist except in >>a proprietary >>platform. Today. There needs to be a horizontal >>layer >>that is more open than anybody >>can use. And I >>would think that's a perfect opportunity for you guys. Just strategically it is. There's no question, and I would argue, Dave, that it's actually >>valuable to take snapshots and to keep the data out at the edge wherever it happens to be collected. But then Federated centrally. It's why I get so excited by an exhibit of data this year going into the cloud, because then you're centralising the aggregation, and that's where you're really going to drive the insights. You're not gonna be writing tensorflow and machine learning and things on premises unless you have a lot of money and a lot of GPS and a lot of capacity. That's the type of thing that is actually better suited for the cloud. And, I would argue, better suited for not your organisation. You're gonna want to delegate that to a third party who has expertise in privacy, data analysis or security forensics or whatever it is that you're trying to do with the data. But you're gonna today when you think about AI. We talked about A. I haven't had a tonne of talk about AI some >>appropriate >>amount. Most of the >>AI today correct me if you think >>this is not true is modelling that's done in the cloud. It's dominant. >>Don't >>you think that's gonna flip when edge >>really starts to take >>off where it's it's more real time >>influencing >>at the edge in new use cases at the edge now how much of that data >>is going to be >>persisted is a >>point of discussion. But what >>are your thoughts on that? I completely agree. So my expectation of the way >>that this will work is that >>the true machine learning will happen in the centralised location, and what it will do is similar to someone will push out to the edge the signatures that drive the inferences. So my example of this is always the Tesla driving down the road. >>There's no way that that >>car should be figuring it sending up to the cloud. Is that a stop sign? Is it not? It can't. It has to be able to figure out what the stop sign is before it gets to it, so we'll do the influencing at the edge. But when it doesn't know what to do with the data, then it should send it to the court to determine, to learn about it and send signatures back out, not just to that edge location, but all the edge locations within the within the ecosystem. So I get what you're saying. They might >>send data back >>when there's an anomaly, >>or I always use the example of a deer running in front of the car. David Floyd gave me that one. That's when I want to. I do want to send the data back to the cloud because Tesla doesn't persist. A tonne of data, I presume, right, right less than 5% of it. You know, I want to. Usually I'm here to dive into the weeds. I want kind of uplevel this >>to sort of the >>larger picture. From an I T perspective. >>There's been a lot of consolidation going on if you divide the >>world into vendors >>and customers. On the customer side, there are only if there's a finite number of seats at the table for truly strategic partners. Those get gobbled up often by hyper >>scale cloud >>providers. The challenge there, and I'm part of a CEO accreditation programme. So this >>is aimed at my students who >>are CEOs and CIOs. The challenge is that a lot of CEOs and CIOs on the customer side don't exhaustively drag out of their vendor partners like a theme everything that Saveem >>can do for >>them. Maybe they're leveraging a point >>solution, >>but I guarantee you they don't all know that you've got cast in in the portfolio. Not every one of them does yet, let alone this idea of a super >>cloud and and and >>how much of a strategic role you can play. So I don't know if it's a blanket admonition to folks out there, but you have got to leverage the people who are building the solutions that are going to help you solve problems in the business. And I guess, as in the form of >>a question, >>uh, do you Do you see that as a challenge? Now those the limited number of seats at >>the Table for >>Strategic Partners >>Challenge and >>Opportunity. If you look at the types of partners that we've partnered with storage partners because they own the storage of the data, at the end of the day, we actually just manage it. We don't actually store it the cloud partners. So I see that as the opportunity and my belief is I thought that the storage doesn't matter, >>but I think the >>organisation that can centralise and manage that data is the one that can rule the world, and so >>clearly I'm a team. I think we can do amazing things, but we do have key >>strategic partners hp >>E Amazon. You heard >>them on stage yesterday. >>18 different >>integrations with AWS. So we have very strategic partners. Azure. I go out there all the time. >>So there >>you don't need to be >>in the room at the table because your partners are >>and they have a relationship with the customer as well. Fair enough. But the key to this it's not just technology. It is these relationships and what is possible between our organisations. So I'm sorry to be >>so dense on this, but when you talk about >>centralising that data you're talking about physically centralising it or can actually live across clouds, >>for instance. But you've got >>visibility and your catalogues >>have visibility on >>all that. Is that what you mean by centralised obliterated? We have understanding of all the places that lives, and we can do things with >>it. We can move it from one >>cloud to another. We can take, you know, everyone talks about data warehouses. >>They're actually pretty expensive. >>You got to take data and stream it into this thing, and there's a massive computing power. On the other hand, we're >>not like that. You've storage on there. We can ephemeral e. Spin up a database when you need it for five minutes and then destroy it. We can spin up an image when you need it and then destroy it. And so on your perspective of locations. So irrespective of >>location, it doesn't >>have to be in a central place, and that's been a challenge. You extract, >>transform and load, >>and moving the data to the central >>location has been a problem. We >>have awareness of >>all the data everywhere, >>and then we can make >>decisions as to what you >>do based >>on where it is and >>what it is. And that's a metadata >>innovation. I guess that >>comes back to the catalogue, >>right? Is that correct? >>You have data >>about the data that informs you as to where it is and how to get to it. And yes, so metadata within the data that allows you to recover it and then data across the federation of all that to determine where it is. And machine intelligence plays a role in all that, not yet not yet in that space. Now. I do think there's opportunity in the future to be able to distribute storage across many different locations and that's a whole conversation in itself. But but our machine learning is more just on helping our customers address the problems in their infrastructures rather than determining right now where that data should be. >>These guys they want me to break, But I'm >>refusing. So your >>Hadoop back >>in their rooms via, um that's >>well, >>that scale. A lot of customers. I talked to Renee Dupuis. Hey, we we got there >>was heavy lift. You >>know, we're looking at new >>ways. New >>approaches, uh, going. And of course, it's all in the cloud >>anyway. But what's >>that look like? That future look like we haven't reached bottle and X ray yet on our on our Hadoop clusters, and we do continuously examine >>them for anomalies that might happen. >>Not saying we won't run into a >>bottle like we always do at some >>point, But we haven't yet >>awesome. We've covered a lot of We've certainly covered extensively the research that you did on cyber >>security and ransomware. Um, you're kind of your vision for modern >>data protection. I think we hit on that pretty well casting, you know, we talked to Michael about that, and then, you know, the future product releases the Salesforce data protection. You guys, I think you're the first there. I think you were threatened at first from Microsoft. 3 65. No, there are other vendors in the in the salesforce space. But what I tell people we weren't the first to do data capture at the hyper >>visor level. There's two other >>vendors I won't tell you they were No one remembers them. Microsoft 3 65. We weren't the first one to for that, but we're now >>the largest. So >>there are other vendors in the salesforce space. But we're looking at We're going to be aggressive. Danielle, Thanks >>so much for coming to Cuba and letting us pick your brain like that Really great job today. And congratulations on >>being back >>in semi normal. Thank you for having me. I love being on all right. And thank you for watching. Keep it right there. More coverage. Day volonte for Dave >>Nicholson, By >>the way, check out silicon angle dot com for all the written coverage. All the news >>The cube dot >>net is where all these videos We'll we'll live. Check out wiki bond dot com I published every week. I think I'm gonna dig into the cybersecurity >>research that you guys did this week. If I can >>get a hands my hands on those charts which Dave Russell promised >>me, we'll be right back >>right after this short break. Mm.

(light music playing) >> Welcome back to VEEAMON 2022 in Las Vegas. We're at the Aria. This is theCUBE and we're covering two days of VEEAMON. We've done a number of VEEAMONs before, we did Miami, we did New Orleans, we did Chicago and we're, we're happy to be back live after two years of virtual VEEAMONs. I'm Dave Vellante. My co-host is David Nicholson. Eric Herzog is here. You think he's, Eric's been on theCUBE, I think more than any other guest, including Pat Gelsinger, who at one point was the number one guest. Eric Herzog, CMO of INFINIDAT great to see you again. >> Great, Dave, thank you. Love to be on theCUBE. And of course notice my Hawaiian shirt, except I now am supporting an INFINIDAT badge on it. (Dave laughs) Look at that. >> Is that part of the shirt or is that a clip-on? >> Ah, you know, one of those clip-ons but you know, it looks good. Looks good. >> Hey man, what are you doing at VEEAMON? I mean, you guys started this journey into data protection several years ago. I remember we were actually at one of their competitors' events when you first released it, but tell us what's going on with Veeam. >> So we do a ton of stuff with Veeam. We do custom integration. We got some integration on the snapshotting side, but we do everything and we have a purpose built backup appliance known as InfiniGuard. It works with Veeam. We also actually have some customers who use our regular primary storage device as a backup target. The InfiniGuard product will do the data reduction, the dedupe compression, et cetera. The standard product does not, it's just a standard high performance array. We will compress the data, but we have customers that do it either way. We have a couple customers that started with the InfiniBox and then transitioned to the InfiniGuard, realizing that why would you put it on regular storage? Why not go to something that's customized for it? So we do that. We do stuff in the field with them. We've been at all the VEEAMONs since the, since like, I think the second one was the first one we came to. We're doing the virtual one as well as the live one. So we've got a little booth inside, but we're also doing the virtual one today as well. So really strong work with Veeam, particularly at the field level with the sales guys and in the channel. >> So when INFINIDAT does something, you guys go hardcore, high end, fast recovery, you just, you know, reliable, that's kind of your brand. Do you see this movement into data protection as kind of an adjacency to your existing markets? Is it a land and expand strategy? Can you kind of explain the strategy there. >> Ah, so it's actually for us a little bit of a hybrid. So we have several accounts that started with InfiniBox and now have gone with the InfiniGuard. So they start with primary storage and go with secondary storage/modern data protection. But we also have, in fact, we just got a large PO from a Fortune 50, who was buying the InfiniGuard first and now is buying our InfiniBox. >> Both ways. Okay. >> All flash array. And, but they started with backup first and then moved to, so we've got them moving both directions. And of course, now that we have a full portfolio, our original product, the InfiniBox, which was a hybrid array, outperformed probably 80 to 85% of the all flash arrays, 'cause the way we use DRAM. And what's so known as our mural cash technology. So we could do very well, but there is about, you know, 15, 20% of the workloads we could not outperform the competition. So then we had an all flash array and purpose built backup. So we can do, you know, what I'll say is standard enterprise storage, high performance enterprise storage. And then of course, modern data protection with our partnerships such as what we do with Veeam and we've incorporated across the entire portfolio, intense cyber resilience technology. >> Why does the world, Eric, need another purpose built backup appliance? What do you guys bring that is filling a gap in the marketplace? >> Well, the first thing we brought was much higher performance. So when you look at the other purpose built backup appliances, it's been about our ability to have incredibly high performance. The second area has been CapEx and OpEx reduction. So for example, we have a cloud service provider who happens to be in South Africa. They had 14 purpose built backup appliances from someone else, seven in one data center and seven in another. Now they have two InfiniGuards, one in each data center handling all of their backup. You know, they're selling backup as a service. They happen to be using Veeam as well as one other backup company. So if you're the cloud provider from their perspective, they just dramatically reduce their CapEx and OpEx. And of course they've made it easier for them. So that's been a good story for us, that ability to consolidation, whether it be on primary storage or secondary storage. We have a very strong play with cloud providers, particularly those meeting them in small that have to compete with the hyperscalers right. They don't have the engineering of Amazon or Google, right? They can't compete with what the Azure guys have got, but because the way both the InfiniGuard and the InfiniBox work, they could dramatically consolidate workloads. We probably got 30 or 40 midsize and actually several members of the top 10 telcos use us. And when they do their clouds, both their internal cloud, but actually the clouds that are actually running the transmissions and the traffic, it actually runs on InfiniBox. One of them has close to 200 petabytes of InfiniBox and InfiniBox, all flash technology running one of the largest telcos on the planet in a cloud configuration. So all that's been very powerful for us in driving revenue. >> So phrases of the week have been air gap, logical air gap, immutable. Where does InfiniGuard fit into that universe? And what's the profile of the customer that's going to choose InfiniGuard as the target where they're immutable, Write Once Read Many, data is going to live. >> So we did, we announced our InfiniSafe technology first on the InfiniGuard, which actually earlier this year. So we have what I call the four legs of the stool of cyber resilience. One is immutable snapshots, but that's only part of it. Second is logical air gapping, and we can do both local and remote and we can provide and combine local with remote. So for example, what that air gap does is separate the management plane from the actual data plane. Okay. So in this case, the Veeam data backup sets. So the management cannot touch that immutable, can't change it, can't delete it. can't edit it. So management is separated once you start and say, I want to do an immutable snap of two petabytes of Veeam backup dataset. Then we just do that. And the air gap does it, but then you could take the local air gap because as you know, from inception to the end of an attack can be close to 300 days, which means there could be a fire. There could be a tornado, there could be a hurricane, there could be an earthquake. And in the primary data center, So you might as well have that air gap just as you would do- do a remote for disaster recovery and business continuity. Then we have the ability to create a fenced forensic environment to evaluate those backup data sets. And we can do that actually on the same device. That is the purpose built backup appliance. So when you look at the architectural, these are public from our competitors, including the guys that are in sort of Hopkinton/Austin, Texas. You can see that they show a minimum of two physical devices. And in many cases, a third, we can do that with one. So not only do we get the fence forensic environment, just like they do, but we do it with reduction, both CapEx and OpEx. Purpose built backup is very high performance. And then the last thing is our ability to recover. So some people talk about rapid recovery, I would say, they dunno what they're talking about. So when we launched the InfiniGuard with InfiniSafe, we did a live demo, 1.5 petabytes, a Veeam backup dataset. We recovered it in 12 minutes. So once you've identified and that's on the InfiniGuard. On the InfiniBox, once you've identified a good copy of data to do the recovery where you're free of malware ransomware, we can do the recovery in three to five seconds. >> Okay. >> So really, really quick. Actually want to double click on something because people talk about immutable copies, immutable snapshots in particular, what have the actual advances been? I mean, is this simply a setting that maybe we didn't set for retention at some time in the past, or if you had to engineer something net new into a system so to provide that logical air gap. >> So what's net new is the air gapping part. Immutable snapshots have been around, you know, before we were on screen, you talked about WORM, Write Once Read Many. Well, since I'm almost 70 years old, I actually know what that means. When you're 30 or 40 or 50, you probably don't even know what a WORM is. Okay. And the real use of immutable snapshots, it was to replace WORM which was an optical technology. And what was the primary usage? Regulatory and compliance, healthcare, finance and publicly traded companies that were worried about. The SEC or the EU or the Japanese finance ministry coming down on them because they're out of compliance and regulatory. That was the original use of immutable snap. Then people were, well, wait a second. Malware ransomware could attack me. And if I got something that's not changeable, that makes it tougher. So the real magic of immutability was now creating the air gap part. Immutability has been around, I'd say 25 years. I mean, WORMs sort of died back when I was at Mac store the first time. So that was 1990-ish is when WORMs sort of fell away. And there have been immutable snapshots from most of the major storage vendors, as well as a lot of the small vendors ever since they came out, it's kind of like a checkbox item because again, regulatory and compliance, you're going to sell to healthcare, finance, public trade. If you don't have the immutable snapshot, then they don't have their compliance and regulatory for SEC or tax purposes, right? With they ever end up in an audit, you got to produce data. And no one's using a WORM drive anymore to my knowledge. >> I remember the first storage conference I ever went to was in Monterey. It had me in the early 1980s, 84 maybe. And it was a optical disc drive conference. The Jim Porter of optical. >> Yep. (laughs) >> I forget what the guy's name was. And I remember somebody coming up to me, I think it was like Bob Payton rest his soul, super smart strategy guy said, this is never going to happen because of the cost and that's what it was. And now you've got that capability on flash, you know, hard disk, et cetera. >> Right. >> So the four pillars, immutability, the air gap, both local and remote, the fence forensics and the recovery speed. Right? >> Right. Pick up is one thing. Recovery is everything. Those are the four pillars, right? >> Those are the four things. >> And your contention is that those four things together differentiate you from the competition. You mentioned, you know, the big competition, but how unique is this in the marketplace, those capabilities and how difficult is it to replicate? >> So first of all, if someone really puts their engineering hat to it, it's not that hard to replicate. It takes a while. Particularly if you're doing an enterprise, for example, our solutions all have a hundred percent availability guarantee. That's hard to do. Most guys have seven nines. >> That's hard. >> We really will guarantee a hundred percent availability. We offer an SLA that's included when you buy. We don't charge extra for it. It's like if you want it, like you just get it. Second thing is really making sure on the recovery side is the hardest part, particularly on a purpose built backup appliance. So when you look at other people and you delve into their public material, press releases, white paper, support documentation. No one's talking about. Yeah, we can take a 1.5 petabyte Veeam backup data set and make it available in 12 minutes and 12 seconds, which was the exact time that we did on our live demo when we launched the product in February of 2022. No one's talking that. On primary storage, you're hearing some of the vendors such as my old employer that also who, also starts with an "I", talk about a recovery time of two to three hours once you have a known good copy. On primary storage, once we have a known good copy, we're talking three to five seconds for that copy to be available. So that's just sort of the power of the snapshot technology, how we manage our metadata and what we've done, which previous to cyber resiliency, we were known for our replication capability and our snapshot capability from an enterprise class data store. That's what people said. INFINIDAT really knows how to do the replication snapshot. I remember our founder was one of the technical founders of EMC for a product known as the Symmetric, which then became the DMAX, the VMAX and is now is the PowerMax. That was invented by the guy who founded INFINIDAT. So that team has the real chops at enterprise high-end storage to the global fortune 2000. And what are the key feature checkbox items they need that's in both the InfiniBox and also in the InfiniGuard. >> So the business case for cyber resiliency is changing. As Dave said, we've had a big dose last several months, you know, couple years actually, of the importance of cyber resiliency, given all the ransomware tax, et cetera. But it sounds like the business case is shifting really focused on avoiding that risk, avoiding that downtime time versus the cost. The cost is always important. I mean, you got a consolidation play here, right? >> Yeah, yeah. >> Dedupe, does dedupe come into play? >> So on the InfiniGuard we do both dedupe and compression. On the InfiniBox we only do compression. So we do have data reduction. It depends on which product you're using from a Veeam perspective. Most of that now is with the InfiniGuard. So you get the block level dedupe and you get compression. And if you can do both, depending on the data set, we do both. >> How does that affect recovery time? >> Yeah, good question. >> So it doesn't affect recovery times. >> Explain why. >> So first of all, when you're doing a backup data set, the final final recovery, you recovered the backup data set, whether it's Veeam or one of their competitors, you actually make it available to the backup administrator to do a full restore of a backup data set. Okay. So in that case, we get it ready and expose it to the Veeam admin or some other backup admin. And then they launch the Veeam software or the other software and do a restore. Okay. So it's really a two step process on the secondary storage model and actually three. First identifying a known good backup copy. Second then we recover, which is again 12, 13 minutes. And then the backup admin's got to do a, you know, a restore of the backup 'cause it's backup data set in the format of backup, which is different from every backup vendor. So we support that. We get it ready to go. And then whether it's a Veeam backup administrator and quite honestly, from our perspective, most of our customers in the global fortune 2000, 25% of the fortune 50 use INIFINIDAT products. 25% and we're a tiny company. So we must have some magic fairy dust that appeals to the biggest companies on the planet. But most of our customers in that area and actually say probably in the fortune 500 actually use two to three different backup packages. So we can support all those on a single InfiniGuard or multiples depending on how big their backup data sets. Our biggest InfiniGuard is 50 petabytes counting the data reduction technology. So we get that ready. On the InfiniBox, the recovery really is, you know, a couple of seconds and in that case, it's primary data in block format. So we just make that available. So on the InfiniBox, the recovery is once, well two. Identifying a known good copy, first step, then just doing recovery and it's available 'cause it's blocked data. >> And that recovery doesn't include movement of a whole bunch of data. It's essentially realignment of pointers to where the good data is. >> Right. >> Now in the InfiniBox as well as in InfiniGuard. >> No, it would be, So in the case of that, in the case of the InfiniGuard, it's a full recovery of a backup data set. >> Okay. >> So the backup software just launches and it sees, >> Okay. >> your backup one of Veeam and just starts doing a restore with the Veeam restoration technology. Okay? >> Okay. >> In the case of the block, as long as the physical InfiniBox, if that was the primary storage and then filter box is not damaged when you make it available, it's available right away to the apps. Now, if you had an issue with the app side or the physical server side, and now you're pointing new apps and you had to reload stuff on that side, you have to point it at that InfiniBox which has the data. And then you got to wait for the servers and the SAP or Oracle or Mongo, Cassandra to recognize, oh, this is my primary storage. So it depends on the physical configuration on the server side and the application perspective, how bad were the apps damaged? So let's take malware. Malware is even worse because you either destroying data or messing, playing with the app so that the app is now corrupted as well as the data is corrupted. So then it's going to take longer the block data's ready, the SAP workload. And if the SAP somehow was compromised, which is a malware thing, not a ransomware thing, they got to reload a good copy of SAP before it can see the data 'cause the malware attacked the application as well as the data. Ransomware doesn't do that. It just holds it for ransom and it encrypts. >> So this is exactly what we're talking about. When we talk about operational recovery and automation, Eric is addressing the reality that it doesn't just end at the line above some arbitrary storage box, you know, reaching up real recovery, reaches up into the application space and it's complicated. >> That's when you're actually recovered. >> Right. >> When the application- >> Well, think of it like a disaster. >> Okay. >> Yes, right. >> I'll knock on woods since I was born and still live in California. Dave too. Let's assume there's a massive earthquake in the bay area in LA. >> Let's not. >> Okay. Let's yes, but hypothetically and the data center's cat five. It doesn't matter what they're, they're all toast. Okay. Couple weeks later it's modern. You know, people figure out what to do and certain buildings don't fall down 'cause of the way earthquake standards are in California now. So there's data available. They move into temporary space. Okay. Data's sitting there in the Colorado data center and they could do a restore. Well, they can't do a restore. How many service did they need? Had they reloaded all of the application software to do a restoration. What happened to the people? If no one got injured, like in the 1989 earthquake in California, very few people got injured yet cost billions of dollars. But everyone was watching this San Francisco giants played in Oakland, >> I remember >> so no one was on the road. >> Al Michael's. >> Epic moment. >> Imagine it's in the middle of commute time in LA and San Francisco, hundreds of thousands of people. What if it's your data center team? Right? So there's a whole bunch around disaster recovery and business country that have nothing to do with the storage, the people, what your process. So I would argue that malware ransomware is a disaster and it's exactly the same thing. You know, you got the known good copy. You've got okay. You're sure that the SAP and Oracle, especially on the malware side, weren't compromised. On the ransomware side, you don't have to worry about that. And those things, you got to take a look at just as if it, I would argue malware and ransomware is a disaster and you need to have a process just like you would. If there was an earthquake, a fire or a flood in the data center, you need a similar process. That's slightly different, but the same thing, servers, people, software, the data itself. And when you have that all mapped out, that's how you do successful malware ransomeware recovery. It's a different type of disaster. >> It's absolutely a disaster. It comes down to business continuity and be able to transact business with as little disruption as possible. We heard today from the keynotes and then Jason Buffington came on about the preponderance of ransomware. Okay. We know that. But then the interesting stat was the percentage of customers that paid the ransom about a third weren't able to recover. And so 'cause you kind of had this feeling of all right, well, you know, see it on, you know, CNBC, should you pay the ransom or not? You know, pay the ransom. Okay. You'll get back. But no, it's not the case. You won't necessarily get back. So, you know, Veeam stated, Hey, our goal is to sort of eliminate that problem. Are you- You feel like you guys in a partnership can actually achieve that. >> Yes. >> So, and you have customers that have actually avoided, you know, been hit and were able to- >> We have people who won't publicly say they've been hit, but the way they talk about what they did, like in a meeting, they were hit and they were very thankful. >> (laughs) Yeah. >> And so that's been very good. I- >> So we got proof. >> Yes, we absolutely have proof. And quite honestly, with the recent legislation in the United States, malware and ransomware actually now is also regulatory and compliance. >> Yeah. >> Because the new law states mid-March that whether it's Herzog's bar and grill to bank of America or any large foreign company doing business in the US, you have to report to the United States federal government, any attack, same with the county school district with any local government, any agency, the federal government, as well as every company from the tiniest to the largest in the world that does, they're supposed to report it 'cause the government is trying to figure out how to fight it. Just the way if you don't report burglary, how they catch the burglars. >> Does your solution simplify testing in any way or reduce the risk of testing? >> Well, because the recovery is so rapid, we recommend that people do this on a regular basis. So for example, because the recovery is so quick, you can recover in 12 minutes while we do not practice, let's say once a month or once every couple weeks. And guess what? It also allows you to build a repository of known good copies. Remember when you get ransomeware, no one's going to come say, Hey, I'm Mr. Rans. I'm going to steal your stuff. It's all done surreptitiously. They're all James Bond on the sly who doesn't say "By the way, I'm James Bond". They are truly underneath the radar. And they're very slowly encrypting that data set. So guess what? Your primary data and your backup data that you don't want to be attacked can be attacked. So it's really about finding a known good copy. So if you're doing this on a regular basis, you can get an index of known good copies. >> Right. >> And then, you know, oh, I can go back to last Tuesday and you know that that's good. Otherwise you're literally testing Wednesday, Thursday, Friday, Saturday to try to find a known good copy, which delays the recovery process 'cause you really do have to test. They make sure it's good. >> If you increase that frequency, You're going to protect yourself. That's why I got to go. Thanks so much for coming on theCUBEs. Great to see you. >> Great. Thank you very much. I'll be wearing a different Hawaiian shirt next to. >> All right. That sounds good. >> All right, Eric Herzog, Eric Herzog on theCUBE, Dave Vallante for David Nicholson. We'll be right back at VEEAMON 2022. Right after this short break. (light music playing)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> When Facebook changed its name to Meta last fall, it catalyzed a chain reaction throughout the tech industry. Software firms, gaming companies, chip makers, device manufacturers, and others have joined in hype machine. Now, it's easy to dismiss the metaverse as futuristic hyperbole, but do we really believe that tapping on a smartphone, or staring at a screen, or two-dimensional Zoom meetings are the future of how we work, play, and communicate? As the internet itself proved to be larger than we ever imagined, it's very possible, and even quite likely that the combination of massive processing power, cheap storage, AI, blockchains, crypto, sensors, AR, VR, brain interfaces, and other emerging technologies will combine to create new and unimaginable consumer experiences, and massive wealth for creators of the metaverse. Hello, and welcome to this week's Wiki Bond Cube Insights, powered by ETR. In this "Breaking Analysis" we welcome in cyber expert, hacker gamer, NFT expert, and founder of ORE System, Nick Donarski. Nick, welcome, thanks so much for coming on theCUBE. >> Thank you, sir, glad to be here. >> Yeah, okay, so today we're going to traverse two parallel paths, one that took Nick from security expert and PenTester to NFTs, tokens, and the metaverse. And we'll simultaneously explore the complicated world of cybersecurity in the enterprise, and how the blockchain, crypto, and NFTs will provide key underpinnings for digital ownership in the metaverse. We're going to talk a little bit about blockchain, and crypto, and get things started there, and some of the realities and misconceptions, and how innovations in those worlds have led to the NFT craze. We'll look at what's really going on in NFTs and why they're important as both a technology and societal trend. Then, we're going to dig into the tech and try to explain why and how blockchain and NFTs are going to lay the foundation for the metaverse. And, finally, who's going to build the metaverse. And how long is it going to take? All right, Nick, let's start with you. Tell us a little bit about your background, your career. You started as a hacker at a really, really young age, and then got deep into cyber as a PenTester. You did some pretty crazy stuff. You have some great stories about sneaking into buildings. You weren't just doing it all remote. Tell us about yourself. >> Yeah, so I mean, really, I started a long time ago. My dad was really the foray into technology. I wrote my first program on an Apple IIe in BASIC in 1989. So, I like to say I was born on the internet, if you will. But, yeah, in high school at 16, I incorporated my first company, did just tech support for parents and teachers. And then in 2000 I transitioned really into security and focused there ever since. I joined Rapid7 and after they picked up Medis boy, I joined HP. I was one of their founding members of Shadowlabs and really have been part of the information security and the cyber community all throughout, whether it's training at various different conferences or talking. My biggest thing and my most awesome moments as various things of being broken into, is really when I get to actually work with somebody that's coming up in the industry and who's new and actually has that light bulb moment of really kind of understanding of technology, understanding an idea, or getting it when it comes to that kind of stuff. >> Yeah, and when you think about what's going on in crypto and NFTs and okay, now the metaverse it's you get to see some of the most innovative people. Now I want to first share a little bit of data on enterprise security and maybe Nick get you to comment. We've reported over the past several years on the complexity in the security business and the numerous vendor choices that SecOps Pros face. And this chart really tells that story in the cybersecurity space. It's an X,Y graph. We've shown it many times from the ETR surveys where the vertical axis, it's a measure of spending momentum called net score. And the horizontal axis is market share, which represents each company's presence in the data set, and a couple of points stand out. First, it's really crowded. In that red dotted line that you see there, that's 40%, above that line on the net score axis, marks highly elevated spending momentum. Now, let's just zoom in a bit and I've cut the data by those companies that have more than a hundred responses in the survey. And you can see here on this next chart, it's still very crowded, but a few call-outs are noteworthy. First companies like SentinelOne, Elastic, Tanium, Datadog, Netskope and Darktrace. They were all above that 40% line in the previous chart, but they've fallen off. They still have actually a decent presence in the survey over 60 responses, but under that hundred. And you can see Auth0 now Okta, big $7 billion acquisition. They got the highest net score CrowdStrike's up there, Okta classic they're kind of enterprise business, and Zscaler and others above that line. You see Palo Alto Networks and Microsoft very impressive because they're both big and they're above that elevated spending velocity. So Nick, kind of a long-winded intro, but it was a little bit off topic, but I wanted to start here because this is the life of a SecOps pro. They lack the talent in a capacity to keep bad guys fully at bay. And so they have to keep throwing tooling at the problem, which adds to the complexity and as a PenTester and hacker, this chaos and complexity means cash for the bad guys. Doesn't it? >> Absolutely. You know, the more systems that these organizations find to integrate into the systems, means that there's more components, more dollars and cents as far as the amount of time and the engineers that need to actually be responsible for these tools. There's a lot of reasons that, the more, I guess, hands in the cookie jar, if you will, when it comes to the security architecture, the more links that are, or avenues for attack built into the system. And really one of the biggest things that organizations face is being able to have engineers that are qualified and technical enough to be able to support that architecture as well, 'cause buying it from a vendor and deploying it, putting it onto a shelf is good, but if it's not tuned properly, or if it's not connected properly, that security tool can just hold up more avenues of attack for you. >> Right, okay, thank you. Now, let's get into the meat of the discussion for today and talk a little bit about blockchain and crypto for a bit. I saw sub stack post the other day, and it was ripping Matt Damon for pedaling crypto on TV ads and how crypto is just this big pyramid scheme. And it's all about allowing criminals to be anonymous and it's ransomware and drug trafficking. And yes, there are definitely scams and you got to be careful and lots of dangers out there, but these are common criticisms in the mainstream press, that overlooked the fact by the way that IPO's and specs are just as much of a pyramid scheme. Now, I'm not saying there shouldn't be more regulation, there should, but Bitcoin was born out of the 2008 financial crisis, cryptocurrency, and you think about, it's really the confluence of software engineering, cryptography and game theory. And there's some really powerful innovation being created by the blockchain community. Crypto and blockchain are really at the heart of a new decentralized platform being built out. And where today, you got a few, large internet companies. They control the protocols and the platform. Now the aspiration of people like yourself, is to create new value opportunities. And there are many more chances for the little guys and girls to get in on the ground floor and blockchain technology underpins all this. So Nick, what's your take, what are some of the biggest misconceptions around blockchain and crypto? And do you even pair those two in the same context? What are your thoughts? >> So, I mean, really, we like to separate ourselves and say that we are a blockchain company, as opposed to necessarily saying(indistinct) anything like that. We leverage those tools. We leverage cryptocurrencies, we leverage NFTs and those types of things within there, but blockchain is a technology, which is the underlying piece, is something that can be used and utilized in a very large number of different organizations out there. So, cryptocurrency and a lot of that negative context comes with a fear of something new, without having that regulation in place, without having the rules in place. And we were a big proponent of, we want the regulation, right? We want to do right. We want to do it by the rules. We want to do it under the context of, this is what should be done. And we also want to help write those rules as well, because a lot of the lawmakers, a lot of the lobbyists and things, they have a certain aspect or a certain goal of when they're trying to get these things. Our goal is simplicity. We want the ability for the normal average person to be able to interact with crypto, interact with NFTs, interact with the blockchain. And basically by saying, blockchain in quotes, it's very ambiguous 'cause there's many different things that blockchain can be, the easiest way, right? The easiest way to understand blockchain is simply a distributed database. That's really the core of what blockchain is. It's a record keeping mechanism that allows you to reference that. And the beauty of it, is that it's quote unquote immutable. You can't edit that data. So, especially when we're talking about blockchain, being underlying for technologies in the future, things like security, where you have logging, you have keeping, whether you're talking about sales, where you may have to have multiple different locations (indistinct) users from different locations around the globe. It creates a central repository that provides distribution and security in the way that you're ensuring your data, ensuring the validation of where that data exists when it was created. Those types of things that blockchain really is. If you go to the historical, right, the very early on Bitcoin absolutely was made to have a way of not having to deal with the fed. That was the core functionality of the initial crypto. And then you had a lot of the illicit trades, those black markets that jumped onto it because of what it could do. The maturity of the technology though, of where we are now versus say back in 97 is a much different world of blockchain, and there's a much different world of cryptocurrency. You still have to be careful because with any fed, you're still going to have that FUD that goes out there and sells that fear, uncertainty and doubt, which spurs a lot of those types of scams, and a lot of those things that target end users that we face as security professionals today. You still get mailers that go out, looking for people to give their social security number over during tax time. Snail mail is considered a very ancient technology, but it still works. You still get a portion of the population that falls for those tricks, fishing, whatever it might be. It's all about trying to make sure that you have fear about what is that change. And I think that as we move forward, and move into the future, the simpler and the more comfortable these types of technologies become, the easier it is to utilize and indoctrinate normal users, to be able to use these things. >> You know, I want to ask you about that, Nick, because you mentioned immutability, there's a lot of misconceptions about that. I had somebody tell me one time, "Blockchain's Bs," and they say, "Well, oh, hold on a second. They say, oh, they say it's a mutable, but you can hack Coinbase, whatever it is." So I guess a couple of things, one is that the killer app for blockchain became money. And so we learned a lot through that. And you had Bitcoin and it really wasn't programmable through its interface. And then Ethereum comes out. I know, you know a lot about Ether and you have solidity, which is a lot simpler, but it ain't JavaScript, which is ubiquitous. And so now you have a lot of potential for the initial ICO's and probably still the ones today, the white papers, a lot of security flaws in there. I'm sure you can talk to that, but maybe you can help square that circle about immutability and security. I've mentioned game theory before, it's harder to hack Bitcoin and the Bitcoin blockchain than it is to mine. So that's why people mine, but maybe you could add some context to that. >> Yeah, you know it goes to just about any technology out there. Now, when you're talking about blockchain specifically, the majority of the attacks happen with the applications and the smart contracts that are actually running on the blockchain, as opposed to necessarily the blockchain itself. And like you said, the impact for whether that's loss of revenue or loss of tokens or whatever it is, in most cases that results from something that was a phishing attack, you gave up your credentials, somebody said, paste your private key in here, and you win a cookie or whatever it might be, but those are still the fundamental pieces. When you're talking about various different networks out there, depending on the blockchain, depends on how much the overall security really is. The more distributed it is, and the more stable it is as the network goes, the better or the more stable any of the code is going to be. The underlying architecture of any system is the key to success when it comes to the overall security. So the blockchain itself is immutable, in the case that the owner are ones have to be trusted. If you look at distributed networks, something like Ethereum or Bitcoin, where you have those proof of work systems, that disperses that information at a much more remote location, So the more disperse that information is, the less likely it is to be able to be impacted by one small instance. If you look at like the DAO Hack, or if you look at a lot of the other vulnerabilities that exist on the blockchain, it's more about the code. And like you said, solidity being as new as it is, it's not JavaScript. The industry is very early and very infantile, as far as the developers that are skilled in doing this. And with that just comes the inexperience and the lack of information that you don't learn until JavaScript is 10 or 12 years old. >> And the last thing I'll say about this topic, and we'll move on to NFTs, but NFTs relate is that, again, I said earlier that the big internet giants have pretty much co-opted the platform. You know, if you wanted to invest in Linux in the early days, there was no way to do that. You maybe have to wait until red hat came up with its IPO and there's your pyramid scheme folks. But with crypto it, which is again, as Nick was explaining underpinning is the blockchain, you can actually participate in early projects. Now you got to be careful 'cause there are a lot of scams and many of them are going to blow out if not most of them, but there are some, gems out there, because as Nick was describing, you've got this decentralized platform that causes scaling issues or performance issues, and people are solving those problems, essentially building out a new internet. But I want to get into NFTs, because it's sort of the next big thing here before we get into the metaverse, what Nick, why should people pay attention to NFTs? Why do they matter? Are they really an important trend? And what are the societal and technological impacts that you see in this space? >> Yeah, I mean, NFTs are a very new technology and ultimately it's just another entry on the blockchain. It's just another piece of data in the database. But how it's leveraged in the grand scheme of how we, as users see it, it can be the classic idea of an NFT is just the art, or as good as the poster on your wall. But in the case of some of the new applications, is where are you actually get that utility function. Now, in the case of say video games, video games and gamers in general, already utilize digital items. They already utilize digital points. As in the case of like Call of Duty points, those are just different versions of digital currencies. You know, World of Warcraft Gold, I like to affectionately say, was the very first cryptocurrency. There was a Harvard course taught on the economy of WOW, there was a black market where you could trade your end game gold for Fiat currencies. And there's even places around the world that you can purchase real world items and stay at hotels for World of Warcraft Gold. So the adoption of blockchain just simply gives a more stable and a more diverse technology for those same types of systems. You're going to see that carry over into shipping and logistics, where you need to have data that is single repository for being able to have multiple locations, multiple shippers from multiple global efforts out there that need to have access to that data. But in the current context, it's either sitting on a shipping log, it's sitting on somebody's desk. All of those types of paper transactions can be leveraged as NFTs on the blockchain. It's just simply that representation. And once you break the idea of this is just a piece of art, or this is a cryptocurrency, you get into a world where you can apply that NFT technology to a lot more things than I think most people think of today. >> Yeah, and of course you mentioned art a couple of times when people sold as digital art for whatever, it was 60, 65 million, 69 million, that caught a lot of people's attention, but you're seeing, I mean, there's virtually infinite number of applications for this. One of the Washington wizards, tokenized portions of his contract, maybe he was creating a new bond, that's really interesting use cases and opportunities, and that kind of segues into the latest, hot topic, which is the metaverse. And you've said yourself that blockchain and NFTs are the foundation of the metaverse, they're foundational elements. So first, what is the metaverse to you and where do blockchain and NFTs, fit in? >> Sure, so, I mean, I affectionately refer to the metaverse just a VR and essentially, we've been playing virtual reality games and all the rest for a long time. And VR has really kind of been out there for a long time. So most people's interpretation or idea of what the metaverse is, is a virtual reality version of yourself and this right, that idea of once it becomes yourself, is where things like NFT items, where blockchain and digital currencies are going to come in, because if you have a manufacturer, so you take on an organization like Nike, and they want to put their shoes into the metaverse because we, as humans, want to individualize ourselves. We go out and we want to have that one of one shoe or that, t-shirt or whatever it is, we're going to want to represent that same type of individuality in our virtual self. So NFTs, crypto and all of those digital currencies, like I was saying that we've known as gamers are going to play that very similar role inside of the metaverse. >> Yeah. Okay. So basically you're going to take your physical world into the metaverse. You're going to be able to, as you just mentioned, acquire things- I loved your WOW example. And so let's stay on this for a bit, if we may, of course, Facebook spawned a lot of speculation and discussion about the concept of the metaverse and really, as you pointed out, it's not new. You talked about why second life, really started in 2003, and it's still around today. It's small, I read recently, it's creators coming back into the company and books were written in the early 90s that used the term metaverse. But Nick, talk about how you see this evolving, what role you hope to play with your company and your community in the future, and who builds the metaverse, when is it going to be here? >> Yeah, so, I mean, right now, and we actually just got back from CES last week. And the Metaverse is a very big buzzword. You're going to see a lot of integration of what people are calling, quote unquote, the metaverse. And there was organizations that were showing virtual office space, virtual malls, virtual concerts, and those types of experiences. And the one thing right now that I don't think that a lot of organizations have grasp is how to make one metaverse. There's no real player one, if you will always this yet, There's a lot of organizations that are creating their version of the metaverse, which then again, just like every other software and game vendor out there has their version of cryptocurrency and their version of NFTs. You're going to see it start to pop up, especially as Oculus is going to come down in price, especially as you get new technologies, like some of the VR glasses that look more augmented reality and look more like regular glasses that you're wearing, things like that, the easier that those technologies become as in adopting into our normal lifestyle, as far as like looks and feels, the faster that stuff's going to actually come out to the world. But when it comes to like, what we're doing is we believe that the metaverse should actually span multiple different blockchains, multiple different segments, if you will. So what ORE system is doing, is we're actually building the underlying architecture and technologies for developers to bring their metaverse too. You can leverage the ORE Systems NFTs, where we like to call our utility NFTs as an in-game item in one game, or you can take it over and it could be a t-shirt in another game. The ability for having that cross support within the ecosystem is what really no one has grasp on yet. Most of the organizations out there are using a very classic business model. Get the user in the game, make them spend their money in the game, make all their game stuff as only good in their game. And that's where the developer has you, they have you in their bubble. Our goal, and what we like to affectionately say is, we want to bring white collar tools and technology to blue collar folks, We want to make it simple. We want to make it off the shelf, and we want to make it a less cost prohibitive, faster, and cheaper to actually get out to all the users. We do it by supporting the technology. That's our angle. If you support the technology and you support the platform, you can build a community that will build all of the metaverse around them. >> Well, and so this is interesting because, if you think about some of the big names, we've Microsoft is talking about it, obviously we mentioned Facebook. They have essentially walled gardens. Now, yeah, okay, I could take Tik Tok and pump it into Instagram is fine, but they're really siloed off. And what you're saying is in the metaverse, you should be able to buy a pair of sneakers in one location and then bring it to another one. >> Absolutely, that's exactly it. >> And so my original kind of investment in attractiveness, if you will, to crypto, was that, the little guy can get an early, but I worry that some of these walled gardens, these big internet giants are going to try to co-op this. So I think what you're doing is right on, and I think it's aligned with the objectives of consumers and the users who don't want to be forced in to a pen. They want to be able to live freely. And that's really what you're trying to do. >> That's exactly it. You know, when you buy an item, say a Skin in Fortnite or Skin in Call of Duty, it's only good in that game. And not even in the franchise, it's only good in that version of the game. In the case of what we want to do is, you can not only have that carry over and your character. So say you buy a really cool shirt, and you've got that in your Call of Duty or in our case, we're really Osiris Protocol, which is our proof of concept video game to show that this all thing actually works, but you can actually go in and you can get a gun in Osiris Protocol. And if we release, Osiris Protocol two, you'll be able to take that to Osiris Protocol two. Now the benefit of that is, is you're going to be the only one in the next version with that item, if you haven't sold it or traded it or whatever else. So we don't lock you into a game. We don't lock you into a specific application. You own that, you can trade that freely with other users. You can sell that on the open market. We're embracing what used to be considered the black market. I don't understand why a lot of video games, we're always against the skins and mods and all the rest. For me as a gamer and coming up, through the many, many years of various different Call of Duties and everything in my time, I wish I could still have some this year. I still have a World of Warcraft account. I wasn't on, Vanilla, Burning Crusade was my foray, but I still have a character. If you look at it that way, if I had that wild character and that gear was NFTs, in theory, I could actually pass that onto my kid who could carry on that character. And it would actually increase in value because they're NFT back then. And then if needed, you could trade those on the open market and all the rest. It just makes gaming a much different thing. >> I love it. All right, Nick, hey, we're out of time, but I got to say, Nick Donarski, thanks so much for coming on the program today, sharing your insights and really good luck to you and building out your technology platform and your community. >> Thank you, sir, it's been an absolute pleasure. >> And thank you for watching. Remember, all these episodes are available as podcasts, just search "Breaking Analysis Podcast", and you'll find them. I publish pretty much every week on siliconangle.com and wikibond.com. And you can reach me @dvellante on Twitter or comment on my LinkedIn posts. You can always email me david.vellante@siliconangle.com. And don't forget, check out etr.plus for all the survey data. This is Dave Vellante for theCUBE Insights, powered by ETR, happy 2022 be well, and we'll see you next time. (upbeat music)

>>Hey, welcome back to the cubes. Continuing coverage of AWS reinvent 2021. I'm your host, Lisa Martin. This is day two, our first full day of coverage. But day two, we have two life sets here with AWS and its ecosystem partners to remote sets over a hundred guests on the program. We're going to be talking about the next decade of cloud innovation, and I'm pleased to welcome back to cube alumni to the program. Justin Borkman is here, the co-founder and CEO of Starburst and Teresa Tung, the cloud first chief technologist at Accenture guys. Welcome back to the queue. Thank you. Thank you for having me. Good to have you back. So, so Teresa, I was doing some research on you and I see you are the most prolific prolific inventor at Accenture with over 220 patents and patent applications. That's huge. Congratulations. Thank you. Thank you. And I love your title. I think it's intriguing. I'd like to learn a little bit more about your role cloud-first chief technologist. Tell me about, >>Well, I get to think about the future of cloud and if you think about clouded powers, everything experiences in our everyday lives and our homes and our car in our stores. So pretty much I get to be cute, right? The rest of Accenture's James Bond >>And your queue. I like that. Wow. What a great analogy. Just to talk to me a little bit, I know service has been on the program before, but give me a little bit of an overview of the company, what you guys do. What were some of the gaps in the markets that you saw a few years ago and said, we have an idea to solve this? Sure. >>So Starburst offers a distributed query engine, which essentially means we're able to run SQL queries on data anywhere, uh, could be in traditional relational databases, data lakes in the cloud on-prem. And I think that was the gap that we saw was basically that people had data everywhere and really had a challenge with how they analyze that data. And, uh, my co-founders are the creators of an open source project originally called Presto now called Trino. And it's how Facebook and Netflix and Airbnb and, and a number of the internet companies run their analytics. And so our idea was basically to take that, commercialize that and make it enterprise grade for the thousands of other companies that are struggling with data management, data analytics problems. >>And that's one of the things we've seen explode during the last 22 months, among many other things is data, right? In every company. These days has to be a data company. If they're not, there's a competitor in the rear view rear view mirror, ready to come and take that place. We're going to talk about the data mesh Teresa, we're going to start with you. This is not a new car. This is a new concept. Talk to us about what a data mesh is and why organizations need to embrace this >>Approach. So there's a canonical definition about data mesh with four attributes and any data geek or data architect really resonates with them. So number one, it's really routed decentralized domain ownership. So data is not within a single line of business within a single entity within a single partner has to be across different domains. Second is publishing data as products. And so instead of these really, you know, technology solutions, data sets, data tables, really thinking about the product and who's going to use it. The third one is really around self-service infrastructure. So you want everybody to be able to use those products. And finally, number four, it's really about federated and global governance. So even though their products, you really need to make sure that you're doing the right things, but what's data money. >>We're not talking about a single tool here, right? This is more of a, an approach, a solution. >>It is a data strategy first and foremost, right? So companies, they are multi-cloud, they have many projects going on, they are on premise. So what do you do about it? And so that's the reality of the situation today, and it's first and foremost, a business strategy and framework to think about the data. And then there's a new architecture that underlines and supports that >>Just didn't talk to me about when you're having customer conversations. Obviously organizations need to have a core data strategy that runs the business. They need to be able to, to democratize really truly democratized data access across all business units. What are some of the, what are some of your customer conversations like are customers really embracing the data strategy, vision and approach? >>Yeah, well, I think as you alluded to, you know, every business is data-driven today and the pandemic, if anything has accelerated digital transformation in that move to become data-driven. So it's imperative that every business of every shape and size really put the power of data in the hands of everyone within their organization. And I think part of what's making data mesh resonates so well, is that decentralization concept that Teresa spoke about? Like, I think companies acknowledge that data is inherently decentralized. They have a lot of different database systems, different teams and data mesh is a framework for thinking about that. Then not only acknowledges that reality, but also braces it and basically says there's actually advantages to this decentralized approach. And so I think that's, what's driving the interest level in the data mesh, uh, paradigm. And it's been exciting to work with customers as they think about that strategy. And I think that, you know, essentially every company in the space is, is in transition, whether they're moving from on cloud to the prem, uh, to, uh, sorry, from on-prem to the cloud or from one cloud to another cloud or undergoing that digital transformation, they have left behind data everywhere. And so they're, they're trying to wrestle with how to grasp that. >>And there's, we know that there's so much value in data. The, the need is to be able to get it, to be able to analyze it quickly in real time. I think another thing we learned in the pandemic is it real-time is no longer a nice to have. It is essential for businesses in every organization. So Theresa let's talk about how Accenture and servers are working together to take the data mesh from a concept of framework and put this into production into execution. >>Yeah. I mean, many clients are already doing some aspect of the data mesh as I listed those four attributes. I'm sure everybody thought like I'm already doing some of this. And so a lot of that is reviewing your existing data projects and looking at it from a data product landscape we're at Amazon, right? Amazon famous for being customer obsessed. So in data, we're not always customer obsessed. We put up tables, we put up data sets, feature stores. Who's actually going to use this data. What's the value from it. And I think that's a big change. And so a lot of what we're doing is helping apply that product lens, a literal product lens and thinking about the customer. >>So what are some w you know, we often talk about outcomes, everything being outcomes focused and customers, vendors wanting to help customers deliver big outcomes, you know, cost reduction, et cetera, things like that. How, what are some of the key outcomes Theresa that the data mesh framework unlocks for organizations in any industry to be able to leverage? >>Yeah. I mean, it really depends on the product. Some of it is organizational efficiency and data-driven decisions. So just by the able to see the data, see what's happening now, that's great. But then you have so beyond the, now what the, so what the analytics, right. Both predictive prescriptive analytics. So what, so now I have all this data I can analyze and drive and predict. And then finally, the, what if, if I have this data and my partners have this data in this mesh, and I can use it, I can ask a lot of what if and, and kind of game out scenarios about what if I did things differently, all of this in a very virtualized data-driven fashion, >>Right? Well, we've been talking about being data-driven for years and years and years, but it's one thing to say that it's a whole other thing to actually be able to put that into practice and to use it, to develop new products and services, delight customers, right. And, and really achieve the competitive advantage that businesses want to have. Just so talk to me about how your customer conversations have changed in the last 22 months, as we've seen this massive acceleration of digital transformation companies initially, really trying to survive and figure out how to pivot, not once, but multiple times. How are those customer conversations changing now is as that data strategy becomes core to the survival of every business and its ability to thrive. >>Yeah. I mean, I think it's accelerated everything and, and that's been obviously good for companies like us and like Accenture, cause there's a lot of work to be done out there. Um, but I think it's a transition from a storage centric mindset to more of an analytics centric mindset. You know, I think traditionally data warehousing has been all about moving data into one central place. And, and once you get it there, then you can analyze it. But I think companies don't have the time to wait for that anymore. Right there, there's no time to build all the ETL pipelines and maintain them and get all of that data together. We need to shorten that time to insight. And that's really what we, what we've been focusing on with our, with our customers, >>Shorten that time to insight to get that value out of the data faster. Exactly. Like I said, you know, the time is no longer a nice to have. It's an absolute differentiator for folks in every business. And as, as in our consumer lives, we have this expectation that we can get whatever we want on our phone, on any device, 24 by seven. And of course now in our business lives, we're having the same expectation, but you have to be able to unlock that access to that data, to be able to do the analytics, to make the decisions based on what the data say. Are you, are you finding our total? Let's talk about a little bit about the go to market strategy. You guys go in together. Talk to me about how you're working with AWS, Theresa, we'll start with you. And then Justin we'll head over to you. Okay. >>Well, a lot of this is powered by the cloud, right? So being able to imagine a new data business to run the analytics on it and then push it out, all of that is often cloud-based. But then the great thing about data mesh it's it gives you a framework to look at and tap into multi-cloud on-prem edge data, right? Data that can't be moved because it is a private and secure has to be at the edge and on-prem so you need to have that's their data reality. And the cloud really makes this easier to do. And then with data virtualization, especially coming from the digital natives, we know it scales >>Just to talk to me about it from your perspective that the GTL. >>Yeah. So, I mean, I think, uh, data mesh is really about people process and technology. I think Theresa alluded to it as a strategy. It's, it's more than just technology. Obviously we bring some of that technology to bear by allowing customers to query the data where it lives. But the people in process side is just as important training people to kind of think about how they do data management, data analytics differently is essential thinking about how to create data as a product. That's one of the core principles that Theresa mentioned, you know, that's where I think, um, you know, folks like Accenture can be really instrumental in helping people drive that transformational change within their organization. And that's >>Hard. Transformational change is hard with, you know, the last 22 months. I've been hard on everyone for every reason. How are you facilitating? I'm curious, like to get Theresa, we'll start with you, your perspectives on how our together as servers and Accenture, with the power of AWS, helping to drive that cultural change within organizations. Because like we talked about Justin there, nobody has extra time to waste on anything these days. >>The good news is there's that imperative, right? Every business is a digital business. We found that our technology leaders, right, the top 10% investors in digital, they are outperforming are the laggards. So before pandemic, it's times to post pep devek times five, so there's a need to change. And so data is really the heart of the company. That's how you unlock your technical debt into technical wealth. And so really using cloud and technologies like Starburst and data virtualization is how we can actually do that. >>And so how do you, Justin, how does Starburst help organizations transfer that technical debt or reduce it? How does the D how does the data much help facilitate that? Because we talk about technical debt and it can, it can really add up. >>Yeah, well, a lot of people use us, uh, or think about us as an abstraction layer above the different data sources that they have. So they may have legacy data sources today. Um, then maybe they want to move off of over time, um, could be classical data, warehouses, other classical, uh, relational databases, perhaps they're moving to the cloud. And by leveraging Starburst as this abstraction, they can query the data that they have today, while in the background, moving data into the cloud or moving it into the new data stores that they want to utilize. And it sort of hides that complexity. It decouples the end user experience, the business analyst, the data scientists from where the data lives. And I think that gives people a lot of freedom and a lot of optionality. And I think, you know, the only constant is change. Um, and so creating an architecture that can stand the test of time, I think is really, really important. >>Absolutely. Speaking of change, I just saw the announcement about Starburst galaxy fully managed SAS platform now available in all three major clouds. Of course, here we are at AWS. This is a, is this a big directional shift for servers? >>It is, you know, uh, I think there's great precedent within open source enterprise software companies like Mongo DB or confluent who started with a self managed product, much the way that we did, and then moved in the direction of creating a SAS product, a cloud hosted, fully managed product that really I think, expands the market. And that's really essentially what we're doing with galaxy galaxy is designed to be as easy as possible. Um, you know, Starburst was already powerful. This makes it powerful and easy. And, uh, and, and in our view, can, can hopefully expand the market to thousands of potential customers that can now leverage this technology in a, in a faster, easier way, >>Just in sticking with you for a minute. Talk to me about kind of where you're going in, where services heading in terms of support for the data mesh architecture across industries. >>Yeah. So a couple of things that we've, we've done recently, and whether we're doing, uh, as we speak, one is, uh, we introduced a new capability. We call star gate. Now star gate is a connector between Starburst clusters. So you're going to have a Starbucks cluster, and let's say Azure service cluster in AWS, a Starbucks cluster, maybe an AWS west and AWS east. And this basically pushes the processing to where the data lives. So again, living within this construct of, uh, of decentralized data that a data mesh is all about, this allows you to do that at an even greater level of abstraction. So it doesn't even matter what cloud region the data lives in or what cloud entirely it lives in. And there are a lot of important applications for this, not only latency in terms of giving you fast, uh, ability to join across those different clouds, but also, uh, data sovereignty constraints, right? >>Um, increasingly important, especially in Europe, but increasingly everywhere. And, you know, if your data isn't Switzerland, it needs to stay in Switzerland. So starting date as a way of pushing the processing to Switzerland. So you're minimizing the data that you need to pull back to complete your analysis. And, uh, and so we think that's a big deal about, you know, kind of enabling a data mash on a, on a global scale. Um, another thing we're working on back to the point of data products is how do customers curate and create these data products and share them within their organization. And so we're investing heavily in our product to make that easier as well, because I think back to one of the things, uh, Theresa said, it's, it's really all about, uh, making this practical and finding quick wins that customers can deploy, deploy in their data mess journey, right? >>This quick wins are key. So Theresa, last question to you, where should companies go to get started today? Obviously everybody has gotten, we're still in this work from anywhere environment. Companies have tons of data, tons of sources of data, did it, infrastructure's already in place. How did they go and get started with data? >>I think they should start looking at their data projects and thinking about the best data products. I think just that mindset shift about thinking about who's this for what's the business value. And then underneath that architecture and support comes to bear. And then thinking about who are the products that your product could work better with just like any other practice partnerships, like what we have with AWS, right? Like that's a stronger together sort of thing, >>Right? So there's that kind of that cultural component that really strategic shift in thinking and on the architecture. Awesome guys, thank you so much for joining me on the program, coming back on the cube at re-invent talking about data mesh really help. You can help organizations and industry put that together and what's going on at service. We appreciate your time. Thanks again. All right. For my guests, I'm Lisa Martin, you're watching the cubes coverage of AWS reinvent 2021. The cube is the leader in global live tech coverage. We'll be right back.

>> From theCUBE studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE in ETR. This is Breaking Analysis with Dave Vellante. >> Every CEO is figuring out the right balance for new hybrid business models. Now, regardless of the chosen approach, which is going to vary, technology executives, they understand they have to accelerate their digital and build resilience as well as optionality into their platforms. Now, this is driving a dramatic shift in IT investments. And at the macro level, we expect total spending to increase at as much as 8% or even more in 2021, compared to last year's contraction. Investments in cybersecurity, cloud collaboration that are enabling hybrid work as well as data, including analytics, AI, and automation are at the top of the spending priorities for CXOs. Hello everyone. And welcome to this week's Wiki Bond Cube insights, powered by ETR. In this Breaking Analysis, we're pleased to welcome back Erik Bradley, who is the chief engagement strategist at our partner, ETR. Now in this segment, we're going to share some of the latest findings from ETR's surveys and provide our commentary on what it means for the markets, for sellers, and for buyers. Erik, great to see you, my friend. Welcome back to Breaking Analysis. >> Thank you for having me, always enjoy it. We've got some fresh data to talk about on this beautiful summer Friday, so I'm ready to go. >> All right. I'm excited too. Okay, last year we saw a contraction in IT spending by at least 5%. And now we're seeing a snapback to, as I said, at least 8% growth relative to last year. You got to go back to 2007 just before the financial crisis to see this type of top line growth. The shift to hybrid work, it's exposed us to new insidious security threats. And we're going to discuss that in a lot more detail. Cloud migration of course picked up dramatically last year, and based on the recent earnings results of the big cloud players, for now we got two quarters of data, that trend continues as organizations are accelerating their digital platform build-outs, and this is bringing a lot of complexity and a greater need for so-called observability solutions, which Erik is going to talk about extensively later on in this segment. Data, we think is entering a new era of de-centralization. We see organizations not only focused on analytics and insights, but actually creating data products. Leading technology organizations like JP Morgan, they're heavily leaning into this trend toward packaging and monetizing data products. And finally, as part of the digital transformation trend, we see no slow down in spending momentum for AI and automation, generally in RPA specifically. Erik, anything you want to add to that top level narrative? >> Yeah, there's a lot to take on the macro takeaways. The first thing I want to state is that that 8, 8.5% number that started off at just 3 to 4% beginning of the year. So as the year has continued, we are just seeing this trend in budgets continue to accelerate, and we don't have any reason to believe that's going to stop. So I think we're going to just keep moving on heading into 2021. And we're going to see a banner year of spend this year and probably next as well. >> All right, now we're going to bring up a chart that shows kind of that progression here of spending momentum. So Erik, I'm going to let you comment on this chart that tracks those projections over time. >> Erik: Yeah. Great. So thank you very much for pulling this up. As you can see in the beginning part of the year, when we asked people, "What do you plan to spend throughout 2021?" They were saying it would be about a 4% increase. Which we were happy with because as you said last year, it was all negative. That continues to accelerate and is only hyper accelerating now as we head into the back half of the year. In addition, after we do this data, I always host a panel of IT end users to kind of get their feedback on what we collected, to a man, every one of them expects continued increase throughout next year. There are some concerns and uncertainty about what we're seeing right now with COVID, but even with that, they're planning their budgets now for 2022 and they're planning for even further increases going forward. >> Dave: Great, thank you. So we circled that 8%. That's really kind of where we thought it was going to land. And so we're happy with that number, but let's take a look at where the action is by technology sector. This chart that we're showing you here, it tracks spending priorities back to last September. When I believe that was the point, Erik, that cyber became the top priority in the survey, ahead of cloud collaboration, analytics, and data, and the other sectors that you see there. Now, Erik, we should explain. These areas, they're the top seven, and they outrank all the other sectors. ETR tracks many, many other sectors, but please weigh in here and share your thoughts on this data. >> Erik: Yeah. Security, security, security. It hasn't changed. It had really hasn't. The hybrid work. The fact that you're behind the firewall one day and then you're outside working from home the next, switching in and out of networks. This is just a field day for bad actors. And we have no choice right now, but to continue to spend, because as you're going to talk about in a minute, hybrid's here to stay. So we have to figure out a way to secure behind the firewall on-prem. We also have to secure our employees and our assets that are not in the office. So it is a main priority. One of the things that point out on this chart, I had a couple of ITN users talk to me about customer experience and automation really need to move from the right part of that chart to the left. So they're seeing more in what you were talking about in RPA and automation, starting to creep up heading into next year. As cloud migration matures, as you know, cybersecurity spending has been ramping up. People are going to see a little bit more on the analytics and a little bit more on the automation side going forward. >> Dave: Great. Now, this next data view- well, first of all, one of the great things about the ETR dataset is that you can ask key questions and get a time series. And I will tell you again, I go back to last March, ETR hit it. They were the first on the work from home trend. And so if you were on that trend, you were able to anticipate it. And a lot of investors I think took advantage of that. Now, but we've shown this before, but there's new data points that we want to introduce. So the data tracks how CIOs and IT buyers have responded to the pandemic since last March. Still 70% of the organizations have employees working remotely, but 39% now have employees fully returning to the office and Erik, the rest of the metrics all point toward positives for IT spending, although accelerating IT deployments there at the right peaked last year, as people realized they had to invest in the future. Your thoughts? >> Erik: Yeah, this is the slide for optimism, without a doubt. Of the entire macro survey we did, this is the most optimistic slide. It's great for overall business. It's great for business travel. This is well beyond just IT. Hiring is up. I've had some people tell me that they possibly can't hire enough people right now. They had to furlough employees, they had to stop projects, and they want to re accelerate those now. But talent is very hard to find. Another point to you about your automation and RPA, another underlying trend for there. The one thing I did want to talk about here is the hybrid workplace, but I believe there's another slide on it. So just to recap on this extremely optimistic, we're seeing a lot of hiring. We're seeing increased spending, and I do believe that that's going to continue. >> Yeah I'm glad you brought that up because a session that you and I did a while ago, we pointed out, it was earlier this year, that the skill shortage is one potential risk to our positive scenario. We'll keep an eye on that, but so I want to show another set of data that we've showed previously, but ETR again, has added some new questions in here. So note here that 60% of employees still work remotely with 33% in a hybrid model currently, and the CIO's expect that to land on about 42% hybrid workforce with around 30% working remotely, which is around, it's been consistent by the way on your surveys, but that's about double the historic norm, Eric. >> Erik: Yeah, and even further to your point Dave, recently I did a panel asking people to give me some feedback on this. And three of those four experts basically said to me, if we had greed run this survey right now, that even more people would be saying remote. That they believe that that number, that's saying they're expecting that number of people to be back in office, is actually too optimistic. They're actually saying that maybe if we had- cause as a survey launched about six, seven weeks ago before this little blip on the radar, before the little COVID hiccup we're seeing now, and they're telling me that they believe if we reran this now that it would be even more remote work, even more hybrid and less returned to the office. So that's just an update I wanted to offer on this slide. >> Dave: Yeah. Thank you for that. I mean, we're still in this kind of day to day, week to week, month to month mode, but I want to do a little double click on this. We're not going to share this data, but there was so much ETR data. We got to be selective. But if you double click on the hybrid models, you'll see that 50% of organizations plan to have time roughly equally split between onsite and remote with again around 30 or 31% mostly remote, with onsite space available if they need it. And Erik, very few don't plan to have some type of hybrid model, at least. >> Yeah, I think it was less than 10% that said it was going to be exclusively onsite. And again, that was a more optimistic scenario six, seven weeks ago than we're seeing right now throughout the country. So I agree with you, hybrid is here to stay. There really is no doubt about it. from everyone I speak to when, you know, I basically make a living talking to IT end users. Hybrid is here to stay. They're planning for it. And that's really the drive behind the spending because you have to support both. You have to give people the option. You have to, from an IT perspective, you also have to support both, right? So if somebody is in office, I need the support staff to be in office. Plus I need them to be able to remote in and fix something from home. So they're spending on both fronts right now. >> Okay. Let's get into some of the vendor performance data. And I want to start with the cloud hyperscalers. It's something that we followed pretty closely. I got some Wiki bond data, that we just had earnings released. So here's data that shows the Q2 revenue shares on the left-hand side in the pie and the growth rates for the big four cloud players on the right hand side. It goes back to Q1 2019. Now the first thing I want to say is these players generated just under $39 billion in the quarter with AWS capturing 50% of that number. I said 39, it was 29 billion, sorry, with AWS capturing 50% of that in the quarter. As you're still tracking around a third in Alibaba and GCP in the, you know, eight or 9% range. But what's most interesting to me, Erik, is that AWS, which generated almost 15 billion in the quarter, was the only player to grow its revenue, both sequentially and year over year. And Erik, I think the street is missing the real story here on Amazon. Amazon announced earnings on Thursday night. The company had a 2% miss on the top line revenues and a meaningful 22% beat on earnings per share. So the retail side of the business missed its revenue targets, so that's why everybody's freaked out. But AWS, the cloud side, saw a 4% revenue beat. So the stock was off more than 70% after hours and into Friday. Now to me, a mix shift toward AWS, that's great news for investors. Now, tepid guidance is a negative, but the shift to a more profitable cloud business is a huge positive. >> Yeah, there's a lot that goes into stock price, right? I remember I was a director of research back in the day. One of my analysts said to me, "Am I crazy for putting a $1,000 target on Amazon?" And I laughed and I said, "No, you're crazy if you don't make it $2,000." (both chuckling) So, you know, at that time it was basically the mix shift towards AWS. You're a thousand percent right. I think the tough year over year comps had something to do with that reaction. That, you know, it's just getting really hard. What's that? The law of large numbers, right? It's really hard to grow at that percentage rate when you're getting this big. But from our data perspective, we're seeing no slowdown in AWS, in cloud, none whatsoever. The only slowdown we're seeing in cloud is GCP. But to, you know, to focus on AWS, extremely strong across the board and not only just in cloud, but in all their data products as well, data and analytics. >> Yeah and I think that the AWS, don't forget folks, that funds Amazon's TAM expansion into so many different places. Okay. As we said at the top, the world of digital and hybrid work, and multi-cloud, it's more complicated than it used to be. And that means if you need to resolve issues, which everybody does, like poor application performance, et cetera, what's happening at the user level, you have to have a better way to sort of see what's going on. And that's what the emergence of the observability space is all about. So Erik, let me set this up and you have a lot of comments here because you've recently had some, and you always have had a lot of round table discussions with CXOs on this topic. So this chart plots net score or spending momentum on the vertical axis, and market share or pervasiveness in the dataset on the horizontal axis. And we inserted a table that shows the data points in detail. Now that red dotted line is just sort of Dave Vellante's subjective mark in the sand for elevated spending levels. And there are three other points here. One is Splunk as well off is two-year peak, as highlighted in the red, but Signal FX, which Splunk acquired, has made a big move northward this last quarter. As has Datadog. So Erik, what can you share with us on this hot, but increasingly crowded space? >> Yeah. I could talk about the space for a long time. As you know, I've gotten some flack over the last year and a half about, you know, kind of pointing out this trend, this negative trend in Splunk. So I do want to be the first one to say that this data set is rebounding. Splunk has been horrific in our data for going back almost two years now, straight downward trend. This is the first time we're seeing any increase, any positivity there. So I do want to be fair and state that because I've been accused of being a little too negative on Splunk in the past. But I would basically say for observability right now, it's a rising tide lifts all boats, if I can use a New England phrase. The data across the board in analytics for these observability players is up, is accelerating. None more so than Datadog. And it's exactly your point, David. The complexity, the increased cloud migration is a perfect setup for Datadog, which is a cloud native. It focuses on microservices. It focuses on cloud observability. Old Splunk was just application monitoring. Don't get me wrong, they're changing, but they were on-prem application monitoring, first and foremost. Datadog came out as cloud native. They, you know, do microservices. This is just a perfect setup for them. And not only is Datadog leading the observability, it's leading the entire analytics sector, all of it. Not just the observability niche. So without a doubt, that is the strongest that we're seeing. It's leading Dynatrace new Relic. The only one that really isn't rebounding is Cisco App Dynamics. That's getting the dreaded legacy word really attached to it. But this space is really on fire, elastic as well, really doing well in this space. New Relic has shown a little bit of improvement as well. And what I heard when I asked my panelists about this, is that because of the maturity of cloud migration, that this observability has to grow. Spending on this has to happen. So they all say the chart looks right. And it's really just about the digital transformation maturity. So that's largely what they think is happening here. And they don't really see it getting, you know, changing anytime soon. >> Yeah, and I would add, and you see that it's getting crowded. You saw a service now acquired LightStep, and they want to get into the game. You mentioned, you know, last deck of the elk stack is, you know, the open source alternative, but then we see a company who's raised a fair amount of money, startup, chaos search, coming in, going after kind of the complexity of the elk stack. You've got honeycomb, which has got a really innovative approach, Jeremy Burton's company observes. So you have venture capital coming in. So we'll see if those guys could be disruptive enough or are they, you know, candidates to get acquired? We'll see how that all- you know that well. The M and A space. You think this space is ripe for M and A? >> I think it's ripe for consolidation, M and A. Something has to shake out. There's no doubt. I do believe that all of these can be standalone. So we shall see what's happened to, you mentioned the Splunk acquisition of Signal FX, just a house cleaning point. That was really nice acceleration by Signal FX, but it was only 20 citations. We'd looked into this a little bit deeper. Our data scientists did. It appears as if the majority of people are just signaling spunk and not FX separately. So moving forward for our data set, we're going to combine those two, so we don't have those anomalies going forward. But that type of acquisition does show what we should expect to see more of in this group going forward. >> Well that's I want to mention. That's one of the challenges that any data company has, and you guys do a great job of it. You're constantly having to reevaluate. There's so much M and A going on in the industry. You've got to pick the right spots in terms of when to consolidate. There's some big, you know, Dell and EMC, for example. You know, you've beautifully worked through that transition. You're seeing, you know, open shift and red hat with IBM. You just got to be flexible. And that's where it's valuable to be able to have a pipeline to guys like Erik, to sort of squint through that. So thank you for that clarification. >> Thank you too, because having a resource like you with industry knowledge really helps us navigate some of those as well for everyone out there. So that's a lot to do with you do Dave, >> Thank you. It's going to be interesting to watch Splunk. Doug Merritt's made some, you know, management changes, not the least of which is bringing in Teresa Carlson to run go to market. So if you know, I'd be interested if they are hitting, bouncing off the bottom and rising up again. They have a great customer base. Okay. Let's look at some of the same dimensions. Go ahead. You got a comment? >> A few of ETR's clients looked at our data and then put a billion dollar investment into it too. So obviously I agree. (Dave laughing) Splunk is looking like it's set for a rebound, and it's definitely something to watch, I agree. >> Not to rat hole in this, but I got to say. When I look back, cause theCUBE gives us kind of early visibility. So companies with momentum and you talk to the customers that all these shows that we go to. I will tell you that three companies stood out last decade. It was Splunk. It was Service Now and Tableau. And you could tell just from just discussions with their customers, the enthusiasm in that customer base. And so that's a real asset, and that helps them build them a moat. So we'll see. All right, let's take a look at the same dimensions now for cyber. This is cybersecurity net score in the vertical, and market share in the horizontal. And I filtered by in greater than a hundred shared in because just gets so crowded. Erik, the only things I would point out here is CrowdStrike and Zscaler continue to shine, CyberArk also showing momentum over that 40% line. Very impressively, Palo Alto networks, which has a big presence in the market. They've bounced back. We predicted that a while back. Your round table suggested people like working with Palo Alto. They're a gold standard. You know, we had reported earlier on that divergence with four to net in terms of valuation and some of the challenges they had in cloud, clearly, you know, back with the momentum. And of course, Microsoft in the upper, right. It's just, they're literally off the charts and obviously a major player here, but your thoughts on cyber? >> Erik: Yeah. Going back to the backdrop. Security, security, security. It has been the number one priority going back to last September. No one sees it changing. It has to happen. The threat vectors are actually expanding and we have no choice but to spend here. So it is not surprising to see. You did name our three favorite names. So as you know, we look at the dataset, we see which ones have the most positive inflections, and we put outlooks on those. And you did mention Zscaler, Okta and CrowdStrike, by far the three standouts that we're seeing. I just recently did a huge panel on Okta talking about their acquisition of Auth Zero. They're pushed into Sale Point space, trying to move just from single sign on and MFA to going to really privileged account management. There is some hurdles there. Really Okta's ability to do this on-prem is something that a little bit of the IT end users are concerned about. But what we're seeing right now, both Okta and Auth Zero are two of the main adopted names in security. They look incredibly well set up. Zscaler as well. With the ZTNA push more towards zero trust, Zscaler came out so hot in their IPO. And everyone was wondering if it was going to trail off just like Snowflake. It's not trailing off. This thing just keeps going up into the right, up into the right. The data supports a lot of tremendous growth for the three names that you just mentioned. >> Yeah. Yeah. I'm glad you brought up Auth Zero. We had reported on that earlier. I just feel like that was a great acquisition. You had Okta doing the belly to belly enterprise, you know, selling. And the one thing that they really lacked was that developer momentum. And that's what Auth Zero brings. Just a smart move by Todd McKinnon and company. And I mean, so this, you know, I want to, I want to pull up another chart show a quick snapshot of some of the players in the survey who show momentum and have you comment on this. We haven't mentioned Snowflake so far, but they remain again with like this gold standard of net score, they've consistently had those high marks with regard to spending velocity. But here's some other data. Erik, how should we interpret this? >> Erik: Yeah, just to harp on Snowflake for a second. Right, I mean the rich get richer. They came out- IPO was so hyped, so it was hard for us as a research company to say, "Oh, you know, well, you know, we agree." But we did. The data is incredible. You can't beat the management team. You can't beat what they're doing. They've got so much cash. I can't wait to see what they do with it. And meanwhile, you would expect something that debuted with that high of a net score, that high of spending velocity to trail off. It would be natural. It's not Dave, it's still accelerating. It's gone even higher. It's at all time highs. And we just don't see it stopping anytime soon. It's a really interesting space right now. Maybe another name to look at on here that I think is pretty interesting, kind of a play on return to business is Kupa. It's a great project expense management tool that got hit really hard. Listen, traveling stopped, business expense stopped, and I did a panel on it. And a lot of our guys basically said, "Yeah, it was the first thing I cut." But we're seeing a huge rebound in spending there in that space. So that's a name that I think might be worth being called out on a positive side. Negative, If you look down to the bottom right of that chart, unfortunately we're seeing some issues in RingCentral and Zoom. Anything that's sort of playing in this next, you know, video conferencing, IP telephony space, they seem to be having really decelerating spending. Also now with Zoom's acquisition of five nine. I'm not really sure how RingCentral's going to compete on that. But yeah, that's one where we debuted for the first time with a negative outlook on that name. And looking and asking to some of the people in our community, a lot of them say externally, you still need IP telepany, but internally you don't. Because the You Cast communication systems are getting so sophisticated, that if I have Teams, if I have Slack, I don't need phones anymore. (chuckling) That you and I can just do a Slack call. We can do a Teams call. And many of them are saying I'm truly ripping out my IP Telepany internally as soon as possible because we just don't need it. So this whole collaboration, productivity space is here to stay. And it's got wide ranging implications to some of these more legacy type of tools. >> You know, one of the other things I'd call out on this chart is Accenture. You and I had a session earlier this year, and we had predicted that that skill shortage was going to lead to an uptick in traditional services. We've certainly seen that. I mean, IBM beat its quarter on the strength of services largely. And seeing Accenture on that is I think confirmation. >> Yeah that was our New Year prediction show, right Dave? When we made top 10 predictions? >> That's right. That was part of our predictions show. Exactly, good memory. >> The data is really showing that continue. People want the projects, they need to do the projects, but hiring is very difficult. So obviously the number one beneficiary there are going to be the Accentures of the world. >> All right. So let's do a quick wrap. I'm going to make a few comments and then have you bring us home, Erik. So we laid out our scenario for the tech spending rebound. We definitely believe last year tracked downward, along with GDP contraction. It was interesting. Gardner doesn't believe, at least factions of Gardner don't believe there's a correlation between GDP and tech spending. But, you know, I personally think there generally is some kind of relatively proportional pattern there. And I think we saw contraction last year. People are concerned about inflation. Of course, that adds some uncertainty. And as well, as you mentioned around the Delta variant. But I feel as though that the boards of directors and CEOs, they've mandated that tech execs have to build out digital platforms for the future. They're data centric. They're highly automated, to your earlier points. They're intelligent with AI infused, and that's going to take investment. I feel like the tech community has said, "Look, we know what to do here. We're dealing with hybrid work. We can't just stop doing what we're doing. Let's move forward." You know, and as you say, we're flying again and so forth. You know, getting hybrid right is a major priority that directly impacts strategies. Technology strategies, particularly around security, cloud, the productivity of remote workers with collaboration. And as we've said many times, we are entering a new era of data that's going to focus on decentralized data, building data products, and Erik let's keep an eye on this observability space. Lot of interest there, and buyers have a number of choices. You know, do they go with a specialist, as we saw recently, we've seen in the past, or did they go with the generalist like Service Now with the acquisition of LightStep? You know, it's going to be interesting. A lot of people are going to get into this space, start bundling into larger platforms. And so as you said, there's probably not enough room for all the players. We're going to see some consolidation there. But anyway, let me give you the final word here. >> Yeah, no, I completely agree with all of it. And I think your earlier points are spot on, that analytics and automation are certainly going to be moving more and more to that left of that chart we had of priorities. I think as we continue that survey heading into 2022, we'll have some fresh data for you again in a few months, that's going to start looking at 2022 priorities and overall spend. And the one other area that I keep hearing about over and over and over again is customer experience. There's a transition from good old CRM to CXM. Right now, everything is digital. It is not going away. So you need an omni-channel support to not only track your customer experience, but improve it. Make sure there's a two way communication. And it's a really interesting space. Salesforce is going to migrate into it. We've got Qualtrics out there. You've got Medallia. You've got FreshWorks, you've got Sprinkler. You got some names out there. And everyone I keep talking to on the IT end user side keeps bringing up customer experience. So let's keep an eye on that as well. >> That's a great point. And again, it brings me back to Service Now. We wrote a piece last week that's sort of, Service Now and Salesforce are on a collision course. We've said that for many, many years. And you've got this platform of platforms. They're just kind of sucking in different functions saying, "Hey, we're friends with everybody." But as you know Erik, software companies, they want to own it all. (both chuckling) All right. Hey Erik, thank you so much. I want to thank you for coming back on. It's always a pleasure to have you on Breaking Analysis. Great to see you. >> Love the partnership. Love the collaboration. Let's go enjoy this summer Friday. >> All right. Let's do. Okay, remember everybody, these episodes, they're all available as podcasts, wherever you listen. All you got to do is search Breaking Analysis Podcast, click subscribe to the series. Check out ETR's website at etr.plus. They've just launched a new website. They've got a whole new pricing model. It's great to see that innovation going on. Now remember we also publish a full report every week on WikiBond.com and SiliconAngle.com. You can always email me, appreciate the back channel comments, the metadata insights. David.Vellante@SiliconAngle.com. DM me on Twitter @DVellante or comment on the LinkedIn posts. This is Dave Vellante for Erik Bradley and theCUBE insights powered by ETR. Have a great week, a good rest of summer, be well. And we'll see you next time. (inspiring 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. >> Mobile World Congress is alive, theCUBE will be there and we'll certainly let you know if it's alive and well when we get on the ground. Now, as we approach a delayed mobile world congress, it's really appropriate to reflect in the state of the telecoms industry. Let's face it. Telcos have done of really good job of keeping us all connected during the pandemic, supporting work from home and that whole pivot, accommodating the rapid shift to landline traffic, securing the network and keeping it up and running but it doesn't change the underlying fundamental dilemma that Telco has faced. Telco is a slow growth, no growth industry, with revenue expectations in the low single digits. And at the same time network traffic continues to grow at 20% annually. And last year it grew at 40% to 50%. Despite these challenges, Telcos are still investing in the future. For example, the Telco industry collectively is selling out more than a trillion dollars in the first half of this decade on 5G and fiber infrastructure. And it's estimated that there are now more than 200 5G networks worldwide. But a lot of questions remain, not the least of which is, can and should Telcos go beyond connectivity and fiber. Can the Telcos actually monetize 5G or whatever's next beyond 5G? Or is that going to be left to the ecosystem? Now what about the ecosystem? How is that evolving? And very importantly, what role will the Cloud Hyperscalers play in Telco? Are they infrastructure on which the Telcos can build or are they going to suck the value out of the market as they have done in the enterprise? Hello everyone, and welcome to this week's Wiki Bond Cube Insights powered by ETR. In this breaking analysis, it's my pleasure to welcome a long time telecoms industry analyst and colleague, and the founding director of Lewis Insight, Mr. Chris Lewis. Chris, welcome to the program. Thanks for coming on >> Dave, it's a pleasure to be here. Thank you for having me. >> It is really our pleasure. So, we're going to cover a lot of ground today. And first thing, we're going to talk about Mobile World Congress. I've never been, you're an expert at that and what we can expect. And then we're going to review the current state of telecoms infrastructure, where it should go. We're going to dig into transformation. Is it a mandate? Is it aspirational? Can Telcos enter adjacent markets in ways they haven't been able to in the past? And then how about the ecosystem? We're going to talk about that, and then obviously we're going to talk about Cloud as I said, and we'll riff a little bit on the tech landscape. So Chris, let's get into it, Mobile World Congress, it's back on, what's Mobile World Congress typically like? What's your expectation this year for the vibe compared to previous events? >> Well Dave, the issue of Mobile World Congress is always that we go down there for a week into Barcelona. We stress ourselves building a matrix of meetings in 30 minutes slots and we return at the end of it trying to remember what we'd been told all the way through. The great thing is that with the last time we had a live, with around 110,000 people there, you could see anyone and everyone you needed to within the mobile, and increasingly the adjacent industry and ecosystem. So, he gave you that once a year, big download of everything new, obviously because it's the Mobile World Congress, a lot of it around devices, but increasingly over the last few years, we saw many, many stands with cars on them because the connected car became an issue, a lot more software oriented players there, but always the Telcos, always the people providing the network infrastructure. Increasingly in the last few years people provided the software and IT infrastructure, but all of these people contributing to what the network should be in the future, what needs to be connected. But of course the reach of the network has been growing. You mentioned during lockdown about connecting people in their homes, well, of course we've also been extending that connection to connect things whether it's in the home or the different devices, monitoring of doorbells and lights and all that sort of stuff. And in the industry environment, connecting all of the robots and sensors. So, actually the perimeter, the remit of the industry to connect has been expanding, and so is the sort of remit of Mobile World Congress. So, we set an awful lot of different suppliers coming in, trying to attach to this enormous market of roughly $1.5 trillion globally. >> Chris, what's the buzz in the industry in terms of who's going to show up. I know a lot of people have pulled out, I've got the Mobile World Congress app and I can see who's attending. And it looks like quite a few people are going to go but what's your expectation? >> Well, from an analyst point of view, obviously I'm mainly keeping up with my clients and trying to get new clients. I'm looking at it and going most of my clients are not attending in person. Now, of course, we need the DSMA, we need Mobile World Congress for future for the industry interaction. But of course, like many people having adopted and adapted to be online, then they're putting a lot of the keynotes online, a lot of the activities will be online. But of course many of the vendors have also produced their independent content and content to actually deliver to us as analysts. So, I'm not sure who will be there. I like you, but you'll be on the ground. You'll be able to report back and let us know exactly who turned up. But from my point of view, I've had so many pre-briefs already, the difference between this year and previous years, I used to get loads of pre-briefs and then have to go do the briefs as well. So this year I've got the pre-brief so I can sit back, put my feet up and wait for your report to come back as to what's happening on the ground. >> You got it. Okay, let's get into a little bit and talk about Telco infrastructure and the state, where it is today, where it's going, Chris, how would you describe the current state of Telco infrastructure? Where does it need to go? Like, what is the ideal future state look like for Telcos in your view? >> So there's always a bit of an identity crisis when it comes to Telco. I think going forward, the connectivity piece was seen as being table stakes, and then people thought where can we go beyond connectivity? And we'll come back to that later. But actually to the connectivity under the scenario I just described of people, buildings, things, and society, we've got to do a lot more work to make that connectivity extend, to be more reliable, to be more secure. So, the state of the network is that we have been building out infrastructure, which includes fiber to connect households and businesses. It includes that next move to cellular from 4G to 5G. It obviously includes Wi-Fi, wherever we've got that as well. And actually it's been a pretty good state, as you said in your opening comments they've done a pretty good job keeping us all connected during the pandemic, whether we're a fixed centric market like the UK with a lot of mobile on top and like the US, or in many markets in Africa and Asia, where we're very mobile centric. So, the fact is that every country market is different, so we should never make too many assumptions at a very top level, but building out that network, building out the services, focusing on that connectivity and making sure we get that cost of delivery right, because competition is pushing us towards having and not ever increasing prices, because we don't want to pay a lot extra every time. But the big issue for me is how do we bring together the IT and the network parts of this story to make sure that we build that efficiency in, and that brings in many questions that we going to touch upon now around Cloud and Hyperscalers around who plays in the ecosystem. >> Well, as you know, Telco is not my wheelhouse, but hanging around with you, I've learned, you've talked a lot about the infrastructure being fit for purpose. It's easy from an IT perspective. Oh yeah, it's fossilized, it's hardened, and it's not really flexible, but the flip side of that coin is as you're pointing out, it's super reliable. So, the big talk today is, "Okay, we're going to open up the network, open systems, and Open RAN, and open everything and microservices and containers. And so, the question is this, can you mimic that historical reliability in that open platform? >> Well, for me, this is the big trade-off and in my great Telco debate every year, I always try and put people against each other to try and to literally debate the future. And one of the things we looked at was is a more open network against this desire of the Telcos to actually have a smaller supplier roster. And of course, as a major corporation, these are on a national basis, very large companies, not large compared to the Hyperscalers for example, but they're large organizations, and they're trying to slim down their organization, slim down the supplier ecosystem. So actually in some ways, the more open it becomes, the more someone's got to manage and integrate all those pieces together. And that isn't something we want to do necessarily. So, I see a real tension there between giving more and more to the traditional suppliers. The Nokia's, Ericsson's, Huawei's, Amdocs and so on, the Ciscos. And then the people coming in breaking new ground like Mavenir and come in, and the sort of approach that Rakuten and Curve taken in bringing in more open and more malleable pieces of smaller software. So yeah, it's a real challenge. And I think as an industry which is notorious for being slow moving, actually we've begun to move relatively quickly, but not necessarily all the way through the organization. We've got plenty of stuff sitting on major or mainframes still in the back of the organization. But of course, as mobile has come in, we've started to deal much more closely, uninteractively in real time, God forbid, with the customers. So actually, at that front end, we've had to do things a lot more quickly. And that's where we're seeing the quickest adaptation to what you might see in your IT environment as being much more, continuous development, continuous improvement, and that sort of on demand delivery. >> Yeah, and we're going to get to that sort of in the Cloud space, but I want to now touch on Telco transformation which is sort of the main theme of this episode. And there's a lot of discussion on this topic, can Telcos move beyond connectivity and managing fiber? Is this a mandate? Is it a pipe dream that's just aspirational? Can they attack adjacencies to grow beyond the 1% a year? I mean, they haven't been successful historically. What are those adjacencies that might be, an opportunity and how will that ecosystem develop? >> Sure. >> So Chris, can and should Telcos try to move beyond core connectivity? Let's start there. >> I like what you did there by saying pipe dreams. Normally, pipe is a is a negative comment in the telecom world. But pipe dream gives it a real positive feel. So can they move beyond connectivity? Well, first of all, connectivity is growing in terms of the number of things being connected. So, in that sense, the market is growing. What we pay for that connectivity is not necessarily growing. So, therefore the mandate is absolutely to transform the inner workings and reduce the cost of delivery. So, that's the internal perspective. The external perspective is that we've tried in many Telcos around the world to break into those adjacent markets, being around media, being enterprise, being around IOT, and actually for the most part they've failed. And we've seen some very significant recent announcements from AT&T, Verizon, BT, beginning to move away from, owning content and not delivering content, but owning content. And the same as they've struggled often in the enterprise market to really get into that, because it's a well-established channel of delivery bringing all those ecosystem players in. So, actually rather than the old Telco view of we going to move into adjacent markets and control those markets, actually moving into them and enabling fellow ecosystem players to deliver the service is what I think we're beginning to see a lot more of now. And that's the big change, it's actually learning to play with the other people in the ecosystem. I always use a phrase that there's no room for egos in the ecosystem. And I think Telcos went in initially with an ego thinking we're really important, we are on connectivity. But actually now they're beginning to approach the ecosystem things saying, "How can we support partners? How can we support everyone in this ecosystem to deliver the services to consumers, businesses and whomever in this evolving ecosystem?" So, there are opportunities out there, plenty of them, but of course, like any opportunity, you've got to approach it in the right way. You've got to get the right investment in place. You've got to approach it with the right open API so everyone can integrate with your approach, and approach it, do I say with a little bit of humility to say, "Hey, we can bring this to the table, how do we work together? >> Well, it's an enormous market. I think you've shared with me, it's like 1.4 trillion. And I want to stay on these adjacencies for a minute, because one of the obvious things that Telcos will talk about is managed services. And I know we have to be careful of that term in an IT context, that it's different in a, you're talking about managing connectivity, but there's professional services. That's a logical sort of extension of their business and probably a safe adjacency, maybe not even adjacency, but they're not going to get into devices. I mean, they'll resell devices, but they're not going to be, I would presume not go back to trying to make devices, but there's certainly the edge and that's so, it'll define in opaque, but it's huge. If there's 5G, there's the IT component and that's probably a partnership opportunity. And as you pointed out, there's the ecosystem, but I wonder, how do you think about 5G as an adjacency or indoor opportunity? Is it a revenue opportunity for Telcos or is that just something that is really aspirational? >> Oh, absolutely it's a revenue opportunity, but I prefer to think of 5G as being a sort of a metaphor for the whole future of telecom. So, we usually talk, and MWC would normally talk about 5G just as a mobile solution. Of course, what you can get with, you can use this fixed wireless access approach, where the roots that sits in your house or your building. So, it's a potential replacement for some fixed lines. And of course, it's also, gives you the ability to build out, let's say in a manufacturing or a campus environment, a private 5G network. So, many of the early opportunities we're seeing with 5G are actually in that more private network environment addressing those very low latency, and high bandwidth requirements. So yeah, there are plenty of opportunities. Of course, the question here is, is connectivity enough, or especially with your comment around the edge, at the edge we need to manage connectivity, storage, compute, analytics, and of course the applications. So, that's a blend of players. It's not going to be in the hands of one player. So yes, plenty of opportunities but understanding what comes the other way from the customer base, where that's, you and I in our homes or outward as an about, or from a business point of view, an office or a campus environment, that's what should be driving, and not the technology itself. And I think this is the trap that the industry has fallen into many times, is we've got a great new wave of technology coming, how can we possibly deliver it to everybody rather than listening to what the customers really require and delivering it in a way consumable by all those different markets. >> Yeah now, of course all of these topics blend together. We try to keep them separately, but we're going to talk about Cloud, we're going to talk about competition, But one of the areas that we don't have a specific agenda item on is, is data and AI. And of course there's all this data flowing through the network, so presumably it's an opportunity for the Telcos. At the same time, they're not considered AI experts. They do when you talk about Edge, they would appear to have the latency advantage because of the last mile and their proximity, to various end points. But the Cloud is sort of building out as well. How do you think about data and AI as an opportunity for Telco? >> I think the whole data and AI piece for me sits on top of the cake or pie, whatever you want to call it. What we're doing with all this connectivity, what we're doing with all these moving parts and gathering information around it, and building automation into the delivery of the service, and using the analytics, whether you call it ML or AI, it doesn't really matter. But actually using that information to deliver a better service, a better outcome. Now, of course, Telcos have had much of this data for years and years, for decades, but they've never used it. So, I think what's happening is, the Cloud players are beginning to educate many of the Telcos around how valuable this stuff is. And that then brings in that question of how do we partner with people using open APIs to leverage that data. Now, do the Telcos keep hold of all that data? Do they let the Cloud players do all of it? No, it's going to be a combination depending on particular environments, and of course the people owning their devices also have a vested interest in this as well. So, you've always got to look at it end to end and where the data flows are, and where we can analyze it. But I agree that analysis on the device at the Edge, and perhaps less and less going back to the core, which is of course the original sort of mandate of the Cloud. >> Well, we certainly think that most of the Edge is going to be about AI inferencing, and then most of the data is going to stay at the edge. Some will come back for sure. And that is big opportunity for whether you're selling compute or conductivity, or maybe storage as well, but certainly insights at the Edge. >> Everything. >> Yeah. >> Everything, yeah. >> Let's get into the Cloud discussion and talk about the Hyperscalers, the big Hyperscaler elephant in the room. We're going to try to dig into what role the Cloud will play in the transformation of telecoms on Telecom TV at the great Telco debate. You likened the Hyperscalers, Chris, to Dementors from Harry Potter hovering over the industry. So, the question is, are the Cloud players going to suck the value out of the Telcos? Or are they more like Dobby the elf? They're powerful, there's sometimes friendly but they're unpredictable. >> Thank you for extending that analogy. Yes, it got a lot of reaction when I use that, but I think it indicates some of the direction of power shift where, we've got to remember here that Telcos are fundamentally national, and they're restricted by regulation, and the Cloud players are global, perhaps not as global as they'd like be, but some regional restrictions, but the global players, the Hyperscalers, they will use that power and they they will extend their reach, and they are extending their reach. If you think they now command some fantastic global networks, in some ways they've replaced some of the Telco international networks, all the submarine investments that tend to be done primarily for the Hyperscalers. So, they're building that out. So, as soon as you get onto their network, then you suddenly become part of that environment. And that is reducing some of the spend on the longer distances we might have got in the past approaches from the Telcos. Now, does that mean they're going to go all the way down and take over the Telcos? I don't believe so, because it's a fundamentally different business digging fiber in people's streets and delivering to the buildings, and putting antennas up. So, they will be a coexistence. And in fact, what we've already seen with Cloud and the Hyperscalers is that they're working much more close together than people might imagine. Now, you mentioned about data in the previous question, Google probably the best known of the of the AI and ML delivers from the Cloud side, working with many of the Telcos, even in some cases to actually have all the data outsourced into the Google Cloud for analytics purposes. They've got the power, the heavy lifting to do that. And so, we begin to see that, and obviously with shifting of workloads as appropriate within the Telco networking environment, we're seeing that with AWS, and of course with Azure as well. And Azure of course acquired a couple of companies in affirmed and Metro switch, which actually do some of the formal 5G core and the likes there within the connectivity environment. So, it's not clean cuts. And to go back to the analogy, those Dementors are swooping around and looking for opportunities, and we know that they will pick up opportunities, and they will extend their reach as far as they can down to that edge. But of course, the edge is where, as you rightly say, the Telcos have the control, they don't necessarily own the customer. I don't believe anyone owns the customer in this digital environment, because digital allows you to move your allegiance and your custom elsewhere anyway. So, but they do own that access piece, and that's what's important from a national point of view, from an economic point of view. And that's why we've seen some of the geopolitical activity banning Huawei from certain markets, encouraging more innovation through open ecosystem plays. And so, there is a tension there between the local Telco, the local market and the Hyperscaler market, but fundamentally they've got an absolute brilliant way of working together using the best of both worlds to deliver the services that we need as an economy. >> Well, and we've talked about this you and I in the past where the Telcos, portions of the Telco network could move into the Cloud. And there of course the Telcos all run the big data centers, and portions of that IT infrastructure could move into the Cloud. But it's very clear, they're not going to give up the entire family jewels to the Cloud players. Why would they? But there are portions of their IT that they could move into. Particularly, in the front end, they want to build like everybody. They want to build an abstraction layer. They're not going to move their core systems and their backend Oracle databases, they're going to put a brick wall around those, but they wanted abstraction layer, and they want to take advantage of microservices and use that data from those transaction systems. But the web front end stuff makes sense to put into Cloud. So, how do you think about that? >> I think you've hit the nail on the head. So you can't move those big backend systems straight away, gradually over time, you will, but you've got to go for those easy wins. And certainly in the research I've been doing with many of my clients, they're suggested that front end piece, making sure that you can onboard customers more easily, you can get the right mix of services. You can provide the omnichannel interaction from that customer experience that everybody talks about, for which the industry is not very well known at all by the way. So, any improvement on that is going to be good from an MPS point of view. So yeah, leveraging what we might, what we call BSS OSS in the telecom world, and actually putting that into the Cloud, leveraging both the Hyperscalers, but also by the way, many of the traditional players who people think haven't moved Cloud wards, but they are moving Cloud wards and they're embracing microservices and Cloud native. So, what you would have seen if we'd been in person down in Barcelona next week, would be a lot of the vendors who perhaps traditionally seems a bit slow moving, actually have done a lot of work to move their portfolio into the Cloud and into Cloud native environments. And yes, as you say, we can use that front end, we can use the API openness that's developed by people at the TM forum, to actually make sure we don't have to do the backend straight away, do it over time. Because of course the thing that we're not touching upon here, is the revenue stream is a consistent revenue stream. So, just because you don't need to change the backend to keep your revenue stream going, this is on a new, it keeps delivering every month, we keep paying our 50, 40, whatever bucks a month into the Telco pot. That's why it's such a big market, and people aren't going to stop doing that. So, I think the dynamics of the industry, we often spend a lot of time thinking about the inner workings of it and the potential of adjacent markets, whereas actually, we keep paying for this stuff, we keep pushing revenue into the pockets of all the Telcos. So, it's not a bad industry to be in, even if they were just pushed back to be in the access market, it's a great business. We need it more and more. The elasticity of demand is very inelastic, we need it. >> Yeah, it's the mother of old golden geese. We don't have a separate topic on security, and I want to touch on security here, is such an important topic. And it's top of mind obviously for everybody, Telcos, Hyperscalers, the Hyperscalers have this shared responsibility model, you know it well. A lot of times it's really confusing for customers. They don't realize it until there has been a problem. The Telcos are going to be very much tuned into this. How will all this openness, and we're going to talk about technology in a moment, but how will this transformation in your view, in the Cloud, with the shared responsibility model, how will that affect the whole security posture? >> Security is a great subject, and I do not specialize in it. I don't claim to be an expert by any stretch of the imagination, but I would say security for me is a bit like AI and analytics. It's everywhere. It's part of everything. And therefore you cannot think of it as a separate add on issue. So, every aspect, every element, every service you build into your micro services environment has to think about how do you secure that connection, that transaction, how do you secure the customer's data? Obviously, sovereignty plays a role in that as well in terms of where it sits, but at every level of every connection, every hop that we look through, every route to jump, we've got to see that security is built in. And in some ways, it's seen as being a separate part of the industry, but actually, as we collapse parts of the network down, we're talking about bringing optical and rooting together in many environments, security should be talked about in the same breath. So when I talked about Edge, when I talked about connectivity, storage, compute, analytics, I should've said security as well, because I absolutely believe that is fundamental to every chain in the link and let's face it, we've got a lot of links in the chain. >> Yeah, 100%. Okay, let's hit on technologies and competition, we kind of blend those together. What technology should we be paying attention to that are going to accelerate this transformation. We hear a lot about 5G, Open RAN. There's a lot of new tech coming in. What are you watching? Who are the players that we maybe should be paying attention to, some that you really like, that are well positioned? >> We've touched upon it in various of the questions that have proceeded this. So, the sort of Cloudification of the networking environment is obviously really important. The automation of the process we've got to move away from bureaucratic manual processes within these large organizations, because we've got to be more efficient, we've got to be more reliable. So, anything which is related to automation. And then the Open RAN question is really interesting. Once again, you raised this topic of when you go down an Open RAN routes or any open route, it ultimately requires more integration. You've got more moving parts from more suppliers. So, therefore there are potential security issues there, depending on how it's defined, but everybody is entering the Open RAN market. There are some names that you will see regularly next week, being pushed, I'm not going to push them anymore, because some of them just attract the oxygen of attention. But there are plenty out there. The good news is, the key vendors who come from the more traditional side are also absolutely embracing that and accept the openness. But I think the piece which probably excites me more, apart from the whole shift towards Cloud and microservices, is the coming together, the openness between the IT environment and the networking environment. And you see it, for example, in the Open RAN, this thing called the RIC, the RAN Interconnection Controller. We're actually, we're beginning to find people come from the IT side able to control elements within the wireless controller piece. Now that that starts to say to me, we're getting a real handle on it, anybody can manage it. So, more specialization is required, but understanding how the end to end flow works. What we will see of course is announcements about new devices, the big guys like Apple and Samsung do their own thing during the year, and don't interrupt their beat with it with MWC, but you'll see a lot of devices being pushed by many other providers, and you'll see many players trying to break into the different elements of the market. But I think mostly, you'll see the people approaching it from more and more Cloudified angle where things are much more leveraging, that Cloud capability and not relying on the sort of rigid and stodgy infrastructure that we've seen in the past >> Which is kind of interesting because Cloud, a lot of the Clouds are Walled Gardens, at the same time they host a lot of open technologies, and I think as these two worlds collide, IT and the Telco industry, it's going to be interesting to see how the Telco developer ecosystem evolves. And so, that's something that we definitely want to watch. You've got a comment there? >> Yeah, I think the Telco developer they've not traditionally been very big in that area at all, have they? They've had their traditional, if you go back to when you and I were kids, the plain old telephone service was a, they were a one trick pony, and they've moved onto that. In some ways, I'd like them to move on and to have the one trick of plain old broadband that we just get broadband delivered everywhere. So, there are some issues about delivering service to all parts of every country, and obviously the globe, whether we do that through satellite, we might see some interesting satellite stuff coming out during NWC. There's an awful lot of birds flying up there trying to deliver signal back to the ground. Traditionally, that's not been very well received, with the change in generation of satellite might help do that. But we've known traditionally that a lot of developer activity in there, what it does bring to the four though, Dave, is this issue of players like the Ciscos and Junipers, and all these guys of the world who bring a developer community to the table as well. This is where the ecosystem play comes in, because that's where you get the innovation in the application world, working with channels, working with individual applications. And so it's opening up, it's basically building a massive fabric that anybody can tap into, and that's what becomes so exciting. So, the barriers to entry come down, but I think it will see us settling down, a stabilization of relationship between the Telcos and the Hyperscalers, because they need each other as we talked about previously, then the major providers, the Ciscos, Nokias, Ericssons, Huawei's, the way they interact with the Telcos. And then allowing that level of innovation coming in from the smaller players, whether it's on a national or a global basis. So, it's actually a really exciting environment. >> So I want to continue that theme and just talk about Telco in the enterprise. And Chris, on this topic, I want to just touch on some things and bring in some survey data from ETR, Enterprise Technology Research, our partner. And of course the Telcos, they've got lots of data centers. And as we talked about, they're going to be moving certain portions into the Cloud, lots of the front end pieces in particular, but let's look at the momentum of some of the IT players within the ETR dataset, and look at how they compare to some of the Telcos that ETR captures specifically within the Telco industry. So, we filtered this data on the Telco industry. So, this is our X, Y graph that we show you oftentimes on the vertical axis, is net score which measures spending momentum, and in the horizontal axis is market share, which is a measure of pervasiveness in the dataset. Now, this data is for shared accounts just in the Telco sector. So we filtered on certain sectors, like within the technology sectors, Cloud, networking, and so it's narrow, it's a narrow slice of the 1500. It respondents, it represents about 133 shared accounts. And a couple of things to jump right out. Within the Telco industry, it's no surprise, but Azure and AWS have massive presence on the horizontal axis, but what's notable as they score very highly in the vertical axis, with elevated spending velocity on their platforms within Telco. Google Cloud doesn't have as much of a presence, but it's elevated as well. Chris was talking about their data posture before, Arista and Verizon, along with VMware are also elevated, as is Aruba, which is HPEs networking division, but they don't have the presence on the horizontal axis. And you got Red Hat OpenStack is actually quite prominent in Telco as we've reported in previous segments. Is no surprise You see Akamai there. Now remember, this survey is weighted toward enterprise IT, so you have to take that into consideration, but look at Cisco, very strong presence, nicely elevated as is Equinox, both higher than many of the others including Dell, but you could see Dell actually has pretty respectable spending in Telco. It's an area that they're starting to focus on more. And then you got that cluster below, your Juniper, AT&T, Oracle, the rest of HPE TELUM and Lumen which is formerly, century link via IBM. Now again, I'm going to caution you. This is an enterprise IT heavy survey, but the big takeaway is the Cloud players have a major presence inside of firms that say they're in the telecommunications industry. And certain IT players like Cisco, VMware and Red Hat appear to be well positioned inside these accounts. So Chris, I'm not sure if any of this commentary resonates with you, but it seems that the Telcos would love to partner up with traditional IT vendors and Cloud players, and maybe find ways to grow their respective businesses. >> I think some of the data points you brought out there are very important. So yes, we've seen a Microsoft Azure and AWS very strong working with Telcos. We've seen Google Cloud platform actually really aggressively pushed into the market certainly the last 12, 24 months. So yeah, they're well positioned, and they all come from a slightly different background. As I said, the Google with this, perhaps more data centric approach in its analytics, tools very useful, AWS with this outpost reaching out, connecting out, and as you'll, with its knowledge of the the Microsoft business market certainly pushing into private networks as well, by the way. So yeah, and Cisco, of course in there does have, and it's a mass scale division, a lot of activity there, some of the people collapsing, some of that rooting an obstacle together, their big push on Silicon. So, what you've got here is a sort of cross representation of many of the different sorts of suppliers who are active in this market. Now Telcos is a big spenders, the telecom market, as we said, a $1.4 trillion market, they spend a lot, they probably have to double bubble spend at the moment to get over the hump of 5G investment, to build out fiber where they need to build out. So, any anything that relates to that is of course a major spending opportunity, a major market opportunity for players. And we know when you need the infrastructure behind it, whether it's in data centers or in their own data centers or in the Cloud to deliver against it. So, what I do like about this as an analyst, a lot of people would focus on one particular piece of the market. So you specialize on handsets, people specialize on home markets and home gateways. So, I tend to sit back and try and look at the big picture, the whole picture. And I think we're beginning to see some very good momentum where people are, where companies are building upon, of course their core business within the telecom industry, extending it out. But the lines of demarcation are blurring between enterprise, Telco, and indeed moving down into small business. And you think about the SD-WAN Market, which came from nowhere to build a much more flexible solution for connecting people over the wide area network, which has been brilliant during the pandemic, because it's allowed us to extend that to home, but be of course, build a campus ready for the future as well. So there are plenty of opportunities out there. I think the big question in my mind is always about from going into the Telco, as I said, whether they wannna reduce the number of suppliers on the roster. So that puts a question mark against some of the open approaches, and then from the Telco to the end customer, because it goes to the Telcos, 30% of their revenue comes from the enterprise market, 60% from the consumer market. How do they leverage the channel? Which includes all the channels, we talked about security, all of the IT stuff that you've already touched upon and the Cloud. It's going to be a very interesting mix and balancing act between different channels to get the services that the customers want. And I think increasingly, customers are more aware of the opportunities open to them to reach back into this ecosystem and say, "Yeah, I want a piece of humans to Telco, but I want it to come to me through my local integrated channel, because I need a bit of their expertise on security." So, fascinating market, and I think not telecom's no longer considered in isolation, but very much as part of that broader digital ecosystem. >> Chris, it's very hard to compress an analysis of a $1.4 trillion business into 30 or 35 minutes, but you're just the guy to help me do it. So, I got to really thank you for participating today and bringing your knowledge. Awesome. >> Do you know, it's my pleasure. I love looking at this market. Obviously I love analogies like Harry Potter, which makes it bring things to life. But at the end of the day, we as people, we want to be connected, we as business, we want to be connected, in society we want to be connected. So, the fundamental of this industry are unbelievably strong. Let's hope that governments don't mess with it too much. And let's hope that we get the right technology comes through, and help support that world of connectivity going forward. >> All right, Chris, well, I'll be texting you from Mobile World Congress in Barcelona, and many thanks to my colleague, Chris Lewis, he brought some serious knowledge today and thank you. And remember, I publish each week on wikibond.com and siliconangle.com. And these episodes are all available as podcasts. You just got to search for Breaking Analysis podcasts. You can always connect with me on twitter @dvellante or email me at dave.vellante@siliconangle.com. And you can comment on my LinkedIn post, and don't forget to check out etr.plus for all the survey data. This is Dave Vellante, for theCUBE Insights powered by ETR. Be well, and we'll see you next time. (upbeat music)