(intro music) >> "theCUBE's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) >> We're not going to- >> Hey everybody, welcome back to the Fira in Barcelona. My name is Dave Vellante, I'm here with Dave Nicholson, day four of MWC23. I mean, it's Dave, it's, it's still really busy. And you walking the floors, you got to stop and start. >> It's surprising. >> People are cheering. They must be winding down, giving out the awards. Really excited. Pier, look at you and Elias here. He's the vice president of Engineering Technology for Edge Computing Offers Strategy and Execution at Dell Technologies, and he's joined by Dan Cummins, who's a fellow and vice president of, in the Edge Business Unit at Dell Technologies. Guys, welcome. >> Thank you. >> Thank you. >> I love when I see the term fellow. You know, you don't, they don't just give those away. What do you got to do to be a fellow at Dell? >> Well, you know, fellows are senior technical leaders within Dell. And they're usually tasked to help Dell solve you know, a very large business challenge to get to a fellow. There's only, I think, 17 of them inside of Dell. So it is a small crowd. You know, previously, really what got me to fellow, is my continued contribution to transform Dell's mid-range business, you know, VNX two, and then Unity, and then Power Store, you know, and then before, and then after that, you know, they asked me to come and, and help, you know, drive the technology vision for how Dell wins at the Edge. >> Nice. Congratulations. Now, Pierluca, I'm looking at this kind of cool chart here which is Edge, Edge platform by Dell Technologies, kind of this cube, like cubes course, you know. >> AK project from here. >> Yeah. So, so tell us about the Edge platform. What, what's your point of view on all that at Dell? >> Yeah, absolutely. So basically in a, when we create the Edge, and before even then was bringing aboard, to create this vision of the platform, and now building the platform when we announced project from here, was to create solution for the Edge. Dell has been at the edge for 30 years. We sold a lot of compute. But the reality was people want outcome. And so, and the Edge is a new market, very exciting, but very siloed. And so people at the Edge have different personas. So quickly realize that we need to bring in Dell, people with expertise, quickly realize as well that doing all these solution was not enough. There was a lot of problem to solve because the Edge is outside of the data center. So you are outside of the wall of the data center. And what is going to happen is obviously you are in the land of no one. And so you have million of device, thousand of million of device. All of us at home, we have all connected thing. And so we understand that the, the capability of Dell was to bring in technology to secure, manage, deploy, with zero touch, zero trust, the Edge. And all the edge the we're speaking about right now, we are focused on everything that is outside of a normal data center. So, how we married the computer that we have for many years, the new gateways that we create, so having the best portfolio, number one, having the best solution, but now, transforming the way that people deploy the Edge, and secure the Edge through a software platform that we create. >> You mentioned Project Frontier. I like that Dell started to do these sort of project, Project Alpine was sort of the multi-cloud storage. I call it "The Super Cloud." The Project Frontier. It's almost like you develop, it's like mission based. Like, "Okay, that's our North Star." People hear Project Frontier, they know, you know, internally what you're talking about. Maybe use it for external communications too, but what have you learned since launching Project Frontier? What's different about the Edge? I mean you're talking about harsh environments, you're talking about new models of connectivity. So, what have you learned from Project Frontier? What, I'd love to hear the fellow perspective as well, and what you guys are are learning so far. >> Yeah, I mean start and then I left to them, but we learn a lot. The first thing we learn that we are on the right path. So that's good, because every conversation we have, there is nobody say to us, you know, "You are crazy. "This is not needed." Any conversation we have this week, start with the telco thing. But after five minutes it goes to, okay, how I can solve the Edge, how I can bring the compute near where the data are created, and how I can do that secure at scale, and with the right price. And then can speak about how we're doing that. >> Yeah, yeah. But before that, we have to really back up and understand what Dell is doing with Project Frontier, which is an Edge operations platform, to simplify your Edge use cases. Now, Pierluca and his team have a number of verticalized applications. You want to be able to securely deploy those, you know, at the Edge. But you need a software platform that's going to simplify both the life cycle management, and the security at the Edge, with the ability to be able to construct and deploy distributed applications. Customers are looking to derive value near the point of generation of data. We see a massive explosion of data. But in particular, what's different about the Edge, is the different computing locations, and the constraints that are on those locations. You know, for example, you know, in a far Edge environment, the people that service that equipment are not trained in the IT, or train, trained in it. And they're also trained in the safety and security protocols of that environment. So you necessarily can't apply the same IT techniques when you're managing infrastructure and deploying applications, or servicing in those locations. So Frontier was designed to solve for those constraints. You know, often we see competitors that are doing similar things, that are starting from an IT mindset, and trying to shift down to cover Edge use cases. What we've done with Frontier, is actually first understood the constraints that they have at the Edge. Both the operational constraints and technology constraints, the service constraints, and then came up with a, an architecture and technology platform that allows them to start from the Edge, and bleed into the- >> So I'm laughing because you guys made the same mistake. And you, I think you learned from that mistake, right? You used to take X86 boxes and throw 'em over the fence. Now, you're building purpose-built systems, right? Project Frontier I think is an example of the learnings. You know, you guys an IT company, right? Come on. But you're learning fast, and that's what I'm impressed about. >> Well Glenn, of course we're here at MWC, so it's all telecom, telecom, telecom, but really, that's a subset of Edge. >> Yes. >> Fair to say? >> Yes. >> Can you give us an example of something that is, that is, orthogonal to, to telecom, you know, maybe off to the side, that maybe overlaps a little bit, but give us an, give us an example of Edge, that isn't specifically telecom focused. >> Well, you got the, the Edge verticals. and Pierluca could probably speak very well to this. You know, you got manufacturing, you got retail, you got automotive, you got oil and gas. Every single one of them are going to make different choices in the software that they're going to use, the hyperscaler investments that they're going to use, and then write some sort of automation, you know, to deploy that, right? And the Edge is highly fragmented across all of these. So we certainly could deploy a private wireless 5G solution, orchestrate that deployment through Frontier. We can also orchestrate other use cases like connected worker, or overall equipment effectiveness in manufacturing. But Pierluca you have a, you have a number. >> Well, but from your, so, but just to be clear, from your perspective, the whole idea of, for example, private 5g, it's a feature- >> Yes. >> That might be included. It happened, it's a network topology, a network function that might be a feature of an Edge environment. >> Yes. But it's not the center of the discussion. >> So, it enables the outcome. >> Yeah. >> Okay. >> So this, this week is a clear example where we confirm and establish this. The use case, as I said, right? They, you say correctly, we learned very fast, right? We brought people in that they came from industry that was not IT industry. We brought people in with the things, and we, we are Dell. So we have the luxury to be able to interview hundreds of customers, that just now they try to connect the OT with the IT together. And so what we learn, is really, at the Edge is different personas. They person that decide what to do at the Edge, is not the normal IT administrator, is not the normal telco. >> Who is it? Is it an engineer, or is it... >> It's, for example, the store manager. >> Yeah. >> It's, for example, the, the person that is responsible for the manufacturing process. Those people are not technology people by any means. But they have a business goal in mind. Their goal is, "I want to raise my productivity by 30%," hence, I need to have a preventive maintenance solution. How we prescribe this preventive maintenance solution? He doesn't prescribe the preventive maintenance solution. He goes out, he has to, a consult or himself, to deploy that solution, and he choose different fee. Now, the example that I was doing from the houses, all of us, we have connected device. The fact that in my house, I have a solar system that produce energy, the only things I care that I can read, how much energy I produce on my phone, and how much energy I send to get paid back. That's the only thing. The fact that inside there is a compute that is called Dell or other things is not important to me. Same persona. Now, if I can solve the security challenge that the SI, or the user need to implement this technology because it goes everywhere. And I can manage this in extensively, and I can put the supply chain of Dell on top of that. And I can go every part in the world, no matter if I have in Papua New Guinea, or I have an oil ring in Texas, that's the winning strategy. That's why people, they are very interested to the, including Telco, the B2B business in telco is looking very, very hard to how they recoup the investment in 5g. One of the way, is to reach out with solution. And if I can control and deploy things, more than just SD one or other things, or private mobility, that's the key. >> So, so you have, so you said manufacturing, retail, automotive, oil and gas, you have solutions for each of those, or you're building those, or... >> Right now we have solution for manufacturing, with for example, PTC. That is the biggest company. It's actually based in Boston. >> Yeah. Yeah, it is. There's a company that the market's just coming right to them. >> We have a, very interesting. Another solution with Litmus, that is a startup that, that also does manufacturing aggregation. We have retail with Deep North. So we can do detecting in the store, how many people they pass, how many people they doing, all of that. And all theses solution that will be, when we will have Frontier in the market, will be also in Frontier. We are also expanding to energy, and we going vertical by vertical. But what is they really learn, right? You said, you know you are an IT company. What, to me, the Edge is a pre virtualization area. It's like when we had, you know, I'm, I've been in the company for 24 years coming from EMC. The reality was before there was virtualization, everybody was starting his silo. Nobody thought about, "Okay, I can run this thing together "with security and everything, "but I need to do it." Because otherwise in a manufacturing, or in a shop, I can end up with thousand of devices, just because someone tell to me, I'm a, I'm a store manager, I don't know better. I take this video surveillance application, I take these things, I take a, you know, smart building solution, suddenly I have five, six, seven different infrastructure to run this thing because someone say so. So we are here to democratize the Edge, to secure the Edge, and to expand. That's the idea. >> So, the Frontier platform is really the horizontal platform. And you'll build specific solutions for verticals. On top of that, you'll, then I, then the beauty is ISV's come in. >> Yes. >> 'Cause it's open, and the developers. >> We have a self certification program already for our solution, as well, for the current solution, but also for Frontier. >> What does that involve? Self-certification. You go through you, you go through some- >> It's basically a, a ISV can come. We have a access to a lab, they can test the thing. If they pass the first screen, then they can become part of our ecosystem very easily. >> Ah. >> So they don't need to spend days or months with us to try to architect the thing. >> So they get the premature of being certified. >> They get the Dell brand associated with it. Maybe there's some go-to-market benefits- >> Yes. >> As well. Cool. What else do we need to know? >> So, one thing I, well one thing I just want to stress, you know, when we say horizontal platform, really, the Edge is really a, a distributed edge computing problem, right? And you need to almost create a mesh of different computing locations. So for example, even though Dell has Edge optimized infrastructure, that we're going to deploy and lifecycle manage, customers may also have compute solutions, existing compute solutions in their data center, or at a co-location facility that are compute destinations. Project Frontier will connect to those private cloud stacks. They'll also collect to, connect to multiple public cloud stacks. And then, what they can do, is the solutions that we talked about, they construct that using an open based, you know, protocol, template, that describes that distributed application that produces that outcome. And then through orchestration, we can then orchestrate across all of these locations to produce that outcome. That's what the platform's doing. >> So it's a compute mesh, is what you just described? >> Yeah, it's, it's a, it's a software orchestration mesh. >> Okay. >> Right. And allows customers to take advantage of their existing investments. Also allows them to, to construct solutions based on the ISV of their choice. We're offering solutions like Pierluca had talked about, you know, in manufacturing with Litmus and PTC, but they could put another use case that's together based on another ISV. >> Is there a data mesh analog here? >> The data mesh analog would run on top of that. We don't offer that as part of Frontier today, but we do have teams working inside of Dell that are working on this technology. But again, if there's other data mesh technology or packages, that they want to deploy as a solution, if you will, on top of Frontier, Frontier's extensible in that way as well. >> The open nature of Frontier is there's a, doesn't, doesn't care. It's just a note on the mesh. >> Yeah. >> Right. Now, of course you'd rather, you'd ideally want it to be Dell technology, and you'll make the business case as to why it should be. >> They get additional benefits if it's Dell. Pierluca talked a lot about, you know, deploying infrastructure outside the walls of an IT data center. You know, this stuff can be tampered with. Somebody can move it to another room, somebody can open up. In the supply chain with, you know, resellers that are adding additional people, can open these devices up. We're actually deploying using an Edge technology called Secure Device Onboarding. And it solves a number of things for us. We, as a manufacturer can initialize the roots of trust in the Dell hardware, such that we can validate, you know, tamper detection throughout the supply chain, and securely transfer ownership. And that's different. That is not an IT technique. That's an edge technique. And that's just one example. >> That's interesting. I've talked to other people in IT about how they're using that technique. So it's, it's trickling over to that side of the business. >> I'm almost curious about the friction that you, that you encounter because the, you know, you paint a picture of a, of a brave new world, a brave new future. Ideally, in a healthy organization, they have, there's a CTO, or at least maybe a CIO, with a CTO mindset. They're seeking to leverage technology in the service of whatever the mission of the organization is. But they've got responsibilities to keep the lights on, as well as innovate. In that mix, what are you seeing as the inhibitors? What's, what's the push back against Frontier that you're seeing in most cases? Is it, what, what is it? >> Inside of Dell? >> No, not, I'm saying out, I'm saying with- >> Market friction. >> Market, market, market friction. What is the push back? >> I think, you know, as I explained, do yourself is one of the things that probably is the most inhibitor, because some people, they think that they are better already. They invest a lot in this, and they have the content. But those are again, silo solutions. So, if you go into some of the huge things that they already established, thousand of store and stuff like that, there is an opportunity there, because also they want to have a refresh cycle. So when we speak about softer, softer, softer, when you are at the Edge, the software needs to run on something that is there. So the combination that we offer about controlling the security of the hardware, plus the operating system, and provide an end-to-end platform, allow them to solve a lot of problems that today they doing by themselves. Now, I met a lot of customers, some of them, one actually here in Spain, I will not make the name, but it's a large automotive. They have the same challenge. They try to build, but the problem is this is just for them. And they want to use something that is a backup and provide with the Dell service, Dell capability of supply chain in all the world, and the diversity of the portfolio we have. These guys right now, they need to go out and find different types of compute, or try to adjust thing, or they need to have 20 people there to just prepare the device. We will take out all of this. So I think the, the majority of the pushback is about people that they already established infrastructure, and they want to use that. But really, there is an opportunity here. Because the, as I said, the IT/OT came together now, it's a reality. Three years ago when we had our initiative, they've pointed out, sarcastically. We, we- >> Just trying to be honest. (laughing) >> I can't let you get away with that. >> And we, we failed because it was too early. And we were too focused on, on the fact to going. Push ourself to the boundary of the IOT. This platform is open. You want to run EdgeX, you run EdgeX, you want OpenVINO, you want Microsoft IOT, you run Microsoft IOT. We not prescribe the top. We are locking down the bottom. >> What you described is the inertia of, of sunk dollars, or sunk euro into an infrastructure, and now they're hanging onto that. >> Yeah. >> But, I mean, you know, I, when we say horizontal, we think scale, we think low cost, at volume. That will, that will win every time. >> There is a simplicity at scale, right? There is a, all the thing. >> And the, and the economics just overwhelm that siloed solution. >> And >> That's inevitable. >> You know, if you want to apply security across the entire thing, if you don't have a best practice, and a click that you can do that, or bring down an application that you need, you need to touch each one of these silos. So, they don't know yet, but we going to be there helping them. So there is no pushback. Actually, this particular example I did, this guy said you know, there are a lot of people that come here. Nobody really described the things we went through. So we are on the right track. >> Guys, great conversation. We really appreciate you coming on "theCUBE." >> Thank you. >> Pleasure to have you both. >> Okay. >> Thank you. >> All right. And thank you for watching Dave Vellante for Dave Nicholson. We're live at the Fira. We're winding up day four. Keep it right there. Go to siliconangle.com. John Furrier's got all the news on "theCUBE.net." We'll be right back right after this break. "theCUBE," at MWC 23. (outro music)

(upbeat music) >> Hello, this is Dave Valente with theCUBE, and one of the most gratifying aspects of my role as a host of "theCUBE TV" is I get to cover a wide range of topics. And quite often, we're able to bring to our program a level of expertise that allows us to more deeply explore and unpack some of the topics that we cover throughout the year. And one of our favorite topics, of course, is data. Now, in 2021, after being in isolation for the better part of two years, a group of industry analysts met up at AWS re:Invent and started a collaboration to look at the trends in data and predict what some likely outcomes will be for the coming year. And it resulted in a very popular session that we had last year focused on the future of data management. And I'm very excited and pleased to tell you that the 2023 edition of that predictions episode is back, and with me are five outstanding market analyst, Sanjeev Mohan of SanjMo, Tony Baer of dbInsight, Carl Olofson from IDC, Dave Menninger from Ventana Research, and Doug Henschen, VP and Principal Analyst at Constellation Research. Now, what is it that we're calling you, guys? A data pack like the rat pack? No, no, no, no, that's not it. It's the data crowd, the data crowd, and the crowd includes some of the best minds in the data analyst community. They'll discuss how data management is evolving and what listeners should prepare for in 2023. Guys, welcome back. Great to see you. >> Good to be here. >> Thank you. >> Thanks, Dave. (Tony and Dave faintly speaks) >> All right, before we get into 2023 predictions, we thought it'd be good to do a look back at how we did in 2022 and give a transparent assessment of those predictions. So, let's get right into it. We're going to bring these up here, the predictions from 2022, they're color-coded red, yellow, and green to signify the degree of accuracy. And I'm pleased to report there's no red. Well, maybe some of you will want to debate that grading system. But as always, we want to be open, so you can decide for yourselves. So, we're going to ask each analyst to review their 2022 prediction and explain their rating and what evidence they have that led them to their conclusion. So, Sanjeev, please kick it off. Your prediction was data governance becomes key. I know that's going to knock you guys over, but elaborate, because you had more detail when you double click on that. >> Yeah, absolutely. Thank you so much, Dave, for having us on the show today. And we self-graded ourselves. I could have very easily made my prediction from last year green, but I mentioned why I left it as yellow. I totally fully believe that data governance was in a renaissance in 2022. And why do I say that? You have to look no further than AWS launching its own data catalog called DataZone. Before that, mid-year, we saw Unity Catalog from Databricks went GA. So, overall, I saw there was tremendous movement. When you see these big players launching a new data catalog, you know that they want to be in this space. And this space is highly critical to everything that I feel we will talk about in today's call. Also, if you look at established players, I spoke at Collibra's conference, data.world, work closely with Alation, Informatica, a bunch of other companies, they all added tremendous new capabilities. So, it did become key. The reason I left it as yellow is because I had made a prediction that Collibra would go IPO, and it did not. And I don't think anyone is going IPO right now. The market is really, really down, the funding in VC IPO market. But other than that, data governance had a banner year in 2022. >> Yeah. Well, thank you for that. And of course, you saw data clean rooms being announced at AWS re:Invent, so more evidence. And I like how the fact that you included in your predictions some things that were binary, so you dinged yourself there. So, good job. Okay, Tony Baer, you're up next. Data mesh hits reality check. As you see here, you've given yourself a bright green thumbs up. (Tony laughing) Okay. Let's hear why you feel that was the case. What do you mean by reality check? >> Okay. Thanks, Dave, for having us back again. This is something I just wrote and just tried to get away from, and this just a topic just won't go away. I did speak with a number of folks, early adopters and non-adopters during the year. And I did find that basically that it pretty much validated what I was expecting, which was that there was a lot more, this has now become a front burner issue. And if I had any doubt in my mind, the evidence I would point to is what was originally intended to be a throwaway post on LinkedIn, which I just quickly scribbled down the night before leaving for re:Invent. I was packing at the time, and for some reason, I was doing Google search on data mesh. And I happened to have tripped across this ridiculous article, I will not say where, because it doesn't deserve any publicity, about the eight (Dave laughing) best data mesh software companies of 2022. (Tony laughing) One of my predictions was that you'd see data mesh washing. And I just quickly just hopped on that maybe three sentences and wrote it at about a couple minutes saying this is hogwash, essentially. (laughs) And that just reun... And then, I left for re:Invent. And the next night, when I got into my Vegas hotel room, I clicked on my computer. I saw a 15,000 hits on that post, which was the most hits of any single post I put all year. And the responses were wildly pro and con. So, it pretty much validates my expectation in that data mesh really did hit a lot more scrutiny over this past year. >> Yeah, thank you for that. I remember that article. I remember rolling my eyes when I saw it, and then I recently, (Tony laughing) I talked to Walmart and they actually invoked Martin Fowler and they said that they're working through their data mesh. So, it takes a really lot of thought, and it really, as we've talked about, is really as much an organizational construct. You're not buying data mesh >> Bingo. >> to your point. Okay. Thank you, Tony. Carl Olofson, here we go. You've graded yourself a yellow in the prediction of graph databases. Take off. Please elaborate. >> Yeah, sure. So, I realized in looking at the prediction that it seemed to imply that graph databases could be a major factor in the data world in 2022, which obviously didn't become the case. It was an error on my part in that I should have said it in the right context. It's really a three to five-year time period that graph databases will really become significant, because they still need accepted methodologies that can be applied in a business context as well as proper tools in order for people to be able to use them seriously. But I stand by the idea that it is taking off, because for one thing, Neo4j, which is the leading independent graph database provider, had a very good year. And also, we're seeing interesting developments in terms of things like AWS with Neptune and with Oracle providing graph support in Oracle database this past year. Those things are, as I said, growing gradually. There are other companies like TigerGraph and so forth, that deserve watching as well. But as far as becoming mainstream, it's going to be a few years before we get all the elements together to make that happen. Like any new technology, you have to create an environment in which ordinary people without a whole ton of technical training can actually apply the technology to solve business problems. >> Yeah, thank you for that. These specialized databases, graph databases, time series databases, you see them embedded into mainstream data platforms, but there's a place for these specialized databases, I would suspect we're going to see new types of databases emerge with all this cloud sprawl that we have and maybe to the edge. >> Well, part of it is that it's not as specialized as you might think it. You can apply graphs to great many workloads and use cases. It's just that people have yet to fully explore and discover what those are. >> Yeah. >> And so, it's going to be a process. (laughs) >> All right, Dave Menninger, streaming data permeates the landscape. You gave yourself a yellow. Why? >> Well, I couldn't think of a appropriate combination of yellow and green. Maybe I should have used chartreuse, (Dave laughing) but I was probably a little hard on myself making it yellow. This is another type of specialized data processing like Carl was talking about graph databases is a stream processing, and nearly every data platform offers streaming capabilities now. Often, it's based on Kafka. If you look at Confluent, their revenues have grown at more than 50%, continue to grow at more than 50% a year. They're expected to do more than half a billion dollars in revenue this year. But the thing that hasn't happened yet, and to be honest, they didn't necessarily expect it to happen in one year, is that streaming hasn't become the default way in which we deal with data. It's still a sidecar to data at rest. And I do expect that we'll continue to see streaming become more and more mainstream. I do expect perhaps in the five-year timeframe that we will first deal with data as streaming and then at rest, but the worlds are starting to merge. And we even see some vendors bringing products to market, such as K2View, Hazelcast, and RisingWave Labs. So, in addition to all those core data platform vendors adding these capabilities, there are new vendors approaching this market as well. >> I like the tough grading system, and it's not trivial. And when you talk to practitioners doing this stuff, there's still some complications in the data pipeline. And so, but I think, you're right, it probably was a yellow plus. Doug Henschen, data lakehouses will emerge as dominant. When you talk to people about lakehouses, practitioners, they all use that term. They certainly use the term data lake, but now, they're using lakehouse more and more. What's your thoughts on here? Why the green? What's your evidence there? >> Well, I think, I was accurate. I spoke about it specifically as something that vendors would be pursuing. And we saw yet more lakehouse advocacy in 2022. Google introduced its BigLake service alongside BigQuery. Salesforce introduced Genie, which is really a lakehouse architecture. And it was a safe prediction to say vendors are going to be pursuing this in that AWS, Cloudera, Databricks, Microsoft, Oracle, SAP, Salesforce now, IBM, all advocate this idea of a single platform for all of your data. Now, the trend was also supported in 2023, in that we saw a big embrace of Apache Iceberg in 2022. That's a structured table format. It's used with these lakehouse platforms. It's open, so it ensures portability and it also ensures performance. And that's a structured table that helps with the warehouse side performance. But among those announcements, Snowflake, Google, Cloud Era, SAP, Salesforce, IBM, all embraced Iceberg. But keep in mind, again, I'm talking about this as something that vendors are pursuing as their approach. So, they're advocating end users. It's very cutting edge. I'd say the top, leading edge, 5% of of companies have really embraced the lakehouse. I think, we're now seeing the fast followers, the next 20 to 25% of firms embracing this idea and embracing a lakehouse architecture. I recall Christian Kleinerman at the big Snowflake event last summer, making the announcement about Iceberg, and he asked for a show of hands for any of you in the audience at the keynote, have you heard of Iceberg? And just a smattering of hands went up. So, the vendors are ahead of the curve. They're pushing this trend, and we're now seeing a little bit more mainstream uptake. >> Good. Doug, I was there. It was you, me, and I think, two other hands were up. That was just humorous. (Doug laughing) All right, well, so I liked the fact that we had some yellow and some green. When you think about these things, there's the prediction itself. Did it come true or not? There are the sub predictions that you guys make, and of course, the degree of difficulty. So, thank you for that open assessment. All right, let's get into the 2023 predictions. Let's bring up the predictions. Sanjeev, you're going first. You've got a prediction around unified metadata. What's the prediction, please? >> So, my prediction is that metadata space is currently a mess. It needs to get unified. There are too many use cases of metadata, which are being addressed by disparate systems. For example, data quality has become really big in the last couple of years, data observability, the whole catalog space is actually, people don't like to use the word data catalog anymore, because data catalog sounds like it's a catalog, a museum, if you may, of metadata that you go and admire. So, what I'm saying is that in 2023, we will see that metadata will become the driving force behind things like data ops, things like orchestration of tasks using metadata, not rules. Not saying that if this fails, then do this, if this succeeds, go do that. But it's like getting to the metadata level, and then making a decision as to what to orchestrate, what to automate, how to do data quality check, data observability. So, this space is starting to gel, and I see there'll be more maturation in the metadata space. Even security privacy, some of these topics, which are handled separately. And I'm just talking about data security and data privacy. I'm not talking about infrastructure security. These also need to merge into a unified metadata management piece with some knowledge graph, semantic layer on top, so you can do analytics on it. So, it's no longer something that sits on the side, it's limited in its scope. It is actually the very engine, the very glue that is going to connect data producers and consumers. >> Great. Thank you for that. Doug. Doug Henschen, any thoughts on what Sanjeev just said? Do you agree? Do you disagree? >> Well, I agree with many aspects of what he says. I think, there's a huge opportunity for consolidation and streamlining of these as aspects of governance. Last year, Sanjeev, you said something like, we'll see more people using catalogs than BI. And I have to disagree. I don't think this is a category that's headed for mainstream adoption. It's a behind the scenes activity for the wonky few, or better yet, companies want machine learning and automation to take care of these messy details. We've seen these waves of management technologies, some of the latest data observability, customer data platform, but they failed to sweep away all the earlier investments in data quality and master data management. So, yes, I hope the latest tech offers, glimmers that there's going to be a better, cleaner way of addressing these things. But to my mind, the business leaders, including the CIO, only want to spend as much time and effort and money and resources on these sorts of things to avoid getting breached, ending up in headlines, getting fired or going to jail. So, vendors bring on the ML and AI smarts and the automation of these sorts of activities. >> So, if I may say something, the reason why we have this dichotomy between data catalog and the BI vendors is because data catalogs are very soon, not going to be standalone products, in my opinion. They're going to get embedded. So, when you use a BI tool, you'll actually use the catalog to find out what is it that you want to do, whether you are looking for data or you're looking for an existing dashboard. So, the catalog becomes embedded into the BI tool. >> Hey, Dave Menninger, sometimes you have some data in your back pocket. Do you have any stats (chuckles) on this topic? >> No, I'm glad you asked, because I'm going to... Now, data catalogs are something that's interesting. Sanjeev made a statement that data catalogs are falling out of favor. I don't care what you call them. They're valuable to organizations. Our research shows that organizations that have adequate data catalog technologies are three times more likely to express satisfaction with their analytics for just the reasons that Sanjeev was talking about. You can find what you want, you know you're getting the right information, you know whether or not it's trusted. So, those are good things. So, we expect to see the capabilities, whether it's embedded or separate. We expect to see those capabilities continue to permeate the market. >> And a lot of those catalogs are driven now by machine learning and things. So, they're learning from those patterns of usage by people when people use the data. (airy laughs) >> All right. Okay. Thank you, guys. All right. Let's move on to the next one. Tony Bear, let's bring up the predictions. You got something in here about the modern data stack. We need to rethink it. Is the modern data stack getting long at the tooth? Is it not so modern anymore? >> I think, in a way, it's got almost too modern. It's gotten too, I don't know if it's being long in the tooth, but it is getting long. The modern data stack, it's traditionally been defined as basically you have the data platform, which would be the operational database and the data warehouse. And in between, you have all the tools that are necessary to essentially get that data from the operational realm or the streaming realm for that matter into basically the data warehouse, or as we might be seeing more and more, the data lakehouse. And I think, what's important here is that, or I think, we have seen a lot of progress, and this would be in the cloud, is with the SaaS services. And especially you see that in the modern data stack, which is like all these players, not just the MongoDBs or the Oracles or the Amazons have their database platforms. You see they have the Informatica's, and all the other players there in Fivetrans have their own SaaS services. And within those SaaS services, you get a certain degree of simplicity, which is it takes all the housekeeping off the shoulders of the customers. That's a good thing. The problem is that what we're getting to unfortunately is what I would call lots of islands of simplicity, which means that it leads it (Dave laughing) to the customer to have to integrate or put all that stuff together. It's a complex tool chain. And so, what we really need to think about here, we have too many pieces. And going back to the discussion of catalogs, it's like we have so many catalogs out there, which one do we use? 'Cause chances are of most organizations do not rely on a single catalog at this point. What I'm calling on all the data providers or all the SaaS service providers, is to literally get it together and essentially make this modern data stack less of a stack, make it more of a blending of an end-to-end solution. And that can come in a number of different ways. Part of it is that we're data platform providers have been adding services that are adjacent. And there's some very good examples of this. We've seen progress over the past year or so. For instance, MongoDB integrating search. It's a very common, I guess, sort of tool that basically, that the applications that are developed on MongoDB use, so MongoDB then built it into the database rather than requiring an extra elastic search or open search stack. Amazon just... AWS just did the zero-ETL, which is a first step towards simplifying the process from going from Aurora to Redshift. You've seen same thing with Google, BigQuery integrating basically streaming pipelines. And you're seeing also a lot of movement in database machine learning. So, there's some good moves in this direction. I expect to see more than this year. Part of it's from basically the SaaS platform is adding some functionality. But I also see more importantly, because you're never going to get... This is like asking your data team and your developers, herding cats to standardizing the same tool. In most organizations, that is not going to happen. So, take a look at the most popular combinations of tools and start to come up with some pre-built integrations and pre-built orchestrations, and offer some promotional pricing, maybe not quite two for, but in other words, get two products for the price of two services or for the price of one and a half. I see a lot of potential for this. And it's to me, if the class was to simplify things, this is the next logical step and I expect to see more of this here. >> Yeah, and you see in Oracle, MySQL heat wave, yet another example of eliminating that ETL. Carl Olofson, today, if you think about the data stack and the application stack, they're largely separate. Do you have any thoughts on how that's going to play out? Does that play into this prediction? What do you think? >> Well, I think, that the... I really like Tony's phrase, islands of simplification. It really says (Tony chuckles) what's going on here, which is that all these different vendors you ask about, about how these stacks work. All these different vendors have their own stack vision. And you can... One application group is going to use one, and another application group is going to use another. And some people will say, let's go to, like you go to a Informatica conference and they say, we should be the center of your universe, but you can't connect everything in your universe to Informatica, so you need to use other things. So, the challenge is how do we make those things work together? As Tony has said, and I totally agree, we're never going to get to the point where people standardize on one organizing system. So, the alternative is to have metadata that can be shared amongst those systems and protocols that allow those systems to coordinate their operations. This is standard stuff. It's not easy. But the motive for the vendors is that they can become more active critical players in the enterprise. And of course, the motive for the customer is that things will run better and more completely. So, I've been looking at this in terms of two kinds of metadata. One is the meaning metadata, which says what data can be put together. The other is the operational metadata, which says basically where did it come from? Who created it? What's its current state? What's the security level? Et cetera, et cetera, et cetera. The good news is the operational stuff can actually be done automatically, whereas the meaning stuff requires some human intervention. And as we've already heard from, was it Doug, I think, people are disinclined to put a lot of definition into meaning metadata. So, that may be the harder one, but coordination is key. This problem has been with us forever, but with the addition of new data sources, with streaming data with data in different formats, the whole thing has, it's been like what a customer of mine used to say, "I understand your product can make my system run faster, but right now I just feel I'm putting my problems on roller skates. (chuckles) I don't need that to accelerate what's already not working." >> Excellent. Okay, Carl, let's stay with you. I remember in the early days of the big data movement, Hadoop movement, NoSQL was the big thing. And I remember Amr Awadallah said to us in theCUBE that SQL is the killer app for big data. So, your prediction here, if we bring that up is SQL is back. Please elaborate. >> Yeah. So, of course, some people would say, well, it never left. Actually, that's probably closer to true, but in the perception of the marketplace, there's been all this noise about alternative ways of storing, retrieving data, whether it's in key value stores or document databases and so forth. We're getting a lot of messaging that for a while had persuaded people that, oh, we're not going to do analytics in SQL anymore. We're going to use Spark for everything, except that only a handful of people know how to use Spark. Oh, well, that's a problem. Well, how about, and for ordinary conventional business analytics, Spark is like an over-engineered solution to the problem. SQL works just great. What's happened in the past couple years, and what's going to continue to happen is that SQL is insinuating itself into everything we're seeing. We're seeing all the major data lake providers offering SQL support, whether it's Databricks or... And of course, Snowflake is loving this, because that is what they do, and their success is certainly points to the success of SQL, even MongoDB. And we were all, I think, at the MongoDB conference where on one day, we hear SQL is dead. They're not teaching SQL in schools anymore, and this kind of thing. And then, a couple days later at the same conference, they announced we're adding a new analytic capability-based on SQL. But didn't you just say SQL is dead? So, the reality is that SQL is better understood than most other methods of certainly of retrieving and finding data in a data collection, no matter whether it happens to be relational or non-relational. And even in systems that are very non-relational, such as graph and document databases, their query languages are being built or extended to resemble SQL, because SQL is something people understand. >> Now, you remember when we were in high school and you had had to take the... Your debating in the class and you were forced to take one side and defend it. So, I was was at a Vertica conference one time up on stage with Curt Monash, and I had to take the NoSQL, the world is changing paradigm shift. And so just to be controversial, I said to him, Curt Monash, I said, who really needs acid compliance anyway? Tony Baer. And so, (chuckles) of course, his head exploded, but what are your thoughts (guests laughing) on all this? >> Well, my first thought is congratulations, Dave, for surviving being up on stage with Curt Monash. >> Amen. (group laughing) >> I definitely would concur with Carl. We actually are definitely seeing a SQL renaissance and if there's any proof of the pudding here, I see lakehouse is being icing on the cake. As Doug had predicted last year, now, (clears throat) for the record, I think, Doug was about a year ahead of time in his predictions that this year is really the year that I see (clears throat) the lakehouse ecosystems really firming up. You saw the first shots last year. But anyway, on this, data lakes will not go away. I've actually, I'm on the home stretch of doing a market, a landscape on the lakehouse. And lakehouse will not replace data lakes in terms of that. There is the need for those, data scientists who do know Python, who knows Spark, to go in there and basically do their thing without all the restrictions or the constraints of a pre-built, pre-designed table structure. I get that. Same thing for developing models. But on the other hand, there is huge need. Basically, (clears throat) maybe MongoDB was saying that we're not teaching SQL anymore. Well, maybe we have an oversupply of SQL developers. Well, I'm being facetious there, but there is a huge skills based in SQL. Analytics have been built on SQL. They came with lakehouse and why this really helps to fuel a SQL revival is that the core need in the data lake, what brought on the lakehouse was not so much SQL, it was a need for acid. And what was the best way to do it? It was through a relational table structure. So, the whole idea of acid in the lakehouse was not to turn it into a transaction database, but to make the data trusted, secure, and more granularly governed, where you could govern down to column and row level, which you really could not do in a data lake or a file system. So, while lakehouse can be queried in a manner, you can go in there with Python or whatever, it's built on a relational table structure. And so, for that end, for those types of data lakes, it becomes the end state. You cannot bypass that table structure as I learned the hard way during my research. So, the bottom line I'd say here is that lakehouse is proof that we're starting to see the revenge of the SQL nerds. (Dave chuckles) >> Excellent. Okay, let's bring up back up the predictions. Dave Menninger, this one's really thought-provoking and interesting. We're hearing things like data as code, new data applications, machines actually generating plans with no human involvement. And your prediction is the definition of data is expanding. What do you mean by that? >> So, I think, for too long, we've thought about data as the, I would say facts that we collect the readings off of devices and things like that, but data on its own is really insufficient. Organizations need to manipulate that data and examine derivatives of the data to really understand what's happening in their organization, why has it happened, and to project what might happen in the future. And my comment is that these data derivatives need to be supported and managed just like the data needs to be managed. We can't treat this as entirely separate. Think about all the governance discussions we've had. Think about the metadata discussions we've had. If you separate these things, now you've got more moving parts. We're talking about simplicity and simplifying the stack. So, if these things are treated separately, it creates much more complexity. I also think it creates a little bit of a myopic view on the part of the IT organizations that are acquiring these technologies. They need to think more broadly. So, for instance, metrics. Metric stores are becoming much more common part of the tooling that's part of a data platform. Similarly, feature stores are gaining traction. So, those are designed to promote the reuse and consistency across the AI and ML initiatives. The elements that are used in developing an AI or ML model. And let me go back to metrics and just clarify what I mean by that. So, any type of formula involving the data points. I'm distinguishing metrics from features that are used in AI and ML models. And the data platforms themselves are increasingly managing the models as an element of data. So, just like figuring out how to calculate a metric. Well, if you're going to have the features associated with an AI and ML model, you probably need to be managing the model that's associated with those features. The other element where I see expansion is around external data. Organizations for decades have been focused on the data that they generate within their own organization. We see more and more of these platforms acquiring and publishing data to external third-party sources, whether they're within some sort of a partner ecosystem or whether it's a commercial distribution of that information. And our research shows that when organizations use external data, they derive even more benefits from the various analyses that they're conducting. And the last great frontier in my opinion on this expanding world of data is the world of driver-based planning. Very few of the major data platform providers provide these capabilities today. These are the types of things you would do in a spreadsheet. And we all know the issues associated with spreadsheets. They're hard to govern, they're error-prone. And so, if we can take that type of analysis, collecting the occupancy of a rental property, the projected rise in rental rates, the fluctuations perhaps in occupancy, the interest rates associated with financing that property, we can project forward. And that's a very common thing to do. What the income might look like from that property income, the expenses, we can plan and purchase things appropriately. So, I think, we need this broader purview and I'm beginning to see some of those things happen. And the evidence today I would say, is more focused around the metric stores and the feature stores starting to see vendors offer those capabilities. And we're starting to see the ML ops elements of managing the AI and ML models find their way closer to the data platforms as well. >> Very interesting. When I hear metrics, I think of KPIs, I think of data apps, orchestrate people and places and things to optimize around a set of KPIs. It sounds like a metadata challenge more... Somebody once predicted they'll have more metadata than data. Carl, what are your thoughts on this prediction? >> Yeah, I think that what Dave is describing as data derivatives is in a way, another word for what I was calling operational metadata, which not about the data itself, but how it's used, where it came from, what the rules are governing it, and that kind of thing. If you have a rich enough set of those things, then not only can you do a model of how well your vacation property rental may do in terms of income, but also how well your application that's measuring that is doing for you. In other words, how many times have I used it, how much data have I used and what is the relationship between the data that I've used and the benefits that I've derived from using it? Well, we don't have ways of doing that. What's interesting to me is that folks in the content world are way ahead of us here, because they have always tracked their content using these kinds of attributes. Where did it come from? When was it created, when was it modified? Who modified it? And so on and so forth. We need to do more of that with the structure data that we have, so that we can track what it's used. And also, it tells us how well we're doing with it. Is it really benefiting us? Are we being efficient? Are there improvements in processes that we need to consider? Because maybe data gets created and then it isn't used or it gets used, but it gets altered in some way that actually misleads people. (laughs) So, we need the mechanisms to be able to do that. So, I would say that that's... And I'd say that it's true that we need that stuff. I think, that starting to expand is probably the right way to put it. It's going to be expanding for some time. I think, we're still a distance from having all that stuff really working together. >> Maybe we should say it's gestating. (Dave and Carl laughing) >> Sorry, if I may- >> Sanjeev, yeah, I was going to say this... Sanjeev, please comment. This sounds to me like it supports Zhamak Dehghani's principles, but please. >> Absolutely. So, whether we call it data mesh or not, I'm not getting into that conversation, (Dave chuckles) but data (audio breaking) (Tony laughing) everything that I'm hearing what Dave is saying, Carl, this is the year when data products will start to take off. I'm not saying they'll become mainstream. They may take a couple of years to become so, but this is data products, all this thing about vacation rentals and how is it doing, that data is coming from different sources. I'm packaging it into our data product. And to Carl's point, there's a whole operational metadata associated with it. The idea is for organizations to see things like developer productivity, how many releases am I doing of this? What data products are most popular? I'm actually in right now in the process of formulating this concept that just like we had data catalogs, we are very soon going to be requiring data products catalog. So, I can discover these data products. I'm not just creating data products left, right, and center. I need to know, do they already exist? What is the usage? If no one is using a data product, maybe I want to retire and save cost. But this is a data product. Now, there's a associated thing that is also getting debated quite a bit called data contracts. And a data contract to me is literally just formalization of all these aspects of a product. How do you use it? What is the SLA on it, what is the quality that I am prescribing? So, data product, in my opinion, shifts the conversation to the consumers or to the business people. Up to this point when, Dave, you're talking about data and all of data discovery curation is a very data producer-centric. So, I think, we'll see a shift more into the consumer space. >> Yeah. Dave, can I just jump in there just very quickly there, which is that what Sanjeev has been saying there, this is really central to what Zhamak has been talking about. It's basically about making, one, data products are about the lifecycle management of data. Metadata is just elemental to that. And essentially, one of the things that she calls for is making data products discoverable. That's exactly what Sanjeev was talking about. >> By the way, did everyone just no notice how Sanjeev just snuck in another prediction there? So, we've got- >> Yeah. (group laughing) >> But you- >> Can we also say that he snuck in, I think, the term that we'll remember today, which is metadata museums. >> Yeah, but- >> Yeah. >> And also comment to, Tony, to your last year's prediction, you're really talking about it's not something that you're going to buy from a vendor. >> No. >> It's very specific >> Mm-hmm. >> to an organization, their own data product. So, touche on that one. Okay, last prediction. Let's bring them up. Doug Henschen, BI analytics is headed to embedding. What does that mean? >> Well, we all know that conventional BI dashboarding reporting is really commoditized from a vendor perspective. It never enjoyed truly mainstream adoption. Always that 25% of employees are really using these things. I'm seeing rising interest in embedding concise analytics at the point of decision or better still, using analytics as triggers for automation and workflows, and not even necessitating human interaction with visualizations, for example, if we have confidence in the analytics. So, leading companies are pushing for next generation applications, part of this low-code, no-code movement we've seen. And they want to build that decision support right into the app. So, the analytic is right there. Leading enterprise apps vendors, Salesforce, SAP, Microsoft, Oracle, they're all building smart apps with the analytics predictions, even recommendations built into these applications. And I think, the progressive BI analytics vendors are supporting this idea of driving insight to action, not necessarily necessitating humans interacting with it if there's confidence. So, we want prediction, we want embedding, we want automation. This low-code, no-code development movement is very important to bringing the analytics to where people are doing their work. We got to move beyond the, what I call swivel chair integration, between where people do their work and going off to separate reports and dashboards, and having to interpret and analyze before you can go back and do take action. >> And Dave Menninger, today, if you want, analytics or you want to absorb what's happening in the business, you typically got to go ask an expert, and then wait. So, what are your thoughts on Doug's prediction? >> I'm in total agreement with Doug. I'm going to say that collectively... So, how did we get here? I'm going to say collectively as an industry, we made a mistake. We made BI and analytics separate from the operational systems. Now, okay, it wasn't really a mistake. We were limited by the technology available at the time. Decades ago, we had to separate these two systems, so that the analytics didn't impact the operations. You don't want the operations preventing you from being able to do a transaction. But we've gone beyond that now. We can bring these two systems and worlds together and organizations recognize that need to change. As Doug said, the majority of the workforce and the majority of organizations doesn't have access to analytics. That's wrong. (chuckles) We've got to change that. And one of the ways that's going to change is with embedded analytics. 2/3 of organizations recognize that embedded analytics are important and it even ranks higher in importance than AI and ML in those organizations. So, it's interesting. This is a really important topic to the organizations that are consuming these technologies. The good news is it works. Organizations that have embraced embedded analytics are more comfortable with self-service than those that have not, as opposed to turning somebody loose, in the wild with the data. They're given a guided path to the data. And the research shows that 65% of organizations that have adopted embedded analytics are comfortable with self-service compared with just 40% of organizations that are turning people loose in an ad hoc way with the data. So, totally behind Doug's predictions. >> Can I just break in with something here, a comment on what Dave said about what Doug said, which (laughs) is that I totally agree with what you said about embedded analytics. And at IDC, we made a prediction in our future intelligence, future of intelligence service three years ago that this was going to happen. And the thing that we're waiting for is for developers to build... You have to write the applications to work that way. It just doesn't happen automagically. Developers have to write applications that reference analytic data and apply it while they're running. And that could involve simple things like complex queries against the live data, which is through something that I've been calling analytic transaction processing. Or it could be through something more sophisticated that involves AI operations as Doug has been suggesting, where the result is enacted pretty much automatically unless the scores are too low and you need to have a human being look at it. So, I think that that is definitely something we've been watching for. I'm not sure how soon it will come, because it seems to take a long time for people to change their thinking. But I think, as Dave was saying, once they do and they apply these principles in their application development, the rewards are great. >> Yeah, this is very much, I would say, very consistent with what we were talking about, I was talking about before, about basically rethinking the modern data stack and going into more of an end-to-end solution solution. I think, that what we're talking about clearly here is operational analytics. There'll still be a need for your data scientists to go offline just in their data lakes to do all that very exploratory and that deep modeling. But clearly, it just makes sense to bring operational analytics into where people work into their workspace and further flatten that modern data stack. >> But with all this metadata and all this intelligence, we're talking about injecting AI into applications, it does seem like we're entering a new era of not only data, but new era of apps. Today, most applications are about filling forms out or codifying processes and require a human input. And it seems like there's enough data now and enough intelligence in the system that the system can actually pull data from, whether it's the transaction system, e-commerce, the supply chain, ERP, and actually do something with that data without human involvement, present it to humans. Do you guys see this as a new frontier? >> I think, that's certainly- >> Very much so, but it's going to take a while, as Carl said. You have to design it, you have to get the prediction into the system, you have to get the analytics at the point of decision has to be relevant to that decision point. >> And I also recall basically a lot of the ERP vendors back like 10 years ago, we're promising that. And the fact that we're still looking at the promises shows just how difficult, how much of a challenge it is to get to what Doug's saying. >> One element that could be applied in this case is (indistinct) architecture. If applications are developed that are event-driven rather than following the script or sequence that some programmer or designer had preconceived, then you'll have much more flexible applications. You can inject decisions at various points using this technology much more easily. It's a completely different way of writing applications. And it actually involves a lot more data, which is why we should all like it. (laughs) But in the end (Tony laughing) it's more stable, it's easier to manage, easier to maintain, and it's actually more efficient, which is the result of an MIT study from about 10 years ago, and still, we are not seeing this come to fruition in most business applications. >> And do you think it's going to require a new type of data platform database? Today, data's all far-flung. We see that's all over the clouds and at the edge. Today, you cache- >> We need a super cloud. >> You cache that data, you're throwing into memory. I mentioned, MySQL heat wave. There are other examples where it's a brute force approach, but maybe we need new ways of laying data out on disk and new database architectures, and just when we thought we had it all figured out. >> Well, without referring to disk, which to my mind, is almost like talking about cave painting. I think, that (Dave laughing) all the things that have been mentioned by all of us today are elements of what I'm talking about. In other words, the whole improvement of the data mesh, the improvement of metadata across the board and improvement of the ability to track data and judge its freshness the way we judge the freshness of a melon or something like that, to determine whether we can still use it. Is it still good? That kind of thing. Bringing together data from multiple sources dynamically and real-time requires all the things we've been talking about. All the predictions that we've talked about today add up to elements that can make this happen. >> Well, guys, it's always tremendous to get these wonderful minds together and get your insights, and I love how it shapes the outcome here of the predictions, and let's see how we did. We're going to leave it there. I want to thank Sanjeev, Tony, Carl, David, and Doug. Really appreciate the collaboration and thought that you guys put into these sessions. Really, thank you. >> Thank you. >> Thanks, Dave. >> Thank you for having us. >> Thanks. >> Thank you. >> All right, this is Dave Valente for theCUBE, signing off for now. Follow these guys on social media. Look for coverage on siliconangle.com, theCUBE.net. Thank you for watching. (upbeat music)

[Music] hello everyone welcome back to the live coverage here in monaco for the monaco crypto summit i'm john furrier host of thecube uh we have a great great guest lineup here already in nine interviews small gathering of the influencers and the people making it happen powered by digital bits sponsored by digital bits presented by digital bits of course a lot happening around decentralization web 3 the metaverse we've got a a powerhouse influencer on the qb ryan gills the founder of openmeta been in the issue for a while ryan great to see you thanks for coming on great to be here thank you you know one of the things that we were observing earlier conversations is you have young and old coming together the best and brightest right now in the front line it's been there for a couple years you know get some hype cycles going on but that's normal in these early growth markets but still true north star is in play that is democratize remove the intermediaries create immutable power to the people the same kind of theme has been drum beating on now come the metaverse wave which is the nfts now the meta verses you know at the beginning of this next wave yeah this is where we're at right now what are you working on tell us what's what's open meta working on yeah i mean so there is a reason for all of this right i think we go through all these different cycles and there's an economic incentive engine and it's designed in because people really like making money but there's a deeper reason for it all and the words the buzzwords the terms they change based off of different cycles this one is a metaverse i just saw it a little early you know so i recognized the importance of an open metaverse probably in 2017 and really decided to dedicate 10 years to that um so we're very early into that decade and we're starting to see more of a movement building and uh you know i've catalyzed a lot of that from from the beginning and making sure that while everything moves to a closed corporate side of things there's also an equal bottom-up approach which i think is just more important and more interesting well first of all i want to give you a lot of props for seeing it early and recognizing the impact and potential collateral damage of not not having open and i was joking earlier about the facebook little snafu with the the exercise app and ftc getting involved and you know i kind of common new york times guy comment online like hey i remember aol wanted to monopolize dial up internet and look the open web obviously changed all that they went to sign an extinction not the same comparable here but you know everyone wants to have their own little walled guard and they feel comfortable first-party data the data business so balancing the benefit of data and all the ip that could come into whether it's a visualization or platform it has to be open without open then you're going to have fragmentation you're going to have all kinds of perverse incentives how does the metaverse continue with such big players like meta themselves x that new name for facebook you know big bully tons of cash you know looking to you know get their sins forgiven um so to speak i mean you got google probably will come in apple's right around the corner amazon you get the whales out there how do is it proprietary is walled garden the new proprietary how do you view all that because it's it's still early and so there's a lot of change can happen well it's an interesting story that's really playing out in three acts right we had the first act which was really truly open right there was this idea that the internet is for the end user this is all just networking and then web 2 came and we got a lot of really great business models from it and it got closed up you know and now as we enter this sort of third act we have the opportunity to learn from both of those right and so i think web 3 needs to go back to the values of web one with the lessons in hindsight of web 2. and all of the winners from web 2 are clearly going to want to keep winning in web 3. so you can probably guess every single company and corporation on earth will move into this i think most governments will move into it as well and um but they're not the ones that are leading it the ones that are leading it are are just it's a culture of people it's a movement that's building and accumulating over time you know it's weird it's uh the whole web 2 thing is the history is interesting because you know when i started my podcasting company in 2004 there's only like three of us you know the dave weiner me evan williams and jack dorsey and we thought and the blogging just was getting going and the dream was democratization at the time mainstream media was the enemy and then now blogs are media so and then all sudden it like maybe it was the 2008 area with the that recession it stopped and then like facebook came in obviously twitter was formed from the death of odio podcasting company so the moment in time in history was a glimmic glimmer of hope well we went under my company went under we all went under but then that ended and then you had the era of twitter facebook linkedin reddit was still around so it kind of stopped where did it where did it pick up was it the ethereum bitcoin and ethereum brought that back where'd the open come back well it's a generational thing if you if you go back to like you know apple as a startup they were trying to take down ibm right it was always there's always the bigger thing that was that we we're trying to sort of unbundle or unpackage because they have too much power they have too much influence and now you know facebook and apple and these big tech companies they are that on on the planet and they're doing it bigger than it's ever been done but when they were startups they existed to try to take that from a bigger company so i think you know it's not an it's not a fact that like facebook or zuckerberg is is the villain here it's just the fact that we're reaching peak centralization anything past this point it becomes more and more unhealthy right and an open metaverse is just a way to build a solution instead of more of a problem and i think if we do just allow corporations to build and own them on the metaverse these problems will get bigger and larger more significant they will touch more people on earth and we know what that looks like so why not try something different so what's the playbook what's the current architecture of the open meta verse that you see and how do people get involved is there protocols to be developed is there new things that are needed how does the architecture layout take us through that your mindset vision on that and then how can people get involved yeah so the the entity structure of what i do is a company called crucible out of the uk um but i i found out very quickly that just a purely for-profit closed company a commercial company won't achieve this objective there's limitations to that so i run a dao as well out of switzerland it's called open meta we actually we named it this six months before facebook changed their name and so this is just the track we're on right and what we develop is a protocol uh we believe that the internet built by game developers is how you define the metaverse and that protocol is in the dao it is in the dow it's that's crucial crucible protocol open meta okay you can think of crucible as labs okay no we're building we're building everything so incubator kind of r d kind of thing exactly yeah and i'm making the choice to develop things and open them up create public goods out of them harness things that are more of a bottom-up approach you know and what we're developing is the emergence protocol which is basically defining the interface between the wallets and the game engines right so you have unity and unreal which all the game developers are sort of building with and we have built software that drops into those game engines to map ownership between the wallet and the experience in the game so integration layer basically between the wallet kind of how stripe is viewed from a software developer's campaign exactly but done on open rails and being done for a skill set of world building that is coming and game developers are the best suited for this world building and i like to own what i built yeah i don't like other people to own what i build and i think there's an entire generation that's that's really how do you feel about the owning and sharing component is that where you see the scale coming into play here i can own it and scale it through the relationship of the open rails yeah i mean i think the truth is that the open metaverse will be a smaller network than even one corporate virtual world for a while because these companies have billions of people right yeah every room you've ever been in on earth people are using two or three of facebook's products right they just have that adoption but they don't have trust they don't have passion they don't have the movement that you see in web3 they don't have the talent the level of creative talent those people care about owning what they create on the on what can someone get involved with question is that developer is that a sponsor what do people do to get involved with do you and your team and to make it bigger i mean it shouldn't be too small so if this tracks you can assume it gets bigger if you care about an open metaverse you have a seat at the table if you become a member of the dao you have a voice at the table you can make decisions with us we are building developing technology that can be used openly so if you're a game developer and you use unity or unreal we will open the beta this month later and then we move directly into what's called a game jam so a global hackathon for game developers where we just go through a giant exploration of what is possible i mean you think about gaming i always said the early adopters of all technology and the old web one was porn and that was because they were they were agnostic of vendor pitches or whatever is it made money they've worked we don't tell them we've always been first we don't tolerate vaporware gaming is now the new area where it is so the audience doesn't want vapor they want it to work they want technology to be solid they want community so it's now the new arbiter so gaming is the pretext to metaverse clearly gaming is swallowing all of media and probably most of the world and this game mechanics under the hood and all kinds of underlying stuff now how does that shape the developer community so like take the classic software developer may not be a game developer how do they translate over you seeing crossover from the software developers that are out there to be game developers what's your take on that it's an interesting question because i come to a lot of these events and the entire web 3 movement is web developers it's in the name yeah right and we have a whole wave of exploration and nfts being sold of people who really love games they're they're players they're gamers and they're fans of games but they are not in the skill set of game development this is a whole discipline yeah it's a whole expertise right you have to understand ik retargeting rigging bone meshes and mapping of all of that stuff and environment building and rendering and all these things it's it's a stacked skill set and we haven't gone through any exploration yet with them that is the next cycle that we're going to and that's what i've spent the last three or four years preparing for yeah and getting the low code is going to be good i was saying earlier to the young gun we had on his name was um oscar belly he's argo versus he's 25 years old he's like he made a quote i'm too old to get into esports like 22 old 25 come on i'd love to be in esports i was commenting that there could be someone sitting next to us in the metaverse here on tv on our digital tv program in the future that's going to be possible the first party citizenship between physical experience absolutely and meta versus these cameras all are a layer in which you can blend the two yeah so that that's that's going to be coming sooner and it's really more of the innovation around these engines to make it look real and have someone actually moving their body not like a stick figure yes or a lego block this is where most people have overlooked because what you have is you have two worlds you have web 3 web developers who see this opportunity and are really going for it and then you have game developers who are resistant to it for the most part they have not acclimated to this but the game developers are more of the keys to it because they understand how to build worlds yeah they do they understand how to build they know what success looks like they know what success looks like if you if you talk about the metaverse with anyone the most you'll hear is ready player one yeah maybe snow crash but those things feel like games yeah right so the metaverse and gaming are so why are game developers um like holding back is because they're like ah it's too not ready yet i'm two more elite or is it more this is you know this is an episode on its own yeah um i'm actually a part of a documentary if you go to youtube and you say why gamers hate nfts there's a two-part documentary about an hour long that robin schmidt from the defiant did and it's really a very good deep dive into this but i think we're just in a moment in time right now if you remember henry ford when he he produced the car everybody wanted faster horses yeah they didn't understand the cultural shift that was happening they just wanted an incremental improvement right and you can't say that right now because it sounds arrogant but i do believe that this is a moment in time and i think once we get through this cultural shift it will be much more clear why it's important it's not pure speculation yeah it's not clout it's not purely money there's something happening that's important for humanity yeah and if we don't do it openly it will be more of a problem yeah i totally agree with you on that silent impact is number one and people some people just don't see it because it's around the corner visionaries do like yourselves we do my objective over the next say three to six months is to identify which game developers see the value in web 3 and are leaning into it because we've built technology that solves interoperability between engines mapping ownership from wallets all the sort of blueprints that are needed in order for a game developer to build this way we've developed that we just need to identify where are they right because the loudest voices are the ones that are pushing back against this yeah and if you're not on twitter you don't see how many people really see this opportunity and i talked to epic and unity and nvidia and they all agree that this is where the future is going but the one question mark is who wants it where are they you know it's interesting i talked to lauren besel earlier she's from the music background we were talking about open source and how music i found that is not open it's proprietary i was talking about when i was in college i used to deal software you'd be like what do you mean deal well at t source code was proprietary and that started the linux movement in the 80s that became a systems revolution and then open source then just started to accelerate now people like it's free software is like not a big deal everyone knows it's what it was never proprietary but we were fighting the big proprietary code bases you mentioned that earlier is there a proprietary thing for music well not really because it's licensed rights right so in the metaverse who's the proprietary is it the walled garden is the is it is it the gamers so is it the consoles is it the investment that these gaming companies have in the software itself so i find that that open source vibe is very much circulating around your world actually open maps in the word open but open source software has a trajectory you know foundations contributors community building same kind of mindset music not so much because no one's it's not direct comparable but i think here it's interesting the gaming culture could be that that proprietary ibm the the state the playstation the xbox you know if you dive into the modding community right the modding community has sort of been this like gray area of of gaming and they will modify games that already exist but they do it with the values of open source they do it with composability and there's been a few breakthroughs counter-strike is a mod right some of the largest games of all time came from mods of other games look at quake had a comeback i played first multiplayer doom when it came out in the 90s and that was all mod based exactly yeah quake and quake was better but you know i remember the first time on a 1.5 cable mode and playing with my friends remember vividly now the graphics weren't that good but that was mod it's mod so then you go i mean and then you go into these other subcultures like dungeons and dragons which was considered to be such a nerdy thing but it's just a deeply human thing it's a narrative building collective experience like these are all the bottom-up type approaches modding uh world building so you're going to connect so i'm just kind of thinking out loud here you're going to connect the open concept of source with open meta bring game developers and software drills together create a fabric of a baseline somewhat somewhat collected platform tooling and components and let it just sell form see what happens better self form that's your imposing composability is much faster yeah than a closed system and you got what are your current building blocks you have now you have the wallet and you have so we built an sdk on both unity and unreal okay as a part of a system that is a protocol that plugs into those two engines and we have an inventory service we have an avatar system we basically kind of leaned into this idea of a persona being the next step after a pfp so so folks that are out there girls and boys who are sitting there playing games they could build their own game on this thing absolutely this is the opportunity for them entrepreneurs to circumvent the system and go directly with open meta and build their own open environment like i said before i i like to own the things i built i've had that entrepreneurial lesson but i don't think in the future you should be so okay with other companies or other intermediaries owning you and what you build i think i mean opportunity to build value yeah and i think i think your point the mod culture is not so much going to be the answer it's what that was like the the the the dynamic of modding yes is developing yes and then therefore you get the benefit of sovereign identity yeah you get the benefit of unbanking that's not the way we market this but those are benefits that come along with it and it allows you to live a different life and may the better product win yeah i mean that's what you're enabling yeah ryan thanks so much for coming on real final question what's going on here why are we here in monaco what's going on this is the inaugural event presented by digital bits why are we here monaco crypto summit i'm here uh some friends of mine brittany kaiser and and lauren bissell invited me here yeah i've known al for for a number of years and i'm just here to support awesome congratulations and uh we'll keep in touch we'll follow up on the open meta great story we love it thanks for coming on okay cube coverage continues here live in monaco i'm john furrier and all the action here on the monaco crypto summit love the dame come back next year it'll be great back with more coverage to wrap up here on the ground then the yacht club event we're going to go right there as well that's in a few hours so we're going to be right back [Music] you

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

(upbeat music) >> Okay, welcome to the special CUBE conversation. I'm John Furrier, host of theCUBE. We're here talking about the evolving capabilities of VCF on VxRail. VCF being VMware Cloud Foundation. as VxRail from Dell Technologies. Samuel Niemi is their Product Manager of VCF on VxRail. He's got the keys to the kingdom. He is going to give us the update on what's going on, obviously with all the major IT operational conversations going on with cloud native, how to get the best excellence out of the organization as we come through the pandemic, big stuff happening. Welcome to theCUBE. >> Thank you, happy to be here. >> In June, you guys announced some major updates that's coming on to VMware Cloud Foundation on VxRail that would allow customers to extend their capabilities and their ability to innovate in the landscape and with external storage. Can you take us through what's new what's the situation and tell us what's happening? >> Yeah, absolutely. So, first off if you're, for those who might be watching who are not familiar with VCF on VxRail, VxRail is our hyperconverged infrastructure system that allows for massive data centers scaling at, from node to node to node. VCF on VxRail specifically is the VMware SDDC software suite that allows us to create a private cloud with VxRail deployments. So instead of saying, I want to manage this cluster and this cluster, and this cluster VCF allows us to manage VxRail clusters and deployments at a big scale. So VCF on VxRail, we've gone from in the last two and a half years or so that we have been available as a product we've gone from nothing to tens of thousands of nodes deployed across the world. And it has been a rollercoaster of a ride. And we're just thrilled with the success that we've had so far. >> And what's been new since the release in June but what's new? >> Absolutely. So, one thing that we've realized from a VxRail perspective is that, as we grow and as our data center and enterprise scale customers continue to grow their VCF on the VxRail environments VCF on VxRail has to evolve as well. And in June we announced an ability for VCF on VxRail to consume external storage. Now, hyper-converged means no storage, networking, network virtualization I should say and your server all in one box. External storage gives us the ability to utilize your existing Dell EMC storage arrays and use that data centric kind of storage deployment with your existing or net new VCF on VxRail deployments. It's really exciting stuff. And we're really looking forward to be able to even better provide solutions for our customers at that big enterprise scale. >> So a lot of change happening scale is a big word here, right? We're seeing scale, modern applications looking for environment. You talk about hybrid private cloud. I mean, essentially cloud operations is private cloud if you will. I got to ask you on this big product that you have VCF on VxRail, what are the drivers behind making this option viable for customers, what are they looking for? Why are they consuming it this way? What are the key aspects of drive in this force? >> Absolutely. So, what we found is that with vSAN which has been wildly successful on the VxRail, it's fantastic for general purpose workloads. And we don't see that changing. What we see is an ability for our customers to leverage the extreme speed of our PowerStore T, our PowerMax and our Unity XT storage arrays so that you can get that sub millisecond latency that you're used to out of those storage arrays and have the same benefits in say another workload domain of your existing vSAN deployment. Now, my favorite example of a use case for that is when you have sub millisecond latency, that's something like a PowerMax can provide. Let's say you're standing at the gas pump. It's cold, I'm here in Minnesota it was three degrees here yesterday. When I'm standing at the gas pump, swipe my card. I don't want to wait and wait and wait for that database kit. Put my card to go through I want it now. PowerMax and our PowerStore T, unity XT with those crazy low latencies, they allow our VCF on VxRail customers to not have to wait at the pump. So when our enterprise customers have those things deployed with that crazy low latency for database hits, you're not standing at the pump. You're not waiting awkwardly at the grocery store for your card to go through. You really get that extreme speed that those big storage arrays can provide. >> Yeah, so the weather in Minnesota, and so my brother lives in that area too. He was complaining about it on the family text, but this is an edge case, whether you're swiping your credit card on the pump, this latency discussion, the edge is really a key conversation because that's what you're, you're going to get cold waiting, but still you could be, key data store for say some equipment in a manufacturing operation, or on a farm or somewhere. So again, this brings up the whole edge. >> True. >> That an area is that the driver, one of the drivers, or is it also just in general the performance? >> You know I would say it depends on what you need out of your storage array. If you need that performance at the edge, VCF can deploy remote clusters in a metro distance within 50 milliseconds. So you can have your center and you can have your edges, you can put storage arrays behind those edges. You can have that kind of, speed from place to place, to place to place, or you can use traditional vSAN storage. So it really comes down to what your storage use case is. Maybe you have a need of the data replication that PowerMax can provide from one site to the other, and that's your backup for your edges. Those kinds of things can all be utilized with VCF on VxRail and remote clusters at the edge. >> What a similar customer use case? Can you just walk me through some examples of customers that you have and what they're interested in, what kind of advantages they're seeing with the capability? >> Certainly. So we have a number of customers who have high level of data resiliency requirements that we have that 99 point lots of nines resiliency that the PowerMax, and it's forebears, VMX have provided for 20 something years now, those customers say at our financial institutions where they have to have massive levels of resiliency. We have customers who frankly have separate buying cycles, where they buy their compute one year, and then maybe two years later, that's when their storage comes up for renewal. So those customers are able to leverage both VCF on VxRail and their external storage. I'm not going to drop customer names. I've got a couple that come to mind, but I'll say in the financial institution and in healthcare especially is where we see. >> What problem are they solving? You don't have to name names because I know it's probably the company, everything, but you know what all the reference stuff, but what's the anecdotal, what's the main problem, let's say kind of the use cases that jump out and people, if people are watching might think that they should be using this. What signals and signs should they be looking for? >> Absolutely. I would say first off data resiliency, and I'm just in love with PowerMax. So that's the first thing that jumps to mind. I'm extreme performance, whether it's databases or having a need to get data out to their customers as quickly as possible. Replication comes to mind. Those are the big three. And then of course, where you maybe need a little bit of compute and a lot of storage are dynamic nodes and VCF on VxRail means that we can sell our nodes without any storage. And that really gives us an ability to just say, I need a lot of compute, I need a little compute, whatever it might be, I'm going to scale my nodes and my storage independently of one another. >> Where can people get more information to find out? >> Sure, absolutely. So for more information, you can always go to dell.com. You can reach out to your sales team and talk to your VMware sales team as well, who are well-versed in VCF on VxRail deployments, but we're always here dell.com and we're always just an email away. >> So while I've got your here, say, I want to ask you about this notion of simplifying the IT operational experience. >> Sure. >> In your view, as you look out on the horizon from your perspective, being the product leader on this area, what's on the mind of the customer. What's the psychology out there? What's some of the environmental conditions that they're facing (indistinct) their landscape. Is it do more with less, the classic cliche? Is it actually a replatformin, is it refactoring? Is it application developers? what's some of the big drivers there in terms of the customers that you're seeing? >> So as a customer today, I have so many options for where to put my data and where to put my VMs and my development. I want to look at what is the best route for my business? Is it a hybrid cloud offering? And if yes, what's the easiest way to manage that because at the end of the day, if I'm spending money on maintenance spending money on staff who are not accelerating the business, but just keeping the thing going, what's the best way to do that? And VCF on VxRail today really allows our customers to deploy a private or a hybrid cloud rather, and maintain the entire thing through one interface. That interface being SDDC Manager. When we look at the benefits of it, VCF for on VxRail today provides Tanzu. So for customers who need to have a development platform in their hybrid cloud Tanzu is that the easy option or the easy answer for that. So, it is a big answer. What's driving this, lots of things, but really it's data center modernization. It's moving from a traditional servers with virtual machines on them into the hybrid cloud. >> Yeah, you were missing resilience here on the data. I think that's awesome because I mean, at the end of the day it's data driven. Everyone wants more data. Database has been around for a while. So making that go faster is really critical. Awesome, awesome conversation. And now on the VCF on VxRail, what's the bottom line, if you had to summarize the evolution capabilities that are coming on, they're evolving, you're the Product Manager, you got the keys to the kingdom, what's next, what's happening? >> If I'm looking at VCF and what's next and what's on the way, really lifecycle management. So, when our customers talk about what it looks like to lifecycle their systems without VCF on VxRail and the complexity of doing that without VCF it's lifecycle management is the reason for being. We look at the, from everything we lifecycle from the hardware of the VxRail nodes, including disc firmware, HPAs, NIC drivers, etc to the VCF SDDC software suite, all of those components they're in vSphere, VCenter ESXi. I'm going through the checklist in my head here. The V realized components, getting all of that lifecycle to a good continuous revalidated state is really, really tough. And then your add storage, that's one more thing. So I want to be able to just have a single click that will go through LCM my entire hybrid cloud environment from hardware to software stack, so that I can manage that external storage that I just added to my system without adding more pain. So really with VCF on VxRail, it's the only jointly engineered solution from an HCI vendor like VxRail and VMware to deliver that single click soup to nuts hardware to software suite LCM. LCM is the name of the game. And we're going to continue to make that innovate on that and new ways that I can't even say yet. >> I can't wait to hear the innovation is a great model. Putting that out there, getting the environmental all scaled up. Sam Niemi, Product Manager, VCF VMware Cloud Foundation on VxRail with Dell Technologies. Thanks for coming on this CUBE conversation. >> Absolutely thanks, John. >> Okay, it's theCUBE here in Palo Alto. I'm John for your host, thanks for watching. (upbeat music)

>> Welcome everybody to this cube conversation. My name is Dave Vellante and we're joined today by Richard Goodwin, who's the group director of IT at Ultraleap and Avishek Kumar, who manages Dell's Power Store, product line, he directs that product line along with several other lines for the company. Gentlemen, welcome to the cube. >> (Avishek) Hi Dave. >> (Richard) Hi >> (Dave) So Richard, Ultraleap, very cool company tracks hand movements, and so forth. Tell us about the company and the technology I'm really interested in how it's used. >> Yeah, we've had many product lines, obviously. We're very innovative, and the organization was spun up from a PhD, a number of PhD students who were the co-founders for Ultraleap, and initially with mid-air haptics, as you, as many people may have seen, but also hand tracking, mid-air touch, sense and feel. So, yeah, it's, it's, it's quite impressive what we have produced and the number of sectors and markets that we were in. And obviously to, to push us to where we are, we have relied upon lots of the Dao technology, both software and hardware. >> (Dave) And what's your role at the company? >> I'm the group IT director, I'm responsible for the IT and business platforms, all infrastructure, network, hardware, software, and also the transition of those platforms to ensure that we're scalable. And we are able to develop our software and hardware as rapidly as possible. >> (Dave) Awesome. Yeah, a lot of data behind that too I bet. Okay Avishek, you direct a number of products at Dell across the portfolio, Unity, Extreme IO, the SC series, and of course power vault. It's quite the portfolio that you look after. So let's get into the case study, if we can, a bit, Richard, maybe you could paint a picture of, of your environment, some of the key applications that you're supporting and maybe what your infrastructure looks like. Give us a high level view. >> Sure. So, pre Power Store, we had quite a disparate architecture, so a fairly significant split and siding on the side of the cloud, not as hybrid as we would like, and not, not as much as on-prem, as we would have liked, and hey, but that's changed quite significantly. So we now have a number of servers and storage and storage arrays that we have on, on-premise, and then we host ourselves. So we are moving quite rapidly, you know as a startup and then moving to a scale-up, we needed that, that scalability and that versatility, and also the whole OPEX versus CAPEX, and also not being driven by lots of SaaS products and architecture and infrastructure, where we needed to be in control because of our development cycles and our products, product development. >> (Dave) So wait, Okay, So, so, too much cloud. I'm hearing you wanted a little bit a dose of on-prem, explain that a little bit more, the cloud wasn't doing it for you in terms of your development cycle, your control. Can you double click on that? >> Yeah. Some of the, some of the control and you know, there's always a balance because there's certain elements of our development cycles and our engineering, software engineering, where we need a very high parallelism for some of the work that we're doing, which then, you know, the CAPEX investment makes things very, very challenging, not commercially the right thing to do. However, there are some of our information, some of IP, some of the secure things that we do, we also do not want upgrades as an example, or any advantages or certain types of server and spec that we need to be quite and unique and that needs to be within our control. >> (Dave) Got it, Okay. Thank you for that. Avishek, we're going to talk about Power Store today. So set it up, please, tell us about Power Store, what it is, you know, why it's important to this conversation. >> Sure. So Power Store is a product that we launched may of 2020, roughly a little bit more than a year now. And it's a brand new architecture that Dell technologies released. And at the end of the day, I'll talk about a few unique aspects of the product, but at the end of the day, where we start with, it's a storage platform, right? So where we see similar to what Richard is saying here, in terms of being able to consolidate the customer's environment, whether it is blog, file, WeVaults, physical, virtual environments, and, and it's, as I said, it's a brand new architecture where we leveraged pieces of existing products, where it made sense, we are using all the latest and greatest technologies delivering the best performance based data reduction. And where we see a lot of traction is the options that it brings to the table for our customers in terms of flexibility, whether they want to add capacity, compute, whether in fact, we have apps on the deployment model where customers can consolidate their compute as well on the static storage platform with needed. So a lot of innovation from a platform perspective itself, and it's not just about the platform itself, but what comes along with it, right? So we refer to it as an ecosystem, part of it, where we work with Ansible playbooks, CSI plugin, you name it, right. And it's the storage platform by itself, doesn't stand by itself in a customer's environment, there are other aspects of the infrastructure that it needs to integrate with as well. Right? So if they are using Ansible playbooks, we want to make sure the integration is there. >> (Dave) Got it. >> And last, but perhaps not the least is the intelligence built into the platform, right? So as we are building these capabilities into the product, there is intelligence built into the product, as well as outside the product where things like Cloud IQ, things like technologies built into power suit itself makes it that much easier for the customers to manage the infrastructure and go from there. >> (Dave) Thank you for that, So, Richard, what was the workload? So it actually, you started with the sort of a Greenfield on-prem. If I understand it correctly, what was the workload that you were sort of building around or workloads? >> So, we had a, a number of different applications. Some of which we cannot really talk about too much, but we had, we had a VxRail, we had a a smaller doubt array and we have lots of what we class as runners, Kubernetes cluster that we run and quite a few different VMs that run on our, on-prem server infrastructure and storage arrays and the issues that we began to hit because of the high IO, from some of our workloads, that we were hitting very high latency, which rapidly stopped, began to cause us issues, especially with some of our software engineering teams. And that is when we embarked upon a competitive RFP for Dell Power Store, Dell were already engaged from an end-user compute where they'd been selected as the end-user compute provider from a previous competitive RFP. And then we engaged them regarding the storage issue that we had and we engaged the, our account lead and count exec, and a number of solution architects were working with us to ensure that we have the optimal solution. Dell were selected over the competitors because of many reasons, you know, the new technology, the de-duplication, the compression, the data, overall data reduction, and the guarantee that also came, came with that, the four-to-one data reduction guarantee, which was significant to us because of their amounts of data that we hold. And we have, you know, as I've mentioned, we're pulling further, further data of ours back into our hosted environments, which will end up on the Power Store, especially with the de-duplication that we're now getting. We've actually hit nine-to-one, which is significant. We were expecting four-to-one, maybe five-to-one with some of the data types. And what was excellent that we were that confident that they did not even review our data types prior, and they were willing to stand by that guarantee of four-to-one. And we've excelled that, we've got significant different data types on, on that array, and we've hit nine-to-one and that's gradually grown over the last nine months, you know, we were kind of at the six then we moved to seven and now we're hitting nine-to-one ratio. >> (Dave) That's great. So you get a little free storage. That's interesting what you're saying, Richard, cause I just assumed that a company that guaranteed four-to-one is going to say okay, let us, let us inspect your workload first and then we'll do the deal. So Avishek, what's the tech behind that data reduction that you're able to, with such confidence, not have to pre inspect the workload in this case anyway. >> Yeah. So, it goes back to the technologies that goes behind the product, right? So, so we, we stand behind the technology and we want to make it simpler for our customers as well where, again we don't want to spend weeks looking at all the data, scanning all the data before giving the guarantee. So we stand behind the technology where we understand that as the data is coming in, we are always going to be de-duplicate it. We are always going to compress it. There is technology within the product where we are offloading some of that to the outside the CPU, so it is not impacting the performance that the applications are going to see. So a data reduction by itself is not good enough, performance by itself is not good enough. Both of them have to be together, right? So, and that's what Power Store brings to the table. >> (Dave) Thank you. So Richard, I'm interested. I mean, I remember the Power Store announcement of, sort of, saw it leading up to it. And one of the big thrusts from Dell was the way I phrase it is essentially trying to create a cloud like experience on-prem. So really focused on simplicity. So my question to you is, let's start with just the deployment. You know, how complicated was it to install? What was that process like? How many clicks, I mean, not that you have to tell me how many clicks, but you know, what I'm asking is, is how difficult was it to get from zero to, you know, up and running? >> Well, we actually stepped our very difficult challenge. We were in quite a difficult situation where we'd pretty much gone off the cliff in terms of our IOPS performance. So the RFP was quite rapid, and then we needed to get whoever which vendor was successful, we needed to get that deployed rather rapidly and on the floor in our data center and server rooms, which we did. And it was very very simplistic, within three weeks of placing the order, we had that array in our server rack and we'd begun the migration, it was very simple to set up. And the management of that array has been, we've seen say 40% reduction in terms of effort to be able to manage our storage because it is very self-contained, you know, even from a reporting perspective, the deployment, the migration was all very, very, very simplistic, and you know, we we've done some work recently where we had to also do some work on the array and some other migrations that we were doing and the resilience came, came to, came to the forefront of where the Juul architecture and no single point of failure enabled us to do some things that we needed to do quite rapidly because of the, the Juul norms and the resilience within, within the unit and within the Power Store itself was considerable where we, we kept performance up, it also prioritize any discreet rebuilds, keeps the incoming ingest rates high, and prioritizes the, you know, the workloads, which is really impressive, especially when we are moving so quickly with our technology. We don't really have much time to, you know, micromanage the estate. >> (Dave) Can you, can you just repeat what you said on the percent reduction? I think I heard you cut out there a little bit, a percent reduction on, on, on management, on, on, on the labor side. >> So our lead storage engineer is estimated around 40% less management. >> (Dave) Wow. Okay. So that's, that's good. So actually, I love this conversation because, you know, in the early days of automation, people like, ah, that's my job, provisioning LUNs. I'm really good at it, but I think people are realizing that it's actually not something that you want to be really good at. It's something that you want to eliminate. So, it now maybe it's that storage engineer got his or her nights and weekends back, but, but what do they do now when they get that extra time, what do you, what do you put them on? You know, no more strategic initiatives or, you know, other, other tech things on the to-do list. What's that like?. >> The last thing that, you know, any of my team, whether it's the storage leads or some of the infrastructure team that were also involved in engaged, cause you know, the organization, we have to be quite versatile as a team in our skillsets. We don't want to be doing those BAU mundane tasks. Even the storage engineer does not want to be allocating LUNs and allocating storage to physical servers, Vms, etc. We want all of that to be automated. And, you know, those engineers, they're working on some of the cutting edge things that we're trying to do with machine learning as an example, which is much more interesting. It's what they want to be doing. You know, that aides, the obvious things like retention, interest and personal development, we don't want to be, you know, that base IT infrastructure management, is not where any of the engineers wants to be. >> (Dave) In terms of the decision to go with Dell Power Store. I'm definitely hearing there was a relationship. There was an existing relationship with Dell. I'm sure that played into it. >> There were many things. So the relationship wasn't really part of this, even though I've mentioned the end-user compute in any sets or anything that we're procuring, we want best of breed, you know, best of sets. And that was done on, the cost is definitely a driver. The technology, you know, is a big trust to us, We're a tech company, new technology to us is also fascinating, not only our own, but also the storage guarantee, the simplicity, the resilience within, within the unit. Also the ability, which was key to us because of what we're trying to do with our hybrid model and bring, bring back repatriate some of the data as it were from the client. We needed that ability to, with ease, to be able to scale up and scale high, and the Power Store gave us that. >> (Dave) When you say cost, I want to dig into that price or you know, the price tag or the, the cost, I mean, when you do the business case. And I wonder if we could add a little color to that. >> (Richard) There's two elements to this, so they're not only the cost of the price tag, but then also cost of ownership and the comparisons that we were running against the other vendors, but also the comparisons that we were running from a CAPEX investment against OPEX and what we have in the cloud, and also the performance, performance that we get from the cloud and our cloud storage and the resilience within that. And then also the initial price tag, and then comparing the CapEx investments to the OPEX where all elements that were key to us making our decision. And I know that there has to be some credit taken by the Dell account team and that their relationship towards the final phrase of that RFP, you know, were key initially, not all, we were just looking for the best possible storage solution for Ultraleap. >> (Dave) And to determine that on your end, was that like a feature, because it's sometimes fuzzy what the business impact is going to be like that 40% you mentioned, or the data reduction at nine to one, when there's a promise of four to one, did you, what did you do? Did you kind of do a feature function analysis and sort of line that up and, and say, okay, I'm going to map that to our business processes our IT processes and try to predict what the impact would be. Is that how you did it? or did you take a different approach? >> (Richard) We did. So we did that, obviously between vendors usually expected an RFP, but then also mapping to how that would impact the business. And that is not an easy process to go through. And we've seen more gains even comparing one vendor to another, some of that because of the technology, the terminology is very very different and sometimes you have to bring that upper level and also gain a much more detailed understanding, which at times can be challenging, but we did a very like-for-like comparison and, and also lots of research, but you're quite right. The business analysis to what we needed. We had quite a good forecast and from summarized stock information data, and also our engineering and business and strategic roadmap, we were able to map those two together, not the easiest of experiences, not one that I want to repeat, but we, we got it. (Dave laughing) >> (Dave)Yeah, a little bit of art and science involved. Avishek, maybe you could talk about Power Store, what, you know, give us the commercial. What makes it different from other products in the market of things like cloud IQ? Maybe you could talk about that a little bit. >> Sure. So, so again, from a, it's music to my ears, when Richard talks about the ease of deployment and the management, because there is a lot of focus on that. But even as I said earlier, from a man technology perspective, a lot of goodness built-in, in terms of being able to consolidate a customer's environment, onto the platform. So that's more from a storage point of view that give the best performance, give the best data reduction, storage efficiencies. The second part, of course, the flexibility, the options that Power Store gives to the customers in terms of sort of desegregating the storage and the compute aspects of it. So if, as a customer, I want to start with different points in terms of what our customer requirements are today, but going forward as the requirements change from a compute capacity perspective, you can use a scale up and scale out capabilities, and then the intelligence built in, right? So, as you scale out your cluster, being able to move storage around right, as needed being able to do that non-disruptively. So instead of saying that Mr. Customer, your, your storage is going to you're at 90% capacity, being able to say that based on your historical trending, we expect you run out of capacity in six months, some small things like that, right? And of course, if the, the dial home, the support assist capabilities that enabled, cloud IQ brings a lot of intelligence to the table as well. In addition to that, as they mentioned earlier, there is apps on capability that gives another level of flexibility to the customers to integrate your storage infrastructure into a virtual environment, if the customer chooses to do that. And last but not the least, it's not just about the product, right? So it's about the programs that we have put around it, anytime upgrade is a big differentiator for us, where it's an investment protection program for customers, where if they want to have the peace of mind, in terms of three months, nine months, three years down the line, if we come out with new technologies, being able to be upgrade to that non-disruptively is a big part of it as well. It's a peace of mind for the customers that, yes I'm getting into the Power Store architecture today, but going forward, I'm protected from that point of view. So anytime upgrade, it's a new business program that we put around leveraging the architectural benefits of Power Store, whether your compute requirement, your storage requirements change, you're covered from that point of view. So again, a very quick overview of, of what Power Store is, why it is different. And again, that's where that comes from. >> (Dave) Thank you for that. Richard, are you actively using cloud IQ? Do you get the, what kind of value do you get from it? >> Not currently. However, we have, we have had plans to do that. The uptake and BCR, our internal Workload is not allowed us, to do that. But one of the other key reasons for selecting Power Store was the non-disruptive element, you know, with other SaaS products, other providers, and other issues that we have experienced. That was one, that was a key decision for us from a Power Store perspective. One of the other, you know, to go back to the conversation slightly, in terms of performance, we are getting, getting there. You know, there's a 400% speed of improvement of publishing. We've got an 80% faster code coverage. Our firmware builds a 1300% quicker than they were previously. and the time savings of the storage engineer and, you know, as a director of IT, I often asked for certain reports from, from the storage array, we're working at, for storage forecast, performance forecast, you know, when we're coming close to product releases, code drops that we're trying to manage, the reporting or the Power Store is impressive. Whereas previously my storage engineer would not be the, the most happiest of people when I would be trying to pull, you know, monthly and quarterly reports, et cetera. Whereas now it's, it's ease and we have live dashboards running and we can easily extract that information. >> (Dave) I love that because, you know, so often we talk about the 40% reduction in IT labor, which okay, that's cool. But then your CFO's going to say, yeah, but it's not like we're getting rid of people. We, you know, we're still spending that money and you're like, okay. You're now into soft dollars, but when you talk about 400%, 80%, 1300% of what you're talking about business impact and that's telephone numbers to a CFO. So I love those metrics. Thank you for sharing. >> Yeah. But what would, they obviously, it's sort of like dashboards when they visualize that they are very hard hitting, you know, the impact. You're quite right the CFO does chase down you know, the availability and the resource profile, however, we're on a huge upward trajectory. So having the right resilience and infrastructure in places is exactly what we need. And as I mentioned before, those engineers are all reallocated to much more interesting work and, you know, the areas that will actually drive our business forward. >> (Dave) Speaking of resilience, are you doing any replication? >> Not currently. However, we've actually got a meeting regarding this today with some of the enterprise and some of their storage specialists, in a couple of hours time, actually, because that is a very high on the agenda for us to be able to replicate and have a high availability cluster and another potentially Power Store need. >> (Dave) Okay. So I was going to ask you where you want to take this thing. I'm hearing, you're looking at cloud IQ, really try to exploit that. So you got some headroom here in terms of the value that you can get out of this platform to do replication, faster recovery, et cetera, maybe protect against, you know, events. Guys, Thanks so much for your time. Really appreciate your insights. >> (Richard) No problem. >> (Avishek) Thank you. >> And thank you for watching this cube conversation. This is Dave Vellante and we'll see you next time.

(calm music)- Hello, and welcome to this CUBE conversation here in Palo Alto, California in theCUBE Studios, I'm John Furrier, host of theCUBE. We are here with the hot startup really working on some real, super important security technology for the cloud, great company, Orca Security, Avi Shua, CEO, and co founder. Avi, thank you for coming on theCUBE and share your story >> Thanks for having me. >> So one of the biggest problems that enterprises and large scale, people who are going to the cloud and are in the cloud and are evolving with cloud native, have realized that the pace of change and the scale is a benefit to the organizations for the security teams, and getting that security equation, right, is always challenging, and it's changing. You guys have a solution for that, I really want to hear what you guys are doing. I like what you're talking about. I like what you're thinking about, and you have some potentially new technologies. Let's get into it. So before we get started, talk about what is Orca Security, what do you guys do? What problem do you solve? >> So what we invented in Orca, is a unique technology called site scanning, that essentially enables us to connect to any cloud environment in a way which is as simple as installing a smartphone application and getting a full stack visibility of your security posture, meaning seeing all of the risk, whether it's vulnerability, misconfiguration, lateral movement risk, work that already been compromised, and more and more, literally in minutes without deploying any agent, without running any network scanners, literally with no change. And while it sounds to many of us like it can't happen, it's snake oil, it's simply because we are so used to on premise environment where it simply wasn't possible in physical server, but it is possible in the cloud. >> Yeah, and you know, we've had many (indistinct) on theCUBE over the years. One (indistinct) told us that, and this is a direct quote, I'll find the clip and share it on Twitter, but he said, "The cloud is more secure than on premise, because it's more changes going on." And I asked him, "Okay, how'd you do?" He says, "It's hard, you got to stay on top of it." A lot of people go to the cloud, and they see some security benefits with the scale. But there are gaps. You guys are building something that solves those gaps, those blind spots, because of things are always changing, you're adding more services, sometimes you're integrating, you now have containers that could have, for instance, you know, malware on it, gets introduced into a cluster, all kinds of things can go on in a cloud environment, that was fine yesterday, you could have a production cluster that's infected. So you have all of these new things. How do you figure out the gaps and the blind spots? That's what you guys do, I believe, what are the gaps in cloud security? Share with us. >> So definitely, you're completely correct. You know, I totally agree the cloud can be dramatically more secluded on-prem. At the end of the day, unlike an on-prem data center, where someone can can plug a new firewall, plug a new switch, change things. And if you don't instrument, it won't see what's inside. This is not possible in the cloud. In the cloud it's all code. It's all running on one infrastructure that can be used for the instrumentation. On the other hand, the cloud enabled businesses to act dramatically faster, by say dramatically, we're talking about order of magnitude faster, you can create new networks in matter of minutes, workloads can come and go within seconds. And this creates a lot of changes that simply haven't happened before. And it involves a lot of challenges, also from security instrumentation point of view. And you cannot use the same methodologies that you used for the on-prem because if you use them, you're going to lose, they were a compromise, that worked for certain physics, certain set of constraints that no longer apply. And our thesis is that essentially, you need to use the capabilities of the cloud itself, for the instrumentation of everything that can runs on the cloud. And when you do that, by definition, you have full coverage, because if it's run on the cloud, it can be instrumented on cloud, this essentially what Docker does. And you're able to have this full visibility for all of the risks and the importance because all of them, essentially filter workload, which we're able to analyze. >> What are some of the blind spots in the public cloud, for instance. I mean, that you guys are seeing that you guys point out or see with the software and the services that you guys have. >> So the most common ones are the things that we have seen in the last decades. I don't think they are materially different simply on steroids. We see things, services that are launched, nobody maintained for years, using things like improper segmentation, that everyone have permission to access everything. And therefore if one environment is breached, everything is breached. We see organization where something goes dramatically hardened. So people find a way to a very common thing is that, and now ever talks about CIM and the tightening their permission and making sure that every workload have only the capabilities that they need. But sometimes developers are a bit lazy. So they'll walk by that, but also have keys that are stored that can bypass the entire mechanism that, again, everyone can do everything on any environment. So at the end of the day, I think that the most common thing is the standard aging issues, making sure that your environment is patched, it's finger tightened, there is no alternative ways to go to the environment, at scale, because the end of the day, they are destined for security professional, you need to secure everything that they can just need to find one thing that was missed. >> And you guys provide that visibility into the cloud. So to identify those. >> Exactly. I think one of the top reasons that we implemented Orca using (indistinct) technology that I've invented, is essentially because it guarantees coverage. For the first time, we can guarantee you that if you scan it, that way, we'll see every instance, every workload, every container, because of its running, is a native workload, whether it's a Kubernetes, whether it's a service function, we see it all because we don't rely on any (indistinct) integration, we don't rely on friction within the organization. So many times in my career, I've been in discussion with customer that has been breached. And when we get to the core of the issue, it was, you couldn't, you haven't installed that agent, you haven't configured that firewall, the IPS was not up to date. So the protections weren't applied. So this is technically true, but it doesn't solve the customer problem, which is, I need the security to be applied to all of my environment, and I can't rely on people to do manual processes, because they will fail. >> Yeah, yeah. I mean, it's you can't get everything now and the velocity, the volume of activity. So let me just get this right, you guys are scanning container. So the risk I hear a lot is, you know, with Kubernetes, in containers is, a fully secure cluster could have a container come in with malware, and penetrate. And even if it's air gapped, it's still there. So problematic, you would scan that? Is that how it would work? >> So yes, but so for nothing but we are not scanning only containers, the essence of Orca is scanning the cloud environment holistically. We scan your cloud configuration, we scan your Kubernetes configuration, we scan your Dockers, the containers that run on top of them, we scan the images that are installed and we scan the permission that these images are one, and most importantly, we combined these data points. So it's not like you buy one solution that look to AWS configuration, is different solution that locate your virtual machines at one cluster, another one that looks at your cluster configuration. Another one that look at a web server and one that look at identity. And then you have resolved from five different tools that each one of them claims that this is the most important issue. But in fact, you need to infuse the data and understand yourself what is the most important items or they're correlated. We do it in an holistic way. And at the end of the day, security is more about thinking case graphs is vectors, rather than list. So it is to tell you something like this is a container, which is vulnerable, it has permission to access your sensitive data, it's running on a pod that is indirectly connected to the internet to this load balancer, which is exposed. So this is an attack vector that can be utilized, which is just a tool that to say you have a vulnerable containers, but you might have hundreds, where 99% of them are not exposed. >> Got it, so it's really more logical, common sense vectoring versus the old way, which was based on perimeter based control points, right? So is that what I get? is that right is that you're looking at it like okay, a whole new view of it. Not necessarily old way. Is that right? >> Yes, it is right, we are looking at as one problem that is entered in one tool that have one unified data model. And on top of that, one scanning technology that can provide all the necessary data. We are not a tool that say install vulnerability scanner, install identity access management tools and infuse all of the data to Orca will make sense, and if you haven't installed the tools to you, it's not our problem. We are scanning your environment, all of your containers, virtual machine serverless function, cloud configuration using guard technology. When standard risk we put them in a graph and essentially what is the attack vectors that matter for you? >> The sounds like a very promising value proposition. if I've workloads, production workloads, certainly in the cloud and someone comes to me and says you could have essentially a holistic view of your security posture at any given point in that state of operations. I'm going to look at it. So I'm compelled by it. Now tell me how it works. Is there overhead involved? What's the cost to, (indistinct) Australian dollars, but you can (indistinct) share the price to would be great. But like, I'm more thinking of me as a customer. What do I have to do? What operational things, what set up? What's my cost operationally, and is there overhead to performance? >> You won't believe me, but it's almost zero. Deploying Orca is literally three clicks, you just go log into the application, you give it the permission to read only permission to the environment. And it does the rest, it doesn't run a single awkward in the environment, it doesn't send a single packet. It doesn't create any overhead we have within our public customer list companies with a very critical workloads, which are time sensitive, I can quote some names companies like Databricks, Robinhood, Unity, SiteSense, Lemonade, and many others that have critical workloads that have deployed it for all of the environment in a very quick manner with zero interruption to the business continuity. And then focusing on that, because at the end of the day, in large organization, friction is the number one thing that kills security. You want to deploy your security tool, you need to talk with the team, the team says, okay, we need to check it doesn't affect the environment, let's schedule it in six months, in six months is something more urgent then times flybys and think of security team in a large enterprise that needs to coordinate with 500 teams, and make sure it's deployed, it can't work, Because we can guarantee, we do it because we leverage the native cloud capabilities, there will be zero impact. This allows to have the coverage and find these really weak spot nobody's been looking at. >> Yeah, I mean, this having the technology you have is also good, but the security teams are burning out. And this is brings up the cultural issue we were talking before we came on camera around the cultural impact of the security assessment kind of roles and responsibilities inside companies. Could you share your thoughts on this because this is a real dynamic, the people involved as a people process technology, the classic, you know, things that are impacted with digital transformation. But really the cultural impact of how developers push code, the business drivers, how the security teams get involved. And sometimes it's about the security teams are not under the CIO or under these different groups, all kinds of impacts to how the security team behaves in context to how code gets shipped. What's your vision and view on the cultural impact of security in the cloud. >> So, in fact, many times when people say that the cloud is not secure, I say that the culture that came with the cloud, sometimes drive us to non secure processes, or less secure processes. If you think about that, only a decade ago, if an organization could deliver a new service in a year, it would be an amazing achievement, from design to deliver. Now, if an organization cannot ship it, within weeks, it's considered a failure. And this is natural, something that was enabled by the cloud and by the technologies that came with the cloud. But it also created a situation where security teams that used to be some kind of a checkpoint in the way are no longer in that position. They're in one end responsible to audit and make sure that things are acting as they should. But on the other end, things happen without involvement. And this is a very, very tough place to be, nobody wants to be the one that tells the business you can't move as fast as you want. Because the business want to move fast. So this is essentially the friction that exists whether can we move fast? And how can we move fast without breaking things, and without breaking critical security requirements. So I believe that security is always about a triode, of educate, there's nothing better than educate about putting the guardrails to make sure that people cannot make mistakes, but also verify an audit because there will be failures in even if you educate, even if you put guardrails, things won't work as needed. And essentially, our position within this, triode is to audit, to verify to empower the security teams to see exactly what's happening, and this is an enabler for a discussion. Because if you see what are the risks, the fact that you have, you know, you have this environment that hasn't been patched for a decade with the password one to six, it's a different case, then I need you to look at this environment because I'm concerned that I haven't reviewed it in a year. >> That's exactly a great comment. You mentioned friction kills innovation earlier. This is one friction point that mismatch off cadence between ownership of process, business owners goals of shipping fast, security teams wanting to be secure. And developers just want to write code faster too. So productivity, burnout, innovation all are a factor in cloud security. What can a company do to get involved? You mentioned easy to deploy. How do I work with Orca? You guys are just, is it a freemium? What is the business model? How do I engage with you if I'm interested in deploying? >> So one thing that I really love about the way that we work is that you don't need to trust a single word I said, you can get a free trial of Orca at website orca.security, one a scan on your cloud environment, and see for yourself, whether there are critical ways that were overlooked, whether everything is said and there is no need for a tool or whether they some areas that are neglected and can be acted at any given moment (indistinct) been breached. We are not a freemium but we offer free trials. And I'm also a big believer in simplicity and pricing, we just price by the average number workload that you have, you don't need to read a long formula to understand the pricing. >> Reducing friction, it's a very ethos sounds like you guys have a good vision on making things easy and frictionless and sets that what we want. So maybe I should ask you a question. So I want to get your thoughts because a lot of conversations in the industry around shifting left. And that's certainly makes a lot of sense. Which controls insecurity do you want to shift left and which ones you want to shift right? >> So let me put it at, I've been in this industry for more than two decades. And like any industry every one's involved, there is a trend and of something which is super valuable. But some people believe that this is the only thing that you need to do. And if you know Gartner Hype Cycle, at the beginning, every technology is (indistinct) of that. And we believe that this can do everything and then it reaches (indistinct) productivity of the area of the value that it provides. Now, I believe that shifting left is similar to that, of course, you want to shift left as much as possible, you want things to be secure as they go out of the production line. This doesn't mean that you don't need to audit what's actually warning, because everything you know, I can quote, Amazon CTO, Werner Vogels about everything that can take will break, everything fails all the time. You need to assume that everything will fail all the time, including all of the controls that you baked in. So you need to bake as much as possible early on, and audit what's actually happening in your environment to find the gaps, because this is the responsibility of security teams. Now, just checking everything after the fact, of course, it's a bad idea. But only investing in shifting left and education have no controls of what's actually happening is a bad idea as well. >> A lot of people, first of all, great call out there. I totally agree, shift left as much as possible, but also get the infrastructure and your foundational data strategies, right and when you're watching and auditing. I have to ask you the next question on the context of the data, right, because you could audit all day long, all night long. But you're going to have a pile of needles looking for haystack of needles, as they say, and you got to have context. And you got to understand when things can be jumped on. You can have alert fatigue, for instance, you don't know what to look at, you can have too much data. So how do you manage the difference between making the developers productive in the shift left more with the shift right auditing? What's the context and (indistinct)? How do you guys talk about that? Because I can imagine, yeah, it makes sense. But I want to get the right alert at the right time when it matters the most. >> We look at risk as a combination of three things. Risk is not only how pickable the lock is. If I'll come to your office and will tell you that you have security issue, is that they cleaning, (indistinct) that lock can be easily picked. You'll laugh at me, technically, it might be the most pickable lock in your environment. But you don't care because the exposure is limited, you need to get to the office, and there's nothing valuable inside. So I believe that we always need to take, to look at risk as the exposure, who can reach that lock, how easily pickable this lock is, and what's inside, is at your critical plan tools, is it keys that can open another lock that includes this plan tools or just nothing. And when you take this into context, and the one wonderful thing about the cloud, is that for the first time in the history of computing, the data that is necessary to understand the exposure and the impact is in the same place where you can understand also the risk of the locks. You can make a very concise decision of easily (indistinct) that makes sense. That is a critical attack vector, that is a (indistinct) critical vulnerability that is exposed, it is an exposed service and the service have keys that can download all of my data, or maybe it's an internal service, but the port is blocked, and it just have a default web server behind it. And when you take that, you can literally quantize 0.1% of the alert, even less than that, that can be actually exploited versus device that might have the same severity scores or sound is critical, but don't have a risk in terms of exposure or business impact. >> So this is why context matters. I want to just connect what you said earlier and see if I get this right. What you just said about the lock being picked, what's behind the door can be more keys. I mean, they're all there and the thieves know, (indistinct) bad guys know exactly what these vectors are. And they're attacking them. But the context is critical. But now that's what you were getting at before by saying there's no friction or overhead, because the old way was, you know, send probes out there, send people out in the network, send packers to go look at things which actually will clutter the traffic up or, you know, look for patterns, that's reliant on footsteps or whatever metaphor you want to use. You don't do that, because you just wire up the map. And then you put context to things that have weights, I'm imagining graph technologies involved or machine learning. Is that right? Am I getting that kind of conceptually, right, that you guys are laying it out holistically and saying, that's a lock that can be picked, but no one really cares. So no one's going to pick and if they do, there's no consequence, therefore move on and focus energy. Is that kind of getting it right? Can you correct me where I got that off or wrong? >> So you got it completely right. On one end, we do the agentless deep assessment to understand your workloads, your virtual machine or container, your apps and service that exists with them. And using the site scanning technology that some people you know, call the MRI for the cloud. And we build the map to understand what are connected to the security groups, the load balancer, the keys that they hold, what these keys open, and we use this graph to essentially understand the risk. Now we have a graph that includes risk and exposure and trust. And we use this graph to prioritize detect vectors that matters to you. So you might have thousands upon thousands of vulnerabilities on servers that are simply internal and these cannot be manifested, that will be (indistinct) and 0.1% of them, that can be exploited indirectly to a load balancer, and we'll be able to highlight these one. And this is the way to solve alert fatigue. We've been in large organizations that use other tools that they had million critical alerts, using the tools before Orca. We ran our scanner, we found 30. And you can manage 30 alerts if you're a large organization, no one can manage a million alerts. >> Well, I got to say, I love the value proposition. I think you're bringing a smart view of this. I see you have the experience there, Avi and team, congratulations, and it makes sense of the cloud is a benefit, it can be leveraged. And I think security being rethought this way, is smart. And I think it's being validated. Now, I did check the news, you guys have raised significant traction as valuation certainly raised around the funding of (indistinct) 10 million, I believe, a (indistinct) Funding over a billion dollar valuation, pushes a unicorn status. I'm sure that's a reflection of your customer interaction. Could you share customer success that you're having? What's the adoption look like? What are some of the things customers are saying? Why do they like your product? Why is this happening? I mean, I can connect the dots myself, but I want to hear what your customers think. >> So definitely, we're seeing huge traction. We grew by thousands of percent year over year, literally where times during late last year, where our sales team, literally you had to wait two or three weeks till you managed to speak to a seller to work with Orca. And we see the reasons as organization have the same problems that we were in, and that we are focusing. They have cloud environments, they don't know their security posture, they need to own it. And they need to own it now in a way which guarantees coverage guarantees that they'll see the important items and there was no other solution that could do that before Orca. And this is the fact. We literally reduce deployment (indistinct) it takes months to minutes. And this makes it something that can happen rather than being on the roadmap and waiting for the next guy to come and do that. So this is what we hear from our customers and the basic value proposition for Orca haven't changed. We're providing literally Cloud security that actually works that is providing full coverage, comprehensive and contextual, in a seamless manner. >> So talk about the benefits to customers, I'll give you an example. Let's just say theCUBE, we have our own cloud. It's growing like crazy. And we have a DevOps team, very small team, and we start working with big companies, they all want to know what our security posture is. I have to go hire a bunch of security people, do I just work with Orca, because that's the more the trend is integration. I just was talking to another CEO of a hot startup and the platform engineering conversations about people are integrating in the cloud and across clouds and on premises. So integration is all about posture, as well, too I want to know, people want to know who they're working with. How does that, does that factor into anything? Because I think, that's a table stakes for companies to have almost a posture report, almost like an MRI you said, or a clean (indistinct) health. >> So definitely, we are both providing the prioritized risk assessment. So let's say that your cloud team want to check their security, the cloud security risk, they'll will connect Orca, they'll see the (indistinct) in a very, very clear way, what's been compromised (indistinct) zero, what's in an imminent compromise meaning the attacker can utilize today. And you probably want to fix it as soon as possible and things that are hazardous in terms that they are very risky, but there is no clear attack vectors that can utilize them today, there might be things that combining other changes will become imminent compromise. But on top of that, when standard people also have compliance requirements, people are subject to a regulation like PCI CCPA (indistinct) and others. So we also show the results in the lens of these compliance frameworks. So you can essentially export a report showing, okay, we were scanned by Orca, and we comply with all of these requirements of SOC 2, etc. And this is another value proposition of essentially not only showing it in a risk lens, but also from the compliance lens. >> You got to be always on with security and cloud. Avi, great conversation. Thank you for sharing nice knowledge and going deep on some of the solution and appreciate your conversation. Thanks for coming on. >> Thanks for having me. >> Obviously, you are CEO and co founder of Orca Security, hot startup, taking on security in the cloud and getting it right. I'm John Furrier with theCUBE. Thanks for watching. (calm music)

>> Voiceover: From around the globe, it's theCUBE with digital coverage of Dell Technologies World Digital Experience brought to you by Dell Technologies. >> Welcome to theCUBE's coverage of Dell Technologies World 2020, the Digital Experience this year. I'm Lisa Martin, pleased to be joined by two CUBE alumni from Dell EMC. Please welcome Joe Caradonna, the VP of Cloud Storage CTO. Joe, good to see you again, even though quite socially distant. >> Yeah, thank you, it's great to be here. >> And Devon Reed is also joining us, the Senior Director of Product Management. Devon, how are you? >> I'm good, how are you doing? >> Good. >> Nice to be here, thank you. >> Nice to be chatting with you guys, although very, very socially distant, following rules. It wouldn't be a Dell Technologies World without having you guys on theCUBE, so we appreciate you joining us. So let's dig in. So much has happened in the world since we last spoke with you. But one of the things that happened last year, around a year ago, was the Dell On Demand program was launched. And now here we are nearly a year later when Michael Dell was just talking about, "Hey, Dell's plan is to go "and deliver everything as a service." We've heard some of your competitors kind of going the same route, some kind of spurned by COVID. Talk to us, Devon, we'll start with you, about what this direction is shift to as-a-service means and what it means specifically for storage. >> Yeah, certainly. So first and foremost, what we talked about last year with respect to On Demand, Dell Technologies On Demand, we've had great success with that program. But before I get into what we're doing with as-a-service, I really want to talk about why we're doing the as-a-service. And when we talk to customers and partners, and when we look at the trends in the market, what we're seeing is that customers are more and more wanting to consume technology infrastructure as a service in an OPEX manner. And analysts are revising those estimates up almost daily. And what we're seeing is one of the things that's driving that is actually why we're here in this remote session as opposed to being in Vegas, doing this. And it's really the global uncertainty around the pandemic. So it's driving the need to free up cash and consume these infrastructure more as a service. Now, as Michael said... Yeah, as Michael said, we have the broadest set of infrastructure offerings in the market and we are number one in most categories. And we're in the process of building out an offer structure that cuts across all the different infrastructure components. But to get real specific on what we're doing with a storage as a service, we are in the process of building out the first true storage or as a service offering for our infrastructure starting with storage. It'll be a private preview as of Q4, by the end of this fiscal year and generally available in the first half of next year. And what we're doing is taking the infrastructure, the Dell Technology's storage and where we're flipping the business model as opposed to buying it outright, the customers actually just consume it as a service. So they have a very simple consumption model where they just pick their outcome, they pick their restored service, they pick their performance, they pick their capacity, and we deliver that service to their on-premise site. >> Let me unpack outcomes of it, 'cause I saw that in some of the information online, outcome driven. What do you mean by that, and can you give us some examples of those outcomes that customers are looking to achieve? >> Yeah, so in today's world, the way people mostly consume infrastructure is, or at least storage, is that they say, "I need a storage product." And what the customers do is they work with our sales representatives and say, "I need a XYZ product. "Maybe it's a PowerStore and I need this much capacity. "I can pick all of the components, "I can pick the number of drives, "the type of drives there are." And that's really from a product perspective. And what we're doing with the, as-a-service, is we're trying to flip the model and really drive to what the business outcome is. So the business outcome here is really, I need block storage, I need this performance level, I need this much capacity. And then we basically ship the infrastructure, we think, that better suits those outcomes. And we're making changes across our entire infrastructure value chain to really deliver these service. So we try to deliver these much quicker for the customer. We actually manage the infrastructure. So it enables customers to spend less time managing their infrastructure and more time actually operating the service, paying attention to their business outcomes. >> Got it, and that's what every customer wants more of is more time to actually deliver this business outcomes and make those course corrections as they need to. Joe, let's talk to you for a bit. Let's talk, what's going on with cloud? The last time we saw you, a lot of change as we talked about, but give us a picture of Dell's cloud strategy. From what you guys are doing on-prem to what you are doing with cloud partners. What is this multi-pronged cloud strategy actually mean? >> Yeah, sure, I mean, our customers want hybrid cloud solutions and we believe that to be the model going forward. And so actually what we're doing is, if you think about it, we're taking the best of public cloud and bringing it on-prem, and we're also taking the best of on-prem and bringing it to the public cloud. So, you know, Devon just talked to you about how we're bringing that public cloud operation model to the data center. But what we've also done is bring our storage arrays to the cloud as a service. And we've done that with PowerStore, we've done that with PowerMax, and we've done that with PowerScale. And in the case of PowerScale for Google cloud, I mean, you get the same performance and capacity scale out in the cloud as you do on-prem. And the systems inter-operate between on-prem and cloud so it makes it easy for fluid data mobility across these environments. And for the first time it enables our customers to get their data to the cloud in a way that they can bring their high performance file workloads to the cloud. >> So talk to me a little bit about, you mentioned PowerScale for Google cloud service, is that a Dell hardware based solution? How does that work? >> Yeah, the adoptions have been great. I mean, we launched back in May and since then we brought on customers in oil and gas and eCommerce and in health as well. And we're growing out the regions, we're going to be announcing a new region in North America soon and we're going to be building out in APJ and EMEA as well. So, customer response has been fantastic, looking forward to growing up. >> Excellent, Devon back to you, let's talk about some of the things that are going on with PowerProtect DD, some new cloud services there too. Can you unpack that for us? >> So Joe, was talking about how we were taking our storage systems and putting them in the cloud. So I just back up in, and kind of introduce real quickly or reintroduce our Dell Technologies Cloud Storage Services. And that's really, we have our primary storage systems from Unity XT, the PowerStore, to PowerScale, to ECS, and that's housed in a co-locations facility right next to hyperscalers. And then that enables us to provide a fully managed service offering to our customers to a multi-cloud. So what we're doing is we're extending the Dell Technologies Cloud Storage Services to include PowerProtect DD. So we're bringing PowerProtect DD into this managed services offering so customers can use it for cloud, longterm retention, backup, archiving, and direct backup from a multicloud environment. So extending what we've already done with the Dell Technologies Cloud Storage Services. >> So is that almost kind of like a cloud based data protection solution for those workloads that are running in the cloud VMs, SaaS applications, physical servers, spiral data, things like that? >> Yeah, there's several use cases. So you could have a primary block storage system on your premises and you could actually be providing direct backup into the cloud. You could have backups that you have on-premise that you could be then replicating with PowerProtect data, data domain replication to cloud. And you could also have data in AWS, or Azure, or Google that you could be backing up directly to the PowerProtect domain into this service. So there's multiple use cases. >> Got it, all right. Joe, let's talk about some of the extensions of cloud you guys have both been talking about the last few minutes. One of the recent announcements was about PowerMax being cloud enabled and that's a big deal to cloudify something like that. Help us understand the nature of that, the impetus, and what that means now and what customers are able to actually use today. >> Yeah sure, I mean, we've launched the PowerMax as a cloud service about a year and a half ago with our partner, Faction. And that's for those customers that want that tier zero enterprise grade data capabilities in the cloud. And not just a cloud, it also offers multicloud capabilities for both file and block. Now, in addition, the Dell Tech World, we're launching additional cloud mobility capabilities for PowerMax, where let's say you have a PowerMax on-prem, you could actually do snapshot shipping to an object repository. And that could be in AWS, that can be in Azure, or it could be locally to our local ECS object store. In addition, in the case of Amazon we go a step further where if you do snapshot shipping into Amazon S3, you can then rehydrate those snapshots directly into EBS. And that way you can do processing on that data in the cloud as well. >> Give us an idea, Joe, the last few months or so what some of your customer conversations have been like? I know you're normally in front of customers all the time. Dell Tech World is a great example. I think last year there was about 14,000 folks there, was huge. And we're all so used to that three dimensional engagement, more challenging to do remotely, but talk to me about some of the customer conversations that you've had, and how they've helped influence some of the recent announcements. >> Yeah sure, customers... It might sound a little cliche, but cloud is a journey. It's a journey for our customers. It's a journey for us too, as we build out our capabilities to best serve them. But their questions are, "I want to take advantage "of that elastic compute in the cloud." But maybe the data storage doesn't keep up with it. In the case of when we go to PowerScale for Google, the reason why we brought that platform to the cloud is 'cause you can get hundreds of gigabytes per second of throughput through that. And for our customers that are doing things like processing genomic sequencing data, they need that level of throughput, and they want to move those workloads into the cloud. The computer's there but the storage systems to keep up with it, were not. So by us bringing a solution like this to the cloud, now they can do that. So we see that with PowerScale, we see a lot of that with file in the cloud because the file services in the cloud aren't as mature as some of the other ones like with block and object. So we're helping filling some of those gaps and getting them to those higher performance tiers. And as I was mentioning, with things like PowerMax and PowerStore, it's extending their on-prem presence into the public cloud. So they can start to make decisions not based on a capability, but more based on the requirements for where they want to run their workloads. >> And let's switch gears to talking about partners now. Dell has a huge partner ecosystem. We always talk with those folks on theCUBE as well, every year. Devon, from a product management perspective, tell me about some of the things that are interesting to partners and what the advantages are for partners with this shift in what, how Dell is going to be delivering, from PCs, to storage, to HCI, for example. >> Yeah exactly, so, Joe mentioned that it's really a journey and Joe talked a lot about how customers aren't maybe not (indistinct) completely going to a hyperscale or to a complete public cloud. And what we're hearing is there's a lot of customers that are actually wanting the cloud-like experience, but wanting it on-prem. And we're hearing from our partners almost on a daily basis. I have a lot of partner customer conversations where they want to be involved in delivering this as a service. Through their customers, they want to maintain that relationship, derive that value, and in some cases even provide the services for them. And that's what we're looking do as the largest infrastructure provider with the broadest base of partnership we have an advantage there. >> Is there any specific partner certification programs that partners can get into to help start rolling this out? >> At this point, we are trying to build it, but at this point we had nothing to announce here but that's something that we're actively working on and stay tuned for that. >> I imagine there will be a lot of virtual conversations at the digital tech world this year, between the partner community when all of these things are announced. And you get those brains collectively together although obviously virtually, to start iterating on ideas and developing things that might be great to programmatize down the road. And, Joe, last question for you, second to last question actually, is this, this year as we talked about a number of times, everyone's remote, everyone's virtual. It's challenging to get that level of engagement. We're all so used to being in-person and all of the hallway conversations even that you have when you're walking around the massive show floor for example, what can participants and attendees expect from your perspective this year at Dell Technologies World? Will they be able to get the education and that engagement that Dell really wants to deliver? >> Yeah, well, clearly we had to scale things back quite, there's no way around that. But we have a lot of sessions that were designed to inform them with a new capabilities we've been building out. And not just for cloud, but across the portfolio. So I hope they get a lot out of that. We have some interactive sessions in there as well, for some interactive Q and A. And you're right, I mean, a challenge for us is connecting with the customer in this virtual reality. We're all at home, right? The customers are at home. So we've been on Zoom, like never before, reaching out to customers to better understand where they want to go, what their challenges are and how we can help them. So I would say we are connecting, it's a little different and requires a little more effort on everyone's part. We just can't all do it in the same day anymore. It is just a little more spread out. >> Well, then it kind of shows the opportunity to consume things on demand. And as consumers, we sort of have this expectation that we can get anything we want on demand. But you mentioned, Joe, in the second to last question, this is the last one. But you mentioned, everybody's at home. You have to tell us about that fantastic guitar behind you. What's the story? >> Every guitar has a story. I'll just say for today, look, this is my tribute to Eddie Van Halen. We're going to miss him for sure. >> And I'll have the audience know, I did ask Joe to play us out. He declined, but I'm going to hold them to that for next time, 'cause we're not sure when we're going to get to see you guys in person again. Joe and Devon, thank you so much for joining me on the program today. It's been great talking to you. Lots of things coming, lots of iterations, lots of influence from the customers, influence from COVID and we're excited to see what is to come. Thanks for your time. >> Both: Thank you so much. >> From my guests, Joe Caradonna and Devon Reed, I'm Lisa Martin. You're watching theCUBE's coverage of Dell Technologies World 2020, the Digital Experience. (soft 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. >> The steepest drop in the stock market since June 11th flipped the narrative and sent investors scrambling. Tech got hammered after a two-month run, and people are asking questions. Is this a bubble popping, or is it a healthy correction? Are we now going to see a rotation into traditional stocks, like banks and maybe certain cyclicals that have lagged behind the technology winners? Hello, everyone, and welcome to this week's episode of Wikibon's CUBE Insights powered by ETR. In this Breaking Analysis, we want to give you our perspective on what's happening in the technology space and unpack what this sentiment flip means for the balance of 2020 and beyond. Let's look at what happened on September 3rd, 2020. The tech markets recoiled this week as the NASDAQ Composite dropped almost 5% in a single day. Apple's market cap alone lost $178 billion. The Big Four: Apple, Microsoft, Amazon, and Google lost a combined value that approached half a trillion dollars. For context, this number is larger than the gross domestic product for countries as large as Thailand, Iran, Austria, Norway, and even the UAE, and many more. The tech stocks that have been running due to COVID, well, they got crushed. These are the ones that we've highlighted as best positioned to thrive during the pandemic, you know, the work-from-home, SaaS, cloud, security stocks. We really have been talking about names like Zoom, ServiceNow, Salesforce, DocuSign, Splunk, and the security names like CrowdStrike, Okta, Zscaler. By the way, DocuSign and CrowdStrike and Okta all had nice earnings beats, but they still got killed underscoring the sentiment shift. Now the broader tech market was off as well on sympathy, and this trend appears to be continuing into the Labor Day holiday. Now why is this happening, and why now? Well, there are a lot of opinions on this. And first, many, like myself, are relatively happy because this market needed to take a little breather. As we've said before, the stock market, it's really not reflecting the realities of the broader economy. Now as we head into September in an election year, uncertainty kicks in, but it really looks like this pullback was fueled by a combination of an overheated market and technical factors. Specifically, take a look at volatility indices. They were high and rising, yet markets kept rising along with them. Robinhood millennial investors who couldn't bet on sports realized that investing in stocks was as much of a rush and potentially more lucrative. The other big wave, which was first reported by the Financial Times, is that SoftBank made a huge bet on tech and bought options tied to around $50 billion worth of high-flying tech stocks. So the option call volumes skyrocketed. The call versus put ratio was getting way too hot, and we saw an imbalance in the market. Now market makers will often buy an underlying stock to hedge call options to ensure liquidity in these cases. So to be more specific, delta in options is a measure of the change in the price of an option relative to the underlying stock, and gamma is a measure of the volatility of the delta. Now usually, volatility is relatively consistent on both sides of the trade, the calls and the puts, because investors often hedge their bets. But in the case of many of these hot stocks, like Tesla, for example, you've seen the call skew be much greater than the skew in the downside. So let's take an example. If people are buying cheap out of the money calls, a market maker might buy the underlying stock to hedge for liquidity. And then if Elon puts out some good news, which he always does, the stock goes up. Market makers have to then buy more of the underlying stock. And then algos kick in to buy even more. And then the price of the call goes up. And as it approaches it at the money price, this forces market makers to keep buying more of that underlying stock. And then the melt up until it stops. And then the market flips like it did this week. When stock prices begin to drop, then market makers were going to rebalance their portfolios and their risk and sell their underlying stocks, and then the rug gets pulled out from the markets. And that's really why some of the stocks that have run dropped so precipitously. Okay, why did I spend so much time on this, and why am I not freaking out? Because I think these market moves are largely technical versus fundamental. It's not like 1999. We had a double whammy of technical rug pulls combined with poor underlying fundamentals for high-flying companies like CMGI and Internet Capital Group, whose businesses, they were all about placing bets on dot-coms that had no business models other than non-monetizable eyeballs. All right, let's take a look at the NASDAQ and dig into the data a little bit. And I think you'll see what I mean and why I'm not too concerned. This is a year-to-date chart of the NASDAQ, and you can see it bottomed on March 23rd at 6,860. And then ran up until June 11th and had that big drop, but was still elevated at 9,492. And then it ran up to over 12,000 and hit an all-time high. And then you see the big drop. And that trend continued on Friday morning. The NASDAQ Composite traded below 11,000. It actually corrected to 10% of its high, 9.8% to be precise, and then it snapped back. But even at its low, that's still up over 20% for the year. In the year of COVID, would that have surprised you in March? It certainly would have surprised me. So to me, this pullback is sort of a relief. It's good and actually very normal and quite predictable. Now the exact timing of these pullbacks, of course, on the other hand is not entirely predictable. Not at all, frankly, at least for this observer. So the big question is where do we go from here? So let's talk about that a little bit. Now the economy continues to get better. Take a look at the August job report; it was good. 1.4 million new jobs, 340,000 came from the government. That was positive numbers. And the other good news is it translates into a drop in unemployment under 10%. It's now at 8.4%. And this is really good relative to expectations. Now the sell-off continued, which suggested that the market wanted to keep correcting, so that's good. Maybe some buying opportunities would emerge in over the next several months, the market snapped back, but for those who have been waiting, I think that's going to happen. And so that snapback, maybe that's an indicator that the market wants to keep going up, we'll see. But I think there are more opportunities ahead because there's really so much uncertainty. What's going to happen with the next round of the stimulus? The jobs report, maybe that's a catalyst for compromise between the Democrats and the Republicans, maybe. The US debt is projected to exceed 100% of GDP this calendar year. That's the highest it's been since World War II. Does that give you a good feeling? That doesn't give me a good feeling. And when we talk about the election, that brings additional uncertainty. So there's a lot to think about for the markets. Now let's talk about what this means for tech. Well, as we've been projecting for months with our colleagues at ETR, despite what's going on in the stock market and its rise, there's those real tech winners, we still see a contraction in 2020 for IT spend of minus 5 to 8%. And we talk a lot about the bifurcation in the market due to COVID accelerating some of these trends that were already in place, like digital transformation and SaaS and cloud. And then the work-from-home kicks in with other trends like video conferencing and the shift to security spend. And we think this is going to continue for years. However, because these stocks have run up so much, they're going to have very tough compares in 2021. So maybe time for a pause. Now let's take a look at the IT spending macroeconomics. This data is from a series of surveys that ETR conducted to try to better understand spending patterns due to COVID. Those yellow slices of the pies show the percent of customers that indicate that their budgets will be impacted by coronavirus. And you can see there's a steady increase from mid-March, which blend into April, and then you can see the June data. It goes from 63% saying yes, which is very high, to 78%, which is very, very high. And the bottom part of the chart shows the degree of that change. So 22% say no change in the latest survey, but you can see much more of a skew to the red declines on the left versus the green upticks on the right-hand side of the chart. Now take a look at how IT buyers are seeing the response to the pandemic. This chart shows what companies are doing as a result of COVID in another recent ETR survey. Now of course, it's no surprise, everybody's working from home. Nobody's traveling for business, not nobody, but most people aren't, we know that. But look at the increase in hiring freezes and freezing new IT deployments, and the sharp rise in layoffs. So IT is yet again being asked to do more with less. They're used to it. Well, we see this driving an acceleration to automation, and that's going to benefit, for instance, the RPA players, cloud providers, and modern software vendors. And it will also precipitate a tailwind for more aggressive AI implementations. And many other selected names are going to continue to do well, which we'll talk about in a second, but they're in the work-from-home, the cloud, the SaaS, and the modern data sectors. But the problem is those sectors are not large enough to offset the declines in the core businesses of the legacy players who have a much higher market share, so the overall IT spend declines. Now where it gets kind of interesting is the legacy companies, look, they all have growth businesses. They're making acquisitions, they're making other bets. IBM, for example, has its hybrid cloud business in Red Hat, Dell has VMware and it's got work-from-home solutions, Oracle has SaaS and cloud, Cisco has its security business, HPE, it's as a service initiative, and so forth. And again, these businesses are growing faster, but they are not large enough to offset the decline in core on-prem legacy and drive anything more than flat growth, overall, for these companies at best. And by the time they're large enough, we'll be into the next big thing, so the cycle continues. But these legacy companies are going to compete with the upstarts, and that's where it gets interesting. So let's get into some of the specific names that we've been talking about for over a year now and make some comments around their prospects. So what we want to do is let's start with one of our favorites: Snowflake. Now Snowflake, along with Asana, JFrog, Sumo Logic, and Unity, has a highly anticipated upcoming IPO. And this chart shows new adoptions in the database sector. And you can see that Snowflake, while down from the October 19th survey, is far outpacing its competitors, with the exception of Google, where BigQuery is doing very well. But you see Mongo and AWS remain strong, and I'm actually quite encouraged that it looks like Cloudera has righted the ship and you kind of saw that in their earnings recently. But my point is that Snowflake is a share gainer, and we think will likely continue to be one for a number of quarters and years if they can execute and compete with the big cloud players, and that's a topic that we've covered extensively in previous Breaking Analysis segments, and, as you know, we think Snowflake can compete. Now let's look at automation. This is another space that we've been talking about quite a bit, and we've largely focused on two leaders: UiPath and Automation Anywhere. But I have to say, I still like Blue Prism. I think they're well-positioned. And I especially like Pegasystems, which has, for years, been embarking on a broader automation agenda. What this chart shows is net score or spending velocity data for those customers who said they were decreasing spend in 2020. Those red bars that we showed earlier are the ones who are decreasing. And you can see both Automation Anywhere and UiPath show elevated levels within that base where spending is declining, so that's a real positive. Now Microsoft, as we've reported, is elbowing its way into the market with what is currently an inferior point product, but, you know, it's Microsoft, so we can't ignore that. And finally, let's have a look at the all-important security sector, which we've covered extensively and put out a report recently. So what this next chart does is cherry-picks of a few of our favorite names, and it shows the net score or spending momentum and the granularity for some of the leaders and emerging players. All of these players are in the green, as you can see in the upper right, and they all have decent presence in the dataset as indicated by the shared NS. Okta is at the top of the list with 58% net score. Palo Alto, they're a more mature player, but still, they have an elevated net score. CrowdStrike's net score dropped this quarter, which was a bit of a concern, but it's still high. And it followed by SailPoint and Zscaler, who are right there. The big three trends in this space right now are cloud security, identity access management, and endpoint security. Those are the tailwinds, and we think these trends have legs. Remember, net score in this survey is a forward-looking metric, so we'll come back and look at the next survey, which is running this month in the field from ETR. Now everyone on this chart has reported earnings, except Zscaler, which reports on September 9th, and all of these companies are doing well and exceeding expectations, but as I said earlier, next year's compares won't be so easy. Oh, and by the way, their stock prices, they all got killed this week as a result of the rug pull that we explained earlier. So we really feel this isn't a fundamental problem for these firms that we're talking about. It's more of a technical in the market. Now Automation Anywhere and UiPath, you really don't know because they're not public and I think they need to get their house in order so they can IPO, so we'll see when they make it to public markets. I don't think that's an if, that I think they will IPO, but the fact that they haven't filed yet says they're not ready. Now why wouldn't you IPO if you are ready in this market despite the recent pullbacks? Okay, let's summarize. So listen, all you new investors out there that think stock picking is easy, look, any fool can make money in a market that goes up every day, but trees don't grow to the moon and there are bulls and bears and pigs, and pigs get slaughtered. And I can throw a dozen other cliches at you, but I am excited that you're learning. You maybe have made a few bucks playing the options game. It's not as easy as you might think. And I'm hoping that you're not trading on margin. But look, I think there are going to be some buying opportunities ahead, there always are, be patient. It's very hard, actually impossible, to time markets, and I'm a big fan of dollar-cost averaging. And young people, if you make less than $137,000 a year, load up on your Roth, it's a government gift that I wish I could have tapped when I was a newbie. And as always, please do your homework. Okay, that's it for today. Remember, these episodes, they're all available as podcasts, wherever you listen, so please subscribe. I publish weekly on wikibon.com and siliconangle.com, so check that out, and please do comment on my LinkedIn posts. Don't forget, check out etr.plus for all the survey action. Get in touch on Twitter, I'm @dvellante, or email me at david.vellante@siliconangle.com. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, everyone. Be well, and we'll see you next time. (gentle upbeat music)

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi I'm Stu minimun and welcome to the cube from our Boston area studios we've been doing a series of CXO leadership discussions talking about how everyone's dealing with the global global endemic I've been welcome program a first-time guest Katy Bullard she is the president of a tile guru of course a cloud guru a online learning company we've had on the cube many times over the years Katy thanks so much thank you so much sue for having me I really appreciate it all right so Katie I remember I saw the in I think the announce was the end of at the beginning of the year your based at the headquarters in Austin you know online you know learning is a huge topic cloud of course you know one of those mega waves that we've been walking a long time and then you know out of nowhere global pandemic you know it's striking us so you know bring us inside you know obviously you know taking a new role in a new organization as it own challenges normally it's like okay what am I going to do for the first 90 days and make that plan tell us you know how were you reacted in how the company has reacted with the koban 19 did you get a chance to look at my 90-day plan dude that was exactly where it was no well let me take you back I'll take you back to kind of why I chose to come to ECG because I think it informs actually what's happening right now as well when I when I was looking for the next opportunity what I look for is I look for two things primarily in a company one is a product that's in a market that's growing really really fast and a product that has raving customer bands and obviously ACG really you know check both of those boxes you think about this is pre Co but if you think about the cloud computing market growing you know 50 60 % a year and the number one challenge for people who are both moving to the cloud or moving to a multi cloud strategy was having enough skilled workers to to do that effectively there really wasn't a better intersection of two you know two who value propositions than what a CG offered which was serving the cloud computing market and skilling up workers in that market fast forward to February you know was interesting I actually went out to Australia offices in mid-february as this was starting to heat up came back just in time I think to not go into quarantine but we very quickly saw the impact and you know this isn't easy for anybody in in any situation but what we are hearing from our customers and from the market is that that move to the cloud is even more important now I think the latest that I saw from the the 2028 odd report said 65 percent of companies are planning a cloud migration 95 percent are of companies are employing a multi cloud strategy so that is accelerating and then of course we're all sitting at home right now and you're getting me in my in my dining room and we have the both learn online versus in person there's no longer in-person training there's no longer events for us to go to lives we're doing that online we also are seeing that you know the way that we use our time is changing so we're not spending hours anymore muting we have a lot of customers who are saying let's use that time instead of muting to learn improve ourselves improve our skills so you know everything is very unpredictable in this environment but we do feel like at ACG our fundamental mission is to help customers get through this to give them the skills that they need so that hopefully as everybody emerges from this later this year they're better positioned to take advantage of the opportunities in front of them ya know you hit on a lot of topics you know so much right now you know remote learning remote work or you know a big discussion the developer world has been looking at that for a long time and you know when I see you know the the the elementary and high school children as well as you know colleges and how they're handling distant learning I was well come on the Cronenberg's brothers you know built something in you know two or three week from your mother's basement Amazon and serverless and they framed millions of people now yeah you know good absolutely translate but it's challenging so I'm curious yes you know and you're working with the team is there anything you're doing to connect to some of the broader audience you know lessons that can be learned as I said you're you know highly scalable you know large scale and you know you have nowhere near the budget of you know these municipalities and colleges yet you do reach you know a very broad audience with some very important skill yes I mean if I think about the actual products itself and why it worked worked so well previously right why the Cronenberg brothers brought to market something that was so beloved but but more importantly why I think it's working so well now is that there was a recognition that we learn these days in bite-sized chunks right most of us don't have four hours a day or three days a week just to sit leave our job and go learn something and so from the very beginning their concept was let's break every single lesson up into these 20 minutes chunks so whether you know I'm on my commute in a previous world or whether I'm you know using some time that I used to spend on the road learning something new I can do it in very digestible forms and in a way that's really engaging to me so I think that model that they've employed from day one is even more valuable now in today's environment I think the other thing is that there was a recognition that we all have different learning styles right we all learn a little bit differently and so whether it's learning in 20 minute chunks so that's learning through video and PowerPoint or whether it's learning hands-on testing things breaking things building things the platform has evolved in a way to enable people no matter where they are in that cloud learning journey whether they're novice that's just getting started and wanting to learn kind of you know the PowerPoint basics like me when I first came on board right of the or a seasoned architect who's trying to get in and build new applications so I think those things are the things that allowed the platform to really resonate with the developer audience for so long and now as we have you know added out of the platform specifically for enterprises where previously you know is for individual developers we now have both I think that's the other thing that is really attractive to large enterprises is the fact that they now right are trying to train thousands of workers at the same time realizing again that every single one of them has a different learning style yeah Katie is as you said before there is you know a broad need or the skill set of cloud computing I'm curious have you seen anything in kind of your customer base either from the enterprise side or individuals is there are there any skill sets that are bubbling up right now that are a critical need or anything that is grown and you know we're curious we're always you know there's some people it's like oh I'm gonna come out of this you know whole experience and you know I love you know work in my home gym and you know learn new languages and become a master baker of sourdough you know me personally I've been really busy so you know I wish I had more spare time travel has definitely reduced thing but it's also given up the time that normally I was gonna you know read a book or you know catch up on raining yeah the sourdough bread is definitely not in my wheelhouse so we well we have seen some really interesting trends actually over the last few months the first one is that we've seen the percentage of our users that are logging in on a daily basis go up about 30 percent so people are taking advantage I think of a little bit of extra time to accelerate their learning the other thing that we are seeing and I was just looking at these stats last week is the kinds of courses and content that are being consumed are changing some of this was happening free covert and some of this was happening post covitz all split those up freako but what we've seen over the last order two 2/4 actually is a pretty significant increase in consumption across multi-cloud skills as you're in particular is seeing about a three times higher increase in consumption than the other two large CCS these although they're all three increasing rapidly so as we think about like the curriculum and our instructors that we're bringing on and what we're building up know historically ACG specifically had grown up in the AWS world but we are responding to that change very very late and in investing in you know a juror GCP and some of the other cloud adjacent courses so that we had been seeing happening over the last couple of quarters most recently what we're seeing is an increase in what i call our beginner or fundamental courses they think that is a direct reflection of people who are looking at this as an opportunity to rescale to set themselves up for a new career i'm so you know our introduction to AWS or introduction to Azure fundamentals or the introduction to DCP those are actually the courses that are increasing the fastest in ranking and anecdotally one of my favorite things to do is to go on LinkedIn or Twitter each day and look at you know what people are saying about ACG and over the last week especially I can't even count the number of folks who've said I'm using my lock down I'm for you know learning or I'm putting my my time and Quarantine to the best use by you know getting trained on ECG and so I think that what we are seeing there is a direct reflection of that alright yeah Katie maybe you can give us a little bit of the update on you know a cloud guru there was the Linux Academy acquisition and if you can share a little bit about this kind of the the the numbers of how many people have gone through your programmed you attract how many people actually get certifications afterwards which I know they need to go to the providers you know pay a fee for that kind of thing yeah we do yeah there's only been a few things happening over here in the last six months right I've got a small acquisition and then you know we're dealing with this now so we acquire Linux Academy in December so actually I came on board about the same time that we acquired the business one of my favorite stories is when I first started talking to Sam and team back in June a cg had about a hundred employees total by the time I was actually accepting an offer in October I think it was 200 employees in total so in a four month span the company had actually doubled we acquired Lenox Academy which was of equivalent size the ACG and so by the end of December we were a 400 person company a company that had been a hundred people know in in the middle of 2019 so 400 people now we are our biggest office is here in Austin we do have a large office in Melbourne Australia which was where the company was originally founded and where Sam is we have an office in London where Ryan is and Linux Academy was actually headquartered right outside of Fort Worth Texas so we've got an office there in Fort Worth as well so it's been amazing to see this company essentially quadruple in size over the last six months everything that goes into scaling a business like that bringing two competitors together integrating the business you know we are in the process of integrating the products and the content and the course dialogues right now so we're excited to bring that market later this year all in the midst of everyone also getting used to this very new and unprecedented environment yeah you know congratulations you know that you know always good to see great growth you know the thing I've noticed is you know ACG just as really goodwill in the community I see the orange shirts at many of the shows I you know right many of the other teams yeah we'll definitely have to get back to you about being on brain feed I was trying to coordinate with my background um one of the other things you know is some of my favorite content over the last few years that we've done the cube has been the serverless cough event so you know any discussion about you know will there be smokers to that or are we just going to need the weight or you know the physical events return before we see those so we actually have just started a new virtual event calendar actually our very first one was yesterday we had almost 3,000 people registering to attend and so it will be a series it's a series of virtual events and webinars that are done in partnership with other leading influencers and practitioners in the industry so expects if anyone's interested you can go online and register for one of the ACG webinars but we'll be having those every two week through the course of this year awesome love that and I guess the last thing Katie there's some other things you've been doing help unity in this need of the pandemic tell us a little bit about that yeah so two things in particular that we've really focused on the first one is across both the Linux Academy and the ACG platforms we have lowered permanently the price of our individual memberships so for individuals from 449 down to 379 we've seen that that has helped enable more people to be able to afford it who otherwise couldn't afford it so that's now in in market the other thing that we're really excited about that we launched this week is a free educational assistance program so we are offering 1,000 subscriptions to ACG for the year so annual subscriptions for people who have been most impacted by kovat so we have a couple of different specific criteria but if you've lost your job due to Ovid and you're in one of the the most heavily impacted industries whether that you know retail or hospitality or travel and are looking to really change careers get into the tech field get your initial certification we do now have a program for that so you go online to our website you're able to apply to that program we launched it yesterday maybe two days ago and I know we already have hundreds of applications so we're really excited to offer that all right well we'll make sure to get this out to the community is build out of that all right Katie thanks so much really pleasure to act up with you and I'm glad Congrats on all the progress thank you so much - thanks for having me alright serverless absolutely one of the topics I've been personally enjoying digging into the last couple years hope you've enjoyed I'm an attorney I'm sue minimun and as always thank you for watching thank you [Music]

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation [Music] hi I'm Stu manna man and welcome to a special cube conversation normally the first week of May we would be at Dell technologies world but that event has been moved to the fall but one of the major announcements from the event are going forward joining me to talk about powering up the mid-range of storage is Caitlin Gordon she is the vice president of marketing at Dell technologies Caitlin you thanks so much for joining thank you so much for having me Stu it's great to be here all right so Caitlin the last couple of years at a dtw different segments of the market as I said it's been powered up as the marketing messaging usually you've got some good t-shirts you've got a lot the labs and demos so tell us about the important announcement that you're sharing with today yeah I mean unfortunately the show is not going on but the product is still launching it actually is already started chipping and we are excited that we're still at be able to announce it this week our store is really probably the most exciting product they've ever gotten to help bring to market and all those demos and labs that you've talked about we're gonna have them all they're all going to be digital this year as well and it's really important for us as a business because it really changes what we're able to do for our customers you know we love speeds and feeds and storage but power store is so much more than that We certainly have designed it to meet the needs of all the workloads lock and file providing performance and efficiency but even more importantly what we built with this platform is something that will help our customers change the way that they're running their data centers and maybe most importantly can adapt with them as their businesses evolve yeah it's so important Caitlin I'm glad you talked about that you know you know the storage industry you know IP in general we can really get wonky and dig down to the speeds and feeds and yeah we want to understand you know how does nvm me and sports class memory and all that thing fit into but I want you to talk about you know what is that customer requirement that you're solving for in the age of AI and cloud you know what are the customers looking for what are those things that your cell for that maybe you know previous generations you go back to like the Unity ie this weren't on the table for discussion yeah I think one of the most interesting thing that's happened for us in the past few years in our conversations with customers is we do have the speeds and feeds the end-to-end nvme and octane and all that wonderful goodness but what they're really helped they're really asking for help on is how do they move towards this vision of having a truly autonomous data center how do they move to a fully self-service model so that all of their infrastructure can be treated like code and that you can automate all of those storage workflows picking out all of the additional costs and time and probably most importantly risk of manual tasks how do we have infrastructure that can be a more intelligent and helped them make more proactive and intelligent decisions that's one part of the equation the other piece is what we've heard loud and clear and this is now true more than ever before that infrastructure investments not only need to make sense for what the needs are today but also need to have the flexibility to adapt with businesses as they're going through this rapid and unpredictable transformation so that they can ensure that there are infrastructure investments today don't become technical debt tomorrow so that ability to have infrastructure that can adapt and evolve that the business is so important to our customers yes so Caitlin how is that done you know traditionally store do you think about it you know I buy a box like why did no way I write it off over 30 number years so what's different about you know the the service is and I'm guessing there's some financial pieces that make you know power store and the rest of the power family different than what I would have bought traditionally from buying a storage array yeah really the whole dynamic changes and it starts really foundationally with the flexible architecture so the product itself is built with the flexible architecture the ability the fact that it's a container based architecture were able to innovate on a container basis which makes our data services across the portfolio more consist enables us to innovate faster it also means that all of our innovation will be delivered to customers in a non disruptive way whether that's a hardware upgrade or a software upgrade all of that will happen without impacting the business that's really the flexible and adaptable architecture but when you look at the deployment that's an even bigger conversation how can we help and deliver infrastructure that gives you a solution that can support a small footprint at the edge collapse that infrastructure at the edge help with data center modernization connect into cloud and the last piece you're just touching on is that consumption more and more and then that's accelerated over the past month or so the ability to consume this as a service it's such an important part of what we're doing here in power stores available all of our Dell technologies on-demand offerings flex on-demand to give you that ability to really consume an infrastructure and an object model really interesting you talked about you know underneath the covers you know containerized architecture you know I think back the previous generations when you know EMC moved on to an intel-based architecture you know there's things where you say there's a major change in the code bases a major change in the architecture and from a customer standpoint they shouldn't have to think about it but I know there's so much work that goes through to make sure that things are rock-solid that it's still gonna provide you know X nines of capability and make sure that you can run your business on it helped us understand a little bit about you know how you know you said a lot of things have changed but we're still talking about things that you know you're running you know our business is on or you know mid-race customers for small enterprises midsize enterprise you know but what's what's still the same I guess is what I'm asking for today's storage compared to what we were looking at that yeah and if you look at it I mean the architecture itself is built as an architecture can pick conserve the broadest set of needs or the biggest set of our customer base so foundationally it supports all physical databases and applications we've got we've all support it's got performance that's really incredible compared to our previous lead mid range all flash solutions seven times faster three times better response times the efficiency of course is critical the ability to support that in a really small footprint with always-on inline data reduction four to one guaranteed the architecture not only scales up of course as a storage appliance but also can independently scale compute so they have the ability to scale up in an appliance and scale out into a cluster and of course you can't resist the buzzwords that's important and an nvme of course the ability to support nvme based flash drives or SEM and it's specifically actually the dual ported octane drive for persistent storage so when you look at it it truly is a best-in-class all flash mid-range storage array but it also does a lot more and that's part of the fun dynamic of what we've built okay so you know we talked about scaling up and scaling out you know of course you know we look at Bay's world two things that are critically important to customers it's my data and my applications obviously you know strong legacy at Dell EMC looking at the data you touched a little bit about the applications but you know tell me more how does this fit for you know my latest cloud native type environments you know how do applications fit into this environment yeah and it's really builds on what we're starting to talk about with that container based architecture so the fact that his container based is interesting and good for us because we can innovate faster it's even more important for customers and we can deliver that to them faster and more consistently what's more interesting is what we can then do for their workloads and their applications because we have this brand-new modular software operating system of course we can deploy that as a standard bare metal on purpose-built hardware or storage appliance what's even more interesting and what's really different about what we can do with our store is we can also abstract that storage OS from the underlying hardware onboard VMware ESXi and run both the storage operating system and applications natively on the appliance so able to collapse the compute and storage layers into a single piece of infrastructure and run a handful of specialized applications on that one appliance which really is game-changing in the data center at the edge to change the way that you can run and consolidate your operations okay yeah if you say specialize to applications so let let's build onion a little bit on that you know I think back obviously you know Dell has a very strong position in hyper-converged infrastructure which is scaling you know compute and storage and doing that an entire environment I remember there were a lot of efforts to say well with a virtualized environment maybe I hate storage and I can put applications on it that was there was use case with Isilon and to say you know I've got a lot of general-purpose compute if I have some excess capacity maybe I can do that it wasn't something that I heard used a lot so what sort of applications and how do kind of compare and contrast this with other things like like HDI yeah and this is power stores apps on capability and really what it's built for is these kind of two classes of applications the first is infrastructure apps so think of these as any type of application that the infrastructure team themselves is is leveraging and wants to simplify their operations antivirus data protection things like that the other category would be what we call data intensive so a data intensive application really is more storage intensive right either has a high demand for capacity and a small demand for compute or is one of these more latency sensitive applications real-time analytics is a good example things like blink and spark the response time is really King and when we look at that in comparison to what HCI is we have been and we are in a great position right with the xrail has been leading the hyper-converged market and we know that our customers are deploying that alongside three-tier architecture and what you look at what we've done with our store what we already have with rail they're highly complementary what we've done in HCI is we've taken storage and brought it into compute what we've done with power store we've taken storage and we brought compute into it and it really solves four different is optimized for different challenges and we really think complementing those in the data center next to each other is going to be an increasingly common deployment model to have the right architecture or the right workloads and then you have VMware consistent operations across the top so you have that consistent operations within your data center whoo edge and also to the cloud all right so end-to-end portfolio is what you're saying there's options for the different applications what one of the big challenges for storage people always is you know I always used to joke it's the four-letter word its migration so customers you know there there are very few Greenfield deployments out there so the existing Dell customers people out there that have been doing things in previous ways how do they get to power store and you know once they're on power Spore what does that mean for you know future you know growth expansion you know migration discussions yeah and I've heard this before right forklifts are not a friendly thing and the good news is with power store it is truly the end of data migration we've built with power store is an architecture that enables you to non-disruptive Li upgrade the controllers when new generations come out you can destructively operate those keep all the capacity in place and don't have any an impact to your business we also know the customers need to get data to powers for now getting to the 2 power store is going to be really really seamless we have invested significantly and a number of different migration options for our portfolio and for third-party to get data to our score and what seamless means could be different to different customers that can be non disruptive it could be agent lists it also could be host based we'll have all of those solutions from day one to enable that transition that happened as seamlessly as possible and on a customer's own time we've actually optimized this to the point where we now enable you to move data from an existing platform to our store in less than 10 players okay that that's great Kaitlyn so you know III remember back when Mendell first finished the acquisition of EMC one of the things we heard loud and clear with Jeff Clarke is a simplification of the portfolio it's something we've heard throughout the ranks remember talking to Jeff Boudreau about hinting at what was happening at the in the mid-range so what does this mean for existing mid-range lines and tell us about what we expect to see as this transition rolls out yeah absolutely so power store is absolutely our lead mid-range all-flash offering we continue to have unity XD is our lead hybrid mid-range solution and we have at end of life any of our other existing mid-range platforms what we know above anything else is that transformation and transitions in the data center and on storage race takes time and the important thing for us is that we enable our customers to do that on their own time and as seamlessly as possible so we have not announced a new end of life when we do we're going to have a long service life and we've built all of these different migration tools to help support that transition so it's going to be very easy for our customers to do that move on their own time and it still enables us to deliver on what we've promised you which is a simplified portfolio great Kaitlyn last thing I want to ask you is what's challenging for people is number one they've got kind of the skill set and the rules that they have today so there needs to be you know an easy migration to go from what they have to the new on the other hand also sometimes it you you want to take a clean sheet of paper and say boy if you could just start over and do it this way it's going to make your life so much easier so tell us how you're balancing that and how you can help both that you know you're your install base as well as you know new people coming in that might not have been traditional storage industry yeah I think the reality is that they're the specialized skill as a storage administrator isn't is something that will not be a growing skill set and we need to help our customers certainly support an operating model that does work like a storage array but does so in a way that is extraordinarily simple and has a lot of intelligence built in so first and foremost this is a storage platform and has really been designed who have the most seamless and simple operating experience from an element manager with our store manager for a storage admin but at the same time we know that for a variety of reasons a lot of customers have a single team that managed their infrastructure and is really moving into more of a cloud operating model and for that we've built in all of the integrations and tools with vmware whether it's Fiero vmware cloud foundation to really help vmware administrator also be able to operate the system as well excellent so it's just on that also how do things like analytics fit into the entire monitoring discussion help us understand how that fits in with some of the rest of the Dell portfolio yeah that's exactly where I was going to go over the last piece of this is why would I Q is something that's really important is Prateek for us cloud IQ of course comes with power so it comes with all of our storage offerings today we're officially announcing it coming across our infrastructure portfolio as well and that's really game-changing for customers in a number of different ways first is it really helps produce risk in the environment because it shows you a health scare or for your data center and if it has an issue it will quickly help you pinpoint that and troubleshoot it before it ever actually becomes a problem that impacts your business you're gonna help you predict your future user needs things like predictive analytics built into cloud IQ help you do capacity forecasting and planning so that you can see exactly when you're going to get to those thresholds of 80 90 100 percent capacity and remedy that board impacts the business and with it now coming across the entire infrastructure portfolio the value it can bring is outside of just storage alone but to the entire data center and one of the biggest things our customers and partners have loved about Cloud IQ is the trusted advisor feature that allows these are our reps or partner to have the ability to be part of that cloud IQ experience he read into from a mobile application or from a web browser have that remote monitoring of the environment and add that human intelligence to the machine intelligence really manage that data center and help our customers stay on top of problems and stay ahead of them before they impact the business well Kaitlyn congratulations the whole power store team we understand a lot of hard work goes into building this and really look forward to by the time we get to Delta technology's world in the fall talking to customers that are using thanks so much for joining us and look forward to talking with you again thanks - great to see you all right be sure to check out the cube dotnet for all the upcoming events that we're doing right now of course a hundred percent remote I'm sue minimun and thank you for watching the Q [Music]

>>It's the queue covering the virtual vertical Big Data Conference 2020. Brought to you by vertical. >>Hello, everybody. Welcome to the new Normal. You're watching the Cube, and it's remote coverage of the vertical big data event on digital or gone Virtual. My name is Dave Volante, and I'm here with Colin Mahoney, who's a senior vice president at Micro Focus and the GM of Vertical Colin. Well, strange times, but the show goes on. Great to see you again. >>Good to see you too, Dave. Yeah, strange times indeed. Obviously, Safety first of everyone that we made >>a >>decision to go Virtual. I think it was absolutely the right all made it in advance of how things have transpired, but we're making the best of it and appreciate your time here, going virtual with us. >>Well, Joe and we're super excited to be here. As you know, the Cube has been at every single BDC since its inception. It's a great event. You just you just presented the key note to your to your audience, You know, it was remote. You didn't have that that live vibe. And you have a lot of fans in the vertical community But could you feel the love? >>Yeah, you know, it's >>it's hard to >>feel the love virtually, but I'll tell you what. The silver lining in all this is the reach that we have for this event now is much broader than it would have been a Z you know, you know, we brought this event back. It's been a few years since we've done it. We're super excited to do it, obviously, you know, in Boston, where it was supposed to be on location, but there wouldn't have been as many people that could participate. So the silver lining in all of this is that I think there's there's a lot of love out there we're getting, too. I have a lot of participants who otherwise would not have been able to participate in this. Both live as well. It's a lot of these assets that we're gonna have available. So, um, you know, it's out there. We've got an amazing customers and of practitioners with vertical. We've got so many have been with us for a long time. We've of course, have a lot of new customers as well that we're welcoming, so it's exciting. >>Well, it's been a while. Since you've had the BDC event, a lot of transpired. You're now part of micro focus, but I know you and I know the vertical team you guys have have not stopped. You've kept the innovation going. We've been following the announcements, but but bridge the gap between the last time. You know, we had coverage of this event and where we are today. A lot has changed. >>Oh, yeah, a lot. A lot has changed. I mean, you know, it's it's the software industry, right? So nothing stays the same. We constantly have Teoh keep going. Probably the only thing that stays the same is the name Vertical. Um and, uh, you know, you're not spending 10 which is just a phenomenal released for us. So, you know, overall, the the organization continues to grow. The dedication and commitment to this great form of vertical continues every single release we do as you know, and this hasn't changed. It's always about performance and scale and adding a whole bunch of new capabilities on that front. But it's also about are our main road map and direction that we're going towards. And I think one of the things have been great about it is that we've stayed true that from day one we haven't tried to deviate too much and get into things that are barred to outside your box. But we've really done, I think, a great job of extending vertical into places where people need a lot of help. And with vertical 10 we know we're going to talk more about that. But we've done a lot of that. It's super exciting for our customers, and all of this, of course, is driven by our customers. But back to the big data conference. You know, everybody has been saying this for years. It was one of the best conferences we've been to just so really it's. It's developers giving tech talks, its customers giving talks. And we have more customers that wanted to give talks than we had slots to fill this year at the event, which is another benefit, a little bit of going virtually accommodate a little bit more about obviously still a tight schedule. But it really was an opportunity for our community to come together and talk about not just America, but how to deal with data, you know, we know the volumes are slowing down. We know the complexity isn't slowing down. The things that people want to do with AI and machine learning are moving forward in a rapid pace as well. There's a lot talk about and share, and that's really huge part of what we try to do with it. >>Well, let's get into some of that. Um, your customers are making bets. Micro focus is actually making a bet on one vertical. I wanna get your perspective on one of the waves that you're riding and where are you placing your bets? >>Yeah, No, it's great. So, you know, I think that one of the waves that we've been writing for a long time, obviously Vertical started out as a sequel platform for analytics as a sequel, database engine, relational engine. But we always knew that was just sort of takes that we wanted to do. People were going to trust us to put enormous amounts of data in our platform and what we owe everyone else's lots of analytics to take advantage of that data in the lots of tools and capabilities to shape that data to get into the right format. The operational reporting but also in this day and age for machine learning and from some pretty advanced regressions and other techniques of things. So a huge part of vertical 10 is just doubling down on that commitment to what we call in database machine learning and ai. Um, And to do that, you know, we know that we're not going to come up with the world's best algorithms. Nor is that our focus to do. Our advantage is we have this massively parallel platform to ingest store, manage and analyze the data. So we made some announcements about incorporating PM ML models into the product. We continue to deepen our python integration. Building off of a new open source project we started with uber has been a great customer and partner on This is one of our great talks here at the event. So you know, we're continuing to do that, and it turns out that when it comes to anything analytics machine learning, certainly so much of what you have to do is actually prepare the big shape the data get the data in the right format, apply the model, fit the model test a model operationalized model and is a great platform to do that. So that's a huge bet that were, um, continuing to ride on, taking advantage of and then some of the other things that we've just been seeing. You continue. I'll take object. Storage is an example on, I think Hadoop and what would you point through ultimately was a huge part of this, but there's just a massive disruption going on in the world around object storage. You know, we've made several bets on S three early we created America Yang mode, which separates computing story. And so for us that separation is not just about being able to take care of your take advantage of cloud economics as we do, or the economics of object storage. It's also about being able to truly isolate workloads and start to set the sort of platform to be able to do very autonomous things in the databases in the database could actually start self analysing without impacting many operational workloads, and so that continues with our partnership with pure storage. On premise, we just announced that we're supporting beyond Google Cloud now. In addition to Amazon, we supported on we've got a CFS now being supported by are you on mode. So we continue to ride on that mega trend as well. Just the clouds in general. Whether it's a public cloud, it's a private cloud on premise. Giving our customers the flexibility and choice to run wherever it makes sense for them is something that we are very committed to. From a flexibility standpoint. There's a lot of lock in products out there. There's a lot of cloud only products now more than ever. We're hearing our customers that they want that flexibility to be able to run anywhere. They want the ease of use and simplicity of native cloud experiences, which we're giving them as well. >>I want to stay in that architectural component for a minute. Talk about separating compute from storage is not just about economics. I mean apart Is that you, you know, green, really scale compute separate from storage as opposed to in chunks. It's more efficient, but you're saying there's other advantages to operational and workload. Specificity. Um, what is unique about vertical In this regard, however, many others separate compute from storage? What's different about vertical? >>Yeah, I think you know, there's a lot of differences about how we do it. It's one thing if you're a cloud native company, you do it and you have a shared catalog. That's key value store that all of your customers are using and are on the same one. Frankly, it's probably more of a security concern than anything. But it's another thing. When you give that capability to each customer on their own, they're fully protected. They're not sharing it with any other customers. And that's something that we hear a lot of insights from our customers. They want to be able to separate compute and storage. But they want to be able to do this in their own environment so that they know that in their data catalog there's no one else is. You share in that catalog, there's no single point of failure. So, um, that's one huge advantage that we have. And frankly, I think it just comes from being a company that's operating on premise and, uh, up in the cloud. I think another huge advantages for us is we don't know what object storage platform is gonna win, nor do we necessarily have. We designed the young vote so that it's an sdk. We started with us three, but it could be anything. It's DFS. That's three. Who knows what what object storage formats were going to be there and then finally, beyond just the object storage. We're really one of the only database companies that actually allows our customers to natively operate on data in very different formats, like parquet and or if you're familiar with those in the Hadoop community. So we not only embrace this kind of object storage disruption, but we really embrace the different data formats. And what that means is our customers that have data pipelines that you know, fully automated, putting this information in different places. They don't have to completely reload everything to take advantage of the Arctic analytics. We can go where the data is connected into it, and we offer them a lot of different ways to take advantage of those analytics. So there are a couple of unique differences with verdict, and again, I think are really advance. You know, in many ways, by not being a cloud native platform is that we're very good at operating in different environments with different formats that changing formats over time. And I don't think a lot of the other companies out there that I think many, particularly many of the SAS companies were scrambling. They even have challenges moving from saying Amazon environment to a Microsoft azure environment with their office because they've got so much unique Band Aid. Excuse me in the background. Just holding the system up that is native to any of those. >>Good. I'm gonna summarize. I'm hearing from you your Ferrari of databases that we've always known. Your your object store agnostic? Um, it's any. It's the cloud experience that you can bring on Prem to virtually any cloud. All the popular clouds hybrid. You know, aws, azure, now Google or on Prem and in a variety of different data formats. And that is, I think, you know, you need the combination of those I think is unique in the marketplace. Um, before we get into the news, I want to ask you about data silos and data silos. You mentioned H DFs where you and I met back in the early days of big data. You know, in some respects, you know, Hadoop help break down the silos with distributing the date and leave it in place, and in other respects, they created Data Lakes, which became silos. And so we have. Yet all these other sales people are trying to get to, Ah, digital transformation meeting, putting data at their core virtually obviously, and leave it in place. What's your thoughts on that in terms of data being a silo buster Buster, How does verdict of way there? >>Yeah, so And you're absolutely right, I think if even if you look at his due for all the new data that gets into the do. In many ways, it's created yet another large island of data that many organizations are struggling with because it's separate from their core traditional data warehouse. It's separate from some of the operational systems that they have, and so there might be a lot of data in there, but they're still struggling with How do I break it out of that large silo and or combine it again? I think some some of the things that verdict it doesn't part of the announcement just attend his migration tools to make it really easy. If you do want to move it from one platform to another inter vertical, but you don't have to move it, you can actually take advantage of a lot of the data where it resides with vertical, especially in the Hadoop brown with our external table storage with our building or compartment natively. So we're very pragmatic about how our customers go about this. Very few customers, Many of them tried it with Hadoop and realize that didn't work. But very few customers want a wholesale. Just say we're going to throw everything out. We're gonna get rid of our data warehouse. We're gonna hit the pause button and we're going to go from there. Just it's not possible to do that. So we've spent a lot of time investing in the product, really work with them to go where the data is and then seamlessly migrate. And when it makes sense to migrate, you mentioned the performance of America. Um, and you talked about it is the variety. It definitely is. And one other thing that we're really proud of this is that it actually is not a gas guzzler. Easy either One of the things that we're seeing, a lot of the other cloud databases pound for pound you get on the 10th the hardware vertical running up there. You get over 10 x performance. We're seeing that a lot, so it's Ah, it's not just about the performance, but it's about the efficiency as well. And I think that efficiency is really important when it comes to silos. Because there's there's just only so much horsepower out there. And it's easier for companies to play tricks and lots of servers environment when they start up for so many organizations and cloud and frankly, looking at the bills they're getting from these cloud workloads that are running. They really conscious of that. >>Yeah. The big, big energy companies love the gas guzzlers. A lot of a lot of cloud. Cute. But let's get into the news. Uh, 10 dot io you shared with your the audience in your keynote. One of the one of the highlights of data. What do we need to know? >>Yeah, so, you know, again doubling down on these mega trends, I'll start with Machine Learning and ai. We've done a lot of work to integrate so that you can take native PM ml models, bring them into vertical, run them massively parallel and help shape you know your data and prepare it. Do all the work that we know is required true machine learning. And for all the hype that there is around it, this is really you know, people want to do a lot of unsupervised machine learning, whether it's for healthcare fraud, detection, financial services. So we've doubled down on that. We now also support things like Tensorflow and, you know, as I mentioned, we're not going to come up with the best algorithms. Our job is really to ensure that those algorithms that people coming up with could be incorporated, that we can run them against massive data sets super efficiently. So that's that's number one number two on object storage. We continue to support Mawr object storage platforms for ya mode in the cloud we're expanding to Google G CPI, Google's cloud beyond just Amazon on premise or in the cloud. Now we're also supporting HD fs with beyond. Of course, we continue to have a great relationship with our partners, your storage on premise. Well, what we continue to invest in the eon mode, especially. I'm not gonna go through all the different things here, but it's not just sort of Hey, you support this and then you move on. There's so many different things that we learn about AP I calls and how to save our customers money and tricks on performance and things on the third areas. We definitely continue to build on that flexibility of deployment, which is related to young vote with. Some are described, but it's also about simplicity. It's also about some of the migration tools that we've announced to make it easy to go from one platform to another. We have a great road map on these abuse on security, on performance and scale. I mean, for us. Those are the things that we're working on every single release. We probably don't talk about them as much as we need to, but obviously they're critically important. And so we constantly look at every component in this product, you know, Version 10 is. It is a huge release for any product, especially an analytic database platform. And so there's We're just constantly revisiting you know, some of the code base and figuring out how we can do it in new and better ways. And that's a big part of 10 as well. >>I'm glad you brought up the machine Intelligence, the machine Learning and AI piece because we would agree that it is really one of the things we've noticed is that you know the new innovation cocktail. It's not being driven by Moore's law anymore. It's really a combination of you. You've collected all this data over the last 10 years through Hadoop and other data stores, object stores, etcetera. And now you're applying machine intelligence to that. And then you've got the cloud for scale. And of course, we talked about you bringing the cloud experience, whether it's on Prem or hybrid etcetera. The reason why I think this is important I wanted to get your take on this is because you do see a lot of emerging analytic databases. Cloud Native. Yes, they do suck up, you know, a lot of compute. Yeah, but they also had a lot of value. And I really wanted to understand how you guys play in that new trend, that sort of cloud database, high performance, bringing in machine learning and AI and ML tools and then driving, you know, turning data into insights and from what I'm hearing is you played directly in that and your differentiation is a lot of the things that we talk about including the ability to do that on from and in the cloud and across clouds. >>Yeah, I mean, I think that's a great point. We were a great cloud database. We run very well upon three major clouds, and you could argue some of the other plants as well in other parts of the world. Um, if you talk to our customers and we have hundreds of customers who are running vertical in the cloud, the experience is very good. I think it would always be better. We've invested a lot in taking advantage of the native cloud ecosystem, so that provisioning and managing vertical is seamless when you're in that environment will continue to do that. But vertical excuse me as a cloud platform is phenomenal. And, um, you know, there's a There's a lot of confusion out there, you know? I think there's a lot of marketing dollars spent that won't name many of the companies here. You know who they are, You know, the cloud Native Data Warehouse and it's true, you know their their software as a service. But if you talk to a lot of our customers, they're getting very good and very similar. experiences with Bernie comic. We stopped short of saying where software is a service because ultimately our customers have that control of flexibility there. They're putting verdict on whichever cloud they want to run it on, managing it. Stay tuned on that. I think you'll you'll hear from or more from us about, you know, that going going even further. But, um, you know, we do really well in the cloud, and I think he on so much of yang. And, you know, this has really been a sort of 2.5 years and never for us. But so much of eon is was designed around. The cloud was designed around Cloud Data Lakes s three, separation of compute and storage on. And if you look at the work that we're doing around container ization and a lot of these other elements, it just takes that to the next level. And, um, there's a lot of great work, so I think we're gonna get continue to get better at cloud. But I would argue that we're already and have been for some time very good at being a cloud analytic data platform. >>Well, since you open the door I got to ask you. So it's e. I hear you from a performance and architectural perspective, but you're also alluding two. I think something else. I don't know what you can share with us. You said stay tuned on that. But I think you're talking about Optionality, maybe different consumption models. That am I getting that right and you share >>your difficult in that right? And actually, I'm glad you wrote something. I think a huge part of Cloud is also has nothing to do with the technology. I think it's how you and seeing the product. Some companies want to rent the product and they want to rent it for a certain period of time. And so we allow our customers to do that. We have incredibly flexible models of how you provision and purchase our product, and I think that helps a lot. You know, I am opening the door Ah, a little bit. But look, we have customers that ask us that we're in offer them or, you know, we can offer them platforms, brawl in. We've had customers come to us and say please take over systems, um, and offer something as a distribution as I said, though I think one thing that we've been really good at is focusing on on what is our core and where we really offer offer value. But I can tell you that, um, we introduced something called the Verdict Advisor Tool this year. One of the things that the Advisor Tool does is it collects information from our customer environments on premise or the cloud, and we run through our own machine learning. We analyze the customer's environment and we make some recommendations automatically. And a lot of our customers have said to us, You know, it's funny. We've tried managed service, tried SAS off, and you guys blow them away in terms of your ability to help us, like automatically managed the verdict, environment and the system. Why don't you guys just take this product and converted into a SAS offering, so I won't go much further than that? But you can imagine that there's a lot of innovation and a lot of thoughts going into how we can do that. But there's no reason that we have to wait and do that today and being able to offer our customers on premise customers that same sort of experience from a managed capability is something that we spend a lot of time thinking about as well. So again, just back to the automation that ease of use, the going above and beyond. Its really excited to have an analytic platform because we can do so much automation off ourselves. And just like we're doing with Perfect Advisor Tool, we're leveraging our own Kool Aid or Champagne Dawn. However you want to say Teoh, in fact, tune up and solve, um, some optimization for our customers automatically, and I think you're going to see that continue. And I think that could work really well in a bunch of different wallets. >>Welcome. Just on a personal note, I've always enjoyed our conversations. I've learned a lot from you over the years. I'm bummed that we can't hang out in Boston, but hopefully soon, uh, this will blow over. I loved last summer when we got together. We had the verdict throwback. We had Stone Breaker, Palmer, Lynch and Mahoney. We did a great series, and that was a lot of fun. So it's really it's a pleasure. And thanks so much. Stay safe out there and, uh, we'll talk to you soon. >>Yeah, you too did stay safe. I really appreciate it up. Unity and, you know, this is what it's all about. It's Ah, it's a lot of fun. I know we're going to see each other in person soon, and it's the people in the community that really make this happen. So looking forward to that, but I really appreciate it. >>Alright. And thank you, everybody for watching. This is the Cube coverage of the verdict. Big data conference gone, virtual going digital. I'm Dave Volante. We'll be right back right after this short break. >>Yeah.

>> Narrator: Live, from San Francisco, celebrating 10 years of high-tech coverage, It's theCUBE! Covering VMworld 2019. Brought to you by VMware, and its ecosystem partners. >> And, welcome back here on theCUBE, we're at the Moscone Center here at downtown San Francisco. Gorgeous day outside, by the way. Picture perfect day. Chamber of Commerce weather, but a lot of big news happening inside here for VMworld 2019, along with John Troyer. I'm John Walls, we're joined by Pierluca Chiodelli, who's the Vice President of Product Management at Dell EMC. And, Pierluca, good to see you, Sir. >> Thank you, it's awesome to be here. >> Great, thanks for being here. And Muneyb Minhazuddin, whose the VP of solutions product marketing at VMware. And Muneyb, I know you're right just hot off the presentation stage. >> Yes I am. >> Catch your breath, it's all going to be fine. How was your audience? I'm sure standing remotely. >> Yeah, it was thirteen hundred plus >> Excellent, yeah. Been a big week, already. >> Of course it has, yeah. >> For you and your team. So, first off, let me just, let's step back, talk about the vibe of the show, the theme of the show we saw Pat on the stage. >> Muneyb: Perfect. >> About an hour and a half this morning, just your thoughts about day one and the big announcements that VMware's been making. >> It's been a great week, and it's actually been a great approaching week. As you know, on Thursday we announced intent and acquire both Pivotal and Carbon Black for close to about $5,000,000,000. So, that's, kind of a big announcement by itself, and then how do you kind of bring in and keep day one where you're not too focused on those two, but get the narrative of VMworld across. And really, you know, where we have, you know, CUBE has been with us on this journey for a long time. >> Right. >> We've seen that data center shift into kind of two tangents. One is, you know, workloads into data center break out into public clouds. Second, rerouting into cloud native applications. And, if you've seen our strategy wall when that was kind of the key messages. Hey, we're embracing both the modern app development, the focus on Kubernetes and Tanzoo announcement, was all about to say, "VMware platforms ready "for the breakout of both tangents." First, Cloud Native, we've got Kubernetes, we're bringing it right into vSphere, so that everybody in the audience can support it. Second, the breadth of our cloud everywhere, right, so, we've gone from Amazon to IBM to Google to Ajour. So, it'll give you the infrastructure for your workloads to be your choice. Modernize or migrate. (chuckles) That was a key message for us to kind of land today. For a lot of our audience who are kind of stuck in that same piece of, "What am I doing with my workloads? "What is that platform I got to build on?" And, you know, the key foundational platform being VMware Cloud Foundation. Right, that was our strategy, and I think last year we called out VMware Cloud Foundation in Pat's keynote, because I wrote it 44 times. (laughs) (group laughter) We didn't do it that many times, this time. We only said that's the platform that lands in Amazon, GCP, Ajour, IBM, and 4,200, you know, cloud provider partners. That gives you really that public cloud extension. The second part being modern apps, Kubernetes is a new, kind of, modern app development platform, vSphere is embedded into that project pacific and the whole Tanzoo announcement, right? So, really, a powerful message, what do you think? Was that successfully landed? >> I think so. John, do you feel good about what you heard today? >> Yeah, absolutely, I think VCF is super interesting. I'm also kind of, so there was an announcement today also about the Dell Technologies Cloud Validated Designs for using VCF. So, VCF the layer, which is kind of the VMware stack with some extra magic in it, that can be in, can make a private hybrid cloud, you know, everywhere. So, talk to us a little bit about Dell Technologies Cloud. As I call it, "DTC." The, it's a lot, there's a lot of stuff in that as well, so, but we have two very complicated solutions stacks that are, we're talking about now, so. >> Yeah, absolutely. >> Can you talk a little bit about the validated design and what came out of that? >> Absolutely, so before we go into the validated design, I think it's very important as Muneyb said. When we think about the Dell Technology Cloud, really, it's a component of the best (murmurs) technology from our storage, networking, and also compute, but we did the VMware VCS on top. So, we work very closely with VMware, and today we are announcing today the Cloud Validated Design. As we announce at the Dell Technology World in May, we said Dell Technology Cloud is this, now we want to tell to the people, how you can easily deploy this. What is make this tangible? So, what we are doing today is rapid time to value. We did design and pretested configuration, that we put in Dell Technologies Cloud Validated Design, as we said. The other important things as Muneyb said, right? It's... And, I heard this also from theCUBE. There was a debate with Stu and other people about, what is the Cloud? How I deploy the Cloud? When we think about Dell Technologies we speak with different peoples, and two set of peoples. One is the app, right? The Cloud app, all the app people that, they want to build have all the automation, DevOps operation and all these things. But, behind those people, there's still an infrastructure. So, we are speaking on both things. So, it's very important this paradigm is there, where you can have people that they can consume the technology, and understand how to build the infrastructure to be automated, and build that automation for the Cloud. So, that's what is the Dell Technologies Font Validation Design. Right. So, one of the biggest things here that we announced, is not only the Cloud Validation Design. It's the first one but also the ability to have compute, storage and network together, and also use it primary storage as a primary citizen of the VCF. So, we should talk about that later but that's-- >> Absolutely, and I think to catch onto that, you know, talking about the applications et cetera, you know, again, in the evolution of Cloud, and we've been on the journey for 10 years is, we've had, the first few years of the Cloud journey was, felt a little like a one way street, which was, kind of meant where people were shutting down data centers and going to all these public cloud providers, was always a one-way street. Now, VMware, and if you followed us closely, we had a service call VMware, you know VCHS, which is VMware Hybrid Cloud Service before the vCloud Air and then we came out with this solution, right? The idea was, we thought there's going to be movement back-and-forth but it wasn't the case. People were seriously shutting down and going one way. As we made all these partnerships of you know, Amazon, IBM, we started seeing, and you heard stories of IHS, Freddie Mac on stage where they take six weeks to move 100 applications one way into the Cloud, customers started asking us some questions, say, 'If it's so easy to go that way, is it also that easy to bring it back?' >> Come back! >> Right? And, that kind of lead to the whole kind of Dell partnership, Dell announcement within the Dell Cloud Foundation, you know, VMware Cloud Foundation, Dell Technologies Cloud Platform to say that, "Hey, it's actually..." There's a notion of not going from hardware-specific, you know, just high-tuned for workloads to commodity hardware in the Public Cloud. There's now a need for having common hardware platform on both on-PRAM, off-PRAM because there is a need for customers to take EC2 workloads or, you know, Ajour workloads and bring it on PRAM again. That was just a notion of how fast it is. I add that point because it is so critical to know that your hardware is performing in tuned, to perform for a high business critical applications. People forgot about them the first few phases of going to the Cloud, and now as they think about a hybrid, true hybrid Cloud nature, they want optimal performance in the software layer, in the hardware layer. You know, hence our announcement of Dell Technologies Cloud, Cloud Foundation, Validated Design. It's really supporting that customer notion. >> So, it's like this optimal, or maximized flexibility is what you're trying to give people. I mean, is that-- >> Pierluca: With the Cloud simplicity, that's really the key. >> But what drives that? I know that you have, you've, you know, whether you're on-PRAM or you're off-PRAM, you're going to decide what workload's going to go on what space on, so forth, but is some of that kind of hedging bets for future workloads because you can't predict where they're going to be done or where you want them done? Or is it just providing flexibility today, and let's not worry about tomorrow? You know, it just seems like there's a lot of runway here, if you will. >> Yeah, and I think there's no right or wrong answer. One of the big workshops I do with our customers is really kind of say have you figured out what's your three to five-year application strategy? Because again, in that first phase of that fast migration to the Public Cloud, people were just like CIOs I know, it's like, I have a cloud for strategy, what does that mean? I'm shutting down all data centers, I'm going to the Cloud. Right or wrong, and that's my Cloud First strategy. Now, what they've come to realize is not all workloads work effectively in the Cloud, right? So, they kind of like, hey, put an application strategy to say what are the most optimal applications that will get the benefit of Cloud? These are like, e-commerce retail. They have to have, you know, Black Friday, expanding elasticity. If you got no slow, mundane, you know backend processes doing batch processes of massive storage of in a bank ledger in the back end, they're not going to get that elasticity. I know what it is, I know how many, you know, batch processes I got to run. So, people are getting smarter about which ones get the benefit of, you know, modern app development, or Cloud elasticity, which ones don't really need to have that. So, we've seen best practice customers actually have a very good app strategy, three to five years, and then decide how much of my app strategy is gone to the right, you know, or gone to the left, right? It's pretty much to say, "I don't have to change." 60, 70% of my Eastern European customers, their banking ledgers are still on mainframes. They're not in a hurry to go to the Cloud, whereas, you know Fintech on the East Coast is going, "I'm going to the, I'm going to the Cloud", right? So, it's really that strategy that's, they should take the app strategy and decide what the infrastructure strategy is on the top shelf. >> I think from the storage business, we see that really clear, right? The app is definitely what is moving the things, right? It's not, people they're not thinking anymore because the transformation is in the way that you consume the infrastructure. They not thinking anymore about what I put there, but is about what app I need to run, how I build my app. So, it's the environment. And, I don't think personally I meet a lot of customer. There is not one right way or wrong way, it's an end, right? As you can see also in VCF we have Vsend, VxRail and primary storage. If you look at two years ago, we will be sitting here and say, you know, "It's only this, not the other things." When we, I been in governor conference, three years ago was like, it's all Cloud. It's reality is the world, the information technology world is always the same, where is a natural genius things. Because people, they need to have the trust, right? You cannot run your entire things on something that you don't know or you didn't prove. So, what we give here today with our technology is the flexibility. You can have a Cloud approach, but use the trusted PowerMax, for example, in conjunction with Vsend, in conjunction with the Unity. So, not all these is the proof that you can preserve your investment. But, is the proof that you can start to build those up. And, if you've seen what paths say today, then those app can live everywhere. So, you can go, you can move, it's much easier to move, and you can just trust what you're doing. >> And, you hit an important point on the move part, right? And, people are so easy, like, "Hey I moved a thousand applications in six weeks "to VMC and AWS." The fundamental notion where that was not possible before, was compute, network, storage. Like, we've been doing vSphere for a long time, you know that. And, it wasn't that easy because what used to happen is people thought, "Hey, a virtualized computer, I can move it." But, what did not happen as you moved that, was your databases, you know, your storage, rules didn't follow you into the Cloud. Your networking QOS and, you know, policies, and you know, priorities didn't follow you into the Cloud. So, that was kind of like, you know, you know, I'm an Australian, so it was a half-assed solution, right? (group laughing) So bear with my language, right. It was a half-assed solution, but really what needs to happen is your compute, your network, your storage has to all work together. And, that's where Cloud Foundation was powerful. And, what we're lighting with this Validated Designs is also that capability that your computer, or storage is one unit from a app. Once you package it and make it available in all the platforms, then that migration becomes six weeks, two weeks to move that. Because once you break it apart, it's a nightmare. There's not a lot of folks who have survived database migrations. (laughs) >> I mean maybe Pierluca, you can kind of sum us up here. This conversation's been a lot around evolution, right? And, there's also been an evolution of data center design and what to expect with that, you know, just buying things off the shelf and getting a Var and, you know, the VMAX, and we've been through this whole, and now, we've talked about VxRail, which can be part of this solution. But, can you talk, just, maybe, take us in, take us out with the, or into the future with the Dell Technologies Cloud as the idea of the Validated Design, the idea of this stack from Dell Technologies in storage et cetera, what can we expect in the near future? And, how much guidance will folks get? >> Yeah, absolutely. So, without breaking any NDA things, but this is only the first step. So, the Cloud Validated Design is just the first step where we said, 'Okay, we are tasked in this, "we putting this together." We are working very closely to also solve the entire things that VCF allow you to do first day deployment, allow you to expand the infrastructure, and allow you also to do life cycle management. For example, with the VxRail we already have the life cycle management part. We are working in way to do that also for our storage and other things. So, if you think about that then it becomes as you said, all the policy we put, like with Vworld, will be strategically in that sense, the policies can be carried over. So, then you can go to VMC, you can go to another place where the software and infrastructure can move back. So, because people can do this on PRAM, a replicate exactly but not only replicate the application, but replicate the (murmurs). What do you do on the QOS, all these key things that makes people running enterprise application, right? So that's, I think, it's very exciting moment. I think it's just the starting of this dream. >> Absolutely. >> Gentlemen, thanks for the time. >> Thank you. >> And you're all, you paint a pretty exciting future, don't ya? >> I hope so. >> So, I can't wait to look forward to even VMworld 2020? >> Wait 'til Barcelona, come on? (laughs) >> All right, well I'm not making that road trip, so unfortunately-- >> We going to more out there. >> But, Barcelona's going to be good. >> Yes, thank you for having us. >> No, I'm not the best guy, so, all right good. Hey, gentlemen, thank you for the time. >> Thank you >> Thank you. >> I appreciate it very much, great discussion. >> Thank you very much. >> Thanks for having us. >> Back with more from San Francisco right after this. (techno music)

>> Male Voiceover: Live from Atlanta, Georgia, it's theCUBE, covering Citrix Synergy Atlanta 2019. Brought to you by Citrix. >> Hi, welcome back to theCUBE. Lisa Martin with Keith Townsend and we're coming to you live from the show floor of Citrix Synergy 2019 in Atlanta, Georgia. And we're welcoming to theCUBE for the first time Adam Jones, the chief revenue officer of the Miami Marlins. Adam, it's great to have you on theCUBE. >> Pleasure to join you both today. >> So, baseball fans, White Sox, San Francisco Giants, Miami Marlins. Always cool to talk sports and technology when we can bring those two things together. I think the San Francisco Giants and the Miami Marlins might have something in common right now, but regardless of the standings, everybody wants to go to a game. You have to deliver, as chief revenue officer, a great a fan experience. You got to make sure all the vendors are there to deliver what those fans want, regardless of the standings. People still want to go to the games. Talk to us a little bit about your role as the CRO of the Miami Marlins, how long you've been doing it, and then we'll get into what you're doing with Citrix. >> Sure. So, joined the Marlins 18 months ago as part of new ownership and the new leadership team brought in to reset the standard for what the Miami Marlins organization could be. We want to be a world class sport entertainment enterprise. That means we're going to evolve beyond a traditional baseball team and ballpark. 26 years into the history of the franchise, eight years into the operating rights of a ballpark, and there's a lot of work to be done around those two assets but as we take the organization forward, we want to continue to broaden that enterprise to focus on more sport and entertainment offerings. >> So, chief revenue officer. We don't get many chief revenue officers at a technology conference. Help make the connection. You're a busy person. What made you take time out of your schedule to come to Citrix Synergy? >> Well, I think it's indicative of the culture we're building within our organization that we're putting data at the very center of our culture. We're going to make informed and timely decisions and we need our technology to enable that culture. And so, when it came to where we were going to align our IT group and it's a group that has built out a very robust, on-prem infrastructure over the past seven years following the opening of Marlins Park, the alignment under strategy, which was my initial title coming in, and now chief revenue officer as I took on more responsibility for the business side of the organization, was a strategic decision to make sure that the infrastructure was meeting the requirements of the organization as we rapidly evolve what our priorities are and what we need in order to deliver on their very aggressive and lofty expectations for their organization. >> So this morning during the keynote, we heard a lot about the digital workplace, the employee experience being really critical for any type of organization's digital transformation, and I just thought it was a really interesting viewpoint because we go to a lot of tech shows here at theCUBE, all over the world, and we don't often talk about employee experience or even culture, as a leading edge indicator of how successful a digital transformation is going to be, but employee experience is really critical to any business because whether those employees are interacting with seven to 10 apps a day based on their job, or they're interacting with your other users, in your case, Marlins fans, making sure those employees are productive, have what they need, in a personalized way, is critical. Talk to us about what the employee experience means for the Marlins, and also, as an indicator on the revenue side. >> Absolutely, so we have an evolving workforce. It's very young across a very diverse enterprise of activities. What we've been able to do in partnership with Citrix since day one of the ballpark, where we went from an organization of roughly 100-150 employees around the team to 300 plus across the team and the ballpark, is build out an infrastructure that was very light in terms of hardware, focused very much on the digital workspace keeps us very nimble, allows us to deploy capital in areas that we see tremendous value back in terms of application and utility. So, as we continue to make our workforce more mobile, I ask them to deliver and work at a higher rate of speed. We need to arm them with the tools that allow them to perform those roles in the office, out of the office, engage beyond more just than a 81 day transactional relationship across Marlins baseball, but how across 12 months out of the year, creating that 365 day touchpoint. They still have tools and access in order to create those memories, those engagements that we want with the market. >> So, talking about customer experience, Marlin baseball is more than just the 300 employees. It is your partners, it's all of your contractors. When I go to a ballpark, I don't see Mark the hot dog vendor I see Mark, the guy that works for the Marlins. My user experience, my customer experience needs to be excellent across that. As CRO, that's part of your responsibility, assuring that the whole Marlin family is presented as one unity. Talk to us about from not just a user experience perspective but also, security expectations of how you need to make that real for your customers. >> Sure, on the experience side, what we are doing is resetting the standard, not only for Marlins and for South Florida, but the industry as a whole. We've brought on a lot of great talent to the organization from across the industry that knows what's worked, what hasn't across our peers. We're applying that. We're challenging conventional practice trying to get out in front of the curve as to what is going to be the future of a game day experience, what is a sport entertainment enterprise more holistically. And so, as a result, we have to arm our employees with those tools that will allow them to engage consistently across all the touchpoints with our fans, with our partners. Try not to centralize data to the point where only a select few have and feel informed and empowered to make decisions and take action, but disseminate that information and empower everyone to deliver consistently across all of those touchpoints. On the security side, being a public interest entity, we're vulnerable. We're a target. There's plenty of precedent around the type of activity that these types of organizations can be prone to try to address, and so, security is a number one priority of ours to make sure that the IP we're creating maintains and stays ours, as well as the information we are collecting around our customers, around our players, stays within that secure environment as well. >> So if I think about going to a baseball game, which I love, there are so many sellable moments there. Whether I'm in the stands and I want to go buy food and beverage, or I want a new hat, or some sort of merchandise for my nephew or something. You have, as CRO, you've got all these different sellable moments, not just in the ballpark, in the physical experience, but even online. So having this kind of cohesive opportunity to sell not just tickets, but food and beverage, merchandise, in person, on mobile, on a tablet, on a desktop, it's got to be a critical part of your strategy Talk about the alignment with yourself and you said a lot of your IT guys have FOMO cause you're here, but I imagine that those experiences are essential that you have the right foundation and technological foundation to deliver sellable moments that deliver. >> That's right. So the ecosystem of a sport is a fairly diverse one from the ticketing transaction to all of the ballpark touchpoints. What we're trying to create is that 12 month relationship with a fan, so that goes into creating a lot of content and how we distribute that content, in order to continue to earn that engagement well beyond 81 plus dates of baseball. And the technology behind there, in terms of our storage and our accessibility, is what allows us to begin to personalize and tailor not only those core, traditional transactions and touchpoints of sport, but how we've begun to transition into more of that broader entertainment enterprise in making sure that we can deliver those as personalized and tailored as we can. >> So there was another Chicago team that showed the age of baseball. It was over 100 years before they won a-- >> Another Chicago team-- >> Yeah, another Chicago team that won a championship. So baseball has a lot of tradition. You're in a unique opportunity that you're coming into a new ownership, but still, baseball has traditions that are hard to compete against. So let's talk about what are some of the cultural changes and opportunities that you see that baseball needs to engage in where technology can help. >> Why I think an interesting thought around baseball and where it's been scrutinized as whether we pace a play or number of games, of not keeping up with the times, not being as snackable, short-form consumption as other sporting content. As everything tracks that way, baseball starts to differentiate itself in terms of the ability to create a very distinct and differentiated experience to a millennial, to a family, to an older consumer who has grown up with the traditions of baseball. And so while baseball needs to continue to innovate and modernize, there's actually this interesting equilibrium as to how much it continues to challenge those traditions that differentiate it from many of other points of contact and where it should continue to preserve those elements to hold what has been generational-type engagement. >> You know a great example of that is mlb.com and being able to watch a game anywhere. Baseball does an amazing job of embracing digital transformation, at least in baseball. One of the things that we talked about, or that David talked about onstage today, is the seven trillion dollar opportunity. That's big, even in baseball numbers. There's no bigger sporting numbers than baseball, but seven trillion dollars is opportunity. What are you excited about coming out of this show when you look at some of the potential game efficiencies from some of the automation announcements that were made today? >> For our organization, while there has been significant investment in infrastructure, great collaboration with Citrix up until this point. The exciting transformation for us is our migration into more of a hybrid cloud environment, which is going to allow us to onboard a number of new applications, tools, for our sales team, our service team, our game presentation groups, to continue to innovate and challenge how they've gone to market in the past. And having Citrix as a partner that has that environment for us to step into, one, gives us a ton of assurance in taking that next step and having someone that continues to bring us new tools within that environment, as well. So our ability to collaborate across the organization, I'd say we've only just skimmed the surface as to the true capability and power of a lot of the tools we've had in place, and very excited about unlocking the true power and potential of that environment moving forward. >> So this is your second season with the Marlins. You spent 15 years at PWC and before we went live, I thought, wow, that must have been a pretty big change going from PWC to major league baseball. But you actually have quite a history in sports. Tell us a little about that and maybe some of the similarities between major league baseball as an industry to other industries that kind of surprised you. >> Sure. Organizations couldn't be different, more different, in terms of profile and in set-up. What I did day-to-day, advising across sport and entertainment leading the sports practice at PWC positioned me for this incredible opportunity or challenge that is the Miami Marlins and what we're building in this aggressive vision that we've set as to how we're going to reset the standard and become world class as an enterprise. PWC and the history with the firm and professional services gave me a unique perspective as to how to take on many of the challenges that we have. Had the opportunity working across sport to really understand what works, what doesn't, so that we can avoid some of those missteps that others who have taken on this roadmap ahead of us have encountered. The breadth of infrastructure that a firm of PWC's size, also gives me a little more of a lens as to what the power and scale of a large organization can deliver in more of a small, mid-size business form, and not accept size or employee base as a constraint as to the types of tools and sophistication of our technology that we can deploy within a sports organization. >> Well, Adam, thank you so much for joining Keith and me on theCUBE this afternoon, talking about how you are helping to make big positive impacts for the Miami Marlins. We appreciate your time. >> I enjoyed it. Thank you. >> Go MLB. All right, for Keith Townsend, I'm Lisa Martin. You're watching theCUBE, live from our first day of coverage of Citrix Synergy 2019. Thanks for watching. (upbeat music)