>> Voiceover: Live, from Atlanta, Georgia, it's theCUBE, covering CITRIX Synergy, Atlanta 2019. Brought to you by: CITRIX. >> Welcome back to theCUBE. Lisa Martin with Keith Townsend coming to you live from Atlanta Georgia, our first day of coverage of Citrix Synergy 2019. Keith and I are very pleased to welcome you to theCUBE. For the first time, Bob O'Donnell, the founder and president of Technalysis. Bob, it's great to have you on theCUBE. >> Thank you. Great to be here I really appreciate it. It's my first chance to do theCUBE. It's exciting. >> We're so excited because you are no stranger to TV. Bloomberg, CNN, CNBC, Squawk Box, now theCUBE! >> Bob: And the now theCUBE! >> Keith: Most importantly- >> Bob: It completes the circle. >> He's a friend of Leo Laporte, which makes him a super star. >> All: (laughing) >> Well there you go. >> We're sitting in the presence of greatness. >> Oh, I don't know about that. But anyway, no, it's a pleasure to be here and it's nice to chat with you guys. It's a very interesting time that we're in. I mean, when we think about what's happening in the world. For years we've seen this move to cloud-based computing, and SaaS, and everything else. And everybody's excited about all of this stuff, and there's all these tools. And then on top of that, we thought, we have all these devices, right? We've got this amazing range of different devices we can use. But ironically, what it is, is we're in a state of too much of a good thing. It's too much. Even though if you think about it, you'd say, "Well, objectively, there's so much that "we could potentially do here. "I mean, we've got these tools that can do "this and this and this." But all of a sudden, "Well, except I got this one and this one, and this one. "And oh, by the way, if I want to send a message, "I can send it five different ways to Sunday, "and therefore if I want to read a message, "I have to be able to read it "five different ways from Sunday." And so, the challenge that you face is, and Citrix talked about it, I thought, quite nicely in their keynote this morning, is people get overwhelmed. And they just can't get productive with what they're trying to do. And so, what you need to do it figure out ways to turn that chaos into structure and order. And that's what they're trying to do with the workspace. And it looks pretty cool. >> Yeah, one of the offline conversations I had was you get all these tools. It's like somebody took a box of 10,000 Legos and just jumped it on your desk and said, "Build a masterpiece." And what I head this morning was the equivalent of what was like a Star Wars kid of like, "This is what you can build. Here's the directions, "and now you can start to deviate and customize it "for your environment." So one of the things that I'd love to get your input on is this concept of AI ML. This ideal of taking tasks and automating them. It's nothing new. We've tried this with macros and other areas. But the thing that was missing was, these tools were pretty dumb. >> Bob: Right. So the promise of ML AI should make these tools become real. What's your impression of the state of the technology versus what was presented today. >> Well, look, we're in very early days of AI and ML. There are some fascinating things out there. There's a lot of the high profile things that we hear about. The ImageNet and the ability to recognize every kind of dog known to mankind, and all the demos we've all seen at every other trade show. It really is, the fascinating part, exactly, to your point, is that the goal with AI and machine learning is to actually makes things understand. And it's fascinating because... I'll take a bit of a sidetrack but bring it back. When devices started to be able to recognize our words, we assumed, because we're human beings, that they recognized what we meant. But, no. There's a big jump between the words that you can transcribe, and what you actually mean. >> Yeah. That context. >> Context is everything. And context is something that, again as human beings, we take it for granted. But you can't take that for granted when it comes to technology and products. So, the beauty of AI as it starts to get deployed is how do we get the context around what it is that we're trying to do, what we meant to say. Of course, we all want that in real life: "What I meant to say was..." But, "what I meant to do was this." Or, "the task I want to do is that." So, taking that back to what Citrix is talking about is there are a lot of rote procedural things that people do in most organizations. And they gave the classic examples of proving the expense reports and this and that. So, clearly, some of those things they can pre-build. The micro apps, in a lot of ways, they really are macros. It's kind of a fancy macro. And that's fine, but the question is are they smart enough to kind of deviate, "Oh, well, there's a conditional branch "that it automatically builds in a macro "that I didn't have to think about "because it realizes in the context of what I'm doing "that it means something else." Or something like that. >> At the end of the day, I want to get the account balance, however that translates. As opposed to: take this column from row A and put it in row B. No, sometimes row A won't be the correct destination. I want the account balance. >> Right, right. >> And the other truth of the matter is we're still getting used to actually talking to our devices. We do that at home to some degree for people who have Alexas, unless they've decided to stop recording everything, and then that's a whole different subject. But, at work we don't. Interestingly, I remember when I first saw Cortana, for example, on a Windows machine. I thought, in a weird way, Cortana makes more sense because I should want... But it hasn't really happened. It hasn't played out. So there's some level of discomfort of talking to our devices and recognizing these things. So, I think there are cultural issues you still have to overcome. There are physical issues in the workplace, now. Now, when you have these open office environments, which doesn't take a rocket scientist to know that that was going to be a disaster. Whoever thought that was smart, man, let's take a look at where their degree came from. But that's the reality that people are in. So, you've got the physical environment challenges. You've got the cultural "how do I work with this?" environment. And then just starting to realize what it can actually do. And then, of course, you have the problem that it didn't recognize what it actually said. That's something stupid, and the original Siri problems that we all had. But, all of these things tie together because they're all different takes on what machine learning has the potential to do and what we think it should do, and what it can actually do. The one thing I will say is as we head towards 2020, I think we're going to start to finally see some of these things do what we thought they were going to do. They're going to start to have the context. They're going to start to have the intelligence. So, in the work space, it's going to have the ability to know what I mean when I say, "I need the account balance." Or, "I need to know where in the sales pipeline "this particular project is," or whatever task it is that I've got to deal with. And so, understanding that and then building the plumbing to do that is critical. One of the interesting things, if you look at what Citrix does, they're really all about plumbing. They have this ability to pull together all these different elements. From the beginning, what we started talking about. All these different applications over different types of network speeds and connections and make them all work. And yet, they present this very simplified, beautiful, nice little, you're like, "oh, this is great!" But, man, buried beneath there is a lot of stuff. And that's, to give them credit, that's what they're really good at doing. And companies now, the challenge is, a lot of companies have really old applications that they've got to kind of modernize in some way shape or form. And some of them are doing it on their own. They're doing the containerization and all the things we hear about as well. Some of them are wrapping them. Citrix, some of their original business, XenApp, was about app virtualization. Taking an old app and giving access in a modern way. So, again, it's doing that, but the other problem you have to bear in mind, excuse me, is that every company has a different combination of apps. They said 500 apps is normal. A lot of companies have more than that. >> Keith: Mhm. (affirmative) >> The problem is, it's not the same five hundred apps. This company has this set of 500 apps. This company has this set of 500 apps. This company has this set of 500 apps, and maybe 150 of them overlap, which means the long tail of 350 per company has to be dealt with and figured out. And that's, again, those are the problems that they're trying to solve and bring in to a unified environment. >> And also manage these growing expectations that all of us that are workers have from the consumer side of our lives. You mentioned Alexa and Siri, and we have these growing experiences that whether I'm talking to a device or I'm going on Amazon, I want it to know what I want. Don't show me something I've already purchased. And we have these expectations as humans and consumers that we want the apps when we get to work to understand the context and of course, we're asking a lot. In your opinion, where is Citrix in starting to help manage, helping their customers, rather, manage those growing expectations? >> I think Citrix has done a lot in that area. Even many, many years ago they were the first to come up with the notion of an enterprise app store. In the early days of the app store, they came out with this concept of, "We want to do an enterprise equivalent of that." When I download an app that I need to install on a work PC, make it easy to get at. So, from way back when they've been building on that. And then, the examples they gave today, the notification from the airline that your flight has changed, or whatever. Those are all the experiences that we're now used to thanks to cloud-based services. And their point is like, "Hey, why shouldn't we "have that at work, as well?" And so that's exactly what they're trying to work towards, is that notion of cloud-based notifications and services, and things, but related to the specific tasks I have to do. Because at the end of the day, they want to drive productivity. Because we all waste stupid amounts of time, and truth be told, the bigger the company you're at, the more time you waste because of just keeping up. I used to work at a big research firm of 1200 people, and literally half my day, every day, was just procedural stuff. I didn't actually work on the stuff that I thought I was hired to do, except for maybe half the day. And with a lot of people, that's very common. So, anything that can be done to reduce that and allow people to get through the procedural stuff a little bit more efficiently, and then actually let them do the work that they were hired to do and that they'd like to do, and oh, by the way, gives them more satisfaction. All of these things tie together. People tend to say, "Oh well, you know, "that's nice to do, this consumerization of IT, "that's nice." It's not just nice. It's actually practical. It's actually a real productivity enhancing capability. And I think Citrix has done an excellent job of driving that message. It's hard to to do because, again, the complexity of the plumbing necessary is super difficult. But their head and their heart are in the right place in terms of trying to achieve that. >> Well, it sounds absolutely like not a "nice to have," but business-critical. One of the stats that David Henshall, their CEO, said this morning, and Keith's been mentioning a number of times, is that he said there's 7 trillion dollars wasted on output because employees are not able to get to their functions that they were hired for in a timely manner. >> Right. >> So, there's a huge addressable market there of opportunity but also the consumerization that's personalization expectation is huge to not just making me, Lisa Martin, as an employee happy, but my business's customers that I'm dealing with. I think of a sales person, or even a call center support person. If they don't have access to that information, "She already called in about this problem 'with her cable ISP," that person is going to go turn, and go find another option that's going to fulfill their needs much better. >> That's absolutely right. And that was the interesting point that they made. And that's what they're trying to do with the intelligent work space is to move beyond just providing these apps, but actually personalizing it to each individual and being able to say, "All right, each of us are going to have a workspace." Sort of, it looks kind of like a news feed kind of a thing. Each one is going to be different though, based upon, obviously, the different tasks that we do, the order with which we do them, the manner with which we do them." So it does get personalized. The notifications, you know, I may want certain notifications that you don't really care about as much. But that's fine. We can each create that level of personalization and customization. And again, what Citrix is trying to do, and it was a key point that P.J. made, is, "Look, we're not just building an application. "We're building a platform." And that's... The significance of that is big. And remember, he came from Microsoft. He worked on Windows. He worked on Office. So, he's got a long history of working on building platform based tools that have tools that you can build on. That have APIs and ways for other people to add to. So, all of those are critical parts of how they tell that story, and how they get people enthralled enough to say, "Hey, I'm going to make the commitment to do it." Because look, it's a lot of work. Let's not kid ourselves. If I'm not a Citrix shop, but I go, "Damn, that's cool!" There's a fair amount of effort to make all this stuff actually happen. So, it's a commitment. But, once they get them hooked it's a pretty sticky type of environment. Especially as they continue to deliver value and personalization and customization. That, at the end of the day, drives productivity. And that's a pretty straight forward message: "Hey, we can save your workers time "and make them happier." Well, who doesn't want that, right? >> So, let's talk about engaging your customers. Like, I can look at this, and I can easily, say I can come to a conference like this and say, "Wow, I really want the output. I don't want "any of that employee experience stuff. "That stuff just sounds hard, "but the output I definitely want." Talk to me about the evolution of your customers as you walk them through if you want the output, here's what you have to do. And talk to me about, specifically, the success stories of where they didn't get it, and then after you've engaged them, they got it. >> Well, there's so many different variations out there. But, at the end of the day, every company out there is dealing with the fact that they have workers that work in a lot of places on a lot of devices and they have to allow them to get stuff done. And so, it's about how much are they willing to do to make that happen? But there's the psychology of it. There is the whole, "how much of this am I willing to outsource?" Versus, "I really want to keep it inside." So, it depends on the industry and the level of if they are a regulated industry, and all those things have an enormous impact on how they do this. But, if you think back, Citrix's original business was, a lot of it, was again, around desktop virtualization, and actually trying to get really old school stuff, I'm taking mainframe green screen stuff, to actually run on an old Windows PC. And that was kind of a lot of what they did, initially. And then, of course, they've built on from there. So, all along the way, you see different organizations. Citrix has been thought of more as more of the old school kind of enterprise software. Along with an SAP or an Oracle so something like that. I think they've done a particularly good job of being cloud native, cloud aware, and working with these cloud-based tools. Because early on, when we think about what happened with SaaS applications, people thought that was going to dramatically change how anybody did software. And it did, but not in the way people expected. So, I'm trying to get an answer, specifically, to your question, but I think what it is is what they're doing, and what companies who deploy it find is that they can take even these completely different types of software and services, and ServiceNow, and Salesforce, and Workday, and all these kinds of things that are dramatically different, but still, again, have overlapping functionality if I use all of them, and conflict or counteract or interact, or need to interact with other tools I already have that I'm working to change. So, again, what I think that what Citrix has done a good job is they're able to look at the wide range of stuff that people have in that 500 group of apps, or whatever it is, and be able to say, "All right, ten of those are cloud-based services. "But we've got 490 other ones we've got to deal with." And they have different levels of technologies to deal with those. So, what companies can do is they can also pick and choose. They can say, "Look, we're not going to get all 500 apps in our workspace." Maybe they just decide, "But we're going to do these twelve, "five of which are SaaS-based, "and then we've got a couple other critical ones "that we have to do, and that hits 80% of our workers." And they can tackle it that way. So, the bottom line is companies who... Look, it's a big investment up front. So the process is you have to psychologically say, "I'm willing to make an investment in," not obviously, just now, but their roadmap. What they're doing. What they're talking about. That's why they talk a lot about the future because if I buy into this ecosystem, I'm committed. Right? Again, I talking about that earlier: The stickiness question. So, companies who are doing this kind of thing, companies who are trying to make sense of all these applications have to be willing to make those big investments. It used to be, it used to have a huge Citrix server farms, as well. Obviously, with the development of the Cloud and Citrix Cloud, that's all changed. But, it's still a big investment, and they have to work to figure out ways to do this. And if they do, to finally get to, you know, they do see productivity savings. I mean, Citrix is, I don't remember the numbers, but they can qualify actual time saved when their solutions are installed, and that's the benefits that these companies get. So, they have to measure how much is my employee time worth versus the cost of getting these things deployed? >> Well, and I think that's going to be a differentiator for them. I wish we had more time because we could keep talking to you for a long time, but you got to add theCUBE to your list of TV: Bloomberg, CNBC, >> Bob: It's all there. Hey, I'm excited. >> Squawk Box, Now, theCUBE. Bob, it has been such a pleasure to have you on theCUBE. >> Thank you. >> We appreciate your time. >> Thanks so much. Appreciate being here, thank you. >> Our pleasure. For Keith Townsend, I am Lisa Martin. You're watching theCUBE, live from CITRIX Synergy 2019. Thanks for watching. (upbeat techno music)

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

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

(bright upbeat music) >> Welcome back, everybody, to the Supercloud 2. My name is Dave Vellante. And I'm pleased to welcome Nir Zuk. He's the founder and CTO of Palo Alto Networks. Nir, good to see you again. Welcome. >> Same here. Good to see you. >> So let's start with the right security architecture in the context of today's fragmented market. You've got a lot of different tools, you've got different locations, on-prem, you've got hardware and software. Tell us about the right security architecture from your standpoint. What's that look like? >> You know, the funny thing is using the word security in architecture rarely works together. (Dave chuckles) If you ask a typical information security person to step up to a whiteboard and draw their security architecture, they will look at you as if you fell from the moon. I mean, haven't you been here in the last 25 years? There's no security architecture. The architecture today is just buying a bunch of products and dropping them into the infrastructure at some relatively random way without really any guiding architecture. And that's a huge challenge in cybersecurity. It's always been, we've always tried to find ways to put an architecture into writing blueprints, whatever you want to call it, and it's always been difficult. Luckily, two things. First, there's something called zero trust, which we can talk a little bit about more, if you want, and zero trust among other things is really a way to create a security architecture, and second, because in the cloud, in the supercloud, we're starting from scratch, we can do things differently. We don't have to follow the way we've always done cybersecurity, again, buying random products, okay, maybe not random, maybe there is some thinking going into it by buying products, one of the other, dropping them in, and doing it over 20 years and ending up with a mess in the cloud, we have an opportunity to do it differently and really have an architecture. >> You know, I love talking to founders and particularly technical founders from StartupNation. I think I saw an article, I think it was Erie Levine, one of the founders or co-founders of Waze, and he had a t-shirt on, it said, "Fall in love with the problem, not the solution." Is that how you approached architecture? You talk about zero trust, it's a relatively new term, but was that in your head when you thought about forming the company? >> Yeah, so when I started Palo Alto Networks, exactly, by the way, 17 years ago, we got funded January, 2006, January 18th, 2006. The idea behind Palo Alto Networks was to create a security platform and over time take more and more cybersecurity functions and deliver them on top of that platform, by the way, as a service, SaaS. Everybody thought we were crazy trying to combine many functions into one platform, best of breed and defense in death and putting all your eggs in the same basket and a bunch of other slogans were flying around, and also everybody thought we were crazy asking customers to send information to the cloud in order to secure themselves. Of course, step forward 17 years, everything is now different. We changed the market. Almost all of cybersecurity today is delivered as SaaS and platforms are ruling more and more the world. And so again, the idea behind the platform was to over time take more and more cybersecurity functions and deliver them together, one brain, one decision being made for each and every packet or system call or file or whatever it is that you're making the decision about and it works really, really well. As a side effect, when you combine that with zero trust and you end up with, let's not call it an architecture yet. You end up with with something where any user, any location, both geographically as well as any location in terms of branch office, headquarters, home, coffee shop, hotel, whatever, so any user, any geographical location, any location, any connectivity method, whether it is SD1 or IPsec or Client VPN or Client SVPN or proxy or browser isolation or whatever and any application deployed anywhere, public cloud, private cloud, traditional data center, SaaS, you secure the same way. That's really zero trust, right? You secure everything, no matter who the user is, no matter where they are, no matter where they go, you secure them exactly the same way. You don't make any assumptions about the user or the application or the location or whatever, just because you trust nothing. And as a side effect, when you do that, you end up with a security architecture, the security architecture I just described. The same thing is true for securing applications. If you try to really think and not just act instinctively the way we usually do in cybersecurity and you say, I'm going to secure my traditional data center applications or private cloud applications and public cloud applications and my SaaS applications the same way, I'm not going to trust something just because it's deployed in the private data center. I'm not going to trust two components of an application or two applications talking to each other just because they're deployed in the same place versus if one component is deployed in one public cloud and the other component is deployed in another public cloud or private cloud or whatever. I'm going to secure all of them the same way without making any trust assumptions. You end up with an architecture for securing your applications, which is applicable for the supercloud. >> It was very interesting. There's a debate I want to pick up on what you said because you said don't call it an architecture yet. So Bob Muglia, I dunno if you know Bob, but he sort of started the debate, said, "Supercloud, think of it as a platform, not an architecture." And there are others that are saying, "No, no, if we do that, then we're going to have a bunch of more stove pipes. So there needs to be standard, almost a purist view. There needs to be a supercloud architecture." So how do you think about it? And it's a bit academic, I know, but do you think of this idea of a supercloud, this layer of value on top of the hyperscalers, do you think of that as a platform approach that each of the individual vendors are responsible for the architecture? Or is there some kind of overriding architecture of standards that needs to emerge to enable the supercloud? >> So we can talk academically or we can talk practically. >> Yeah, let's talk practically. That's who you are. (Dave laughs) >> Practically, this world is ruled by financial interests and none of the public cloud providers, especially the bigger they are has any interest of making it easy for anyone to go multi-cloud, okay? Also, on top of that, if we want to be even more practical, each of those large cloud providers, cloud scale providers have engineers and all these engineers think they're the best in the world, which they are and they all like to do things differently. So you can't expect things in AWS and in Azure and GCP and in the other clouds like Oracle and Ali and so on to be the same. They're not going to be the same. And some things can be abstracted. Maybe cloud storage or bucket storage can be abstracted with the layer that makes them look the same no matter where you're running. And some things cannot be abstracted and unfortunately will not be abstracted because the economical interest and the way engineers work won't let it happen. We as a third party provider, cybersecurity provider, and I'm sure other providers in other areas as well are trying or we're doing our best. We're not trying, we are doing our best, and it's pretty close to being the way you describe the top of your supercloud. We're building something that abstracts the underlying cloud such that securing each of these clouds, and by the way, I would add private cloud to it as well, looks exactly the same. So we use, almost always, whenever possible, the same terminology, no matter which cloud we're securing and the same policy and the same alerts and the same information and so on. And that's also very important because when you look at the people that actually end up using the product, security engineers and more importantly, SOC, security operations center analysts, they're not going to study the details of each and every cloud. It's just going to be too much. So we need to abstract it for them. >> Yeah, we agree by the way that the supercloud definition is inclusive of on-prem, you know, what you call private cloud. And I want to pick up on something else you said. I think you're right that abstracting and making consistent across clouds something like object storage, get put, you know, whether it's an S3 bucket or an Azure Blob, relatively speaking trivial. When you now bring that supercloud concept to something more complex like security, first of all, as a technically feasible and inferring the answer there is yes, and if so, what do you see as the main technical challenges of doing so? >> So it is feasible to the extent that the different cloud provide the same functionality. Then you step into a territory where different cloud providers have different paths services and different cloud providers do things a little bit differently and they have different sets of permissions and different logging that sometimes provides all the information and sometimes it doesn't. So you end up with some differences. And then the question is, do you abstract the lowest common dominator and that's all you support? Or do you find a way to be smarter than that? And yeah, whatever can be abstracted is abstracted and whatever cannot be abstracted, you find an easy way to represent that to your users, security engineers, security analysts, and so on, which is what I believe we do. >> And you do that by what? Inventing or developing technology that presents that experience to users? Could you be more specific there? >> Yeah, so different cloud providers call their storage in different names and you use different ways to configure them and the logs come out the same. So we normalize it. I mean, the keyword is probably normalization. Normalize it. And we try to, you know, then you have to pick a winner here and to use someone's terminology or you need to invent new terminology. So we try to use the terminology of the largest cloud provider so that we have a better chance of doing that but we can't always do that because they don't support everything that other cloud providers provide, but the important thing is, with or thanks to that normalization, our customers both on the engineering side and on the user side, operations side end up having to learn one terminology in order to set policies and understand attacks and investigate incidents. >> I wonder if I could pick your brain on what you see as the ideal deployment model to achieve this supercloud experience. For example, do you think instantiating your stack in multiple regions and multiple clouds is the right way to do it? Or is building a single global instance on top of the clouds a more preferable way? Are maybe other models we should consider? What do you see as the trade off of these different deployment models and which one is ideal in your view? >> Yeah, so first, when you deploy cloud security, you have to decide whether you're going to use agents or not. By agents, I mean something working, something running inside the workload. Inside a virtual machine on the container host attached to function, serverless function and so on and I, of course, recommend using agents because that enables prevention, it enables functionality you cannot get without agents but you have to choose that. Now, of course, if you choose agent, you need to deploy AWS agents in AWS and GCP agents in GCP and Azure agents in Azure and so on. Of course, you don't do it manually. You do it through the CICD pipeline. And then the second thing that you need to do is you need to connect with the consoles. Of course, that can be done over the internet no matter where your security instances is running. You can run it on premise, you can run it in one of the other different clouds. Of course, we don't run it on premise. We prefer not to run it on premise because if you're secured in cloud, you might as well run in the cloud. And then the question is, for example, do you run a separate instance for AWS for GCP or for Azure, or you want to run one instance for all of them in one of these clouds? And there are advantages and disadvantages. I think that from a security perspective, it's always better to run in one place because then when you collect the information, you get information from all the clouds and you can start looking for cross-cloud issues, incidents, attacks, and so on. The downside of that is that you need to send all the information to one of the clouds and you probably know that sending data out of the cloud costs a lot of money versus keeping it in the cloud. So theoretically, you can build an architecture where you keep the data for AWS in AWS, Azure in Azure, GCP in GCP, and then you try to run distributed queries. When you do that, you find out you'd end up paying more for the compute to do that than you would've paid for sending all the data to a central location. So we prefer the approach of running in one place, bringing all the data there, and running all the security, the machine learning or whatever, the rules or whatever it is that you're running in one place versus trying to create a distributed deployment in order to try to save some money on the data, the network data transfers. >> Yeah, thank you for that. That makes a lot of sense. And so basically, should we think about the next layer building security data lake, if you will, and then running machine learning on top of that if I can use that term of a data lake or a lake house? Is that sort of where you're headed? >> Yeah, look, the world is headed in that direction, not just the cybersecurity world. The world is headed from being rule-based to being data-based. So cybersecurity is not different and what we used to do with rules in the past, we're now doing with machine learning. So in the past, you would define rules saying, if you see this, this, and this, it's an attack. Now you just throw the data at the machine, I mean, I'm simplifying it, but you throw data at a machine. You'll tell the machine, find the attack in the data. It's not that simple. You need to build the right machine learning models. It needs to be done by people that are both cybersecurity experts and machine learning experts. We do it mostly with ex-military offensive people that take their offensive knowledge and translate it into machine learning models. But look, the world is moving in that direction and cybersecurity is moving in that direction as well. You need to collect a lot of data. Like I said, I prefer to see all the data in one place so that the machine learning can be much more efficient, pay for transferring the data, save money on the compute. >> I think the drop the mic quote it ignite that you had was within five years, your security operation is going to be AI-powered. And so you could probably apply that to virtually any job over the next five years. >> I don't know if any job. Certainly writing essays for school is automated already as we've seen with ChatGPT and potentially other things. By the way, we need to talk at some point about ChatGPT security. I don't want to think what happens when someone spends a lot of money on creating a lot of fake content and teaches ChatGPT the wrong answer to a question. We start seeing ChatGPT as the oracle of everything. We need to figure out what to do with the security of that. But yeah, things have to be automated in cybersecurity. They have to be automated. They're just too much data to deal with and it's just not even close to being good enough to wait for an incident to happen and then going investigate the incident based on the data that we have. It's better to look at all the data all the time, millions of events per second, and find those incidents before they happen. There's no way to do that without machine learning. >> I'd love to have you back and talk about ChatGPT. I know they're trying to put in some guardrails but there are a lot of unintended consequences, aren't there? >> Look, if they're not going to have a person filtering the data, then with enough money, you can create thousands or tens of thousands of pieces of articles or whatever that look real and teach the machine something that is totally wrong. >> We were talking about the hyper skills before and I agree with you. It's very unlikely they're going to get together, band together, and create these standards. But it's not a static market. It's a moving train, if you will. So assuming you're building this cross cloud experience which you are, what do you want from the hyperscalers? What do you want them to bring to the table? What is a technology supplier like Palo Alto Networks bring? In other words, where do you see ongoing as your unique value add and that moat that you're building and how will that evolve over time vis-a-vis the hyperscaler evolution? >> Yeah, look, we need APIs. The more data we have, the more access we have to more data, the less restricted the access is and the cheaper the access is to the data because someone has to pay today for some reason for accessing that data, the more secure their customers are going to be. So we need help and are helping by the way a lot, all of them in finding easy ways for customers to deploy things in the cloud, access data, and again, a lot of data, very diversified data and do it in a cost-effective way. >> And when we talk about the edge, I presume you look at the edge as just another data center or maybe it's the reverse. Maybe the data center is just another edge location, but you're seeing specific edge security solutions come out. I'm guessing that you would say, that's not what we want. Edge should be part of that architecture that we talked about earlier. Do you agree? >> Correct, it should be part of the architecture. I would also say that the edge provides an opportunity specifically for network security, whereas traditional network security would be deployed on premise. I'm talking about internet security but half network security market, and not just network security but also the other network intelligent functions like routing and QS. We're seeing a trend of pushing those to the edge of the cloud. So what you deploy on premise is technology for bringing packets to the edge of the cloud and then you run your security at the edge, whatever that edge is, whether it's a private edge or public edge, you run it in the edge. It's called SASE, Secure Access Services Edge, pronounced SASE. >> Nir, I got to thank you so much. You're such a clear thinker. I really appreciate you participating in Supercloud 2. >> Thank you. >> All right, keep it right there for more content covering the future of cloud and data. This is Dave Vellante for John Furrier. I'll be right back. (bright upbeat music)

(upbeat music) >> Hello, and welcome to Supercloud2. I'm John Furrier, host of theCUBE here. We're exploring all the new Supercloud trends around multiple clouds, hyper scale gaps in their systems, new innovations, new applications, new companies, new products, new brands emerging from this big inflection point. Got a great guest who's going to unpack it with me today, Ramesh Prabagaran, who's the co-founder and CEO of Prosimo, CUBE alumni. Ramesh, legend in the industry, you've been around. You've seen many cycles. Welcome to Supercloud2. >> Thank you. You're being too kind. >> Well, you know, you guys have been a technical, great technical founding team, multiple ventures, multiple times around the track as they say, but now we're seeing something completely different. This is our second event, kind of we're doing to start the the ball rolling around unpacking this idea of Supercloud which evolved from a riff with me and Dave to now a working group paper, multiple definitions. People are saying they're Supercloud. CloudFlare says this is their version. Someone says there over there. Fitzi over there in the blog is always, you know, challenging us on our definitions, but it's, the consensus is though something's happening. >> Ramesh: Absolutely. >> And what's your take on this kind of big inflection point? >> Absolutely, so if you just look at kind of this in layers right, so you have hyper scalers that are innovating really quickly on underlying capabilities, and then you have enterprises adopting these technologies, right, there is a layer in the middle that I would say is largely missing, right? And one that addresses the gaps introduced by these new capabilities, by the hyper scalers. At the same time, one that actually spans, let's say multiple regions, multiple clouds and so forth. So that to me is kind of the Supercloud layer of sorts. One that helps enterprises adopt the underlying hyper scaler capabilities a lot faster, and at the same time brings a certain level of consistency and homogeneity also. >> What do you think the big driver of Supercloud is? Is it the industry growing up or is it the demand for new kinds of capabilities or both? Or just evolution? What's your take? >> I would say largely it depends on kind of who the entity is that you're talking about, right? And so I would say both. So if you look at one cohort here, it's adoption, right? If I have a externally facing digital presence, for example, then I'm going to scale that up and get to as many subscribers and users no matter what, right? And at that time it's a different set of problems. If you're looking at kind of traditional enterprise inward that are bringing apps into the cloud and so forth, it's a different set of care abouts, right? So both are, I would say, equally important problems to solve for. >> Well, one reality that we're definitely tracking, and it's not really a debate anymore, is hybrid. >> Ramesh: Yep >> Hybrid happened. It happened faster than most people thought. But, you know, we were talking about this in 2015 when it first got kicked around, but now you see hybrid in the cloud, on premises and the edge. This kind of forms that distributed computing paradigm that we've always been predicting. And so if that continues to play out the way it is, you're now going to have a completely distributed, connected internet and sets of systems, intra and external within companies. So again, the world is connected 100%. Everything's changing, right? >> And that introduces. >> It wasn't your grandfather's networking anymore or storage. The game is still the same, but the play, the components are acting differently. What's your take on this? >> Absolutely. No, absolutely. That's a very key important point, and it's one that we always ask our customers right at the front end, right? Because your starting assumptions matter. If you have workloads of workloads in the cloud and data center is something that you want to connect into, then you'll make decisions kind of keeping cloud in the center and then kind of bolt on technologies for what that means to extend it to the data center. If your center of gravity is in the data center, and then cloud is let's say 10% right now, but you see that growing, then what choices do you have? Right, do you want to bring your data center technologies into the cloud because you want that consistency in operations? Or do you want to start off fresh, right? So this is a really key, important question, and one that many of our customers are actually are grappling with, right? They have this notion that going cloud native is the right approach, but at the same time that means I have a bifurcation in kind of how do I operate my data center versus my cloud, right? Two different operating models, and slowly it'll shift over to one. But you're going to have to deal with dual reality for a while. >> I was talking to an old friend of mine, CIO, very experienced CIO. Big time company, large deployment, a lot of IT. I said, so what's the big trend everyone's telling me about IT's going. He goes no, not really. IT's not going away for me. It's going everywhere in the company. >> Ramesh: Exactly. >> So I need to scale my IT-like capabilities everywhere and then make it invisible. >> Ramesh: Correct. >> Which is essentially code words for saying it's going to be completely cloud native everywhere. This is what is happening. Do you agree? >> Absolutely right, and so if you look at what do enterprises care about it? The reason to go to the cloud is to get speed of operations, and it's apps, apps, apps, right? Do you ever have a conversation on networking and infrastructure first? No, that kind of gets brought into the conversation because you want to deal with users, applications and services, right? And so the end goal is essentially how do users communicate with apps and get the right experience, security and whatnot, and how do apps talk to each other and make sure that you get all of the connectivity and security requirements? Underneath the covers, what does this mean for infrastructure, networking, security and whatnot? It's actually going to be someone else's job, right? And you shouldn't have to think too much about it. So this whole notion of kind of making that transparent is real actually, right? But at the same time, us and all the guys that we talk to on the customer side, that's their job, right? Like we have to work towards making that transparent. Some are going to be in the form of capability, some are going to be driven by data, but that's really where the two worlds are going to come together. >> Lots of debates going on. We just heard from Bob Muglia here on Supercloud2. He said Supercloud's a platform that provides programmatically consistent services hosted on heterogeneous cloud providers. So the question that's being debated is is Supercloud a platform or an architecture in your view? >> Okay, that's a tough one actually. I'm going to side on the side on kind of the platform side right, and the reason for that is architectural choices are things that you make ahead of time. And you, once you're in, there really isn't a fork in the road, right? Platforms continue to evolve. You can iterate, innovate and so on and so forth. And so I'm thinking Supercloud is more of a platform because you do have a choice. Hey, am I going AWS, Azure, GCP. You make that choice. What is my center of gravity? You make that choice. That's kind of an architectural decision, right? Once you make that, then how do I make things work consistently across like two or three clouds? That's a platform choice. >> So who's responsible for the architecture as the platform, the vendor serving the platform or is the platform vendor agnostic? >> You know, this is where you have to kind of peel the onion in layers, right? If you talk about applications, you can't go to a developer team or an app team and say I want you to operate on Google or AWS. They're like I'll pick the cloud that I want, right? Now who are we talking to? The infrastructure guys and the networking guys, right? They want to make sure that it's not bifurcated. It's like, hey, I want to make sure whatever I build for AWS I can equally use that on Azure. I can equally use that on GCP. So if you're talking to more of the application centric teams who really want infrastructure to be transparent, they'll say, okay, I want to make this choice of whether this is AWS, Azure, GCP, and stick to that. And if you come kind of down the layers of the stack into infrastructure, they are thinking a little more holistically, a little more Supercloud, a little more multicloud, and that. >> That's a good point. So that brings up the deployment question. >> Ramesh: Exactly! >> I want to ask you the next question, okay, what is the preferred deployment in your opinion for a Supercloud narrative? Is it single instance, spread it around everywhere? What's the, do you have a single global instance or do you have everything synchronized? >> So I would say first layer of that Supercloud really kind of fix the holes that have been introduced as a result of kind of adopting the hyper scaler technologies, right? So each, the hyper scalers have been really good at innovating and providing really massive scale elastic capabilities, right? But once you start to build capabilities on top of that to help serve the application, there's a few holes start to show up. So first job of Supercloud really is to plug those holes, right? Second is can I get to an operating model, so that I can replicate this not just in a single region, but across multiple regions, same cloud, and then across multiple clouds, right? And so both of those need to be solved for in order to be (cross talking). >> So is that multiple instantiations of the stack or? >> Yeah, so this again depends on kind of the capability, right? So if you take a more solution view, and so I can speak for kind of networking security combined right? There you always take a solution view. You don't ever look at, you know, what does this mean for a single instance in a single region. You take a macro view, and then you then break it down into what does this mean for region, what does it mean for instance, what does this mean for AZs? And so on and so forth. So you kind of have to go top to bottom. >> Okay, welcome you down into the trap now. Okay, synchronizing the data, latency, these are all questions. So what does the network Supercloud look like to you? Because networking is big here. >> Ramesh: Yes, absolutely. >> This is what you guys do. >> Exactly, yeah. So the different set of problems as you go up the stack, right? So if you have hundreds of workloads in a single region, the set of problems you're dealing with there are kind of app native connectivity, how do I go from kind of east/west, all of those fun things, right? Which are usually bound in terms of latency. You don't have those challenges as much, but can you build your entire enterprise application architecture in one region? No, you're going to have to create multiple instances, right? So my data lake is invariably going to be in one place. My business logic is going to be spread across a few places. What does that bring in? I need to go across regions. Am I going to put those two regions right next to each other? No, I'm not going to, right? I'm going to have places in Europe. I'm going to have APAC, and I'm going to have a North American presence, and I need to bring all these things together. So this is where, back to your point, latency really matters, right? Because I need to be able to find out not just best path but also how do I reduce the millisecond, microseconds that my application cares about, which brings in a layer of optimization and then so on and so on and so forth. So this is what we call kind of to borrow the Prosimo language full stack networking, right? Because I'm not just dealing with how do I go from one region to another because that's laws of physics. I can only control so much. But there are a few elements up the application stack in software that you can tweak to actually bring these things closer and closer. >> And on that point, you're seeing security being talked a lot more at the network layer. So how do you secure the Supercloud at the network layer? What's that look like? >> Yeah, we've been grappling with essentially is security kind of foundational, and then is the network on top. And then we had an alternative viewpoint which is kind of network and then security on top. And the answer is actually it's neither, right? It's almost like a meshed up sandwich of sorts. So you need to have networking security work really well together, right? Case in point, I mean we were talking to a customer yesterday. He said, hey, I have my data lake in one region that needs to talk to an analytics service in a completely different region of a different cloud. These two things just need to be able to talk to each other, which means I need to bring elements of networking. I need to bring elements of security, secure access, app segmentation, all of those things. Very simple, I have an analytics service that needs to contact a data lake. That's what he starts with, but then before you know it, it actually brings up a whole stack underneath, so that's. >> VMware calls that cloud chaos. >> Ramesh: Yes, exactly. >> And then that's the halfway point between cloud smart. Cloud first, cloud chaos, cloud smart, and the next thing, you can skip that whole step. But again, again, it's pick your strategy right? Again, this comes back down to your earlier point. I want to ask you from a customer standpoint, you got the hyper scalers doing very, very well. >> Ramesh: Yep, absolutely. >> And I love what their Amazon's doing. I think Microsoft again though they had a little bit of downgrade are catching up fast, and they have their installed base. So you got the land of the installed bases. >> Correct. >> First and greater, better cloud. Install base getting better, almost as good, almost as good is a gift, but close. Now you have them specializing. Silicon, special silicon. So there's gaps for other services. >> Ramesh: Correct. >> And Amazon Web Services, Adam Selipsky's a open book saying, hey, we want our ecosystem to pick up these gaps and build on them. Go ahead, go to town. >> So this is where I think choices are tough, right? Because if you had one choice, you would work with it, and you would work around it, right? Now I have five different choices. Now what do I do? Our viewpoint is there are a bunch of things that say AWS does really, really well. Use that as a foundational layer, right? Like don't reinvent the wheel on those things. Transit gateways, global accelerators and whatnot, they exist for a reason. Billions of dollars have gone into building those things. Use that foundational layer, right? But what you want to build on top of that is actually driven by the application. The requirements of a lambda application that's serverless, it's very different than a packaged application that's responding for transactions, right? Like it's just completely very, very different. And so bring in the right set of capabilities required for those set of applications, and then you go based on that. This is also where I think whether something is a regional construct versus an overall global construct really, really matters, right? Because if you start with the assumption that everything is going to be built regionally, then it's someone else's job to make sure that all of these things are connected. But if you start with kind of the global purview, then the rest of them start to (cross talking). >> What are some of the things that the enterprises might want that are gaps that are going to be filled by the, by startups like you guys and the ecosystem because we're seeing the ecosystem form into two big camps. >> Ramesh: Yep. >> ISVs, which is an old school definition of independent software vendor, aka someone who writes software. >> Ramesh: Exactly. >> SaaS app. >> Ramesh: Correct. >> And then ecosystem software players that were once ISVs now have people building on top of them. >> Ramesh: Correct. >> They're building on top of the cloud. So you have that new hyper scale effect going on. >> Ramesh: Exactly. >> You got ISVs, which is software developers, software vendors. >> Ramesh: Correct. >> And ecosystems. >> Yep. >> What's that impact of that? Cause it's a new dynamic. >> Exactly, so if you take kind of enterprises, want to make sure that that their apps and the data center migrate to the cloud, new apps are developed the right way in the cloud, right? So that's kind of table stakes. So now what choices do they have? They listen to AWS and say, okay, I have all these cloud native services. I want to be able to instantiate all that. Now comes the interesting choice that they have to make. Do I go hire a whole bunch of people and do it myself or do I go there on the platform route, right? Because I made an architectural choice. Now I have to decide whether I want to do this myself or the platform choice. DIY works great for some, but you don't know what you're getting into, and it's people involved, right? People, process, all those fun things involved, right? So we show up there and say, you don't know what you don't know, right? Like because that's the nature of it. Why don't you invest in a platform like what what we provide, and then you actually build on top of it. We will, it's our job to make sure that we keep up with the innovation happening underneath the covers. And at the same time, this is not a closed ended system. You can actually build on top of our platform, right? And so that actually gives you a good mix. Now the care abouts are interesting. Some apps care about experience. Some apps care about latency. Some apps are extremely charty and extremely data intensive, but nobody wants to pay for it, right? And so it's a interesting Jenga that you have to play between experience versus security versus cost, right? And that makes kind of head of infrastructure and cloud platform teams' life really, really, really interesting. >> And this is why I love your background, and Stu Miniman, when he was with theCUBE, and now he's at Red Hat, we used to riff about the network and how network folks are now, those concepts are now up the top of the stack because the cloud is one big network effect. >> Ramesh: Exactly, correct. >> It's a computer. >> Yep, absolutely. No, and case in point, right, like say we're in let's say in San Jose here or or Palo Alto here, and let's say my application is sitting in London, right? The cloud gives you different express lanes. I can go down to my closest pop location provided by AWS and then I can go ride that all the way up to up to London. It's going to give me better performance, low latency, but I'm going to have to incur some costs associated with it. Or I can go all the wild internet all the way from Palo Alta up to kind of the ingress point into London and then go access, but I'm spending time on the wild internet, which means all kinds of fun things happen, right? But I'm not paying much, but my experience is not going to be so great. So, and there are various degrees of shade in them, of gray in the middle, right? So how do you pick what? It all kind of is driven by the applications. >> Well, we certainly want you back for Supercloud3, our next version of this virtual/live event here in our Palo Alto studios. Really appreciate you coming on. >> Absolutely. >> While you're here, give a quick plug for the company. Next minute, we can take a minute to talk about the success of the company. >> Ramesh: Absolutely. >> I know you got a fresh financing this past year. Plenty of money in the bank, going to ride this new wave, Supercloud wave. Give us a quick plug. >> Absolutely, yeah. So three years going on to four this calendar year. So it's an interesting time for the company. We have proven that our technology, product and our initial customers are quite happy with it. Now comes essentially more of those and scale and so forth. That's kind of the interesting phase that we are in. Also heartened to see quite a few of kind of really large and dominant players in the market, partners, channels and so forth, invest in us to take this to the next set of customers. I would say there's been a dramatic shift in the conversation with our customers. The first couple of years or so of the company, we are about three years old right now, was really about us educating them. This is what you need. This is what you need. Now actually it's a lot of just pull, right? We've seen a good indication, as much as a hate RFIs, a good indication is the number of RFIs that show up at our door saying we want you to participate in this because we want to understand more, right? And so as a, I think we are at an interesting point of the, of that shift. >> RFIs always like do all this work and hope for the best. Pray for a deal. You know, you guys on the right side of history. If a customer asks with respect to Supercloud, multicloud, is that your focus? Is that the direction you guys are going into? >> Yeah, so I would say we are kind of both, right? Supercloud and multicloud because we, our customers are hybrid, multiple clouds, all of the above, right? Our main pitch and kind of value back to the customers is go embrace cloud native because that's the right approach, right? It doesn't make sense to go reinvent the wheel on that one, but then make a really good choice about whether you want to do this yourself or invest in a platform to make your life easy. Because we have seen this story play out with many many enterprises, right? They pick the right technologies. They do a simple POC overnight, and they say, yeah, I can make this work for two apps, right? And then they say, yes, I can make this work for 100. You go down a certain path. You hit a wall. You hit a wall, and it's a hard wall. It's like, no, there isn't a thing that you can go around it. >> A lot of dead bodies laying around. >> Ramesh: Exactly. >> Dead wall. >> And then they have to unravel around that, and then they come talk to us, and they say, okay, now what? Like help me, help me through this journey. So I would say to the extent that you can do this diligence ahead of time, do that, and then, and then pick the right platform. >> You've got to have the talent. And you got to be geared up. You got to know what you're getting into. >> Ramesh: Exactly. >> You got to have the staff to do this. >> And cloud talent and skillset in particular, I mean there's lots available but it's in pockets right? And if you look at kind of web three companies, they've gone and kind of amassed all those guys, right? So enterprises are not left with the cream of the crop. >> John: They might be coming back. >> Exactly, exactly, so. >> With this downturn. Ramesh, great to see you and thanks for contributing to Supercloud2, and again, love your team. Very technical team, and you're in the right side of history in this one. Congratulations. >> Ramesh: No, and thank you, thank you very much. >> Okay, this is Supercloud2. I'm John Furrier with Dave Vellante. We'll be back right after this short break. (upbeat music)

(lively music) >> Hello, everyone. Welcome back to the Supercloud presentation here. I'm theCUBE, I'm John Furrier, your host. What a great segment here. We're going to unpack the networking aspect of the cloud, how that translates into what Supercloud architecture and platform deployment scenarios look like. And demystify multi-cloud, hybridcloud. We've got two great experts. Amir Khan, the Co-Founder and CEO of Alkira, Atif Khan, Co-Founder and CTO of Alkira. These guys been around since 2018 with the startup, but before that story, history in the tech industry. I mean, routing early days, multiple waves, multiple cycles. >> Welcome three decades. >> Welcome to Supercloud. >> Thanks. >> Thanks for coming on. >> Thank you so much for having us. >> So, let's get your take on Supercloud because it's been one of those conversations that really galvanized the industry because it kind of highlights almost this next wave, this next side of the street that everyone's going to be on that's going to be successful. The laggards on the legacy seem to be stuck on the old model. SaaS is growing up, it's ISVs, it's ecosystems, hyperscale, full hybrid. And then multi-cloud around the corners cause all this confusion, everyone's hand waving. You know, this is a solution, that solution, where are we? What do you guys see as this supercloud dynamic? >> So where we start from is always focusing on the customer problem. And in 2018 when we identified the problem, we saw that there were multiple clouds with many diverse ways of doing things from the network perspective, and customers were struggling with that. So we delved deeper into that and looked at each one of the cloud architectures completely independent. And there was no common solution and customers were struggling with that from the perspective. They wanted to be in multiple clouds, either through mergers and acquisitions or running an application which may be more cost effective to run in something or maybe optimized for certain reasons to run in a different cloud. But from the networking perspective, everything needed to come together. So that's, we are starting to define it as a supercloud now, but basically, it's a common infrastructure across all clouds. And then integration of high lift services like, you know, security or IPAM services or many other types of services like inter-partner routing and stuff like that. So, Amir, you agree then that multi-cloud is simply a default result of having whatever outcomes, either M&A, some productivity software, maybe Azure. >> Yes. >> Amazon has this and then I've got on-premise application, so it's kinds mishmash. >> So, I would qualify it with hybrid multi-cloud because everything is going to be interconnected. >> John: Got it. >> Whether it's on-premise, remote users or clouds. >> But have CTO perspective, obviously, you got developers, multiple stacks, got AWS, Azure and GCP, other. Not everyone wants to kind of like go all in, but yet they don't want to hedge too much because it's a resource issue. And I got to learn this stack, I got to learn that stack. So then now, you have this default multi-cloud, hybrid multi-cloud, then it's like, okay, what do I do? How do you spread that around? Is it dangerous? What's the the approach technically? What's some of the challenges there? >> Yeah, certainly. John, first, thanks for having us here. So, before I get to that, I'll just add a little bit to what Amir was saying, like how we started, what we were seeing and how it, you know, correlates with the supercloud. So, as you know, before this company, Alkira, we were doing, we did the SD-WAN company, which was Viptela. So there, we started seeing when people started deploying SD-WAN at like a larger scale. We started like, you know, customers coming to us and saying they needed connectivity into the cloud from the SD-WAN. They wanted to extend the SD-WAN fabric to the cloud. So we came up with an architecture, which was like later we started calling them Cloud onRamps, where we built, you know, a transit VPC and put like the virtual instances of SD-WAN appliances extended from there to the cloud. But before we knew, like it started becoming very complicated for the customers because it wasn't just connectivity, it also required, you know, other use cases. You had to instantiate or bring in security appliances in there. You had to secure all of that stuff. There were requirements for, you know, different regions. So you had to bring up the same thing in different regions. Then multiple clouds, what did you do? You had to replicate the same thing in multiple clouds. And now if there was was requirement between clouds, how were you going to do it? You had to route traffic from somewhere, and come up with all those routing controls and stuff. So, it was very complicated. >> Like spaghetti code, but on network. >> The games begin, in fact, one of our customers called it spaghetti mess. And so, that's where like we thought about where was the industry going and which direction the industry was going into? And we came up with the Alkira where what we are doing is building a common infrastructure across multiple clouds, across in, you know, on-prem locations, be it data centers or physical sites, branches sites, et cetera, with integrated security and network networking services inside. And, you know, nowadays, networking is not only about connectivity, you have to secure everything. So, security has to be built in. Redundancy, high availability, disaster recovery. So all of that needs to be built in. So that's like, you know, kind of a definition of like what we thought at that time, what is turning into supercloud now. >> Yeah. It's interesting too, you mentioned, you know, VPCs is not, configuration of loans a hassle. Nevermind the manual mistakes could be made, but as you decide to do something you got to, "Oh, we got to get these other things." A lot of the hyper scales and a lot of the alpha cloud players now, and cloud native folks, they're kind of in that mode of, "Wow, look at what we've built." Now, they're got to maintain, how do I refresh it? Like, how do I keep the talent? So they got this similar chaotic environment where it's like, okay, now they're already already through, so I think they're going to be okay. But then some people want to bypass it completely. So there's a lot of customers that we see out there that fit the makeup of, I'm cloud first, I've lifted and shifted, I move some stuff to the cloud. But I want to bypass all that learnings from all the people that are gone through the past three years. Can I just skip that and go to a multi-cloud or coherent infrastructure? What do you think about that? What's your view? >> So yeah, so if you look at these enterprises, you know, many of them just to find like the talent, which for one cloud as far as the IT staff is concerned, it's hard enough. And now, when you have multiple clouds, it's hard to find people the talent which is, you know, which has expertise across different clouds. So that's where we come into the picture. So our vision was always to simplify all of this stuff. And simplification, it cannot be just simplification because you cannot just automate the workflows of the cloud providers underneath. So you have to, you know, provide your full data plane on top of it, fed full control plane, management plane, policy and management on top of it. And coming back to like your question, so these nowadays, those people who are working on networking, you know, before it used to be like CLI. You used to learn about Cisco CLI or Juniper CLI, and you used to work on it. Nowadays, it's very different. So automation, programmability, all of that stuff is the key. So now, you know, Ops guys, the DevOps guys, so these are the people who are in high demand. >> So what do you think about the folks out there that are saying, okay, you got a lot of fragmentation. I got the stacks, I got a lot of stove pipes, if you will, out there on the stack. I got to learn this from Azure. Can you guys have with your product abstract the way that's so developers don't need to know the ins and outs of stack's, almost like a gateway, if you will, the old days. But like I'm a developer or team develop, why should I have to learn the management layer of Azure? >> That's exactly what we started, you know, out with to solve. So it's, what we have built is a platform and the platform sits inside the cloud. And customers are able to build their own network or a virtual network on top using that platform. So the platform has its own data plane, own control plane and management plane with a policy layer on top of it. So now, it's the platform which is sitting in different clouds, but from a customer's point of view, it's one way of doing networking. One way of instantiating or bringing in services or security services in the middle. Whether those are our security services or whether those are like services from our partners, like Palo Alto or Checkpoint or Cisco. >> So you guys brought the SD-WAN mojo and refactored it for the cloud it sounds like. >> No. >> No? (chuckles) >> We cannot said. >> All right, explain. >> It's way more than that. >> I mean, SD-WAN was wan. I mean, you're talking about wide area networks, talking about connected, so explain the difference. >> SD-WAN was primarily done for one major reason. MPLS was expensive, very strong SLAs, but very low speed. Internet, on the other hand, you sat at home and you could access your applications much faster. No SLA, very low cost, right? So we wanted to marry the two together so you could have a purely private infrastructure and a public infrastructure and secure both of them by creating a common secure fabric across all those environments. And then seamlessly tying it into your internal branch and data center and cloud network. So, it merely brought you to the edge of the cloud. It didn't do anything inside the cloud. Now, the major problem resides inside the clouds where you have to optimize the clouds themselves. Take a step back. How were the clouds built? Basically, the cloud providers went to the Ciscos and Junipers and the rest of the world, built the network in the data centers or across wide area infrastructure, and brought it all together and tried to create a virtualized layer on top of that. But there were many limitations of this underlying infrastructure that they had built. So number of routes per region, how inter region connectivity worked, or how many routes you could carry to the VPCs of V nets? That all those were becoming no common policy across, you know, these environments, no segmentation across these environments, right? So the networking constructs that the enterprise customers were used to as enterprise class carry class capabilities, they did not exist in the cloud. So what did the customer do? They ended up stitching it together all manually. And that's why Atif was alluding to earlier that it became a spaghetti mess for the customers. And then what happens is, as a result, day two operations, you know, troubleshooting, everything becomes a nightmare. So what do you do? You have to build an infrastructure inside the cloud. Cloud has enough raw capabilities to build the solutions inside there. Netflix's of the world. And many different companies have been born in the cloud and evolved from there. So why could we not take the raw capabilities of the clouds and build a network cloud or a supercloud on top of these clouds to optimize the whole infrastructure and seamlessly connecting it into the on-premise and remote user locations, right? So that's your, you know, hybrid multi-cloud solution. >> Well, great call out on the SD-WAN in common versus cloud. 'Cause I think this is important because you're building a network layer in the cloud that spans out so the customers don't have to get into the, there's a gap in the system that I'm used to, my operating environment, of having lockdown security and network. >> So yeah. So what you do is you use the raw capabilities like bandwidth or virtual machines, or you know, containers, or, you know, different types of serverless capabilities. And you bring it all together in a way to solve the networking problems, thereby creating a supercloud, which is an abstraction layer which hides all the complexity of the underlying clouds from the customer, right? And it provides a common infrastructure across all environments to that customer, right? That's the beauty of it. And it does it in a way that it looks like, if they have the networking knowledge, they can apply it to this new environment and carry it forward. One way of doing security across all clouds and hybrid environments. One way of doing routing. One way of doing large-scale network address translation. One way of doing IPAM services. So people are tired of doing individual things and individual clouds and on-premise locations, right? So now they're getting something common. >> You guys brought that, you brought all that to bear and flexible for the customer to essentially self-serve their network cloud. >> Yes, yeah. Is that the wave? >> And nowadays, from business perspective, agility is the key, right? You have to move at the pace of the business. If you don't, you are losing. >> So, would it be safe to say that you guys have a network supercloud? >> Absolutely, yeah. >> We, pretty much, yeah. Absolutely. >> What does that mean to our customer? What's in it for them? What's the benefit to the customer? I got a network supercloud, it connects, provides SLA, all the capabilities I need. What do they get? What's the end point for them? What's the end? >> Atif, maybe you can talk some examples. >> The IT infrastructure is all like distributed now, right? So you have applications running in data centers. You have applications running in one cloud. Other cloud, public clouds, enterprises are depending on so many SaaS applications. So now, these are, you can call these endpoints. So a supercloud or a network cloud, from our perspective, it's a cloud in the middle or a network in the middle, which provides connectivity from any endpoint to any endpoint. So, you are able to connect to the supercloud or network cloud in one way no matter where you are. So now, whichever cloud you are in, whichever cloud you need to connect to. And also, it's not just connecting to the cloud. So you need to do a lot of stuff, a lot of networking inside the cloud also. So now, as Amir was saying, every cloud has its own from a networking, you know, the concept perspective or the construct, they are different. There are limitations in there also. So this supercloud, which is sitting on top, basically, your platform is sitting into the cloud, but the supercloud is built on top of using your platform. So that abstracts all those complexities, all those limitations. So now your limitations are whatever the limitations of that platform are. So now your platform, that platform is in our control. So we can keep building it, we can keep scaling it horizontally. Because one of the things is that, you know, in this cloud era, one of the things is autoscaling these services. So why can't the network now autoscale also, just like your other services. >> Network autoscaling is a genius idea, and I think that's a killer. I want to ask the the follow on question because I think, first of all, I love what you guys are doing. So, I think it's a great example of this new innovation. It's not obvious until you see it, right? Geographical is huge. So, you know, single instance, global instances, multiple instances, you're seeing global. How do you guys look at that global equation? Because as companies expand their clouds into geos, and then ultimately, you know, it's obviously continent, region and locales. You're going to have geographic issues. So, this is an extension of your network cloud? >> Amir: It is the extension of the network cloud because if you look at this hyperscalers, they're sitting pretty much everywhere in the globe. So, wherever their regions are, the beauty of building a supercloud is that you can by definition, be available in those regions. It literally takes a day or two of testing for our stack to run in those regions, to make sure there are no nuances that we run into, you know, for that region. The moment we bring it up in that region, all customers can onboard into that solution. So literally, what used to take months or years to build a global infrastructure, now, you can configure it in 10 minutes basically, and bring it up in less than one hour. Since when did we see any solution- >> And by the way, >> that can come up with. >> when the edge comes out too, you're going to start to see more clouds get bolted on. >> Exactly. And you can expand to the edge of the network. That's why we call cloud the new edge, right? >> John: Yeah, it is. Now, I think you guys got a good solutions, network clouds, superclouds, good. So the question on the premise side, so I get the cloud play. It's very cool. You can expand out. It's a nice layer. I'm sure you manage the SLAs between latency and all kinds of things. Knowing when not to do things. Physics or physics. Okay. Now, you've got the on-premise. What's the on-premise equation look like? >> So on-premise, the kind of customers, we are working with large enterprises, mid-size enterprises. So they have on-prem networks, they have deployed, in many cases, they have deployed SD-WAN. In many cases, they have MPLS. They have data centers also. And a lot of these companies are, you know, moving the applications from the data center into the cloud. But we still have large enterprise- >> But for you guys, you can sit there too with non server or is it a box or what is it? >> It's a software stack, right? So, we are a software company. >> Okay, so no box. >> No box. >> Okay, got it. >> No box. >> It's even better. So, we can connect any, as I mentioned, any endpoint, whether it's data centers. So, what happens is usually these enterprises from the data centers- >> John: It's a cloud endpoint for you. >> Cloud endpoint for us. And they need highspeed connectivity into the cloud. And our network cloud is sitting inside the or supercloud is sitting inside the cloud. So we need highspeed connectivity from the data centers. This is like multi-gig type of connectivity. So we enable that connectivity as a service. And as Amir was saying, you are able to bring it up in minutes, pretty much. >> John: Well, you guys have a great handle on supercloud. I really appreciate you guys coming on. I have to ask you guys, since you have so much experience in the industry, multiple inflection points you've guys lived through and we're all old, and we can remember those glory days. What's the big deal going on right now? Because you can connect the dots and you can imagine, okay, like a Lambda function spinning up some connectivity. I need instant access to a new route, throw some, I need to send compute to an edge point for process data. A lot of these kind of ad hoc services are going to start flying around, which used to be manually configured as you guys remember. >> Amir: And that's been the problem, right? The shadow IT, that was the biggest problem in the enterprise environment. So that's what we are trying to get the customers away from. Cloud teams came in, individuals or small groups of people spun up instances in the cloud. It was completely disconnected from the on-premise environment or the existing IT environment that the customer had. So, how do you bring it together? And that's what we are trying to solve for, right? At a large scale, in a carrier cloud center (indistinct). >> What do you call that? Shift right or shift left? Shift left is in the cloud native world security. >> Amir: Yes. >> Networking and security, the two hottest areas. What are you shifting? Up or down? I mean, the network's moving up the stack. I mean, you're seeing the run times at Kubernetes later' >> Amir: Right, right. It's true we're end-to-end virtualization. So you have plumbing, which is the physical infrastructure. Then on top of that, now for the first time, you have true end-to-end virtualization, which the cloud-like constructs are providing to us. We tried to virtualize the routers, we try to virtualize instances at the server level. Now, we are bringing it all together in a truly end-to-end virtualized manner to connect any endpoint anywhere across the globe. Whether it's on-premise, home, multiple clouds, or SaaS type environments. >> Yeah. If you talk about the technical benefits beyond virtualizations, you kind of see in virtualization be abstracted away. So you got end-to-end virtualization, but you don't need to know virtualization to take advantage of it. >> Exactly. Exactly. >> What are some of the tech involved where, what's the trend around on top of virtual? What's the easy button for that? >> So there are many, many use cases from the customers and they're, you know, some of those use cases, they used to deliver out of their data centers before. So now, because you, know, it takes a long time to spend something up in the data center and stuff. So the trend is and what enterprises are looking for is agility. And to achieve that agility, they are moving those services or those use cases into the cloud. So another technical benefit of like something like a supercloud and what we are doing is we allow customers to, you know, move their services from existing data centers into the cloud as well. And I'll give you some examples. You know, these enterprises have, you know, tons of partners. They provide connectivity to their partners, to select resources. It used to happen inside the data center. You would bring in connectivity into the data center and apply like tons of ACLs and whatnot to make sure that you are able to only connect. And now those use cases are, they need to be enabled inside the cloud. And the customer's customers are also, it's not just coming from the on-prem, they're coming from the cloud as well. So, if they're coming from the cloud as well as from on-prem, so you need like an infrastructure like supercloud, which is sitting inside the cloud and is able to handle all these use cases. So all of these use cases have to be, so that requires like moving those services from the data center into the cloud or into the supercloud. So, they're, oh, as we started building this service over the last four years, we have come across so many use cases. And to deliver those use cases, you have to have a platform. So you have to have your own platform because otherwise you are depending on somebody else's, you know, capabilities. And every time their capabilities change, you have to change. >> John: I'm glad you brought up the platform 'cause I want to get your both reaction to this. So Bob Muglia just said on theCUBE here at Supercloud, that supercloud is a platform that provides programmatically consistent services hosted on heterogeneous cloud providers. So the question is, is supercloud a platform or an architecture in your view? >> That's an interesting view on things, you know? I mean, if you think of it, you have to design or architect a solution before we turn it into a platform. >> John: It's a trick question actually. >> So it's a, you know, so we look at it as that you have to have an architectural approach end to end, right? And then you build a solution based on that approach. So, I don't think that they are mutually exclusive. I think they go hand in hand. It's an architecture that you turn into a solution and provide that agility and high availability and disaster recovery capability that it built into that. >> It's interesting that these definitions might be actually redefined with this new configuration. >> Amir: Yes. >> Because architecture and platform used to mean something, like, aight here's a platform, you buy this platform. >> And then you architecture solution. >> Architect it via vendor. >> Right, right, right. >> Okay. And they have to deal with that architecture in the place of multiple superclouds. If you have too many stove pipes, then what's the purpose of supercloud? >> Right, right, right. And because, you know, historically, you built a router and you sold it to the customer. And the poor customer was supposed to install it all, you know, and interconnect all those things. And if you have 40, 50,000 router network, which we saw in our lifetime, 'cause there used to be many more branches when we were growing up in the networking industry, right? You had to create hierarchy and all kinds of things to figure out how to solve that problem. We are no longer living in that world anymore. You cannot deploy individual virtual instances. And that's what approach a lot of people are taking, which is a pure overly network. You cannot take that approach anymore. You have to evolve the architecture and then build the solution based on that architecture so that it becomes a platform which is readily available, highly scalable, and available. And at the same time, it's very, very easy to deploy. It's a SaaS type solution, right? >> So you're saying, do the architecture to get the solution for the platform that the customer has. >> Amir: Yes. >> They're not buying a platform, they end up with a platform- >> With the platform. >> as a result of Supercloud path. All right. So that's what's, so you mentioned, that's a great point. I want to double click on what you just said. 'Cause I like that what you said. What's the deployment strategy in your mind for supercloud? I'm an architect. I'm at an enterprise in the Midwest. I'm an insurance company, got some cloud action going on. I'm mostly on-premise. I've got the mandate to transform the company. We have apps. We'll be fully transformed in five years. What's my strategy? What do I do? >> Amir: The resources. >> What's the deployment strategy? Single global instance, code in every region, on every cloud? >> It needs to be a solution which is available as a SaaS service, right? So from the customer's perspective, they are onboarding into the supercloud. And then the supercloud is allowing them to do whatever they used to do, you know, historically and in the new world, right? That needs to come together. And that's what we have built is that, we have brought everything together in a way that what used to take months or years, and now taking an hour or two hours, and then people test it for a week or so and deploy it in production. >> I want to bring up something we were talking about before we were on camera about the TCP/IP, the OSI model. That was a concept that destroyed the proprietary narcissist. Work operating systems of the mini computers, which brought in an era of tech prosperity for generations. TCP/IP was kind of the magical moment that allowed for that kind of super networking connection. Inter networking is what's called as a category. It feels like something's going on here with supercloud. The way you describe it, it feels like there's this unification idea. Like the reality is we've got multiple stuff sitting around by default, you either clean it up or get rid of it, right? Or it's almost a, it's either a nuance, a new nuisance or chaos. >> Yeah. And we live in the new world now. We don't have the luxury of time. So we need to move as fast as possible to solve the business problems. And that's what we are running into. If we don't have automated solutions which scale, which solve our problems, then it's going to be a problem. And that's why SaaS is so important in today's world. Why should we have to deploy the network piecemeal? Why can't we have a solution? We solve our problem as we move forward and we accomplish what we need to accomplish and move forward. >> And we don't really need standards here, dude. It's not that we need a standards body if you have unification. >> So because things move so fast, there's no time to create a standards body. And that's why you see companies like ours popping up, which are trying to create a common infrastructure across all clouds. Otherwise if we vent the standardization path may take long. Eventually, we should be going in that direction. But we don't have the luxury of time. That's what I was trying to get to. >> Well, what's interesting is, is that to your point about standards and ratification, what ratifies a defacto anything? In the old days there was some technical bodies involved, but here, I think developers drive everything. So if you look at the developers and how they're voting with their code. They're instantly, organically defining everything as a collective intelligence. >> And just like you're putting out the paper and making it available, everybody's contributing to that. That's why you need to have APIs and terra form type constructs, which are available so that the customers can continue to improve upon that. And that's the Net DevOps, right? So that you need to have. >> What was once sacrilege, just sayin', in business school, back in the days when I got my business degree after my CS degree was, you know, no one wants to have a better mousetrap, a bad business model to have a better mouse trap. In this case, the better mouse trap, the better solution actually could be that thing. >> It is that thing. >> I mean, that can trigger, tips over the industry. >> And that that's where we are seeing our customers. You know, I mean, we have some publicly referenceable customers like Coke or Warner Music Group or, you know, multiple others and chart industries. The way we are solving the problem. They have some of the largest environments in the industry from the cloud perspective. And their whole network infrastructure is running on the Alkira infrastructure. And they're able to adopt new clouds within days rather than waiting for months to architect and then deploy and then figure out how to manage it and operate it. It's available as a service. >> John: And we've heard from your customer, Warner, they were just on the program. >> Amir: Yes. Okay, okay. >> So they're building a supercloud. So superclouds aren't just for tech companies. >> Amir: No. >> You guys build a supercloud for networking. >> Amir: It is. >> But people are building their own superclouds on top of all this new stuff. Talk about that dynamic. >> Healthcare providers, financials, high-tech companies, even startups. One of our startup customers, Tekion, right? They have these dealerships that they provide sales and support services to across the globe. And for them to be able to onboard those dealerships, it is 80% less time to production. That is real money, right? So, maybe Atif can give you a lot more examples of customers who are deploying. >> Talk about some of the customer activity. What are they like? Are they laggards, they innovators? Are they trying to hit the easy button? Are they coming in late or are you got some high customers? >> Actually most of our customers, all of our customers or customers in general. I don't think they have a choice but to move in this direction because, you know, the cloud has, like everything is quick now. So the cloud teams are moving faster in these enterprises. So now that they cannot afford the network nor to keep up pace with the cloud teams. So, they don't have a choice but to go with something similar where you can, you know, build your network on demand and bring up your network as quickly as possible to meet all those use cases. So, I'll give you an example. >> John: So the demand's high for what you guys do. >> Demand is very high because the cloud teams have- >> John: Yeah. They're going fast. >> They're going fast and there's no stopping. And then network teams, they have to keep up with them. And you cannot keep deploying, you know, networks the way you used to deploy back in the day. And as far as the use cases are concerned, there are so many use cases which our customers are using our platform for. One of the use cases, I'll give you an example of these financial customers. Some of the financial customers, they have their customers who they provide data, like stock exchanges, that provide like market data information to their customers out of data centers part. But now, their customers are moving into the cloud as well. So they need to come in from the cloud. So when they're coming in from the cloud, you cannot be giving them data from your data center because that takes time, and your hair pinning everything back. >> Moving data is like moving, moving money, someone said. >> Exactly. >> Exactly. And the other thing is like you have to optimize your traffic flows in the cloud as well because every time you leave the cloud, you get charged a lot. So, you don't want to leave the cloud unless you have to leave the cloud, your traffic. So, you have to come up or use a service which allows you to optimize all those traffic flows as well, you know? >> My final question to you guys, first of all, thanks for coming on Supercloud Program. Really appreciate it. Congratulations on your success. And you guys have a great positioning and I'm a big fan. And I have to ask, you guys are agile, nimble startup, smart on the cutting edge. Supercloud concept seems to resonate with people who are kind of on the front range of this major wave. While all the incumbents like Cisco, Microsoft, even AWS, they're like, I think they're looking at it, like what is that? I think it's coming up really fast, this trend. Because I know people talk about multi-cloud, I get that. But like, this whole supercloud is not just SaaS, it's more going on there. What do you think is going on between the folks who get it, supercloud, get the concept, and some are who are scratching their heads, whether it's the Ciscos or someone, like I don't get it. Why is supercloud important for the folks that aren't really seeing it? >> So first of all, I mean, the customers, what we saw about six months, 12 months ago, were a little slower to adopt the supercloud kind of concept. And there were leading edge customers who were coming and adopting it. Now, all of a sudden, over the last six to nine months, we've seen a flurry of customers coming in and they are from all disciplines or all very diverse set of customers. And they're starting to see the value of that because of the practical implications of what they're doing. You know, these shadow IT type environments are no longer working and there's a lot of pressure from the management to move faster. And then that's where they're coming in. And perhaps, Atif, if you can give a few examples of. >> Yeah. And I'll also just add to your point earlier about the network needing to be there 'cause the cloud teams are like, let's go faster. And the network's always been slow because, but now, it's been almost turbocharged. >> Atif: Yeah. Yeah, exactly. And as I said, like there was no choice here. You had to move in this industry. And the other thing I would add a little bit is now if you look at all these enterprises, most of their traffic is from, even from which is coming from the on-prem, it's going to the cloud SaaS applications or public clouds. And it's more than 50% of traffic, which is leaving your, you know, what you used to call, your network or the private network. So now it's like, you know, before it used to just connect sites to data centers and sites together. Now, it's a cloud as well as the SaaS application. So it's either internet bound or the public cloud bound. So now you have to build a network quickly, which caters to all these use cases. And that's where like something- >> And you guys, your solution to me is you eliminate all that work for the customer. Now, they can treat the cloud like a bag of Legos. And do their thing. Well, I oversimplify. Well, you know I'm talking about. >> Atif: Right, exactly. >> And to answer your question earlier about what about the big companies coming in and, you know, now they slow to adopt? And, you know, what normally happens is when Cisco came up, right? There used to be 16 different protocols suites. And then we finally settled on TCP/IP and DECnet or AppleTalk or X&S or, you know, you name it, right? Those companies did not adapt to the networking the way it was supposed to be done. And guess what happened, right? So if the companies in the networking space do not adopt this new concept or new way of doing things, I think some of them will become extinct over time. >> Well, I think the force and function too is the cloud teams as well. So you got two evolutions. You got architectural relevance. That's real as impact. >> It's very important. >> Cost, speed. >> And I look at it as a very similar disruption to what Cisco's the world, very early days did to, you know, bring the networking out, right? And it became the internet. But now we are going through the cloud. It's the cloud era, right? How does the cloud evolve over the next 10, 15, 20 years? Everything's is going to be offered as a service, right? So slowly data centers go away, the network becomes a plumbing thing. Very, you know, simple to deploy. And everything on top of that is virtualized in the cloud-like manners. >> And that makes the networks hardened and more secure. >> More secure. >> It's a great way to be secure. You remember the glory days, we'll go back 15 years. The Cisco conversation was, we got to move up to stack. All the manager would fight each other. Now, what does that actually mean? Stay where we are. Stay in your lane. This is kind of like the network's version of moving up the stack because not so much up the stack, but the cloud is everywhere. It's almost horizontally scaled. >> It's extending into the on-premise. It is already moving towards the edge, right? So, you will see a lot- >> So, programmability is a big program. So you guys are hitting programmability, compatibility, getting people into an environment they're comfortable operating. So the Ops people love it. >> Exactly. >> Spans the clouds to a level of SLA management. It might not be perfectly spanning applications, but you can actually know latencies between clouds, measure that. And then so you're basically managing your network now as the overall infrastructure. >> Right. And it needs to be a very intelligent infrastructure going forward, right? Because customers do not want to wait to be able to troubleshoot. They don't want to be able to wait to deploy something, right? So, it needs to be a level of automation. >> Okay. So the question for you guys both on we'll end on is what is the enablement that, because you guys are a disruptive enabler, right? You create this fabric. You're going to enable companies to do stuff. What are some of the things that you see and your customers might be seeing as things that they're going to do as a result of having this enablement? So what are some of those things? >> Amir: Atif, perhaps you can talk through the some of the customer experience on that. >> It's agility. And we are allowing these customers to move very, very quickly and build these networks which meet all these requirements inside the cloud. Because as Amir was saying, in the cloud era, networking is changing. And if you look at, you know, going back to your comment about the existing networking vendors. Some of them still think that, you know, just connecting to the cloud using some concepts like Cloud OnRamp is cloud networking, but it's changing now. >> John: 'Cause there's apps that are depending upon. >> Exactly. And it's all distributed. Like IT infrastructure, as I said earlier, is all distributed. And at the end of the day, you have to make sure that wherever your user is, wherever your app is, you are able to connect them securely. >> Historically, it used to be about building a router bigger and bigger and bigger and bigger, you know, and then interconnecting those routers. Now, it's all about horizontal scale. You don't need to build big, you need to scale it, right? And that's what cloud brings to the customer. >> It's a cultural change for Cisco and Juniper because they have to understand that they're still could be in the game and still win. >> Exactly. >> The question I have for you, what are your customers telling you that, what's some of the anecdotal, like, 'cause you guys have a good solution, is it, "Oh my god, you guys saved my butt." Or what are some of the commentary that you hear from the customers in terms of praise and and glory from your solution? >> Oh, some even say, when we do our demo and stuff, they say it's too hard to believe. >> Believe. >> Like, too hard. It's hard, you know, it's >> I dont believe you. They're skeptics. >> I don't believe you that because now you're able to bring up a global network within minutes. With networking services, like let's say you have APAC, you know, on-prem users, cloud also there, cloud here, users here, you can bring up a global network with full routed connectivity between all these endpoints with security services. You can bring up like a firewall from a third party or our services in the middle. This is a matter of minutes now. And this is all high speed connectivity with SLAs. Imagine like before connecting, you know, Singapore to U.S. East or Hong Kong to Frankfurt, you know, if you were putting your infrastructure in columns like E-connects, you would have to go, you know, figure out like, how am I going to- >> Seal line In, connect to it? Yeah. A lot of hassles, >> If you had to put like firewalls in the middle, segmentation, you had to, you know, isolate different entities. >> That's called heavy lifting. >> So what you're seeing is, you know, it's like customer comes in, there's a disbelief, can you really do that? And then they try it out, they go, "Wow, this works." Right? It's deployed in a small environment. And then all of a sudden they start taking off, right? And literally we have seen customers go from few thousand dollars a month or year type deployments to multi-million dollars a year type deployments in very, very short amount of time, in a few months. >> And you guys are pay as you go? >> Pay as you go. >> Pay as go usage cloud-based compatibility. >> Exactly. And it's amazing once they get to deploy the solution. >> What's the variable on the cost? >> On the cost? >> Is it traffic or is it. >> It's multiple different things. It's packaged into the overall solution. And as a matter of fact, we end up saving a lot of money to the customers. And not only in one way, in multiple different ways. And we do a complete TOI analysis for the customers. So it's bandwidth, it's number of connections, it's the amount of compute power that we are using. >> John: Similar things that they're used to. >> Just like the cloud constructs. Yeah. >> All right. Networking supercloud. Great. Congratulations. >> Thank you so much. >> Thanks for coming on Supercloud. >> Atif: Thank you. >> And looking forward to seeing more of the demand. Translate, instant networking. I'm sure it's going to be huge with the edge exploding. >> Oh yeah, yeah, yeah, yeah. >> Congratulations. >> Thank you so much. >> Thank you so much. >> Okay. So this is Supercloud 2 event here in Palo Alto. I'm John Furrier. The network Supercloud is here. Checkout Alkira. I'm John Furry, the host. Thanks for watching. (lively music)

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

(upbeat music) >> Welcome back everyone to Supercloud 2, live here in Palo Alto, our studio, where we're doing a live stage performance and virtually syndicating out around the world. I'm John Furrier with Dave Vellante, my co-host with the The Cube here. We've got Kit Colbert, the CTO of VM. We're doing a keynote on Cloud Chaos, the evolution of SuperCloud Architecture Kit. Great to see you, thanks for coming on. >> Yeah, thanks for having me back. It's great to be here for Supercloud 2. >> And so we're going to dig into it. We're going to do a Q&A. We're going to let you present. You got some slides. I really want to get this out there, it's really compelling story. Do the presentation and then we'll come back and discuss. Take it away. >> Yeah, well thank you. So, we had a great time at the original Supercloud event, since then, been talking to a lot of customers, and started to better formulate some of the thinking that we talked about last time So, let's jump into it. Just a few quick slides to sort of set the tone here. So, if we go to the the next slide, what that shows is the journey that we see customers on today, going from what we call Cloud First into this phase that many customers are stuck in, called Cloud Chaos, and where they want to get to, and this is the term customers actually use, we didn't make this up, we heard it from customers. This notion of Cloud Smart, right? How do they use cloud more effectively, more intelligently? Now, if you walk through this journey, customers start with Cloud First. They usually select a single cloud that they're going to standardize on, and when they do that, they have to build out a whole bunch of functionality around that cloud. Things you can see there on the screen, disaster recovery, security, how do they monitor it or govern it? Like, these are things that are non-negotiable, you've got to figure it out, and typically what they do is, they leverage solutions that are specific for that cloud, and that's fine when you have just one cloud. But if we build out here, what we see is that most customers are using more than just one, they're actually using multiple, not necessarily 10 or however many on the screen, but this is just as an example. And so what happens is, they have to essentially duplicate or replicate that stack they've built for each different cloud, and they do so in a kind of a siloed manner. This results in the Cloud Chaos term that that we talked about before. And this is where most businesses out there are, they're using two, maybe three public clouds. They've got some stuff on-prem and they've also got some stuff out at the edge. This is apps, data, et cetera. So, this is the situation, this is sort of that Cloud Chaos. So, the question is, how do we move from this phase to Cloud Smart? And this is where the architecture comes in. This is why architecture, I think, is so important. It's really about moving away from these single cloud services that just solve a problem for one cloud, to something we call a Cross-Cloud service. Something that can support a set of functionality across all clouds, and that means not just public clouds, but also private clouds, edge, et cetera, and when you evolve that across the board, what you get is this sort of Supercloud. This notion that we're talking about here, where you combine these cross-cloud services in many different categories. You can see some examples there on the screen. This is not meant to be a complete set of things, but just examples of what can be done. So, this is sort of the transition and transformation that we're talking about here, and I think the architecture piece comes in both for the individual cloud services as well as that Supercloud concept of how all those services come together. >> Great presentation., thanks for sharing. If you could pop back to that slide, on the Cloud Chaos one. I just want to get your thoughts on something there. This is like the layout of the stack. So, this slide here that I'm showing on the screen, that you presented, okay, take us through that complexity. This is the one where I wanted though, that looks like a spaghetti code mix. >> Yes. >> So, do you turn this into a Supercloud stack, right? Is that? >> well, I think it's, it's an evolving state that like, let's take one of these examples, like security. So, instead of implementing security individually in different ways, using different technologies, different tooling for each cloud, what you would do is say, "Hey, I want a single security solution that works across all clouds", right? A concrete example of this would be secure software supply chain. This is probably one of the top ones that I hear when I talk to customers. How do I know that the software I'm building is truly what I expect it to be, and not something that some hacker has gotten into, and polluted with malicious code? And what they do is that, typically today, their teams have gone off and created individual secure software supply chain solutions for each cloud. So, now they could say, "Hey, I can take a single implementation and just have different endpoints." It could go to Google, or AWS, or on-prem, or wherever have you, right? So, that's the sort of architectural evolution that we're talking about. >> You know, one of the things we hear, Dave, you've been on theCUBE all the time, and we, when we talk privately with customers who are asking us like, what's, what's going on? They have the same complaint, "I don't want to build a team, a dev team, for that stack." So, if you go back to that slide again, you'll see that, that illustrates the tech stack for the clouds and the clouds at the bottom. So, the number one complaint we hear, and I want to get your reaction to that, "I don't want to have a team to have to work on that. So, I'm going to pick one and then have a hedge secondary one, as a backup." Here, that's one, that's four, five, eight, ten, ten environments. >> Yeah, I got a lot. >> That's going to be the reality, so, what's the technical answer to that? >> Yeah, well first of all, let me just say, this picture is again not totally representative of reality oftentimes, because while that picture shows a solution for every cloud, oftentimes that's not the case. Oftentimes it's a line of business going off, starting to use a new cloud. They might solve one or two things, but usually not security, usually not some of these other things, right? So, I think from a technical standpoint, where you want to get to is, yes, that sort of common service, with a common operational team behind it, that is trained on that, that can work across clouds. And that's really I think the important evolution here, is that you don't need to replicate these operational teams, one for each cloud. You can actually have them more focused across all those clouds. >> Yeah, in fact, we were commenting on the opening today. Dave and I were talking about the benefits of the cloud. It's heterogeneous, which is a good thing, but it's complex. There's skill gaps and skill required, but at the end of the day, self-service of the cloud, and the elastic nature of it makes it the benefit. So, if you try to create too many common services, you lose the value of the cloud. So, what's the trade off, in your mind right now as customers start to look at okay, identity, maybe I'll have one single sign on, that's an obvious one. Other ones? What are the areas people are looking at from a combination, common set of services? Where do they start? What's the choices? What are some of the trade offs? 'Cause you can't do it everything. >> No, it's a great question. So, that's actually a really good point and as I answer your question, before I answer your question, the important point about that, as you saw here, you know, across cloud services or these set of Cross-Cloud services, the things that comprise the Supercloud, at least in my view, the point is not necessarily to completely abstract the underlying cloud. The point is to give a business optionality and choice, in terms of what it wants to abstract, and I think that gets to your question, is how much do you actually want to abstract from the underlying cloud? Now, what I find, is that typically speaking, cloud choice is driven at least from a developer or app team perspective, by the best of breed services. What higher level application type services do you need? A database or AI, you know, ML systems, for your application, and that's going to drive your choice of the cloud. So oftentimes, businesses I talk to, want to allow those services to shine through, but for other things that are not necessarily highly differentiated and yet are absolutely critical to creating a successful application, those are things that you want to standardize. Again, like things like security, the supply chain piece, cost management, like these things you need to, and you know, things like cogs become really, really important when you start operating at scale. So, those are the things in it that I see people wanting to focus on. >> So, there's a majority model. >> Yes. >> All right, and we heard of earlier from Walmart, who's fairly, you know, advanced, but at the same time their supercloud is pretty immature. So, what are you seeing in terms of supercloud momentum, crosscloud momentum? What's the starting point for customers? >> Yeah, so it's interesting, right, on that that three-tiered journey that I talked about, this Cloud Smart notion is, that is adoption of what you might call a supercloud or architecture, and most folks aren't there yet. Even the really advanced ones, even the really large ones, and I think it's because of the fact that, we as an industry are still figuring this out. We as an industry did not realize this sort of Cloud Chaos state could happen, right? We didn't, I think most folks thought they could standardize on one cloud and that'd be it, but as time has shown, that's simply not the case. As much as one might try to do that, that's not where you end up. So, I think there's two, there's two things here. Number one, for folks that are early in to the cloud, and are in this Cloud Chaos phase, we see the path out through standardization of these cross-cloud services through adoption of this sort of supercloud architecture, but the other thing I think is particularly exciting, 'cause I talked to a number of of businesses who are not yet in the Cloud Chaos phase. They're earlier on in the cloud journey, and I think the opportunity there is that they don't have to go through Cloud Chaos. They can actually skip that whole phase if they adopt this supercloud architecture from the beginning, and I think being thoughtful around that is really the key here. >> It's interesting, 'cause we're going to hear from Ionis Pharmaceuticals later, and they, yes there are multiple clouds, but the multiple clouds are largely separate, and so it's a business unit using that. So, they're not in Cloud Chaos, but they're not tapping the advantages that you could get for best of breed across those business units. So, to your point, they have an opportunity to actually build that architecture or take advantage of those cross-cloud services, prior to reaching cloud chaos. >> Well, I, actually, you know, I'd love to hear from them if, 'cause you say they're not in Cloud Chaos, but are they, I mean oftentimes I find that each BU, each line of business may feel like they're fine, in of themselves. >> Yes, exactly right, yes. >> But when you look at it from an overall company perspective, they're like, okay, things are pretty chaotic here. We don't have standardization, I don't, you know, like, again, security compliance, these things, especially in many regulated industries, become huge problems when you're trying to run applications across multiple clouds, but you don't have any of those company-wide standardizations. >> Well, this is a point. So, they have a big deal with AstraZeneca, who's got this huge ecosystem, they want to start sharing data across those ecosystem, and that's when they will, you know, that Cloud Chaos will, you know, come, come to fore, you would think. I want to get your take on something that Bob Muglia said earlier, which is, he kind of said, "Hey Dave, you guys got to tighten up your definition a little bit." So, he said a supercloud is a platform that provides programmatically consistent services hosted on heterogeneous cloud providers. So, you know, thank you, that was nice and simple. However others in the community, we're going to hear from Dr. Nelu Mihai later, says, no, no, wait a minute, it's got to be an architecture, not a platform. Where do you land on this architecture v. platform thing? >> I look at it as, I dunno if it's, you call it maturity or just kind of a time horizon thing, but for me when I hear the word platform, I typically think of a single vendor. A single vendor provides this platform. That's kind of the beauty of a platform, is that there is a simplicity usually consistency to it. >> They did the architecture. (laughing) >> Yeah, exactly but I mean, well, there's obviously architecture behind it, has to be, but you as a customer don't necessarily need to deal with that. Now, I think one of the opportunities with Supercloud is that it's not going to be, or there is no single vendor that can solve all these problems. It's got to be the industry coming together as a community, inter-operating, working together, and so, that's why, for me, I think about it as an architecture, that there's got to be these sort of, well-defined categories of functionality. There's got to be well-defined interfaces between those categories of functionality to enable modularity, to enable businesses to be able to pick and choose the right sorts of services, and then weave those together into an overall supercloud. >> Okay, so you're not pitching, necessarily the platform, you're saying, hey, we have an architecture that's open. I go back to something that Vittorio said on August 9th, with the first Supercloud, because as well, remember we talked about abstracting, but at the same time giving developers access to those primitives. So he said, and this, I think your answer sort of confirms this. "I want to have my cake eat it too and not gain weight." >> (laughing) Right. Well and I think that's where the platform aspect can eventually come, after we've gotten aligned architecture, you're going to start to naturally see some vendors step up to take on some of the remaining complexity there. So, I do see platforms eventually emerging here, but I think where we have to start as an industry is around aligning, okay, what does this definition mean? What does that architecture look like? How do we enable interoperability? And then we can take the next step. >> Because it depends too, 'cause I would say Snowflake has a platform, and they've just defined the architecture, but we're not talking about infrastructure here, obviously, we're talking about something else. >> Well, I think that the Snowflake talks about, what he talks about, security and data, you're going to start to see the early movement around areas that are very spanning oriented, and I think that's the beginning of the trend and I think there's going to be a lot more, I think on the infrastructure side. And to your point about the platform architecture, that's actually a really good thought exercise because it actually makes you think about what you're designing in the first place, and that's why I want to get your reaction. >> Quote from- >> Well I just have to interrupt since, later on, you're going to hear from near Nir Zuk of Palo Alto Network. He says architecture and security historically, they don't go hand in hand, 'cause it's a big mess. >> It depends if you're whacking the mole or you actually proactively building something. Well Kit, I want to get your reaction from a quote from someone in our community who said about Supercloud, you know, "The Supercloud's great, there are issues around computer science rigors, and customer requirements." So, there's some issues around the science itself as well as not just listen to the customer, 'cause if that's the case, we'd have a better database, a better Oracle, right, so, but there's other, this tech involved, new tech. We need an open architecture with universal data modeling interconnecting among them, connectivity is a part of security, and then, once we get through that gate, figuring out the technical, the data, and the customer requirements, they say "Supercloud should be a loosely coupled platform with open architecture, plug and play, specialized services, ready for optimization, automation that can stand the test of time." What's your reaction to that sentiment? You like it, is that, does that sound good? >> Yeah, no, broadly aligns with my thinking, I think, and what I see from talking with customers as well. I mean, I like the, again, the, you know, listening to customer needs, prioritizing those things, focusing on some of the connective tissue networking, and data and some of these aspects talking about the open architecture, the interoperability, those are all things I think are absolutely critical. And then, yeah, like I think at the end. >> On the computer science side, do you see some science and engineering things that need to be engineered differently? We heard databases are radically going to change and that are inadequate for the new architecture. What are some of the things like that, from a science standpoint? >> Yeah, yeah, yeah. Some of the more academic research type things. >> More tech, or more better tech or is it? >> Yeah, look, absolutely. I mean I think that there's a bunch around, certainly around the data piece, around, you know, there's issues of data gravity, data mobility. How do you want to do that in a way that's performant? There's definitely issues around security as well. Like how do you enable like trust in these environments, there's got to be some sort of hardware rooted trusts, and you know, a whole bunch of various types of aspects there. >> So, a lot of work still be done. >> Yes, I think so. And that's why I look at this as, this is not a one year thing, or you know, it's going to be multi-years, and I think again, it's about all of us in the industry working together to come to an aligned picture of what that looks like. >> So, as the world's moved from private cloud to public cloud and now Cross-cloud services, supercloud, metacloud, whatever you want to call it, how have you sort of changed the way engineering's organized, developers sort of approached the problem? Has it changed and how? >> Yeah, absolutely. So, you know, it's funny, we at VMware, going through the same challenges as our customers and you know, any business, right? We use multiple clouds, we got a big, of course, on-prem footprint. You know, what we're doing is similar to what I see in many other customers, which, you see the evolution of a platform team, and so the platform team is really in charge of trying to develop a lot of these underlying services to allow our lines of business, our product teams, to be able to move as quickly as possible, to focus on the building, while we help with a lot of the operational overheads, right? We maintain security, compliance, all these other things. We also deal with, yeah, just making the developer's life as simple as possible. So, they do need to know some stuff about, you know, each public cloud they're using, those public cloud services, but at the same, time we can abstract a lot of the details they don't need to be in. So, I think this sort of delineation or separation, I should say, between the underlying platform team and the product teams is a very, very common pattern. >> You know, I noticed the four layers you talked about were observability, infrastructure, security and developers, on that slide, the last slide you had at the top, that was kind of the abstraction key areas that you guys at VMware are working? >> Those were just some groupings that we've come up with, but we like to debate them. >> I noticed data's in every one of them. >> Yeah, yep, data is key. >> It's not like, so, back to the data questions that security is called out as a pillar. Observability is just kind of watching everything, but it's all pretty much data driven. Of the four layers that you see, I take that as areas that you can. >> Standardize. >> Consistently rely on to have standard services. >> Yes. >> Which one do you start with? What's the, is there order of operations? >> Well, that's, I mean. >> 'Cause I think infrastructure's number one, but you had observability, you need to know what's going on. >> Yeah, well it really, it's highly dependent. Again, it depends on the business that we talk to and what, I mean, it really goes back to, what are your business priorities, right? And we have some customers who may want to get out of a data center, they want to evacuate the data center, and so what they want is then, consistent infrastructure, so they can just move those applications up to the cloud. They don't want to have to refactor them and we'll do it later, but there's an immediate and sort of urgent problem that they have. Other customers I talk to, you know, security becomes top of mind, or maybe compliance, because they're in a regulated industry. So, those are the sort of services they want to prioritize. So, I would say there is no single right answer, no one size fits all. The point about this architecture is really around the optionality of it, as it allows you as a business to decide what's most important and where you want to prioritize. >> How about the deployment models kit? Do, does a customer have that flexibility from a deployment model standpoint or do I have to, you know, approach it a specific way? Can you address that? >> Yeah, I mean deployment models, you're talking about how they how they consume? >> So, for instance, yeah, running a control plane in the cloud. >> Got it, got it. >> And communicating elsewhere or having a single global instance or instantiating that instance, and? >> So, that's a good point actually, and you know, the white paper that we released back in August, around this sort of concept, the Cross-cloud service. This is some of the stuff we need to figure out as an industry. So, you know when we talk about a Cross-cloud service, we can mean actually any of the things you just talked about. It could be a single instance that runs, let's say in one public cloud, but it supports all of 'em. Or it could be one that's multi-instance and that runs in each of the clouds, and that customers can take dependencies on whichever one, depending on what their use cases are or the, even going further than that, there's a type of Cross-cloud service that could actually be instantiated even in an air gapped or offline environment, and we have many, many businesses, especially heavily regulated ones that have that requirement, so I think, you know. >> Global don't forget global, regions, locales. >> Yeah, there's all sorts of performance latency issues that can be concerned about. So, most services today are the former, there are single sort of instance or set of instances within a single cloud that support multiple clouds, but I think what we're doing and where we're going with, you know, things like what we see with Kubernetes and service meshes and all these things, will better enable folks to hit these different types of cross-cloud service architectures. So, today, you as a customer probably wouldn't have too much choice, but where we're going, you'll see a lot more choice in the future. >> If you had to summarize for folks watching the importance of Supercloud movement, multi-cloud, cross-cloud services, as an industry in flexible, 'cause I'm always riffing on the whole old school network protocol stacks that got disrupted by TCP/IP, that's a little bit dated, we got people on the chat that are like, you know, 20 years old that weren't even born then. So, but this is a, one of those inflection points that's once in a generation inflection point, I'm sure you agree. What scoped the order of magnitude of the change and the opportunity around the marketplace, the business models, the technology, and ultimately benefits the society. >> Yeah. Wow. Getting bigger. >> You have 10 seconds, go. >> I know. Yeah. (laughing) No, look, so I think it is what we're seeing is really the next phase of what you might call cloud, right? This notion of delivering services, the way they've been packaged together, traditionally by the hyperscalers is now being challenged. and what we're seeing is really opening that up to new levels of innovation, and I think that will be huge for businesses because it'll help meet them where they are. Instead of needing to contort the businesses to, you know, make it work with the technology, the technology will support the business and where it's going. Give people more optionality, more flexibility in order to get there, and I think in the end, for us as individuals, it will just make for better experiences, right? You can get better performance, better interactivity, given that devices are so much of what we do, and so much of what we interact with all the time. This sort of flexibility and optionality will fundamentally better for us as individuals in our experiences. >> And we're seeing that with ChatGPT, everyone's talking about, just early days. There'll be more and more of things like that, that are next gen, like obviously like, wow, that's a fall out of your chair moment. >> It'll be the next wave of innovation that's unleashed. >> All right, Kit Colbert, thanks for coming on and sharing and exploring the Supercloud architecture, Cloud Chaos, the Cloud Smart, there's a transition progression happening and it's happening fast. This is the supercloud wave. If you're not on this wave, you'll be driftwood. That's a Pat Gelsinger quote on theCUBE. This is theCUBE Be right back with more Supercloud coverage, here in Palo Alto after this break. (upbeat music) (upbeat music continues)

(light music) >> Hi everybody. Welcome to this Cube Conversation. I'm really pleased to announce a collaboration with Rob Strechay. He's a guest cube analyst, and we'll be working together to extract the signal from the noise. Rob is a long-time product pro, working at a number of firms including AWS, HP, HPE, NetApp, Snowplow. I did a stint as an analyst at Enterprise Strategy Group. Rob, good to see you. Thanks for coming into our Marlboro Studios. >> Well, thank you for having me. It's always great to be here. >> I'm really excited about working with you. We've known each other for a long time. You've been in the Cube a bunch. You know, you're in between gigs, and I think we can have a lot of fun together. Covering events, covering trends. So. let's get into it. What's happening out there? We're sort of exited the isolation economy. Things were booming. Now, everybody's tapping the brakes. From your standpoint, what are you seeing out there? >> Yeah. I'm seeing that people are really looking how to get more out of their data. How they're bringing things together, how they're looking at the costs of Cloud, and understanding how are they building out their SaaS applications. And understanding that when they go in and actually start to use Cloud, it's not only just using the base services anymore. They're looking at, how do I use these platforms as a service? Some are easier than others, and they're trying to understand, how do I get more value out of that relationship with the Cloud? They're also consolidating the number of Clouds that they have, I would say to try to better optimize their spend, and getting better pricing for that matter. >> Are you seeing people unhook Clouds, or just reduce maybe certain Cloud activities and going maybe instead of 60/40 going 90/10? >> Correct. It's more like the 90/10 type of rule where they're starting to say, Hey I'm not going to get rid of Azure or AWS or Google. I'm going to move a portion of this over that I was using on this one service. Maybe I got a great two-year contract to start with on this platform as a service or a database as a service. I'm going to unhook from that and maybe go with an independent. Maybe with something like a Snowflake or a Databricks on top of another Cloud, so that I can consolidate down. But it also gives them more flexibility as well. >> In our last breaking analysis, Rob, we identified six factors that were reducing Cloud consumption. There were factors and customer tactics. And I want to get your take on this. So, some of the factors really, you got fewer mortgage originations. FinTech, obviously big Cloud user. Crypto, not as much activity there. Lower ad spending means less Cloud. And then one of 'em, which you kind of disagreed with was less, less analytics, you know, fewer... Less frequency of calculations. I'll come back to that. But then optimizing compute using Graviton or AMD instances moving to cheaper storage tiers. That of course makes sense. And then optimize pricing plans. Maybe going from On Demand, you know, to, you know, instead of pay by the drink, buy in volume. Okay. So, first of all, do those make sense to you with the exception? We'll come back and talk about the analytics piece. Is that what you're seeing from customers? >> Yeah, I think so. I think that was pretty much dead on with what I'm seeing from customers and the ones that I go out and talk to. A lot of times they're trying to really monetize their, you know, understand how their business utilizes these Clouds. And, where their spend is going in those Clouds. Can they use, you know, lower tiers of storage? Do they really need the best processors? Do they need to be using Intel or can they get away with AMD or Graviton 2 or 3? Or do they need to move in? And, I think when you look at all of these Clouds, they always have pricing curves that are arcs from the newest to the oldest stuff. And you can play games with that. And understanding how you can actually lower your costs by looking at maybe some of the older generation. Maybe your application was written 10 years ago. You don't necessarily have to be on the best, newest processor for that application per se. >> So last, I want to come back to this whole analytics piece. Last June, I think it was June, Dev Ittycheria, who's the-- I call him Dev. Spelled Dev, pronounced Dave. (chuckles softly) Same pronunciation, different spelling. Dev Ittycheria, CEO of Mongo, on the earnings call. He was getting, you know, hit. Things were starting to get a little less visible in terms of, you know, the outlook. And people were pushing him like... Because you're in the Cloud, is it easier to dial down? And he said, because we're the document database, we support transaction applications. We're less discretionary than say, analytics. Well on the Snowflake earnings call, that same month or the month after, they were all over Slootman and Scarpelli. Oh, the Mongo CEO said that they're less discretionary than analytics. And Snowflake was an interesting comment. They basically said, look, we're the Cloud. You can dial it up, you can dial it down, but the area under the curve over a period of time is going to be the same, because they get their customers to commit. What do you say? You disagreed with the notion that people are running their calculations less frequently. Is that because they're trying to do a better job of targeting customers in near real time? What are you seeing out there? >> Yeah, I think they're moving away from using people and more expensive marketing. Or, they're trying to figure out what's my Google ad spend, what's my Meta ad spend? And what they're trying to do is optimize that spend. So, what is the return on advertising, or the ROAS as they would say. And what they're looking to do is understand, okay, I have to collect these analytics that better understand where are these people coming from? How do they get to my site, to my store, to my whatever? And when they're using it, how do they they better move through that? What you're also seeing is that analytics is not only just for kind of the retail or financial services or things like that, but then they're also, you know, using that to make offers in those categories. When you move back to more, you know, take other companies that are building products and SaaS delivered products. They may actually go and use this analytics for making the product better. And one of the big reasons for that is maybe they're dialing back how many product managers they have. And they're looking to be more data driven about how they actually go and build the product out or enhance the product. So maybe they're, you know, an online video service and they want to understand why people are either using or not using the whiteboard inside the product. And they're collecting a lot of that product analytics in a big way so that they can go through that. And they're doing it in a constant manner. This first party type tracking within applications is growing rapidly by customers. >> So, let's talk about who wins in that. So, obviously the Cloud guys, AWS, Google and Azure. I want to come back and unpack that a little bit. Databricks and Snowflake, we reported on our last breaking analysis, it kind of on a collision course. You know, a couple years ago we were thinking, okay, AWS, Snowflake and Databricks, like perfect sandwich. And then of course they started to become more competitive. My sense is they still, you know, compliment each other in the field, right? But, you know, publicly, they've got bigger aspirations, they get big TAMs that they're going after. But it's interesting, the data shows that-- So, Snowflake was off the charts in terms of spending momentum and our EPR surveys. Our partner down in New York, they kind of came into line. They're both growing in terms of market presence. Databricks couldn't get to IPO. So, we don't have as much, you know, visibility on their financials. You know, Snowflake obviously highly transparent cause they're a public company. And then you got AWS, Google and Azure. And it seems like AWS appears to be more partner friendly. Microsoft, you know, depends on what market you're in. And Google wants to sell BigQuery. >> Yeah. >> So, what are you seeing in the public Cloud from a data platform perspective? >> Yeah. I think that was pretty astute in what you were talking about there, because I think of the three, Google is definitely I think a little bit behind in how they go to market with their partners. Azure's done a fantastic job of partnering with these companies to understand and even though they may have Synapse as their go-to and where they want people to go to do AI and ML. What they're looking at is, Hey, we're going to also be friendly with Snowflake. We're also going to be friendly with a Databricks. And I think that, Amazon has always been there because that's where the market has been for these developers. So, many, like Databricks' and the Snowflake's have gone there first because, you know, Databricks' case, they built out on top of S3 first. And going and using somebody's object layer other than AWS, was not as simple as you would think it would be. Moving between those. >> So, one of the financial meetups I said meetup, but the... It was either the CEO or the CFO. It was either Slootman or Scarpelli talking at, I don't know, Merrill Lynch or one of the other financial conferences said, I think it was probably their Q3 call. Snowflake said 80% of our business goes through Amazon. And he said to this audience, the next day we got a call from Microsoft. Hey, we got to do more. And, we know just from reading the financial statements that Snowflake is getting concessions from Amazon, they're buying in volume, they're renegotiating their contracts. Amazon gets it. You know, lower the price, people buy more. Long term, we're all going to make more money. Microsoft obviously wants to get into that game with Snowflake. They understand the momentum. They said Google, not so much. And I've had customers tell me that they wanted to use Google's AI with Snowflake, but they can't, they got to go to to BigQuery. So, honestly, I haven't like vetted that so. But, I think it's true. But nonetheless, it seems like Google's a little less friendly with the data platform providers. What do you think? >> Yeah, I would say so. I think this is a place that Google looks and wants to own. Is that now, are they doing the right things long term? I mean again, you know, you look at Google Analytics being you know, basically outlawed in five countries in the EU because of GDPR concerns, and compliance and governance of data. And I think people are looking at Google and BigQuery in general and saying, is it the best place for me to go? Is it going to be in the right places where I need it? Still, it's still one of the largest used databases out there just because it underpins a number of the Google services. So you almost get, like you were saying, forced into BigQuery sometimes, if you want to use the tech on top. >> You do strategy. >> Yeah. >> Right? You do strategy, you do messaging. Is it the right call by Google? I mean, it's not a-- I criticize Google sometimes. But, I'm not sure it's the wrong call to say, Hey, this is our ace in the hole. >> Yeah. >> We got to get people into BigQuery. Cause, first of all, BigQuery is a solid product. I mean it's Cloud native and it's, you know, by all, it gets high marks. So, why give the competition an advantage? Let's try to force people essentially into what is we think a great product and it is a great product. The flip side of that is, they're giving up some potential partner TAM and not treating the ecosystem as well as one of their major competitors. What do you do if you're in that position? >> Yeah, I think that that's a fantastic question. And the question I pose back to the companies I've worked with and worked for is, are you really looking to have vendor lock-in as your key differentiator to your service? And I think when you start to look at these companies that are moving away from BigQuery, moving to even, Databricks on top of GCS in Google, they're looking to say, okay, I can go there if I have to evacuate from GCP and go to another Cloud, I can stay on Databricks as a platform, for instance. So I think it's, people are looking at what platform as a service, database as a service they go and use. Because from a strategic perspective, they don't want that vendor locking. >> That's where Supercloud becomes interesting, right? Because, if I can run on Snowflake or Databricks, you know, across Clouds. Even Oracle, you know, they're getting into business with Microsoft. Let's talk about some of the Cloud players. So, the big three have reported. >> Right. >> We saw AWSs Cloud growth decelerated down to 20%, which is I think the lowest growth rate since they started to disclose public numbers. And they said they exited, sorry, they said January they grew at 15%. >> Yeah. >> Year on year. Now, they had some pretty tough compares. But nonetheless, 15%, wow. Azure, kind of mid thirties, and then Google, we had kind of low thirties. But, well behind in terms of size. And Google's losing probably almost $3 billion annually. But, that's not necessarily a bad thing by advocating and investing. What's happening with the Cloud? Is AWS just running into the law, large numbers? Do you think we can actually see a re-acceleration like we have in the past with AWS Cloud? Azure, we predicted is going to be 75% of AWS IAS revenues. You know, we try to estimate IAS. >> Yeah. >> Even though they don't share that with us. That's a huge milestone. You'd think-- There's some people who have, I think, Bob Evans predicted a while ago that Microsoft would surpass AWS in terms of size. You know, what do you think? >> Yeah, I think that Azure's going to keep to-- Keep growing at a pretty good clip. I think that for Azure, they still have really great account control, even though people like to hate Microsoft. The Microsoft sellers that are out there making those companies successful day after day have really done a good job of being in those accounts and helping people. I was recently over in the UK. And the UK market between AWS and Azure is pretty amazing, how much Azure there is. And it's growing within Europe in general. In the states, it's, you know, I think it's growing well. I think it's still growing, probably not as fast as it is outside the U.S. But, you go down to someplace like Australia, it's also Azure. You hear about Azure all the time. >> Why? Is that just because of the Microsoft's software state? It's just so convenient. >> I think it has to do with, you know, and you can go with the reasoning they don't break out, you know, Office 365 and all of that out of their numbers is because they have-- They're in all of these accounts because the office suite is so pervasive in there. So, they always have reasons to go back in and, oh by the way, you're on these old SQL licenses. Let us move you up here and we'll be able to-- We'll support you on the old version, you know, with security and all of these things. And be able to move you forward. So, they have a lot of, I guess you could say, levers to stay in those accounts and be interesting. At least as part of the Cloud estate. I think Amazon, you know, is hitting, you know, the large number. Laws of large numbers. But I think that they're also going through, and I think this was seen in the layoffs that they were making, that they're looking to understand and have profitability in more of those services that they have. You know, over 350 odd services that they have. And you know, as somebody who went there and helped to start yet a new one, while I was there. And finally, it went to beta back in September, you start to look at the fact that, that number of services, people, their own sellers don't even know all of their services. It's impossible to comprehend and sell that many things. So, I think what they're going through is really looking to rationalize a lot of what they're doing from a services perspective going forward. They're looking to focus on more profitable services and bringing those in. Because right now it's built like a layer cake where you have, you know, S3 EBS and EC2 on the bottom of the layer cake. And then maybe you have, you're using IAM, the authorization and authentication in there and you have all these different services. And then they call it EMR on top. And so, EMR has to pay for that entire layer cake just to go and compete against somebody like Mongo or something like that. So, you start to unwind the costs of that. Whereas Azure, went and they build basically ground up services for the most part. And Google kind of falls somewhere in between in how they build their-- They're a sort of layer cake type effect, but not as many layers I guess you could say. >> I feel like, you know, Amazon's trying to be a platform for the ecosystem. Yes, they have their own products and they're going to sell. And that's going to drive their profitability cause they don't have to split the pie. But, they're taking a piece of-- They're spinning the meter, as Ziyas Caravalo likes to say on every time Snowflake or Databricks or Mongo or Atlas is, you know, running on their system. They take a piece of the action. Now, Microsoft does that as well. But, you look at Microsoft and security, head-to-head competitors, for example, with a CrowdStrike or an Okta in identity. Whereas, it seems like at least for now, AWS is a more friendly place for the ecosystem. At the same time, you do a lot of business in Microsoft. >> Yeah. And I think that a lot of companies have always feared that Amazon would just throw, you know, bodies at it. And I think that people have come to the realization that a two pizza team, as Amazon would call it, is eight people. I think that's, you know, two slices per person. I'm a little bit fat, so I don't know if that's enough. But, you start to look at it and go, okay, if they're going to start out with eight engineers, if I'm a startup and they're part of my ecosystem, do I really fear them or should I really embrace them and try to partner closer with them? And I think the smart people and the smart companies are partnering with them because they're realizing, Amazon, unless they can see it to, you know, a hundred million, $500 million market, they're not going to throw eight to 16 people at a problem. I think when, you know, you could say, you could look at the elastic with OpenSearch and what they did there. And the licensing terms and the battle they went through. But they knew that Elastic had a huge market. Also, you had a number of ecosystem companies building on top of now OpenSearch, that are now domain on top of Amazon as well. So, I think Amazon's being pretty strategic in how they're doing it. I think some of the-- It'll be interesting. I think this year is a payout year for the cuts that they're making to some of the services internally to kind of, you know, how do we take the fat off some of those services that-- You know, you look at Alexa. I don't know how much revenue Alexa really generates for them. But it's a means to an end for a number of different other services and partners. >> What do you make of this ChatGPT? I mean, Microsoft obviously is playing that card. You want to, you want ChatGPT in the Cloud, come to Azure. Seems like AWS has to respond. And we know Google is, you know, sharpening its knives to come up with its response. >> Yeah, I mean Google just went and talked about Bard for the first time this week and they're in private preview or I guess they call it beta, but. Right at the moment to select, select AI users, which I have no idea what that means. But that's a very interesting way that they're marketing it out there. But, I think that Amazon will have to respond. I think they'll be more measured than say, what Google's doing with Bard and just throwing it out there to, hey, we're going into beta now. I think they'll look at it and see where do we go and how do we actually integrate this in? Because they do have a lot of components of AI and ML underneath the hood that other services use. And I think that, you know, they've learned from that. And I think that they've already done a good job. Especially for media and entertainment when you start to look at some of the ways that they use it for helping do graphics and helping to do drones. I think part of their buy of iRobot was the fact that iRobot was a big user of RoboMaker, which is using different models to train those robots to go around objects and things like that, so. >> Quick touch on Kubernetes, the whole DevOps World we just covered. The Cloud Native Foundation Security, CNCF. The security conference up in Seattle last week. First time they spun that out kind of like reinforced, you know, AWS spins out, reinforced from reinvent. Amsterdam's coming up soon, the CubeCon. What should we expect? What's hot in Cubeland? >> Yeah, I think, you know, Kubes, you're going to be looking at how OpenShift keeps growing and I think to that respect you get to see the momentum with people like Red Hat. You see others coming up and realizing how OpenShift has gone to market as being, like you were saying, partnering with those Clouds and really making it simple. I think the simplicity and the manageability of Kubernetes is going to be at the forefront. I think a lot of the investment is still going into, how do I bring observability and DevOps and AIOps and MLOps all together. And I think that's going to be a big place where people are going to be looking to see what comes out of CubeCon in Amsterdam. I think it's that manageability ease of use. >> Well Rob, I look forward to working with you on behalf of the whole Cube team. We're going to do more of these and go out to some shows extract the signal from the noise. Really appreciate you coming into our studio. >> Well, thank you for having me on. Really appreciate it. >> You're really welcome. All right, keep it right there, or thanks for watching. This is Dave Vellante for the Cube. And we'll see you next time. (light music)

(electronic music) >> Hi everybody, welcome back to Supercloud 2. I'm Dave Vellante with my co-host John Furrier. We're here at our tricked out Palo Alto studio. We're going live wall to wall all day. We're inserting a number of pre-recorded interviews, folks like Walmart. We just heard from Nir Zuk of Palo Alto Networks, and I'm really pleased to welcome in David Flynn. David Flynn, you may know as one of the people behind Fusion-io, completely changed the way in which people think about storing data, accessing data. David Flynn now the founder and CEO of a company called Hammerspace. David, good to see you, thanks for coming on. >> David: Good to see you too. >> And Dr. Nelu Mihai is the CEO and founder of Cloud of Clouds. He's actually built a Supercloud. We're going to get into that. Nelu, thanks for coming on. >> Thank you, Happy New Year. >> Yeah, Happy New Year. So I'm going to start right off with a little debate that's going on in the community if you guys would bring out this slide. So Bob Muglia early today, he gave a definition of Supercloud. He felt like we had to tighten ours up a little bit. He said a Supercloud is a platform, underscoring platform, that provides programmatically consistent services hosted on heterogeneous cloud providers. Now, Nelu, we have this shared doc, and you've been in there. You responded, you said, well, hold on. Supercloud really needs to be an architecture, or else we're going to have this stove pipe of stove pipes, really. And then you went on with more detail, what's the information model? What's the execution model? How are users going to interact with Supercloud? So I start with you, why architecture? The inference is that a platform, the platform provider's responsible for the architecture? Why does that not work in your view? >> No, the, it's a very interesting question. So whenever I think about platform, what's the connotation, you think about monolithic system? Yeah, I mean, I don't know whether it's true or or not, but there is this connotation of of monolithic. On the other hand, if you look at what's a problem right now with HyperClouds, from the customer perspective, they're very complex. There is a heterogeneous world where actually every single one of this HyperClouds has their own architecture. You need rocket scientists to build a cloud applications. Always there is this contradiction between cost and performance. They fight each other. And I'm quoting here a former friend of mine from Bell Labs who work at AWS who used to say "Cloud is cheap as long as you don't use it too much." (group chuckles) So clearly we need something that kind of plays from the principle point of view the role of an operating system, that seats on top of this heterogeneous HyperCloud, and there's nothing wrong by having these proprietary HyperClouds, think about processors, think about operating system and so on, so forth. But in order to build a system that is simple enough, I think we need to go deeper and understand. >> So the argument, the counterargument to that, David, is you'll never get there. You need a proprietary system to get to market sooner, to solve today's problem. Now I don't know where you stand on this platform versus architecture. I haven't asked you, but. >> I think there are aspects of both for sure. I mean it needs to be an architecture in the sense that it's broad based and open and so forth. But you know, platform, you could say as long as people can instantiate it themselves, on their own infrastructure, as long as it's something that can be deployed as, you know, software defined, you don't want the concept of platform being the monolith, you know, combined hardware and software. So it really depends on what you're focused on when you're saying platform, you know, I'd say as long as they software defined thing, to where it can literally run anywhere. I mean, because I really think what we're talking about here is the original concept of cloud computing. The ability to run anything anywhere, without having to care about the physical infrastructure. And what we have today is not that, the cloud today is a big mainframe in the sky, that just happens to be large enough that once you select which region, generally you have enough resources. But, you know, nowadays you don't even necessarily have enough resources in one region. and then you're kind of stuck. So we haven't really gotten to that utility model of computing. And you're also asked to rewrite your application, you know, to abandon the conveniences of high performance file access. You got to rewrite it to use object storage stuff. We have to get away from that. >> Okay, I want to just drill on that, 'cause I think I like that point about, there's not enough availability, but on the developer cloud, the original AWS premise was targeting developers, 'cause at that time, you have to provision a Sun box get a Cisco DSU/CSU, now you get on the cloud. But I think you're giving up the scale question, 'cause I think right now, scale is huge, enterprise grade versus cloud for developers. >> That's Right. >> Because I mean look at, Amazon, Azure, they got compute, they got storage, they got queuing, and some stuff. If you're doing a startup, you throw your app up there, localhost to cloud, no big deal. It's the scale thing that gets me- >> And you can tell by the fact that, in regions that are under high demand, right, like in London or LA, at least with the clients we work with in the median entertainment space, it costs twice as much for the exact same cloud instances that do the exact same amount of work, as somewhere out in rural Canada. So why is it you have such a cost differential, it has to do with that supply and demand, and the fact that the clouds aren't really the ability to run anything anywhere. Even within the same cloud vendor, you're stuck in a specific region. >> And that was never the original promise, right? I mean it was, we turned it into that. But the original promise was get rid of the heavy lifting of IT. >> Not have to run your own, yeah, exactly. >> And then it became, wow, okay I can run anywhere. And then you know, it's like web 2.0. You know people say why Supercloud, you and I talked about this, why do you need a name for Supercloud? It's like web 2.0. >> It's what Cloud was supposed to be. >> It's what cloud was supposed to be, (group laughing and talking) exactly, right. >> Cloud was supposed to be run anything anywhere, or at least that's what we took it as. But you're right, originally it was just, oh don't have to run your own infrastructure, and you can choose somebody else's infrastructure. >> And you did that >> But you're still bound to that. >> Dave: And People said I want more, right? >> But how do we go from here? >> That's, that's actually, that's a very good point, because indeed when the first HyperClouds were designed, were designed really focus on customers. I think Supercloud is an opportunity to design in the right way. Also having in mind the computer science rigor. And we should take advantage of that, because in fact actually, if cloud would've been designed properly from the beginning, probably wouldn't have needed Supercloud. >> David: You wouldn't have to have been asked to rewrite your application. >> That's correct. (group laughs) >> To use REST interfaces to your storage. >> Revisist history is always a good one. But look, cloud is great. I mean your point is cloud is a good thing. Don't hold it back. >> It is a very good thing. >> Let it continue. >> Let it go as as it is. >> Yeah, let that thing continue to grow. Don't impose restrictions on the cloud. Just refactor what you need to for scale or enterprise grade or availability. >> And you would agree with that, is that true or is it problem you're solving? >> Well yeah, I mean it, what the cloud is doing is absolutely necessary. What the public cloud vendors are doing is absolutely necessary. But what's been missing is how to provide a consistent interface, especially to persistent data. And have it be available across different regions, and across different clouds. 'cause data is a highly localized thing in current architecture. It only exists as rendered by the storage system that you put it in. Whether that's a legacy thing like a NetApp or an Isilon or even a cloud data service. It's localized to a specific region of the cloud in which you put that. We have to delocalize data, and provide a consistent interface to it across all sites. That's high performance, local access, but to global data. >> And so Walmart earlier today described their, what we call Supercloud, they call it the Walmart cloud native platform. And they use this triplet model. They have AWS and Azure, no, oh sorry, no AWS. They have Azure and GCP and then on-prem, where all the VMs live. When you, you know, probe, it turns out that it's only stateless in the cloud. (John laughs) So, the state stuff- >> Well let's just admit it, there is no such thing as stateless, because even the application binaries and libraries are state. >> Well I'm happy that I'm hearing that. >> Yeah, okay. >> Because actually I have a lot of debate (indistinct). If you think about no software running on a (indistinct) machine is stateless. >> David: Exactly. >> This is something that was- >> David: And that's data that needs to be distributed and provided consistently >> (indistinct) >> Across all the clouds, >> And actually, it's a nonsense, but- >> Dave: So it's an illusion, okay. (group talks over each other) >> (indistinct) you guys talk about stateless. >> Well, see, people make the confusion between state and persistent state, okay. Persistent state it's a different thing. State is a different thing. So, but anyway, I want to go back to your point, because there's a lot of debate here. People are talking about data, some people are talking about logic, some people are talking about networking. In my opinion is this triplet, which is data logic and connectivity, that has equal importance. And actually depending on the application, can have the center of gravity moving towards data, moving towards what I call execution units or workloads. And connectivity is actually the most important part of it. >> David: (indistinct). >> Some people are saying move the logic towards the data, some other people, and you are saying actually, that no, you have to build a distributed data mesh. What I'm saying is actually, you have to consider all these three variables, all these vector in order to decide, based on application, what's the most important. Because sometimes- >> John: So the application chooses >> That's correct. >> Well it it's what operating systems were in the past, was principally the thing that runs and manages the jobs, the job scheduler, and the thing that provides your persistent data (indistinct). >> Okay. So we finally got operating system into the equation, thank you. (group laughs) >> Nelu: I actually have a PhD in operating system. >> Cause what we're talking about is an operating system. So forget platform or architecture, it's an operating environment. Let's use it as a general term. >> All right. I think that's about it for me. >> All right, let's take (indistinct). Nelu, I want ask you quick, 'cause I want to give a, 'cause I believe it's an operating system. I think it's going to be a reset, refactored. You wrote to me, "The model of Supercloud has to be open theoretical, has to satisfy the rigors of computer science, and customer requirements." So unique to today, if the OS is going to be refactored, it's not going to be, may or may not be Red Hat or somebody else. This new OS, obviously requirements are for customers too but is what's the computer science that is needed? Where are we, what's the missing? Where's the science in this shift? It's not your standard OS it's not like an- (group talks over each other) >> I would beg to differ. >> (indistinct) truly an operation environment. But the, if you think about, and make analogies, what you need when you design a distributed system, well you need an information model, yeah. You need to figure out how the data is located and distributed. You need a model for the execution units, and you need a way to describe the interactions between all these objects. And it is my opinion that we need to go deeper and formalize these operations in order to make a step forward. And when we design Supercloud, and design something that is better than the current HyperClouds. And actually that is when we design something better, you make a system more efficient and it's going to be better from the cost point of view, from the performance point of view. But we need to add some math into all this customer focus centering and I really admire AWS and their executive team focusing on the customer. But now it's time to go back and see, if we apply some computer science, if you try to formalize to build a theoretical model of cloud, can we build a system that is better than existing ones? >> So David, how do you- >> this is what I'm saying. >> That's a good question >> How do You see the operating system of a, or operating environment of a decentralized cloud? >> Well I think it's layered. I mean we have operating systems that can run systems quite efficiently. Linux has sort of one in the data center, but we're talking about a layer on top of that. And I think we're seeing the emergence of that. For example, on the job scheduling side of things, Kubernetes makes a really good example. You know, you break the workload into the most granular units of compute, the containerized microservice, and then you use a declarative model to state what is needed and give the system the degrees of freedom that it can choose how to instantiate it. Because the thing about these distributed systems, is that the complexity explodes, right? Running a piece of hardware, running a single server is not a problem, even with all the many cores and everything like that. It's when you start adding in the networking, and making it so that you have many of them. And then when it's going across whole different data centers, you know, so, at that level the way you solve this is not manually (group laughs) and not procedurally. You have to change the language so it's intent based, it's a declarative model, and what you're stating is what is intended, and you're leaving it to more advanced techniques, like machine learning to decide how to instantiate that service across the cluster, which is what Kubernetes does, or how to instantiate the data across the diverse storage infrastructure. And that's what we do. >> So that's a very good point because actually what has been neglected with HyperClouds is really optimization and automation. But in order to be able to do both of these things, you need, I'm going back and I'm stubborn, you need to have a mathematical model, a theoretical model because what does automation mean? It means that we have to put machines to do the work instead of us, and machines work with what? Formula, with algorithms, they don't work with services. So I think Supercloud is an opportunity to underscore the importance of optimization and automation- >> Totally agree. >> In HyperCloud, and actually by doing that, we can also have an interesting connotation. We are also contributing to save our planet, because if you think right now. we're consuming a lot of energy on this HyperClouds and also all this AI applications, and I think we can do better and build the same kind of application using less energy. >> So yeah, great point, love that call out, the- you know, Dave and I always joke about the old, 'cause we're old, we talk about, you know, (Nelu Laughs) old history, OS/2 versus DOS, okay, OS's, OS/2 is silly better, first threaded OS, DOS never went away. So how does legacy play into this conversation? Because I buy the theoretical, I love the conversation. Okay, I think it's an OS, totally see it that way myself. What's the blocker? Is there a legacy that drags it back? Is the anchor dragging from legacy? Is there a DOS OS/2 moment? Is there an opportunity to flip the script? This is- >> I think that's a perfect example of why we need to support the existing interfaces, Operating Systems, real operating systems like Linux, understands how to present data, it's called a file system, block devices, things that that plumb in there. And by, you know, going to a REST interface and S3 and telling people they have to rewrite their applications, you can't even consume your application binaries that way, the OS doesn't know how to pull that sort of thing. So we, to get to cloud, to get to the ability to host massive numbers of tenants within a centralized infrastructure, you know, we abandoned these lower level interfaces to the OS and we have to go back to that. It's the reason why DOS ultimately won, is it had the momentum of the install base. We're seeing the same thing here. Whatever it is, it has to be a real file system and not a come down file system >> Nelu, what's your reaction, 'cause you're in the theoretical bandwagon. Let's get your reaction. >> No, I think it's a good, I'll give, you made a good analogy between OS/2 and DOS, but I'll go even farther saying, if you think about the evolution operating system didn't stop the evolution of underlying microprocessors, hardware, and so on and so forth. On the contrary, it was a catalyst for that. So because everybody could develop their own hardware, without worrying that the applications on top of operating system are going to modify. The same thing is going to happen with Supercloud. You're going to have the AWSs, you're going to have the Azure and the the GCP continue to evolve in their own way proprietary. But if we create on top of it the right interface >> The open, this is why open is important. >> That's correct, because actually you're going to see sometime ago, everybody was saying, remember venture capitals were saying, "AWS killed the world, nobody's going to come." Now you see what Oracle is doing, and then you're going to see other players. >> It's funny, Amazon's trying to be more like Microsoft. Microsoft's trying to be more like Amazon and Google- Oracle's just trying to say they have cloud. >> That's, that's correct, (group laughs) so, my point is, you're going to see a multiplication of this HyperClouds and cloud technology. So, the system has to be open in order to accommodate what it is and what is going to come. Okay, so it's open. >> So the the legacy- so legacy is an opportunity, not a blocker in your mind. And you see- >> That's correct, I think we should allow them to continue to to to be their own actually. But maybe you're going to find a way to connect with it. >> Amazon's the processor, and they're on the 80 80 80 right? >> That's correct. >> You're saying you love people trying to get put to work. >> That's a good analogy. >> But, performance levels you say good luck, right? >> Well yeah, we have to be able to take traditional applications, high performance applications, those that consume file system and persistent data. Those things have to be able to run anywhere. You need to be able to put, put them onto, you know, more elastic infrastructure. So, we have to actually get cloud to where it lives up to its billing. >> And that's what you're solving for, with Hammerspace, >> That's what we're solving for, making it possible- >> Give me the bumper sticker. >> Solving for how do you have massive quantities of unstructured file data? At the end of the day, all data ultimately is unstructured data. Have that persistent data available, across any data center, within any cloud, within any region on-prem, at the edge. And have not just the same APIs, but have the exact same data sets, and not sucked over a straw remote, but at extreme high performance, local access. So how do you have local access to globally shared distributed data? And that's what we're doing. We are orchestrating data globally across all different forms of storage infrastructure, so you have a consistent access at the highest performance levels, at the lowest level innate built into the OS, how to consume it as (indistinct) >> So are you going into the- all the clouds and natively building in there, or are you off cloud? >> So This is software that can run on cloud instances and provide high performance file within the cloud. It can take file data that's on-prem. Again, it's software, it can run in virtual or on physical servers. And it abstracts the data from the existing storage infrastructure, and makes the data visible and consumable and orchestratable across any of it. >> And what's the elevator pitch for Cloud of Cloud, give that too. >> Well, Cloud of Clouds creates a theoretical model of cloud, and it describes every single object in the cloud. Where is data, execution units, and connectivity, with one single class of very simple object. And I can, I can give you (indistinct) >> And the problem that solves is what? >> The problem that solves is, it creates this mathematical model that is necessary in order to do other interesting things, such as optimization, using sata engines, using automation, applying ML for instance. Or deep learning to automate all this clouds, if you think about in the industrial field, we know how to manage and automate huge plants. Why wouldn't it do the same thing in cloud? It's the same thing you- >> That's what you mean by theoretical model. >> Nelu: That's correct. >> Lay out the architecture, almost the bones of skeleton or something, or, and then- >> That's correct, and then on top of it you can actually build a platform, You can create your services, >> when you say math, you mean you put numbers to it, you kind of index it. >> You quantify this thing and you apply mathematical- It's really about, I can disclose this thing. It's really about describing the cloud as a knowledge graph for every single object in the graph for node, an edge is a vector. And then once you have this model, then you can apply the field theory, and linear algebra to do operation with these vectors. And it's, this creates a very interesting opportunity to let the math do this thing for us. >> Okay, so what happens with hyperscale, or it's like AWS in your model. >> So in, in my model actually, >> Are they happy with this, or they >> I'm very happy with that. >> Will they be happy with you? >> We create an interface to every single HyperCloud. We actually, we don't need to interface with the thousands of APIs, but you know, if we have the 80 20 rule, and we map these APIs into this graph, and then every single operation that is done in this graph is done from the beginning, in an optimized manner and also automation ready. >> That's going to be great. David, I want us to go back to you before we close real quick. You've had a lot of experience, multiple ventures on the front end. You talked to a lot of customers who've been innovating. Where are the classic (indistinct)? Cause you, you used to sell and invent product around the old school enterprises with storage, you know that that trajectory storage is still critical to store the data. Where's the classic enterprise grade mindset right now? Those customers that were buying, that are buying storage, they're in the cloud, they're lifting and shifting. They not yet put the throttle on DevOps. When they look at this Supercloud thing, Are they like a deer in the headlights, or are they like getting it? What's the, what's the classic enterprise look like? >> You're seeing people at different stages of adoption. Some folks are trying to get to the cloud, some folks are trying to repatriate from the cloud, because they've realized it's better to own than to rent when you use a lot of it. And so people are at very different stages of the journey. But the one thing that's constant is that there's always change. And the change here has to do with being able to change the location where you're doing your computing. So being able to support traditional workloads in the cloud, being able to run things at the edge, and being able to rationalize where the data ought to exist, and with a declarative model, intent-based, business objective-based, be able to swipe a mouse and have the data get redistributed and positioned across different vendors, across different clouds, that, we're seeing that as really top of mind right now, because everybody's at some point on this journey, trying to go somewhere, and it involves taking their data with them. (John laughs) >> Guys, great conversation. Thanks so much for coming on, for John, Dave. Stay tuned, we got a great analyst power panel coming right up. More from Palo Alto, Supercloud 2. Be right back. (bouncy music)

>> Welcome back everyone to the closing remarks here before we kick off our ecosystem portion of the program. We're live in Palo Alto for theCUBE special presentation of Supercloud 2. It's the second edition, the first one was in August. I'm John Furrier with Dave Vellante. Here to wrap up with our special guest analyst George Gilbert, investor and industry legend former colleague of ours, analyst at Wikibon. George great to see you. Dave, you know, wrapping up this day what in a phenomenal program. We had a contribution from industry vendors, industry experts, practitioners and customers building and redefining their company's business model. Rolling out technology for Supercloud and multicloud and ultimately changing how they do data. And data was the theme today. So very, very great program. Before we jump into our favorite parts let's give a shout out to the folks who make this possible. Free contents our mission. We'll always stay true to that mission. We want to thank VMware, alkira, ChaosSearch, prosimo for being sponsors of this great program. We will have Supercloud 3 coming up in a month or so, or two months. We'll see. Or sooner, we don't know. But it'll be more about security, but a lot more momentum. Okay, so that's... >> And don't forget too that this program not going to end now. We've got a whole ecosystem speaks track so stay tuned for that. >> John: Yeah, we got another 20 interviews. Feels like it. >> Well, you're going to hear from Saks, Veronika Durgin. You're going to hear from Western Union, Harveer Singh. You're going to hear from Ionis Pharmaceuticals, Nick Taylor. Brian Gracely chimes in on Supecloud. So he's the man behind the cloud cast. >> Yeah, and you know, the practitioners again, pay attention to also to the cloud networking interviews. Lot of change going on there that's going to be disruptive and actually change the landscape as well. Again, as Supercloud progresses to be the next big thing. If you're not on this next wave, you'll drift what, as Pat Gelsinger says. >> Yep. >> To kick off the closing segments, George, Dave, this is a wave that's been identified. Again, people debate the word all you want Supercloud. It is a gateway to multicloud eventually it is the standard for new applications, new ways to do data. There's new computer science being generated and customer requirements being addressed. So it's the confluence of, you know, tectonic plates shifting in the industry, new computer science seeing things like AI and machine learning and data at the center of it and new infrastructure all kind of coming together. So, to me, that's my takeaway so far. That is the big story and it's going to change society and ultimately the business models of these companies. >> Well, we've had 10, you know, you think about it we came out of the financial crisis. We've had 10, 12 years despite the Covid of tech success, right? And just now CIOs are starting to hit the brakes. And so my point is you've had all this innovation building up for a decade and you've got this massive ecosystem that is running on the cloud and the ecosystem is saying, hey, we can have even more value by tapping best of of breed across clouds. And you've got customers saying, hey, we need help. We want to do more and we want to point our business and our intellectual property, our software tooling at our customers and monetize our data. So you have all these forces coming together and it's sort of entering a new era. >> George, I want to go to you for a second because you are big contributor to this event. Your interview with Bob Moglia with Dave was I thought a watershed moment for me to hear that the data apps, how databases are being rethought because we've been seeing a diversity of databases with Amazon Web services, you know, promoting no one database rules of the world. Now it's not one database kind of architecture that's puling these new apps. What's your takeaway from this event? >> So if you keep your eye on this North Star where instead of building apps that are based on code you're building apps that are defined by data coming off of things that are linked to the real world like people, places, things and activities. Then the idea is, and the example we use is, you know, Uber but it could be, you know, amazon.com is defined by stuff coming off data in the Amazon ecosystem or marketplace. And then the question is, and everyone was talking at different angles on this, which was, where's the data live? How much do you hide from the developer? You know, and when can you offer that? You know, and you started with Walmart which was describing apps, traditional apps that are just code. And frankly that's easier to make that cross cloud and you know, essentially location independent. As soon as you have data you need data management technology that a customer does not have the sophistication to build. And then the argument was like, so how much can you hide from the developer who's building data apps? Tristan's version was you take the modern data stack and you start adding these APIs that define business concepts like bookings, billings and revenue, you know, or in the Uber example like drivers and riders, you know, and ETA's and prices. But those things execute still on the data warehouse or data lakehouse. Then Bob Muglia was saying you're not really hiding enough from the developer because you still got to say how to do all that. And his vision is not only do you hide where the data is but you hide how to sort of get at all that code by just saying what you want. You define how a car and how a driver and how a rider works. And then those things automatically figure out underneath the cover. >> So huge challenges, right? There's governance, there's security, they could be big blockers to, you know, the Supercloud but the industry's going to be attacking that problem. >> Well, what's your take? What's your favorite segment? Zhamak Dehghani came on, she's starting in that company, exclusive news. That was big notable moment for theCUBE. She launched her company. She pioneered the data mesh concept. And I think what George is saying and what data mesh points to is something that we've been saying for a long time. That data is now going to flip the script on how apps behave. And the Uber example I think is illustrated 'cause people can relate to Uber. But imagine that for every business whether it's a manufacturing business or retail or oil and gas or FinTech, they can look at their business like a game almost gamify it with data, riders, cars you know, moving data around the value of data. This is something that Adam Selipsky teased out at AWS, Dave. So what's your takeaway from this Supercloud? Where are we in your mind? Well big thing is data products and decentralizing your data architecture, but putting data in the hands of domain experts who can actually monetize the data. And I think that's, to me that's really exciting. Because look, data products financial industry has always been doing building data products. Mortgage backed securities is a data product. But why should the financial industry have all the fun? I mean virtually every organization can tap its ecosystem build data products, take its internal IP and processes and software and point it to the world and actually begin to make money out of it. >> Okay, so let's go around the horn. I'll start, I'll get you guys some time to think. Next question, what did you learn today? I learned that I think it's an infrastructure game and talking to Kit Colbert at VMware, I think it's all about infrastructure refactoring and I think the data's going to be an ingredient that's going to be operating system like. I think you're going to see the infrastructure influencing operations that will enable Superclouds to be real. And developers won't even know what a Supercloud is because they'll be using it. It's the operations focus is going to be very critical. Just like DevOps movements started Cloud native I think you're going to see a data native movement and I think infrastructure is critical as people go to the next level. That's my big takeaway today. And I'll say the data conversation is at the center. I think security, data are going to be always active horizontally scalable concepts, but every company's going to reset their infrastructure, how it looks and if it's not set up for data and or things that there need to be agile on, it's going to be a non-starter. So I think that's the cloud NextGen, distributed computing. >> I mean, what came into focus for me was I think the hyperscaler is going to continue to do their thing, you know, and be very, very successful and they're each coming at it from different approaches. We talk about this all the time in theCUBE. Amazon the best infrastructure, you know, Google's got its you know, data and AI thing and it's playing catch up and Microsoft's got this massive estate. Okay, cool. Check. The next wave of innovation which is coming from data, I've always said follow the data. That's where the where the money's going to be is going to come from other places. People want to be able to, organizations want to be able to share data across clouds across their organization, outside of their ecosystem and make money with that data sharing. They don't want to FTP it anymore. I got it. You take it. They want to work with live data in real time and I think the edge, we didn't talk much about the edge today is going to even take that to a new level real time inferencing at the edge, AI and and being able to do new things with data that we haven't even seen. But playing around with ChatGPT, it's blowing our mind. And I think you're right, it's like when we first saw the browser, holy crap, this is going to change the world. >> Yeah. And the ChatGPT by the way is going to create a wave of machine learning and data refactoring for sure. But also Howie Liu had an interesting comment, he was asked by a VC how much to replicate that and he said it's in the hundreds of millions, not billions. Now if you asked that same question how much does it cost to replicate AWS? The CapEx alone is unstoppable, they're already done. So, you know, the hyperscalers are going to continue to boom. I think they're going to drive the infrastructure. I think Amazon's going to be really strong at silicon and physics and squeeze every ounce atom out of every physical thing and then get latency as your bottleneck and the rest is all going to be... >> That never blew me away, a hundred million to create kind of an open AI, you know, competitor. Look at companies like Lacework. >> John: Some people have that much cash on the balance sheet. >> These are security companies that have raised a billion dollars, right? To compete. You know, so... >> If you're not shifting left what do you do with data, shift up? >> But, you know. >> What did you learn, George? >> I'm listening to you and I think you're helping me crystallize something which is the software infrastructure to enable the data apps is wide open. The way Zhamak described it is like if you want a data product like a sales and operation plan, that is built on other data products, like a sales plan which has a forecast in it, it has a production plan, it has a procurement plan and then a sales and operation plan is actually a composition of all those and they call each other. Now in her current platform, you need to expose to the developer a certain amount of mechanics on how to move all that data, when to move it. Like what happens if something fails. Now Muglia is saying I can hide that completely. So all you have to say is what you want and the underlying machinery takes care of everything. The problem is Muglia stuff is still a few years off. And Tristan is saying, I can give you much of that today but it's got to run in the data warehouse. So this trade offs all different ways. But again, I agree with you that the Cloud platform vendors or the ecosystem participants who can run across Cloud platforms and private infrastructure will be the next platform. And then the cloud platform is sort of where you run the big honking centralized stuff where someone else manages the operations. >> Sounds like middleware to me, Dave >> And key is, I'll just end with this. The key is being able to get to the data, whether it's in a data warehouse or a data lake or a S3 bucket or an object store, Oracle database, whatever. It's got to be inclusive that is critical to execute on the vision that you just talked about 'cause that data's in different systems and you're not going to put it all into some new system. >> So creating middleware in the cloud that sounds what it sounds like to me. >> It's like, you discovered PaaS >> It's a super PaaS. >> But it's platform services 'cause PaaS connotes like a tightly integrated platform. >> Well this is the real thing that's going on. We're going to see how this evolves. George, great to have you on, Dave. Thanks for the summary. I enjoyed this segment a lot today. This ends our stage performance live here in Palo Alto. As you know, we're live stage performance and syndicate out virtually. Our afternoon program's going to kick in now you're going to hear some great interviews. We got ChaosSearch. Defining the network Supercloud from prosimo. Future of Cloud Network, alkira. We got Saks, a retail company here, Veronika Durgin. We got Dave with Western Union. So a lot of customers, a pharmaceutical company Warner Brothers, Discovery, media company. And then you know, what is really needed for Supercloud, good panels. So stay with us for the afternoon program. That's part two of Supercloud 2. This is a wrap up for our stage live performance. I'm John Furrier with Dave Vellante and George Gilbert here wrapping up. Thanks for watching and enjoy the program. (bright music)

>> Narrator: From theCUBE Studios in Palo Alto and Boston bringing you data-driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante >> Enterprise tech practitioners, like most of us they want to make their lives easier so they can focus on delivering more value to their businesses. And to do so, they want to tap best of breed services in the public cloud, but at the same time connect their on-prem intellectual property to emerging applications which drive top line revenue and bottom line profits. But creating a consistent experience across clouds and on-prem estates has been an elusive capability for most organizations, forcing trade-offs and injecting friction into the system. The need to create seamless experiences is clear and the technology industry is starting to respond with platforms, architectures, and visions of what we've called the Supercloud. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis we give you a preview of Supercloud 2, the second event of its kind that we've had on the topic. Yes, folks that's right Supercloud 2 is here. As of this recording, it's just about four days away 33 guests, 21 sessions, combining live discussions and fireside chats from theCUBE's Palo Alto Studio with prerecorded conversations on the future of cloud and data. You can register for free at supercloud.world. And we are super excited about the Supercloud 2 lineup of guests whereas Supercloud 22 in August, was all about refining the definition of Supercloud testing its technical feasibility and understanding various deployment models. Supercloud 2 features practitioners, technologists and analysts discussing what customers need with real-world examples of Supercloud and will expose thinking around a new breed of cross-cloud apps, data apps, if you will that change the way machines and humans interact with each other. Now the example we'd use if you think about applications today, say a CRM system, sales reps, what are they doing? They're entering data into opportunities they're choosing products they're importing contacts, et cetera. And sure the machine can then take all that data and spit out a forecast by rep, by region, by product, et cetera. But today's applications are largely about filling in forms and or codifying processes. In the future, the Supercloud community sees a new breed of applications emerging where data resides on different clouds, in different data storages, databases, Lakehouse, et cetera. And the machine uses AI to inspect the e-commerce system the inventory data, supply chain information and other systems, and puts together a plan without any human intervention whatsoever. Think about a system that orchestrates people, places and things like an Uber for business. So at Supercloud 2, you'll hear about this vision along with some of today's challenges facing practitioners. Zhamak Dehghani, the founder of Data Mesh is a headliner. Kit Colbert also is headlining. He laid out at the first Supercloud an initial architecture for what that's going to look like. That was last August. And he's going to present his most current thinking on the topic. Veronika Durgin of Sachs will be featured and talk about data sharing across clouds and you know what she needs in the future. One of the main highlights of Supercloud 2 is a dive into Walmart's Supercloud. Other featured practitioners include Western Union Ionis Pharmaceuticals, Warner Media. We've got deep, deep technology dives with folks like Bob Muglia, David Flynn Tristan Handy of DBT Labs, Nir Zuk, the founder of Palo Alto Networks focused on security. Thomas Hazel, who's going to talk about a new type of database for Supercloud. It's several analysts including Keith Townsend Maribel Lopez, George Gilbert, Sanjeev Mohan and so many more guests, we don't have time to list them all. They're all up on supercloud.world with a full agenda, so you can check that out. Now let's take a look at some of the things that we're exploring in more detail starting with the Walmart Cloud native platform, they call it WCNP. We definitely see this as a Supercloud and we dig into it with Jack Greenfield. He's the head of architecture at Walmart. Here's a quote from Jack. "WCNP is an implementation of Kubernetes for the Walmart ecosystem. We've taken Kubernetes off the shelf as open source." By the way, they do the same thing with OpenStack. "And we have integrated it with a number of foundational services that provide other aspects of our computational environment. Kubernetes off the shelf doesn't do everything." And so what Walmart chose to do, they took a do-it-yourself approach to build a Supercloud for a variety of reasons that Jack will explain, along with Walmart's so-called triplet architecture connecting on-prem, Azure and GCP. No surprise, there's no Amazon at Walmart for obvious reasons. And what they do is they create a common experience for devs across clouds. Jack is going to talk about how Walmart is evolving its Supercloud in the future. You don't want to miss that. Now, next, let's take a look at how Veronica Durgin of SAKS thinks about data sharing across clouds. Data sharing we think is a potential killer use case for Supercloud. In fact, let's hear it in Veronica's own words. Please play the clip. >> How do we talk to each other? And more importantly, how do we data share? You know, I work with data, you know this is what I do. So if you know I want to get data from a company that's using, say Google, how do we share it in a smooth way where it doesn't have to be this crazy I don't know, SFTP file moving? So that's where I think Supercloud comes to me in my mind, is like practical applications. How do we create that mesh, that network that we can easily share data with each other? >> Now data mesh is a possible architectural approach that will enable more facile data sharing and the monetization of data products. You'll hear Zhamak Dehghani live in studio talking about what standards are missing to make this vision a reality across the Supercloud. Now one of the other things that we're really excited about is digging deeper into the right approach for Supercloud adoption. And we're going to share a preview of a debate that's going on right now in the community. Bob Muglia, former CEO of Snowflake and Microsoft Exec was kind enough to spend some time looking at the community's supercloud definition and he felt that it needed to be simplified. So in near real time he came up with the following definition that we're showing here. I'll read it. "A Supercloud is a platform that provides programmatically consistent services hosted on heterogeneous cloud providers." So not only did Bob simplify the initial definition he's stressed that the Supercloud is a platform versus an architecture implying that the platform provider eg Snowflake, VMware, Databricks, Cohesity, et cetera is responsible for determining the architecture. Now interestingly in the shared Google doc that the working group uses to collaborate on the supercloud de definition, Dr. Nelu Mihai who is actually building a Supercloud responded as follows to Bob's assertion "We need to avoid creating many Supercloud platforms with their own architectures. If we do that, then we create other proprietary clouds on top of existing ones. We need to define an architecture of how Supercloud interfaces with all other clouds. What is the information model? What is the execution model and how users will interact with Supercloud?" What does this seemingly nuanced point tell us and why does it matter? Well, history suggests that de facto standards will emerge more quickly to resolve real world practitioner problems and catch on more quickly than consensus-based architectures and standards-based architectures. But in the long run, the ladder may serve customers better. So we'll be exploring this topic in more detail in Supercloud 2, and of course we'd love to hear what you think platform, architecture, both? Now one of the real technical gurus that we'll have in studio at Supercloud two is David Flynn. He's one of the people behind the the movement that enabled enterprise flash adoption, that craze. And he did that with Fusion IO and he is now working on a system to enable read write data access to any user in any application in any data center or on any cloud anywhere. So think of this company as a Supercloud enabler. Allow me to share an excerpt from a conversation David Flore and I had with David Flynn last year. He as well gave a lot of thought to the Supercloud definition and was really helpful with an opinionated point of view. He said something to us that was, we thought relevant. "What is the operating system for a decentralized cloud? The main two functions of an operating system or an operating environment are one the process scheduler and two, the file system. The strongest argument for supercloud is made when you go down to the platform layer and talk about it as an operating environment on which you can run all forms of applications." So a couple of implications here that will be exploring with David Flynn in studio. First we're inferring from his comment that he's in the platform camp where the platform owner is responsible for the architecture and there are obviously trade-offs there and benefits but we'll have to clarify that with him. And second, he's basically saying, you kill the concept the further you move up the stack. So the weak, the further you move the stack the weaker the supercloud argument becomes because it's just becoming SaaS. Now this is something we're going to explore to better understand is thinking on this, but also whether the existing notion of SaaS is changing and whether or not a new breed of Supercloud apps will emerge. Which brings us to this really interesting fellow that George Gilbert and I RIFed with ahead of Supercloud two. Tristan Handy, he's the founder and CEO of DBT Labs and he has a highly opinionated and technical mind. Here's what he said, "One of the things that we still don't know how to API-ify is concepts that live inside of your data warehouse inside of your data lake. These are core concepts that the business should be able to create applications around very easily. In fact, that's not the case because it involves a lot of data engineering pipeline and other work to make these available. So if you really want to make it easy to create these data experiences for users you need to have an ability to describe these metrics and then to turn them into APIs to make them accessible to application developers who have literally no idea how they're calculated behind the scenes and they don't need to." A lot of implications to this statement that will explore at Supercloud two versus Jamma Dani's data mesh comes into play here with her critique of hyper specialized data pipeline experts with little or no domain knowledge. Also the need for simplified self-service infrastructure which Kit Colbert is likely going to touch upon. Veronica Durgin of SAKS and her ideal state for data shearing along with Harveer Singh of Western Union. They got to deal with 200 locations around the world in data privacy issues, data sovereignty how do you share data safely? Same with Nick Taylor of Ionis Pharmaceutical. And not to blow your mind but Thomas Hazel and Bob Muglia deposit that to make data apps a reality across the Supercloud you have to rethink everything. You can't just let in memory databases and caching architectures take care of everything in a brute force manner. Rather you have to get down to really detailed levels even things like how data is laid out on disk, ie flash and think about rewriting applications for the Supercloud and the MLAI era. All of this and more at Supercloud two which wouldn't be complete without some data. So we pinged our friends from ETR Eric Bradley and Darren Bramberm to see if they had any data on Supercloud that we could tap. And so we're going to be analyzing a number of the players as well at Supercloud two. Now, many of you are familiar with this graphic here we show some of the players involved in delivering or enabling Supercloud-like capabilities. On the Y axis is spending momentum and on the horizontal accesses market presence or pervasiveness in the data. So netscore versus what they call overlap or end in the data. And the table insert shows how the dots are plotted now not to steal ETR's thunder but the first point is you really can't have supercloud without the hyperscale cloud platforms which is shown on this graphic. But the exciting aspect of Supercloud is the opportunity to build value on top of that hyperscale infrastructure. Snowflake here continues to show strong spending velocity as those Databricks, Hashi, Rubrik. VMware Tanzu, which we all put under the magnifying glass after the Broadcom announcements, is also showing momentum. Unfortunately due to a scheduling conflict we weren't able to get Red Hat on the program but they're clearly a player here. And we've put Cohesity and Veeam on the chart as well because backup is a likely use case across clouds and on-premises. And now one other call out that we drill down on at Supercloud two is CloudFlare, which actually uses the term supercloud maybe in a different way. They look at Supercloud really as you know, serverless on steroids. And so the data brains at ETR will have more to say on this topic at Supercloud two along with many others. Okay, so why should you attend Supercloud two? What's in it for me kind of thing? So first of all, if you're a practitioner and you want to understand what the possibilities are for doing cross-cloud services for monetizing data how your peers are doing data sharing, how some of your peers are actually building out a Supercloud you're going to get real world input from practitioners. If you're a technologist, you're trying to figure out various ways to solve problems around data, data sharing, cross-cloud service deployment there's going to be a number of deep technology experts that are going to share how they're doing it. We're also going to drill down with Walmart into a practical example of Supercloud with some other examples of how practitioners are dealing with cross-cloud complexity. Some of them, by the way, are kind of thrown up their hands and saying, Hey, we're going mono cloud. And we'll talk about the potential implications and dangers and risks of doing that. And also some of the benefits. You know, there's a question, right? Is Supercloud the same wine new bottle or is it truly something different that can drive substantive business value? So look, go to Supercloud.world it's January 17th at 9:00 AM Pacific. You can register for free and participate directly in the program. Okay, that's a wrap. I want to give a shout out to the Supercloud supporters. VMware has been a great partner as our anchor sponsor Chaos Search Proximo, and Alura as well. For contributing to the effort I want to thank Alex Myerson who's on production and manages the podcast. Ken Schiffman is his supporting cast as well. Kristen Martin and Cheryl Knight to help get the word out on social media and at our newsletters. And Rob Ho is our editor-in-chief over at Silicon Angle. Thank you all. Remember, these episodes are all available as podcast. Wherever you listen we really appreciate the support that you've given. We just saw some stats from from Buzz Sprout, we hit the top 25% we're almost at 400,000 downloads last year. So really appreciate your participation. All you got to do is search Breaking Analysis podcast and you'll find those I publish each week on wikibon.com and siliconangle.com. Or if you want to get ahold of me you can email me directly at David.Vellante@siliconangle.com or dm me DVellante or comment on our LinkedIn post. I want you to check out etr.ai. They've got the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching. We'll see you next week at Supercloud two or next time on breaking analysis. (light music)

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

>> On January 17th, 2023 join theCUBE community for SuperCloud2 where we explore the intersection of cloud and data. One of our gold sponsors is ChaosSearch and I'm here with Ed Walsh, CEO of the company. Ed, why should people attend SuperCloud2? >> That's good question. Listen, Supercloud is a mega trend, just like you said, data and cloud, I would also add analytics to it and some companies but also some end user enterprise and some companies are using it for great, things you couldn't possibly do without this design principle. In fact, if you're doing anything around cloud, data analytics, you need to look at these things or you're not going to keep up with your data growth. >> Awesome. January 17th, go to SuperCloud.World and register. You don't want to miss the conversations with data mesh founders, Zhamak Dehghani, technologists like Bob Muglia and customers building super clouds like Wal-Mart. Don't miss it.

>> Ed, Supercloud 2, why should I go? >> So I would say supercloud is really a megatrend. You always talk about cloud and data analytics who are the megatrends? And if you're focused on those things, you need to be looking at the principles. I look at design principles about you can use your supercloud. 'Cause companies, end user companies and big companies are using these type of principles to get results that you just simply can't do if you're not looking at these principles. So if you're looking at cloud, data or analytics, these are the principles you got to understand 'cause it's really a megatrend in the industry. >> January 17th, go to supercloud.world and register. You don't want to miss the conversations with data mesh founder, Zhamak Dehghani, technologists like Bob Muglia, and customers building superclouds like Walmart. Don't miss it.

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

>>How do y'all, and welcome back to Supercomputing 2022. We're the Cube, and we are live from Dallas, Texas. I'm joined by my co-host, David Nicholson. David, hello. Hello. We are gonna be talking about data and enterprise AI at scale during this segment. And we have the pleasure of being joined by both Dell and Navidia. Anthony and Bob, welcome to the show. How you both doing? Doing good. >>Great. Great show so far. >>Love that. Enthusiasm, especially in the afternoon on day two. I think we all, what, what's in that cup? Is there something exciting in there that maybe we should all be sharing with you? >>Just say it's just still Yeah, water. >>Yeah. Yeah. I love that. So I wanna make sure that, cause we haven't talked about this at all during the show yet, on the cube, I wanna make sure that everyone's on the same page when we're talking about data unstructured versus structured data. I, it's in your title, Anthony, tell me what, what's the difference? >>Well, look, the world has been based in analytics around rows and columns, spreadsheets, data warehouses, and we've made predictions around the forecast of sales maintenance issues. But when we take computers and we give them eyes, ears, and fingers, cameras, microphones, and temperature and vibration sensors, we now translate that into more human experience. But that kind of data, the sensor data, that video camera is unstructured or semi-structured, that's what that >>Means. We live in a world of unstructured data structure is something we add to later after the fact. But the world that we see and the world that we experience is unstructured data. And one of the promises of AI is to be able to take advantage of everything that's going on around us and augment that, improve that, solve problems based on that. And so if we're gonna do that job effectively, we can't just depend on structured data to get the problem done. We have to be able to incorporate everything that we can see here, taste, smell, touch, and use >>That as, >>As part of the problem >>Solving. We want the chaos, bring it. >>Chaos has been a little bit of a theme of our >>Show. It has been, yeah. And chaos is in the eye of the beholder. You, you think about, you think about the reason for structuring data to a degree. We had limited processing horsepower back when everything was being structured as a way to allow us to be able to, to to reason over it and gain insights. So it made sense to put things into rows and tables. How does, I'm curious, diving right into where Nvidia fits into this, into this puzzle, how does NVIDIA accelerate or enhance our ability to glean insight from or reason over unstructured data in particular? >>Yeah, great question. It's really all about, I would say it's all about ai and Invidia is a leader in the AI space. We've been investing and focusing on AI since at least 2012, if not before, accelerated computing that we do it. Invidia is an important part of it, really. We believe that AI is gonna revolutionize nearly every aspect of computing. Really nearly every aspect of problem solving, even nearly every aspect of programming. And one of the reasons is for what we're talking about now is it's a little impact. Being able to incorporate unstructured data into problem solving is really critical to being able to solve the next generation of problems. AI unlocks, tools and methodologies that we can realistically do that with. It's not realistic to write procedural code that's gonna look at a picture and solve all the problems that we need to solve if we're talking about a complex problem like autonomous driving. But with AI and its ability to naturally absorb unstructured data and make intelligent reason decisions based on it, it's really a breakthrough. And that's what NVIDIA's been focusing on for at least a decade or more. >>And how does NVIDIA fit into Dell's strategy? >>Well, I mean, look, we've been partners for many, many years delivering beautiful experiences on workstations and laptops. But as we see the transition away from taking something that was designed to make something pretty on screen to being useful in solving problems in life sciences, manufacturing in other places, we work together to provide integrated solutions. So take for example, the dgx a 100 platform, brilliant design, revolutionary bus technologies, but the rocket ship can't go to Mars without the fuel. And so you need a tank that can scale in performance at the same rate as you throw GPUs at it. And so that's where the relationship really comes alive. We enable people to curate the data, organize it, and then feed those algorithms that get the answers that Bob's been talking about. >>So, so as a gamer, I must say you're a little shot at making things pretty on a screen. Come on. That was a low blow. That >>Was a low blow >>Sassy. What I, >>I Now what's in your cup? That's what I wanna know, Dave, >>I apparently have the most boring cup of anyone on you today. I don't know what happened. We're gonna have to talk to the production team. I'm looking at all of you. We're gonna have to make that better. One of the themes that's been on this show, and I love that you all embrace the chaos, we're, we're seeing a lot of trend in the experimentation phase or stage rather. And it's, we're in an academic zone of it with ai, companies are excited to adopt, but most companies haven't really rolled out their strategy. What is necessary for us to move from this kind of science experiment, science fiction in our heads to practical application at scale? Well, >>Let me take this, Bob. So I've noticed there's a pattern of three levels of maturity. The first level is just what you described. It's about having an experience, proof of value, getting stakeholders on board, and then just picking out what technology, what algorithm do I need? What's my data source? That's all fun, but it is chaos over time. People start actually making decisions based on it. This moves us into production. And what's important there is normality, predictability, commonality across, but hidden and embedded in that is a center of excellence. The community of data scientists and business intelligence professionals sharing a common platform in the last stage, we get hungry to replicate those results to other use cases, throwing even more information at it to get better accuracy and precision. But to do this in a budget you can afford. And so how do you figure out all the knobs and dials to turn in order to make, take billions of parameters and process that, that's where casual, what's >>That casual decision matrix there with billions of parameters? >>Yeah. Oh, I mean, >>But you're right that >>That's, that's exactly what we're, we're on this continuum, and this is where I think the partnership does really well, is to marry high performant enterprise grade scalability that provides the consistency, the audit trail, all of the things you need to make sure you don't get in trouble, plus all of the horsepower to get to the results. Bob, what would you >>Add there? I think the thing that we've been talking about here is complexity. And there's complexity in the AI problem solving space. There's complexity everywhere you look. And we talked about the idea that NVIDIA can help with some of that complexity from the architecture and the software development side of it. And Dell helps with that in a whole range of ways, not the least of which is the infrastructure and the server design and everything that goes into unlocking the performance of the technology that we have available to us today. So even the center of excellence is an example of how do I take this incredibly complex problem and simplify it down so that the real world can absorb and use this? And that's really what Dell and Vidia are partnering together to do. And that's really what the center of excellence is. It's an idea to help us say, let's take this extremely complex problem and extract some good value out of >>It. So what is Invidia's superpower in this realm? I mean, look, we're we are in, we, we are in the era of Yeah, yeah, yeah. We're, we're in a season of microprocessor manufacturers, one uping, one another with their latest announcements. There's been an ebb and a flow in our industry between doing everything via the CPU versus offloading processes. Invidia comes up and says, Hey, hold on a second, gpu, which again, was focused on graphics processing originally doing something very, very specific. How does that translate today? What's the Nvidia again? What's, what's, what's the superpower? Because people will say, well, hey, I've got a, I've got a cpu, why do I need you? >>I think our superpower is accelerated computing, and that's really a hardware and software thing. I think your question is slanted towards the hardware side, which is, yes, it is very typical and we do make great processors, but the processor, the graphics processor that you talked about from 10 or 20 years ago was designed to solve a very complex task. And it was exquisitely designed to solve that task with the resources that we had available at that time. Time. Now, fast forward 10 or 15 years, we're talking about a new class of problems called ai. And it requires both exquisite, soft, exquisite processor design as well as very complex and exquisite software design sitting on top of it as well. And the systems and infrastructure knowledge, high performance storage and everything that we're talking about in the solution today. So Nvidia superpower is really about that accelerated computing stack at the bottom. You've got hardware above that, you've got systems above that, you have middleware and libraries and above that you have what we call application SDKs that enable the simplification of this really complex problem to this domain or that domain or that domain, while still allowing you to take advantage of that processing horsepower that we put in that exquisitely designed thing called the gpu >>Decreasing complexity and increasing speed to very key themes of the show. Shocking, no one, you all wanna do more faster. Speaking of that, and I'm curious because you both serve a lot of different unique customers, verticals and use cases, is there a specific project that you're allowed to talk about? Or, I mean, you know, you wanna give us the scoop, that's totally cool too. We're here for the scoop on the cube, but is there a specific project or use case that has you personally excited Anthony? We'll start with that. >>Look, I'm, I've always been a big fan of natural language processing. I don't know why, but to derive intent based on the word choices is very interesting to me. I think what compliments that is natural language generation. So now we're having AI programs actually discover and describe what's inside of a package. It wouldn't surprise me that over time we move from doing the typical summary on the economic, the economics of the day or what happened in football. And we start moving that towards more of the creative advertising and marketing arts where you are no longer needed because the AI is gonna spit out the result. I don't think we're gonna get there, but I really love this idea of human language and computational linguistics. >>What a, what a marriage. I agree. Think it's fascinating. What about you, Bob? It's got you >>Pumped. The thing that really excites me is the problem solving, sort of the tip of the spear in problem solving. The stuff that you've never seen before, the stuff that you know, in a geeky way kind of takes your breath away. And I'm gonna jump or pivot off of what Anthony said. Large language models are really one of those areas that are just, I think they're amazing and they're just kind of surprising everyone with what they can do here on the show floor. I was looking at a demonstration from a large language model startup, basically, and they were showing that you could ask a question about some obscure news piece that was reported only in a German newspaper. It was about a little shipwreck that happened in a hardware. And I could type in a query to this system and it would immediately know where to find that information as if it read the article, summarized it for you, and it even could answer questions that you could only only answer by looking pic, looking at pictures in that article. Just amazing stuff that's going on. Just phenomenal >>Stuff. That's a huge accessibility. >>That's right. And I geek out when I see stuff like that. And that's where I feel like all this work that Dell and Invidia and many others are putting into this space is really starting to show potential in ways that we wouldn't have dreamed of really five years ago. Just really amazing. And >>We see this in media and entertainment. So in broadcasting, you have a sudden event, someone leaves this planet where they discover something new where they get a divorce and they're a major quarterback. You wanna go back somewhere in all of your archives to find that footage. That's a very laborist project. But if you can use AI technology to categorize that and provide the metadata tag so you can, it's searchable, then we're off to better productions, more interesting content and a much richer viewer experience >>And a much more dynamic picture of what's really going on. Factoring all of that in, I love that. I mean, David and I are both nerds and I know we've had take our breath away moments, so I appreciate that you just brought that up. Don't worry, you're in good company. In terms of the Geek Squad over >>Here, I think actually maybe this entire show for Yes, exactly. >>I mean, we were talking about how steampunk some of the liquid cooling stuff is, and you know, this is the only place on earth really, or the only show where you would come and see it at this level in scale and, and just, yeah, it's, it's, it's very, it's very exciting. How important for the future of innovation in HPC are partnerships like the one that Navia and Dell have? >>You wanna start? >>Sure, I would, I would just, I mean, I'm gonna be bold and brash and arrogant and say they're essential. Yeah, you don't not, you do not want to try and roll this on your own. This is, even if we just zoomed in to one little beat, little piece of the technology, the software stack that do modern, accelerated deep learning is incredibly complicated. There can be easily 20 or 30 components that all have to be the right version with the right buttons pushed, built the right way, assembled the right way, and we've got lots of technologies to help with that. But you do not want to be trying to pull that off on your own. That's just one little piece of the complexity that we talked about. And we really need, as technology providers in this space, we really need to do as much as we do to try to unlock the potential. We have to do a lot to make it usable and capable as well. >>I got a question for Anthony. All >>Right, >>So in your role, and I, and I'm, I'm sort of, I'm sort of projecting here, but I think, I think, I think your superpower personally is likely in the realm of being able to connect the dots between technology and the value that that technology holds in a variety of contexts. That's right. Whether it's business or, or whatever, say sentences. Okay. Now it's critical to have people like you to connect those dots. Today in the era of pervasive ai, how important will it be to have AI have to explain its answer? In other words, words, should I trust the information the AI is giving me? If I am a decision maker, should I just trust it on face value? Or am I going to want a demand of the AI kind of what you deliver today, which is No, no, no, no, no, no. You need to explain this to me. How did you arrive at that conclusion, right? How important will that be for people to move forward and trust the results? We can all say, oh hey, just trust us. Hey, it's ai, it's great, it's got Invidia, you know, Invidia acceleration and it's Dell. You can trust us, but come on. So many variables in the background. It's >>An interesting one. And explainability is a big function of ai. People want to know how the black box works, right? Because I don't know if you have an AI engine that's looking for potential maladies in an X-ray, but it misses it. Do you sue the hospital, the doctor or the software company, right? And so that accountability element is huge. I think as we progress and we trust it to be part of our everyday decision making, it's as simply as a recommendation engine. It isn't actually doing all of the decisions. It's supporting us. We still have, after decades of advanced technology algorithms that have been proven, we can't predict what the market price of any object is gonna be tomorrow. And you know why? You know why human beings, we are so unpredictable. How we feel in the moment is radically different. And whereas we can extrapolate for a population to an individual choice, we can't do that. So humans and computers will not be separated. It's a, it's a joint partnership. But I wanna get back to your point, and I think this is very fundamental to the philosophy of both companies. Yeah, it's about a community. It's always about the people sharing ideas, getting the best. And anytime you have a center of excellence and algorithm that works for sales forecasting may actually be really interesting for churn analysis to make sure the employees or students don't leave the institution. So it's that community of interest that I think is unparalleled at other conferences. This is the place where a lot of that happens. >>I totally agree with that. We felt that on the show. I think that's a beautiful note to close on. Anthony, Bob, thank you so much for being here. I'm sure everyone feels more educated and perhaps more at peace with the chaos. David, thanks for sitting next to me asking the best questions of any host on the cube. And thank you all for being a part of our community. Speaking of community here on the cube, we're alive from Dallas, Texas. It's super computing all week. My name is Savannah Peterson and I'm grateful you're here. >>So I.