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

(gentle music) >> Announcer: "theCube's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) (logo whooshing) >> Hey, everyone, welcome back to "theCube's" coverage of day one, MWC23 live from Barcelona, Lisa Martin here with Dave Vellante. Dave, we've got some great conversations so far This is the biggest, most packed show I've been to in years. About 80,000 people here so far. >> Yeah, down from its peak of 108, but still pretty good. You know, a lot of folks from China come to this show, but with the COVID situation in China, that's impacted the attendance, but still quite amazing. >> Amazing for sure. We're going to be talking about trends and mobility, and all sorts of great things. We have a couple of guests joining us for the first time on "theCUBE." Please welcome Dr. Srinivas Mukkamala or Sri, chief product officer at Ivanti. And Dave Shepherd, VP Ivanti. Guys, welcome to "theCUBE." Great to have you here. >> Thank you. >> So, day one of the conference, Sri, we'll go to you first. Talk about some of the trends that you're seeing in mobility. Obviously, the conference renamed from Mobile World Congress to MWC mobility being part of it, but what are some of the big trends? >> It's interesting, right? I mean, I was catching up with Dave. The first thing is from the keynotes, it took 45 minutes to talk about security. I mean, it's quite interesting when you look at the shore floor. We're talking about Edge, we're talking about 5G, the whole evolution. And there's also the concept of are we going into the Cloud? Are we coming back from the Cloud, back to the Edge? They're really two different things. Edge is all decentralized while you recompute. And one thing I observed here is they're talking about near real-time reality. When you look at automobiles, when you look at medical, when you look at robotics, you can't have things processed in the Cloud. It'll be too late. Because you got to make millisecond-based stations. That's a big trend for me. When I look at staff... Okay, the compute it takes to process in the Cloud versus what needs to happen on-prem, on device, is going to revolutionize the way we think about mobility. >> Revolutionize. David, what are some of the things that you're saying? Do you concur? >> Yeah, 100%. I mean, look, just reading some of the press recently, they're predicting 22 billion IoT devices by 2024. Everything Sri just talked about there. It's growing exponentially. You know, problems we have today are a snapshot. We're probably in the slowest place we are today. Everything's just going to get faster and faster and faster. So it's a, yeah, 100% concur with that. >> You know, Sri, on your point, so Jose Maria Alvarez, the CEO of Telefonica, said there are three pillars of the future of telco, low latency, programmable networks, and Cloud and Edge. So, as to your point, Cloud and low latency haven't gone hand in hand. But the Cloud guys are saying, "All right, we're going to bring the Cloud to the Edge." That's sort of an interesting dynamic. We're going to bypass them. We heard somebody, another speaker say, "You know, Cloud can't do it alone." You know? (chuckles) And so, it's like these worlds need each other in a way, don't they? >> Definitely right. So that's a fantastic way to look at it. The Cloud guys can say, "We're going to come closer to where the computer is." And if you really take a look at it with data localization, where are we going to put the Cloud in, right? I mean, so the data sovereignty becomes a very interesting thing. The localization becomes a very interesting thing. And when it comes to security, it gets completely different. I mean, we talked about moving everything to a centralized compute, really have massive processing, and give you the addition back wherever you are. Whereas when you're localized, I have to process everything within the local environment. So there's already a conflict right there. How are we going to address that? >> Yeah. So another statement, I think, it was the CEO of Ericsson, he was kind of talking about how the OTT guys have heard, "We can't let that happen again. And we're going to find new ways to charge for the network." Basically, he's talking about monetizing the API access. But I'm interested in what you're hearing from customers, right? 'Cause our mindset is, what value you're going to give to customers that they're going to pay for, versus, "I got this data I'm going to charge developers for." But what are you hearing from customers? >> It's amazing, Dave, the way you're looking at it, right? So if we take a look at what we were used to perpetual, and we said we're going to move to a subscription, right? I mean, everybody talks about subscription economy. Telcos on the other hand, had subscription economy for a long time, right? They were always based on usage, right? It's a usage economy. But today, we are basically realizing on compute. We haven't even started charging for compute. If you go to AWS, go to Azure, go to GCP, they still don't quite charge you for actual compute, right? It's kind of, they're still leaning on it. So think about API-based, we're going to break the bank. What people don't realize is, we do millions of API calls for any high transaction environment. A consumer can't afford that. What people don't realize is... I don't know how you're going to monetize. Even if you charge a cent a call, that is still going to be hundreds and thousands of dollars a day. And that's where, if you look at what you call low-code no-code motion? You see a plethora of companies being built on that. They're saying, "Hey, you don't have to write code. I'll give you authentication as a service. What that means is, Every single time you call my API to authenticate a user, I'm going to charge you." So just imagine how many times we authenticate on a single day. You're talking a few dozen times. And if I have to pay every single time I authenticate... >> Real friction in the marketplace, David. >> Yeah, and I tell you what. It's a big topic, right? And it's a topic that we haven't had to deal with at the Edge before, and we hear it probably daily really, complexity. The complexity's growing all the time. That means that we need to start to get insight, visibility. You know? I think a part of... Something that came out of the EU actually this week, stated, you know, there's a cyber attack every 11 seconds. That's fast, right? 2016, that was 40 seconds. So actually that speed I talked about earlier, everything Sri says that's coming down to the Edge, we want to embrace the Edge and that is the way we're going to move. But customers are mindful of the complexity that's involved in that. And that, you know, lens thought to how are we going to deal with those complexities. >> I was just going to ask you, how are you planning to deal with those complexities? You mentioned one ransomware attack every 11 seconds. That's down considerably from just a few years ago. Ransomware is a household word. It's no longer, "Are we going to get attacked?" It's when, it's to what extent, it's how much. So how is Ivanti helping customers deal with some of the complexities, and the changes in the security landscape? >> Yeah. Shall I start on that one first? Yeah, look, we want to give all our customers and perspective customers full visibility of their environment. You know, devices that are attached to the environment. Where are they? What are they doing? How often are we going to look for those devices? Not only when we find those devices. What applications are they running? Are those applications secure? How are we going to manage those applications moving forward? And overall, wrapping it round, what kind of service are we going to do? What processes are we going to put in place? To Sri's point, the low-code no-code angle. How do we build processes that protect our organization? But probably a point where I'll pass to Sri in a moment is how do we add a level of automation to that? How do we add a level of intelligence that doesn't always require a human to be fixing or remediating a problem? >> To Sri, you mentioned... You're right, the keynote, it took 45 minutes before it even mentioned security. And I suppose it's because they've historically, had this hardened stack. Everything's controlled and it's a safe environment. And now that's changing. So what would you add? >> You know, great point, right? If you look at telcos, they're used to a perimeter-based network. >> Yep. >> I mean, that's what we are. Boxed, we knew our perimeter. Today, our perimeter is extended to our home, everywhere work, right? >> Yeah- >> We don't have a definition of a perimeter. Your browser is the new perimeter. And a good example, segueing to that, what we have seen is horizontal-based security. What we haven't seen is verticalization, especially in mobile. We haven't seen vertical mobile security solutions, right? Yes, you hear a little bit about automobile, you hear a little bit about healthcare, but what we haven't seen is, what about food sector? What about the frontline in food? What about supply chain? What security are we really doing? And I'll give you a simple example. You brought up ransomware. Last night, Dole was attacked with ransomware. We have seen the beef producer colonial pipeline. Now, if we have seen agritech being hit, what does it mean? We are starting to hit humanity. If you can't really put food on the table, you're starting to really disrupt the supply chain, right? In a massive way. So you got to start thinking about that. Why is Dole related to mobility? Think about that. They don't carry service and computers. What they carry is mobile devices. that's where the supply chain works. And then that's where you have to start thinking about it. And the evolution of ransomware, rather than a single-trick pony, you see them using multiple vulnerabilities. And Pegasus was the best example. Spyware across all politicians, right? And CEOs. It is six or seven vulnerabilities put together that actually was constructed to do an attack. >> Yeah. How does AI kind of change this? Where does it fit in? The attackers are going to have AI, but we could use AI to defend. But attackers are always ahead, right? (chuckles) So what's your... Do you have a point of view on that? 'Cause everybody's crazy about ChatGPT, right? The banks have all banned it. Certain universities in the United States have banned it. Another one's forcing his students to learn how to use ChatGPT to prompt it. It's all over the place. You have a point of view on this? >> So definitely, Dave, it's a great point. First, we all have to have our own generative AI. I mean, I look at it as your digital assistant, right? So when you had calculators, you can't function without a calculator today. It's not harmful. It's not going to take you away from doing multiplication, right? So we'll still teach arithmetic in school. You'll still use your calculator. So to me, AI will become an integral part. That's one beautiful thing I've seen on the short floor. Every little thing there is a AI-based solution I've seen, right? So ChatGPT is well played from multiple perspective. I would rather up level it and say, generated AI is the way to go. So there are three things. There is human intense triaging, where humans keep doing easy work, minimal work. You can use ML and AI to do that. There is human designing that you need to do. That's when you need to use AI. >> But, I would say this, in the Enterprise, that the quality of the AI has to be better than what we've seen so far out of ChatGPT, even though I love ChatGPT, it's amazing. But what we've seen from being... It's got to be... Is it true that... Don't you think it has to be cleaner, more accurate? It can't make up stuff. If I'm going to be automating my network with AI. >> I'll answer that question. It comes down to three fundamentals. The reason ChatGPT is giving addresses, it's not trained on the latest data. So for any AI and ML method, you got to look at three things. It's your data, it's your domain expertise, who is training it, and your data model. In ChatGPT, it's older data, it's biased to the people that trained it, right? >> Mm-hmm. >> And then, the data model is it's going to spit out what it's trained on. That's a precursor of any GPT, right? It's pre-trained transformation. >> So if we narrow that, right? Train it better for the specific use case, that AI has huge potential. >> You flip that to what the Enterprise customers talk about to us is, insight is invaluable. >> Right. >> But then too much insight too quickly all the time means we go remediation crazy. So we haven't got enough humans to be fixing all the problems. Sri's point with the ChatGPT data, some of that data we are looking at there could be old. So we're trying to triage something that may still be an issue, but it might have been superseded by something else as well. So that's my overriding when I'm talking to customers and we talk ChatGPT, it's in the news all the time. It's very topical. >> It's fun. >> It is. I even said to my 13-year-old son yesterday, your homework's out a date. 'Cause I knew he was doing some summary stuff on ChatGPT. So a little wind up that's out of date just to make that emphasis around the model. And that's where we, with our Neurons platform Ivanti, that's what we want to give the customers all the time, which is the real-time snapshot. So they can make a priority or a decision based on what that information is telling them. >> And we've kind of learned, I think, over the last couple of years, that access to real-time data, real-time AI, is no longer nice to have. It's a massive competitive advantage for organizations, but it's going to enable the on-demand, everything that we expect in our consumer lives, in our business lives. This is going to be table stakes for organizations, I think, in every industry going forward. >> Yeah. >> But assumes 5G, right? Is going to actually happen and somebody's going to- >> Going to absolutely. >> Somebody's going to make some money off it at some point. When are they going to make money off of 5G, do you think? (all laughing) >> No. And then you asked a very good question, Dave. I want to answer that question. Will bad guys use AI? >> Yeah. Yeah. >> Offensive AI is a very big thing. We have to pay attention to it. It's got to create an asymmetric war. If you look at the president of the United States, he said, "If somebody's going to attack us on cyber, we are going to retaliate." For the first time, US is willing to launch a cyber war. What that really means is, we're going to use AI for offensive reasons as well. And we as citizens have to pay attention to that. And that's where I'm worried about, right? AI bias, whether it's data, or domain expertise, or algorithmic bias, is going to be a big thing. And offensive AI is something everybody have to pay attention to. >> To your point, Sri, earlier about critical infrastructure getting hacked, I had this conversation with Dr. Robert Gates several years ago, and I said, "Yeah, but don't we have the best offensive, you know, technology in cyber?" And he said, "Yeah, but we got the most to lose too." >> Yeah, 100%. >> We're the wealthiest nation of the United States. The wealthiest is. So you got to be careful. But to your point, the president of the United States saying, "We'll retaliate," right? Not necessarily start the war, but who started it? >> But that's the thing, right? Attribution is the hardest part. And then you talked about a very interesting thing, rich nations, right? There's emerging nations. There are nations left behind. One thing I've seen on the show floor today is, digital inequality. Digital poverty is a big thing. While we have this amazing technology, 90% of the world doesn't have access to this. >> Right. >> What we have done is we have created an inequality across, and especially in mobility and cyber, if this technology doesn't reach to the last mile, which is emerging nations, I think we are creating a crater back again and putting societies a few miles back. >> And at much greater risk. >> 100%, right? >> Yeah. >> Because those are the guys. In cyber, all you need is a laptop and a brain to attack. >> Yeah. Yeah. >> If I don't have it, that's where the civil war is going to start again. >> Yeah. What are some of the things in our last minute or so, guys, David, we'll start with you and then Sri go to you, that you're looking forward to at this MWC? The theme is velocity. We're talking about so much transformation and evolution in the telecom industry. What are you excited to hear and learn in the next couple of days? >> Just getting a complete picture. One is actually being out after the last couple of years, so you learn a lot. But just walking around and seeing, from my perspective, some vendor names that I haven't seen before, but seeing what they're doing and bringing to the market. But I think goes back to the point made earlier around APIs and integration. Everybody's talking about how can we kind of do this together in a way. So integrations, those smart things is what I'm kind of looking for as well, and how we plug into that as well. >> Excellent, and Sri? >> So for us, there is a lot to offer, right? So while I'm enjoying what I'm seeing here, I'm seeing at an opportunity. We have an amazing portfolio of what we can do. We are into mobile device management. We are the last (indistinct) company. When people find problems, somebody has to go remediators. We are the world's largest patch management company. And what I'm finding is, yes, all these people are embedding software, pumping it like nobody's business. As you find one ability, somebody has to go fix them, and we want to be the (indistinct) company. We had the last smile. And I find an amazing opportunity, not only we can do device management, but do mobile threat defense and give them a risk prioritization on what needs to be remediated, and manage all that in our ITSM. So I look at this as an amazing, amazing opportunity. >> Right. >> Which is exponential than what I've seen before. >> So last question then. Speaking of opportunities, Sri, for you, what are some of the things that customers can go to? Obviously, you guys talk to customers all the time. In terms of learning what Ivanti is going to enable them to do, to take advantage of these opportunities. Any webinars, any events coming up that we want people to know about? >> Absolutely, ivanti.com is the best place to go because we keep everything there. Of course, "theCUBE" interview. >> Of course. >> You should definitely watch that. (all laughing) No. So we have quite a few industry events we do. And especially there's a lot of learning. And we just raised the ransomware report that actually talks about ransomware from a global index perspective. So one thing what we have done is, rather than just looking at vulnerabilities, we showed them the weaknesses that led to the vulnerabilities, and how attackers are using them. And we even talked about DHS, how behind they are in disseminating the information and how it's actually being used by nation states. >> Wow. >> And we did cover mobility as a part of that as well. So there's a quite a bit we did in our report and it actually came out very well. >> I have to check that out. Ransomware is such a fascinating topic. Guys, thank you so much for joining Dave and me on the program today, sharing what's going on at Ivanti, the changes that you're seeing in mobile, and the opportunities that are there for your customers. We appreciate your time. >> Thank you >> Thank you. >> Yes. Thanks, guys. >> Thanks, guys. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching "theCUBE" live from MWC23 in Barcelona. As you know, "theCUBE" is the leader in live tech coverage. Dave and I will be right back with our next guest. (gentle upbeat music)

(upbeat music) >> Hello and welcome back to theCUBE. We're here in Palo Alto, California. I'm your host, John Furrier with a special guest here in the studio. As part of our Cloud Native SecurityCon Coverage we had an opportunity to bring in Jon Turow who is the partner at Madrona Venture Partners formerly with AWS and to talk about machine learning, foundational models, and how the future of AI is going to be impacted by some of the innovation around what's going on in the industry. ChatGPT has taken the world by storm. A million downloads, fastest to the million downloads there. Before some were saying it's just a gimmick. Others saying it's a game changer. Jon's here to break it down, and great to have you on. Thanks for coming in. >> Thanks John. Glad to be here. >> Thanks for coming on. So first of all, I'm glad you're here. First of all, because two things. One, you were formerly with AWS, got a lot of experience running projects at AWS. Now a partner at Madrona, a great firm doing great deals, and they had this future at modern application kind of thesis. Now you are putting out some content recently around foundational models. You're deep into computer vision. You were the IoT general manager at AWS among other things, Greengrass. So you know a lot about data. You know a lot about some of this automation, some of the edge stuff. You've been in the middle of all these kind of areas that now seem to be the next wave coming. So I wanted to ask you what your thoughts are of how the machine learning and this new automation wave is coming in, this AI tools are coming out. Is it a platform? Is it going to be smarter? What feeds AI? What's your take on this whole foundational big movement into AI? What's your general reaction to all this? >> So, thanks, Jon, again for having me here. Really excited to talk about these things. AI has been coming for a long time. It's been kind of the next big thing. Always just over the horizon for quite some time. And we've seen really compelling applications in generations before and until now. Amazon and AWS have introduced a lot of them. My firm, Madrona Venture Group has invested in some of those early players as well. But what we're seeing now is something categorically different. That's really exciting and feels like a durable change. And I can try and explain what that is. We have these really large models that are useful in a general way. They can be applied to a lot of different tasks beyond the specific task that the designers envisioned. That makes them more flexible, that makes them more useful for building applications than what we've seen before. And so that, we can talk about the depths of it, but in a nutshell, that's why I think people are really excited. >> And I think one of the things that you wrote about that jumped out at me is that this seems to be this moment where there's been a multiple decades of nerds and computer scientists and programmers and data thinkers around waiting for AI to blossom. And it's like they're scratching that itch. Every year is going to be, and it's like the bottleneck's always been compute power. And we've seen other areas, genome sequencing, all kinds of high computation things where required high forms computing. But now there's no real bottleneck to compute. You got cloud. And so you're starting to see the emergence of a massive acceleration of where AI's been and where it needs to be going. Now, it's almost like it's got a reboot. It's almost a renaissance in the AI community with a whole nother macro environmental things happening. Cloud, younger generation, applications proliferate from mobile to cloud native. It's the perfect storm for this kind of moment to switch over. Am I overreading that? Is that right? >> You're right. And it's been cooking for a cycle or two. And let me try and explain why that is. We have cloud and AWS launch in whatever it was, 2006, and offered more compute to more people than really was possible before. Initially that was about taking existing applications and running them more easily in a bigger scale. But in that period of time what's also become possible is new kinds of computation that really weren't practical or even possible without that vast amount of compute. And so one result that came of that is something called the transformer AI model architecture. And Google came out with that, published a paper in 2017. And what that says is, with a transformer model you can actually train an arbitrarily large amount of data into a model, and see what happens. That's what Google demonstrated in 2017. The what happens is the really exciting part because when you do that, what you start to see, when models exceed a certain size that we had never really seen before all of a sudden they get what we call emerging capabilities of complex reasoning and reasoning outside a domain and reasoning with data. The kinds of things that people describe as spooky when they play with something like ChatGPT. That's the underlying term. We don't as an industry quite know why it happens or how it happens, but we can measure that it does. So cloud enables new kinds of math and science. New kinds of math and science allow new kinds of experimentation. And that experimentation has led to this new generation of models. >> So one of the debates we had on theCUBE at our Supercloud event last month was, what's the barriers to entry for say OpenAI, for instance? Obviously, I weighed in aggressively and said, "The barriers for getting into cloud are high because all the CapEx." And Howie Xu formerly VMware, now at ZScaler, he's an AI machine learning guy. He was like, "Well, you can spend $100 million and replicate it." I saw a quote that set up for 180,000 I can get this other package. What's the barriers to entry? Is ChatGPT or OpenAI, does it have sustainability? Is it easy to get into? What is the market like for AI? I mean, because a lot of entrepreneurs are jumping in. I mean, I just read a story today. San Francisco's got more inbound migration because of the AI action happening, Seattle's booming, Boston with MIT's been working on neural networks for generations. That's what we've found the answer. Get off the neural network, Boston jump on the AI bus. So there's total excitement for this. People are enthusiastic around this area. >> You can think of an iPhone versus Android tension that's happening today. In the iPhone world, there are proprietary models from OpenAI who you might consider as the leader. There's Cohere, there's AI21, there's Anthropic, Google's going to have their own, and a few others. These are proprietary models that developers can build on top of, get started really quickly. They're measured to have the highest accuracy and the highest performance today. That's the proprietary side. On the other side, there is an open source part of the world. These are a proliferation of model architectures that developers and practitioners can take off the shelf and train themselves. Typically found in Hugging face. What people seem to think is that the accuracy and performance of the open source models is something like 18 to 20 months behind the accuracy and performance of the proprietary models. But on the other hand, there's infinite flexibility for teams that are capable enough. So you're going to see teams choose sides based on whether they want speed or flexibility. >> That's interesting. And that brings up a point I was talking to a startup and the debate was, do you abstract away from the hardware and be software-defined or software-led on the AI side and let the hardware side just extremely accelerate on its own, 'cause it's flywheel? So again, back to proprietary, that's with hardware kind of bundled in, bolted on. Is it accelerator or is it bolted on or is it part of it? So to me, I think that the big struggle in understanding this is that which one will end up being right. I mean, is it a beta max versus VHS kind of thing going on? Or iPhone, Android, I mean iPhone makes a lot of sense, but if you're Apple, but is there an Apple moment in the machine learning? >> In proprietary models, here does seem to be a jump ball. That there's going to be a virtuous flywheel that emerges that, for example, all these excitement about ChatGPT. What's really exciting about it is it's really easy to use. The technology isn't so different from what we've seen before even from OpenAI. You mentioned a million users in a short period of time, all providing training data for OpenAI that makes their underlying models, their next generation even better. So it's not unreasonable to guess that there's going to be power laws that emerge on the proprietary side. What I think history has shown is that iPhone, Android, Windows, Linux, there seems to be gravity towards this yin and yang. And my guess, and what other people seem to think is going to be the case is that we're going to continue to see these two poles of AI. >> So let's get into the relationship with data because I've been emerging myself with ChatGPT, fascinated by the ease of use, yes, but also the fidelity of how you query it. And I felt like when I was doing writing SQL back in the eighties and nineties where SQL was emerging. You had to be really a guru at the SQL to get the answers you wanted. It seems like the querying into ChatGPT is a good thing if you know how to talk to it. Labeling whether your input is and it does a great job if you feed it right. If you ask a generic questions like Google. It's like a Google search. It gives you great format, sounds credible, but the facts are kind of wrong. >> That's right. >> That's where general consensus is coming on. So what does that mean? That means people are on one hand saying, "Ah, it's bullshit 'cause it's wrong." But I look at, I'm like, "Wow, that's that's compelling." 'Cause if you feed it the right data, so now we're in the data modeling here, so the role of data's going to be critical. Is there a data operating system emerging? Because if this thing continues to go the way it's going you can almost imagine as you would look at companies to invest in. Who's going to be right on this? What's going to scale? What's sustainable? What could build a durable company? It might not look what like what people think it is. I mean, I remember when Google started everyone thought it was the worst search engine because it wasn't a portal. But it was the best organic search on the planet became successful. So I'm trying to figure out like, okay, how do you read this? How do you read the tea leaves? >> Yeah. There are a few different ways that companies can differentiate themselves. Teams with galactic capabilities to take an open source model and then change the architecture and retrain and go down to the silicon. They can do things that might not have been possible for other teams to do. There's a company that that we're proud to be investors in called RunwayML that provides video accelerated, sorry, AI accelerated video editing capabilities. They were used in everything, everywhere all at once and some others. In order to build RunwayML, they needed a vision of what the future was going to look like and they needed to make deep contributions to the science that was going to enable all that. But not every team has those capabilities, maybe nor should they. So as far as how other teams are going to differentiate there's a couple of things that they can do. One is called prompt engineering where they shape on behalf of their own users exactly how the prompt to get fed to the underlying model. It's not clear whether that's going to be a durable problem or whether like Google, we consumers are going to start to get more intuitive about this. That's one. The second is what's called information retrieval. How can I get information about the world outside, information from a database or a data store or whatever service into these models so they can reason about them. And the third is, this is going to sound funny, but attribution. Just like you would do in a news report or an academic paper. If you can state where your facts are coming from, the downstream consumer or the human being who has to use that information actually is going to be able to make better sense of it and rely better on it. So that's prompt engineering, that's retrieval, and that's attribution. >> So that brings me to my next point I want to dig in on is the foundational model stack that you published. And I'll start by saying that with ChatGPT, if you take out the naysayers who are like throwing cold water on it about being a gimmick or whatever, and then you got the other side, I would call the alpha nerds who are like they can see, "Wow, this is amazing." This is truly NextGen. This isn't yesterday's chatbot nonsense. They're like, they're all over it. It's that everybody's using it right now in every vertical. I heard someone using it for security logs. I heard a data center, hardware vendor using it for pushing out appsec review updates. I mean, I've heard corner cases. We're using it for theCUBE to put our metadata in. So there's a horizontal use case of value. So to me that tells me it's a market there. So when you have horizontal scalability in the use case you're going to have a stack. So you publish this stack and it has an application at the top, applications like Jasper out there. You're seeing ChatGPT. But you go after the bottom, you got silicon, cloud, foundational model operations, the foundational models themselves, tooling, sources, actions. Where'd you get this from? How'd you put this together? Did you just work backwards from the startups or was there a thesis behind this? Could you share your thoughts behind this foundational model stack? >> Sure. Well, I'm a recovering product manager and my job that I think about as a product manager is who is my customer and what problem he wants to solve. And so to put myself in the mindset of an application developer and a founder who is actually my customer as a partner at Madrona, I think about what technology and resources does she need to be really powerful, to be able to take a brilliant idea, and actually bring that to life. And if you spend time with that community, which I do and I've met with hundreds of founders now who are trying to do exactly this, you can see that the stack is emerging. In fact, we first drew it in, not in January 2023, but October 2022. And if you look at the difference between the October '22 and January '23 stacks you're going to see that holes in the stack that we identified in October around tooling and around foundation model ops and the rest are organically starting to get filled because of how much demand from the developers at the top of the stack. >> If you look at the young generation coming out and even some of the analysts, I was just reading an analyst report on who's following the whole data stacks area, Databricks, Snowflake, there's variety of analytics, realtime AI, data's hot. There's a lot of engineers coming out that were either data scientists or I would call data platform engineering folks are becoming very key resources in this area. What's the skillset emerging and what's the mindset of that entrepreneur that sees the opportunity? How does these startups come together? Is there a pattern in the formation? Is there a pattern in the competency or proficiency around the talent behind these ventures? >> Yes. I would say there's two groups. The first is a very distinct pattern, John. For the past 10 years or a little more we've seen a pattern of democratization of ML where more and more people had access to this powerful science and technology. And since about 2017, with the rise of the transformer architecture in these foundation models, that pattern has reversed. All of a sudden what has become broader access is now shrinking to a pretty small group of scientists who can actually train and manipulate the architectures of these models themselves. So that's one. And what that means is the teams who can do that have huge ability to make the future happen in ways that other people don't have access to yet. That's one. The second is there is a broader population of people who by definition has even more collective imagination 'cause there's even more people who sees what should be possible and can use things like the proprietary models, like the OpenAI models that are available off the shelf and try to create something that maybe nobody has seen before. And when they do that, Jasper AI is a great example of that. Jasper AI is a company that creates marketing copy automatically with generative models such as GPT-3. They do that and it's really useful and it's almost fun for a marketer to use that. But there are going to be questions of how they can defend that against someone else who has access to the same technology. It's a different population of founders who has to find other sources of differentiation without being able to go all the way down to the the silicon and the science. >> Yeah, and it's going to be also opportunity recognition is one thing. Building a viable venture product market fit. You got competition. And so when things get crowded you got to have some differentiation. I think that's going to be the key. And that's where I was trying to figure out and I think data with scale I think are big ones. Where's the vulnerability in the stack in terms of gaps? Where's the white space? I shouldn't say vulnerability. I should say where's the opportunity, where's the white space in the stack that you see opportunities for entrepreneurs to attack? >> I would say there's two. At the application level, there is almost infinite opportunity, John, because almost every kind of application is about to be reimagined or disrupted with a new generation that takes advantage of this really powerful new technology. And so if there is a kind of application in almost any vertical, it's hard to rule something out. Almost any vertical that a founder wishes she had created the original app in, well, now it's her time. So that's one. The second is, if you look at the tooling layer that we discussed, tooling is a really powerful way that you can provide more flexibility to app developers to get more differentiation for themselves. And the tooling layer is still forming. This is the interface between the models themselves and the applications. Tools that help bring in data, as you mentioned, connect to external actions, bring context across multiple calls, chain together multiple models. These kinds of things, there's huge opportunity there. >> Well, Jon, I really appreciate you coming in. I had a couple more questions, but I will take a minute to read some of your bios for the audience and we'll get into, I won't embarrass you, but I want to set the context. You said you were recovering product manager, 10 plus years at AWS. Obviously, recovering from AWS, which is a whole nother dimension of recovering. In all seriousness, I talked to Andy Jassy around that time and Dr. Matt Wood and it was about that time when AI was just getting on the radar when they started. So you guys started seeing the wave coming in early on. So I remember at that time as Amazon was starting to grow significantly and even just stock price and overall growth. From a tech perspective, it was pretty clear what was coming, so you were there when this tsunami hit. >> Jon: That's right. >> And you had a front row seat building tech, you were led the product teams for Computer Vision AI, Textract, AI intelligence for document processing, recognition for image and video analysis. You wrote the business product plan for AWS IoT and Greengrass, which we've covered a lot in theCUBE, which extends out to the whole edge thing. So you know a lot about AI/ML, edge computing, IOT, messaging, which I call the law of small numbers that scale become big. This is a big new thing. So as a former AWS leader who's been there and at Madrona, what's your investment thesis as you start to peruse the landscape and talk to entrepreneurs as you got the stack? What's the big picture? What are you looking for? What's the thesis? How do you see this next five years emerging? >> Five years is a really long time given some of this science is only six months out. I'll start with some, no pun intended, some foundational things. And we can talk about some implications of the technology. The basics are the same as they've always been. We want, what I like to call customers with their hair on fire. So they have problems, so urgent they'll buy half a product. The joke is if your hair is on fire you might want a bucket of cold water, but you'll take a tennis racket and you'll beat yourself over the head to put the fire out. You want those customers 'cause they'll meet you more than halfway. And when you find them, you can obsess about them and you can get better every day. So we want customers with their hair on fire. We want founders who have empathy for those customers, understand what is going to be required to serve them really well, and have what I like to call founder-market fit to be able to build the products that those customers are going to need. >> And because that's a good strategy from an emerging, not yet fully baked out requirements definition. >> Jon: That's right. >> Enough where directionally they're leaning in, more than in, they're part of the product development process. >> That's right. And when you're doing early stage development, which is where I personally spend a lot of my time at the seed and A and a little bit beyond that stage often that's going to be what you have to go on because the future is going to be so complex that you can't see the curves beyond it. But if you have customers with their hair on fire and talented founders who have the capability to serve those customers, that's got me interested. >> So if I'm an entrepreneur, I walk in and say, "I have customers that have their hair on fire." What kind of checks do you write? What's the kind of the average you're seeing for seed and series? Probably seed, seed rounds and series As. >> It can depend. I have seen seed rounds of double digit million dollars. I have seen seed rounds much smaller than that. It really depends on what is going to be the right thing for these founders to prove out the hypothesis that they're testing that says, "Look, we have this customer with her hair on fire. We think we can build at least a tennis racket that she can use to start beating herself over the head and put the fire out. And then we're going to have something really interesting that we can scale up from there and we can make the future happen. >> So it sounds like your advice to founders is go out and find some customers, show them a product, don't obsess over full completion, get some sort of vibe on fit and go from there. >> Yeah, and I think by the time founders come to me they may not have a product, they may not have a deck, but if they have a customer with her hair on fire, then I'm really interested. >> Well, I always love the professional services angle on these markets. You go in and you get some business and you understand it. Walk away if you don't like it, but you see the hair on fire, then you go in product mode. >> That's right. >> All Right, Jon, thank you for coming on theCUBE. Really appreciate you stopping by the studio and good luck on your investments. Great to see you. >> You too. >> Thanks for coming on. >> Thank you, Jon. >> CUBE coverage here at Palo Alto. I'm John Furrier, your host. More coverage with CUBE Conversations after this break. (upbeat music)

(upbeat music) (upbeat music) >> Hi everybody, welcome back to our coverage of the Cloud Native Security Con. I'm Dave Vellante, here in our Boston studio. We're connecting today with Palo Alto, with John Furrier and Lisa Martin. We're also live from the show floor in Seattle. But right now, I'm here with Andy Thurai who's from Constellation Research, friend of theCUBE, and we're going to discuss the intersection of AI and security, the potential of AI, the risks and the future. Andy, welcome, good to see you again. >> Good to be here again. >> Hey, so let's get into it, can you talk a little bit about, I know this is a passion of yours, the ethical considerations surrounding AI. I mean, it's front and center in the news, and you've got accountability, privacy, security, biases. Should we be worried about AI from a security perspective? >> Absolutely, man, you should be worried. See the problem is, people don't realize this, right? I mean, the ChatGPT being a new shiny object, it's all the craze that's about. But the problem is, most of the content that's produced either by ChatGPT or even by others, it's an access, no warranties, no accountability, no whatsoever. Particularly, if it is content, it's okay. But if it is something like a code that you use for example, one of their site projects that GitHub's co-pilot, which is actually, open AI + Microsoft + GitHub's combo, they allow you to produce code, AI writes code basically, right? But when you write code, problem with that is, it's not exactly stolen, but the models are created by using the GitHub code. Actually, they're getting sued for that, saying that, "You can't use our code". Actually there's a guy, Tim Davidson, I think he's named the professor, he actually demonstrated how AI produces exact copy of the code that he has written. So right now, it's a lot of security, accountability, privacy issues. Use it either to train or to learn. But in my view, it's not ready for enterprise grade yet. >> So, Brian Behlendorf today in his keynotes said he's really worried about ChatGPT being used to automate spearfishing. So I'm like, okay, so let's unpack that a little bit. Is the concern there that it just, the ChatGPT writes such compelling phishing content, it's going to increase the probability of somebody clicking on it, or are there other dimensions? >> It could, it's not necessarily just ChatGPT for that matter, right? AI can, actually, the hackers are using it to an extent already, can use to individualize content. For example, one of the things that you are able to easily identify when you're looking at the emails that are coming in, the phishing attack is, you look at some of the key elements in it, whether it's a human or even if it's an automated AI based system. They look at certain things and they say, "Okay, this is phishing". But if you were to read an email that looks exact copy of what I would've sent to you saying that, "Hey Dave, are you on for tomorrow? Or click on this link to do whatever. It could individualize the message. That's where the volume at scale to individual to masses, that can be done using AI, which is what scares me. >> Is there a flip side to AI? How is it being utilized to help cybersecurity? And maybe you could talk about some of the more successful examples of AI in security. Like, are there use cases or are there companies out there, Andy, that you find, I know you're close to a lot of firms that are leading in this area. You and I have talked about CrowdStrike, I know Palo Alto Network, so is there a positive side to this story? >> Yeah, I mean, absolutely right. Those are some of the good companies you mentioned, CrowdStrike, Palo Alto, Darktrace is another one that I closely follow, which is a good company as well, that they're using AI for security purposes. So, here's the thing, right, when people say, when they're using malware detection systems, most of the malware detection systems that are in today's security and malware systems, use some sort of a signature and pattern scanning in the malware. You know how many identified malwares are there today in the repository, in the library? More than a billion, a billion. So, if you are to check for every malware in your repository, that's not going to work. The pattern based recognition is not going to work. So, you got to figure out a different way of identification of pattern of usage, not just a signature in a malware, right? Or there are other areas you could use, things like the usage patterns. For example, if Andy is coming in to work at a certain time, you could combine a facial recognition saying, that should he be in here at that time, and should he be doing things, what he is supposed to be doing. There are a lot of things you could do using that, right? And the AIOps use cases, which is one of my favorite areas that I work, do a lot of work, right? That it has use cases for detecting things that are anomaly, that are not supposed to be done in a way that's supposed to be, reducing the noise so it can escalate only the things what you're supposed to. So, AIOps is a great use case to use in security areas which they're not using it to an extent yet. Incident management is another area. >> So, in your malware example, you're saying, okay, known malware, pretty much anybody can deal with that now. That's sort of yesterday's problem. >> The unknown is the problem. >> It's the unknown malware really trying to understand the patterns, and the patterns are going to change. It's not like you're saying a common signature 'cause they're going to use AI to change things up at scale. >> So, here's the problem, right? The malware writers are also using AI now, right? So, they're not going to write the old malware, send it to you. They are actually creating malware on the fly. It is possible entirely in today's world that they can create a malware, drop in your systems and it'll it look for the, let me get that name right. It's called, what are we using here? It's called the TTPs, Tactics, Techniques and procedures. It'll look for that to figure out, okay, am I doing the right pattern? And then malware can sense it saying that, okay, that's the one they're detecting. I'm going to change it on the fly. So, AI can code itself on the fly, rather malware can code itself on the fly, which is going to be hard to detect. >> Well, and when you talk about TTP, when you talk to folks like Kevin Mandia of Mandiant, recently purchased by Google or other of those, the ones that have the big observation space, they'll talk about the most malicious hacks that they see, involve lateral movement. So, that's obviously something that people are looking for, AI's looking for that. And of course, the hackers are going to try to mask that lateral movement, living off the land and other things. How do you see AI impacting the future of cyber? We talked about the risks and the good. One of the things that Brian Behlendorf also mentioned is that, he pointed out that in the early days of the internet, the protocols had an inherent element of trust involved. So, things like SMTP, they didn't have security built in. So, they built up a lot of technical debt. Do you see AI being able to help with that? What steps do you see being taken to ensure that AI based systems are secure? >> So, the major difference between the older systems and the newer systems is the older systems, sadly even today, a lot of them are rules-based. If it's a rules-based systems, you are dead in the water and not able, right? So, the AI-based systems can somewhat learn from the patterns as I was talking about, for example... >> When you say rules-based systems, you mean here's the policy, here's the rule, if it's not followed but then you're saying, AI will blow that away, >> AI will blow that away, you don't have to necessarily codify things saying that, okay, if this, then do this. You don't have to necessarily do that. AI can somewhat to an extent self-learn saying that, okay, if that doesn't happen, if this is not a pattern that I know which is supposed to happen, who should I escalate this to? Who does this system belong to? And the other thing, the AIOps use case we talked about, right, the anomalies. When an anomaly happens, then the system can closely look at, saying that, okay, this is not normal behavior or usage. Is that because system's being overused or is it because somebody's trying to access something, could look at the anomaly detection, anomaly prevention or even prediction to an extent. And that's where AI could be very useful. >> So, how about the developer angle? 'Cause CNCF, the event in Seattle is all around developers, how can AI be integrated? We did a lot of talk at the conference about shift-left, we talked about shift-left and protect right. Meaning, protect the run time. So, both are important, so what steps should be taken to ensure that the AI systems are being developed in a secure and ethically sound way? What's the role of developers in that regard? >> How long do you got? (Both laughing) I think it could go for base on that. So, here's the problem, right? Lot of these companies are trying to see, I mean, you might have seen that in the news that Buzzfeed is trying to hire all of the writers to create the thing that ChatGPT is creating, a lot of enterprises... >> How, they're going to fire their writers? >> Yeah, they replace the writers. >> It's like automated automated vehicles and automated Uber drivers. >> So, the problem is a lot of enterprises still haven't done that, at least the ones I'm speaking to, are thinking about saying, "Hey, you know what, can I replace my developers because they are so expensive? Can I replace them with AI generated code?" There are a few issues with that. One, AI generated code is based on some sort of a snippet of a code that has been already available. So, you get into copyright issues, that's issue number one, right? Issue number two, if AI creates code and if something were to go wrong, who's responsible for that? There's no accountability right now. Or you as a company that's creating a system that's responsible, or is it ChatGPT, Microsoft is responsible. >> Or is the developer? >> Or the developer. >> The individual developer might be. So, they're going to be cautious about that liability. >> Well, so one of the areas where I'm seeing a lot of enterprises using this is they are using it to teach developers to learn things. You know what, if you're to code, this is a good way to code. That area, it's okay because you are just teaching them. But if you are to put an actual production code, this is what I advise companies, look, if somebody's using even to create a code, whether with or without your permission, make sure that once the code is committed, you validate that the 100%, whether it's a code or a model, or even make sure that the data what you're feeding in it is completely out of bias or no bias, right? Because at the end of the day, it doesn't matter who, what, when did that, if you put out a service or a system out there, it is involving your company liability and system, and code in place. You're going to be screwed regardless of what, if something were to go wrong, you are the first person who's liable for it. >> Andy, when you think about the dangers of AI, and what keeps you up at night if you're a security professional AI and security professional. We talked about ChatGPT doing things, we don't even, the hackers are going to get creative. But what worries you the most when you think about this topic? >> A lot, a lot, right? Let's start off with an example, actually, I don't know if you had a chance to see that or not. The hackers used a bank of Hong Kong, used a defect mechanism to fool Bank of Hong Kong to transfer $35 million to a fake account, the money is gone, right? And the problem that is, what they did was, they interacted with a manager and they learned this executive who can control a big account and cloned his voice, and clone his patterns on how he calls and what he talks and the whole name he has, after learning that, they call the branch manager or bank manager and say, "Hey, you know what, hey, move this much money to whatever." So, that's one way of kind of phishing, kind of deep fake that can come. So, that's just one example. Imagine whether business is conducted by just using voice or phone calls itself. That's an area of concern if you were to do that. And imagine this became an uproar a few years back when deepfakes put out the video of Tom Cruise and others we talked about in the past, right? And Tom Cruise looked at the video, he said that he couldn't distinguish that he didn't do it. It is so close, that close, right? And they are doing things like they're using gems... >> Awesome Instagram account by the way, the guy's hilarious, right? >> So, they they're using a lot of this fake videos and fake stuff. As long as it's only for entertainment purposes, good. But imagine doing... >> That's right there but... >> But during the election season when people were to put out saying that, okay, this current president or ex-president, he said what? And the masses believe right now whatever they're seeing in TV, that's unfortunate thing. I mean, there's no fact checking involved, and you could change governments and elections using that, which is scary shit, right? >> When you think about 2016, that was when we really first saw, the weaponization of social, the heavy use of social and then 2020 was like, wow. >> To the next level. >> It was crazy. The polarization, 2024, would deepfakes... >> Could be the next level, yeah. >> I mean, it's just going to escalate. What about public policy? I want to pick your brain on this because I I've seen situations where the EU, for example, is going to restrict the ability to ship certain code if it's involved with critical infrastructure. So, let's say, example, you're running a nuclear facility and you've got the code that protects that facility, and it can be useful against some other malware that's outside of that country, but you're restricted from sending that for whatever reason, data sovereignty. Is public policy, is it aligned with the objectives in this new world? Or, I mean, normally they have to catch up. Is that going to be a problem in your view? >> It is because, when it comes to laws it's always miles behind when a new innovation happens. It's not just for AI, right? I mean, the same thing happened with IOT. Same thing happened with whatever else new emerging tech you have. The laws have to understand if there's an issue and they have to see a continued pattern of misuse of the technology, then they'll come up with that. Use in ways they are ahead of things. So, they put a lot of restrictions in place and about what AI can or cannot do, US is way behind on that, right? But California has done some things, for example, if you are talking to a chat bot, then you have to basically disclose that to the customer, saying that you're talking to a chat bot, not to a human. And that's just a very basic rule that they have in place. I mean, there are times that when a decision is made by the, problem is, AI is a black box now. The decision making is also a black box now, and we don't tell people. And the problem is if you tell people, you'll get sued immediately because every single time, we talked about that last time, there are cases involving AI making decisions, it gets thrown out the window all the time. If you can't substantiate that. So, the bottom line is that, yes, AI can assist and help you in making decisions but just use that as a assistant mechanism. A human has to be always in all the loop, right? >> Will AI help with, in your view, with supply chain, the software supply chain security or is it, it's always a balance, right? I mean, I feel like the attackers are more advanced in some ways, it's like they're on offense, let's say, right? So, when you're calling the plays, you know where you're going, the defense has to respond to it. So in that sense, the hackers have an advantage. So, what's the balance with software supply chain? Are the hackers have the advantage because they can use AI to accelerate their penetration of the software supply chain? Or will AI in your view be a good defensive mechanism? >> It could be but the problem is, the velocity and veracity of things can be done using AI, whether it's fishing, or malware, or other security and the vulnerability scanning the whole nine yards. It's scary because the hackers have a full advantage right now. And actually, I think ChatGPT recently put out two things. One is, it's able to direct the code if it is generated by ChatGPT. So basically, if you're trying to fake because a lot of schools were complaining about it, that's why they came up with the mechanism. So, if you're trying to create a fake, there's a mechanism for them to identify. But that's a step behind still, right? And the hackers are using things to their advantage. Actually ChatGPT made a rule, if you go there and read the terms and conditions, it's basically honor rule suggesting, you can't use this for certain purposes, to create a model where it creates a security threat, as that people are going to listen. So, if there's a way or mechanism to restrict hackers from using these technologies, that would be great. But I don't see that happening. So, know that these guys have an advantage, know that they're using AI, and you have to do things to be prepared. One thing I was mentioning about is, if somebody writes a code, if somebody commits a code right now, the problem is with the agile methodologies. If somebody writes a code, if they commit a code, you assume that's right and legit, you immediately push it out into production because need for speed is there, right? But if you continue to do that with the AI produced code, you're screwed. >> So, bottom line is, AI's going to speed us up in a security context or is it going to slow us down? >> Well, in the current version, the AI systems are flawed because even the ChatGPT, if you look at the the large language models, you look at the core piece of data that's available in the world as of today and then train them using that model, using the data, right? But people are forgetting that's based on today's data. The data changes on a second basis or on a minute basis. So, if I want to do something based on tomorrow or a day after, you have to retrain the models. So, the data already have a stale. So, that in itself is stale and the cost for retraining is going to be a problem too. So overall, AI is a good first step. Use that with a caution, is what I want to say. The system is flawed now, if you use it as is, you'll be screwed, it's dangerous. >> Andy, you got to go, thanks so much for coming in, appreciate it. >> Thanks for having me. >> You're very welcome, so we're going wall to wall with our coverage of the Cloud Native Security Con. I'm Dave Vellante in the Boston Studio, John Furrier, Lisa Martin and Palo Alto. We're going to be live on the show floor as well, bringing in keynote speakers and others on the ground. Keep it right there for more coverage on theCUBE. (upbeat music) (upbeat music) (upbeat music) (upbeat music)

>> Presenter: theCUBE presents Ignite '22, brought to you by Palo Alto Networks. >> Hey guys and girls. Welcome back to theCube's live coverage at Palo Alto Ignite '22. We're live at the MGM Grand Hotel in beautiful Las Vegas. Lisa Martin here with Dave Vellante. This is day one of our coverage. We've been talking with execs from Palo Alto, Partners, but one of our most exciting things is talking with Founders day. We get to do that next. >> The thing is, it's like I wrote this weekend in my breaking analysis. Understanding the problem in cybersecurity is really easy, but figuring out how to fix it ain't so much. >> It definitely isn't. >> So I'm excited to have Nir here. >> Very excited. Nir Zuk joins us, the founder and CTO of Palo Alto Networks. Welcome, Nir. Great to have you on the program. >> Thank you. >> So Palo Alto Networks, you founded it back in 2005. It's hard to believe that's been 18 years, almost. You did something different, which I want to get into. But tell us, what was it back then? Why did you found this company? >> I thought the world needed another cybersecurity company. I thought it's because there were so many cybersecurity vendors in the world, and just didn't make any sense. This industry has evolved in a very weird way, where every time there was a new challenge, rather than existing vendors dealing with a challenge, you had new vendors dealing with it, and I thought I could put a stop to it, and I think I did. >> You did something differently back in 2005, looking at where you are now, the leader, what was different in your mind back then? >> Yeah. When you found a new company, you have really two good options. There's also a bad option, but we'll skip that. You can either disrupt an existing market, or you can create a new market. So first, I decided to disrupt an existing market, go into an existing market first, network security, then cyber security, and change it. Change the way it works. And like I said, the challenges that every problem had a new vendor, and nobody just stepped back and said, "I think I can solve it with the platform." Meaning, I think I can spend some time not solving a specific problem, but building a platform that then can be used to solve many different problems. And that's what I've done, and that's what Palo Alto Networks has done, and that's where we are today. >> So you look back, you call it now, I think you call it a next gen firewall, but nothing in 2005, can it be next gen? Do you know the Silicon Valley Show? Do you know the show Silicon Valley? >> Oh! Yeah. >> Yeah, of course. >> You got to have a box. But it was a different kind of box- >> Actually. >> Explain that. >> Actually, it's exactly the same thing. You got to have a box. So I actually wanted to call it a necessary evil. Marketing wouldn't go for that. >> No. >> And the reason I wanted to call it a necessary evil, because one of the things that we've done in order to platform our cyber security, again, first network security now, also cloud security, and security operations, is to turn it into a SaaS delivered industry. Today every cyber security professional knows that, when they buy cyber security, they buy usually a SaaS delivered service. Back then, people thought I was crazy to think that customers are going to send their data to their vendor in order to process, and they wanted everything on premise and so on, but I said, "No, customers are going to send information to us for processing, because we have much more processing power than they have." And we needed something in the infrastructure to send us the information. So that's why I wanted to call it the necessary evil. We ended up calling it next generation firewall, which was probably a better term. >> Well, even Veritas. Remember Veritas? They had the no hardware agenda. Even they have a box. So it is like you say, you got to have it. >> It's necessary. >> Okay. You did this, you started this on your own cloud, kind of like Salesforce, ServiceNow. >> Correct. >> Similar now- >> Build your own data centers. >> Build your own data center. Okay, I call it a cloud, but no. >> No, it's the same. There's no cloud, it's just someone else's computer. >> According to Larry Ellison, he was actually probably right about that. But over time, you've had this closer partnership with the public clouds. >> Correct. >> What does that bring you and your customers, and how hard was that to navigate? >> It wasn't that hard for us, because we didn't have that many services. Usually it's harder. Of course, we didn't do a lift and shift, which is their own thing to do with the cloud. We rebuild things for the cloud, and the benefits, of course, are time to market, scale, agility, and in some cases also, cost. >> Yeah, some cases. >> In some cases. >> So you have a sort of a hybrid model today. You still run your own data centers, do you not? >> Very few. >> Really? >> There are very, very few things that we have to do on hardware, like simulating malware and things that cannot be done in a virtual machine, which is pretty much the only option you have in the cloud. They provide bare metal, but doesn't serve our needs. I think that we don't view cloud, and your viewers should not be viewing cloud, as a place where they're going to save money. It's a place where they're going to make money. >> I like that. >> You make much more money, because you're more agile. >> And that's why this conversation is all about, your cost of goods sold they're going to be so high, you're going to have to come back to your own data centers. That's not on your mind right now. What's on your mind is advancing the unit, right? >> Look, my own data center would limit me in scale, would limit my agility. If you want to build something new, you don't have all the PaaS services, the platform as a service, services like database, and AI, and so on. I have to build them myself. It takes time. So yeah, it's going to be cheaper, but I'm not going to be delivering the same thing. So my revenues will be much lower. >> Less top line. What can humans do better than machines? You were talking about your keynote... I'm just going to chat a little bit. You were talking about your keynote. Basically, if you guys didn't see the keynote, that AI is going to run every soc within five years, that was a great prediction that you made. >> Correct. >> And they're going to do things that you can't do today, and then in the future, they're going to do things that you can't... Better than you can do. >> And you just have to be comfortable with that. >> So what do you think humans can do today and in the future better than machines? >> Look, humans can always do better than machines. The human mind can do things that machines cannot do. We are conscious, I don't think machines will be conscious. And you can do things... My point was not that machines can do things that humans cannot do. They can just do it better. The things that humans do today, machines can do better, once machines do that, humans will be free to do things that they don't do today, that machines cannot do. >> Like what? >> Like finding the most difficult, most covert attacks, dealing with the most difficult incidents, things that machines just can't do. Just that today, humans are consumed by finding attacks that machines can find, by dealing with incidents that machines can deal with. It's a waste of time. We leave it to the machines and go and focus on the most difficult problems, and then have the machines learn from you, so that next time or a hundred or a thousand times from now, they can do it themselves, and you focus on the even more difficult. >> Yeah, just like after 9/11, they said that we lack the creativity. That's what humans have, that machines don't, at least today. >> Machines don't. Yeah, look, every airplane has two pilots, even though airplanes have been flying themselves for 30 years now, why do you have two pilots, to do the things that machines cannot do? Like land on the Hudson, right? You always need humans to do the things that machines cannot do. But to leave the things that machines can do to the machines, they'll do it better. >> And autonomous vehicles need breaks. (indistinct) >> In your customer conversations, are customers really grappling with that, are they going, "Yeah, you're right?" >> It depends. It's hard for customers to let go of old habits. First, the habit of buying a hundred different solutions from a hundred different vendors, and you know what? Why would I trust one vendor to do everything, put all my eggs in the same basket? They have all kind of slogans as to why not to do that, even though it's been proven again and again that, doing everything in one system with one brain, versus a hundred systems with a hundred brains, work much better. So that's one thing. The second thing is, we always have the same issue that we've had, I think, since the industrial revolution, of what machines are going to take away my job. No, they're just going to make your job better. So I think that some of our customers are also grappling with that, like, "What do I do if the machines take over?" And of course, like we've said, the machines aren't taking over. They're going to do the benign work, you're going to do the interesting work. You should embrace it. >> When I think about your history as a technology pro, from Check Point, a couple of startups, one of the things that always frustrated you, is when when a larger company bought you out, you ended up getting sucked into the bureaucratic vortex. How do you avoid that at Palo Alto Networks? >> So first, you mean when we acquire company? >> Yes. >> The first thing is that, when we acquire companies, we always acquire for integration. Meaning, we don't just buy something and then leave it on the side, and try to sell it here and there. We integrate it into the core of our products. So that's very important, so that the technology lives, thrives and continues to grow as part of our bigger platform. And I think that the second thing that is very important, from past experience what we've learned, is to put the people that we acquire in key positions. Meaning, you don't buy a company and then put the leader of that company five levels below the CEO. You always put them in very senior positions. Almost always, we have the leaders of the companies that we acquire, be two levels below the CEO, so very senior in the company, so they can influence and make changes. >> So two questions related to that. One is, as you grow your team, can you be both integrated? And second part of the question, can you be both integrated and best of breed? Second part of the question is, do you even have to be? >> So I'll answer it in the third way, which is, I don't think you can be best of breed without being integrated in cybersecurity. And the reason is, again, this split brain that I've mentioned twice. When you have different products do a part of cybersecurity and they don't talk to each other, and they don't share a single brain, you always compromise. You start looking for things the wrong way. I can be a little bit technical here, but please. Take the example of, traditionally you would buy an IDS/IPS, separately from your filtering, separately from DNS security. One of the most important things we do in network security is to find combining control connections. Combining control connections where the adversaries controlling something behind your firewall and is now going around your network, is usually the key heel of the attack. That's why attacks like ransomware, that don't have a commanding control connection, are so difficult to deal with, by the way. So commanding control connections are a key seal of the attacks, and there are three different technologies that deal with it. Neural filtering for neural based commanding control, DNS security for DNS based commanding control, and IDS/IPS for general commanding control. If those are three different products, they'll be doing the wrong things. The oral filter will try to find things that it's not really good at, that the IPS really need to find, and the DN... It doesn't work. It works much better when it's one product doing everything. So I think the choice is not between best of breed and integrated. I think the only choice is integrated, because that's the only way to be best of breed. >> And behind that technology is some kind of realtime data store, I'll call it data lake, database. >> Yeah. >> Whatever. >> It's all driven by the same data. All the URLs, all the domain graph. Everything goes to one big data lake. We collect about... I think we collect about, a few petabytes per day. I don't write the exact number of data. It's all going to the same data lake, and all the intelligence is driven by that. >> So you mentioned in a cheeky comment about, why you founded the company, there weren't enough cybersecurity companies. >> Yeah. >> Clearly the term expansion strategy that Palo Alto Networks has done has been very successful. You've been, as you talked about, very focused on integration, not just from the technology perspective, but from the people perspective as well. >> Correct. >> So why are there still so many cybersecurity companies, and what are you thinking Palo Alto Networks can do to change that? >> So first, I think that there are a lot of cybersecurity companies out there, because there's a lot of money going into cybersecurity. If you look at the number of companies that have been really successful, it's a very small percentage of those cybersecurity companies. And also look, we're not going to be responsible for all the innovation in cybersecurity. We need other people to innovate. It's also... Look, always the question is, "Do you buy something or do you build it yourself?" Now we think we're the smartest people in the world. Of course, we can build everything, but it's not always true that we can build everything. Know that we're the smartest people in the world, for sure. You see, when you are a startup, you live and die by the thing that you build. Meaning if it's good, it works. If it's not good, you die. You run out of money, you shut down, and you just lost four years of your life to this, at least. >> At least. >> When you're a large company, yeah, I can go and find a hundred engineers and hire them. And especially nowadays, it becomes easier, as it became easier, and give them money, and have them go and build the same thing that the startup is building, but they're part of a bigger company, and they'll have more coffee breaks, and they'll be less incentive to go and do that, because the company will survive with or without them. So that's why startups can do things much better, sometimes than larger companies. We can do things better than startups, when it comes to being data driven because we have the data, and nobody can compete against the amount of data that we have. So we have a good combination of finding the right startups that have already built something, already proven that it works with some customers, and of course, building a lot of things internally that we cannot do outside. >> I heard you say in one of the, I dunno, dozens of videos I've listened to you talked to. The industry doesn't need or doesn't want another IoT stovepipe. Okay, I agree. So you got on-prem, AWS, Azure, Google, maybe Alibaba, IoT is going to be all over the place. So can you build, I call it the security super cloud, in other words, a consistent experience with the same policies and edicts across all my estates, irrespective of physical location? Is that technically feasible? Is it what you are trying to do? >> Certainly, what we're trying to do with Prisma Cloud, with our cloud security product, it works across all the clouds that you mentioned, and Oracle as well. It's almost entirely possible. >> Almost. >> Almost. Well, the things that... What you do is you normalize the language that the different cloud scale providers use, into one language. This cloud calls it a S3, and so, AWS calls it S3, and (indistinct) calls it GCS, and so on. So you normalize their terminology, and then build policy using a common terminology that your customers have to get used to. Of course, there are things that are different between the different cloud providers that cannot be normalized, and there, it has to be cloud specific. >> In that instance. So is that, in part, your strategy, is to actually build that? >> Of course. >> And does that necessitate running on all the major clouds? >> Of course. It's not just part of our strategy, it's a major part of our strategy. >> Compulsory. >> Look, as a standalone vendor that is not a cloud provider, we have two advantages. The first one is we're security product, security focused. So we can do much better than them when it comes to security. If you are a AWS, GCP, Azure, and so on, you're not going to put your best people on security, you're going to put them on the core business that you have. So we can do much better. Hey, that's interesting. >> Well, that's not how they talk. >> I don't care how they talk. >> Now that's interesting. >> When something is 4% of your business, you're not going to put it... You're not going to put your best people there. It's just, why would you? You put your best people on 96%. >> That's not driving their revenue. >> Look, it's simple. It's not what we- >> With all due respect. With all due respect. >> So I think we do security much better than them, and they become the good enough, and we become the premium. But certainly, the second thing that give us an advantage and the right to be a standalone security provider, is that we're multicloud, private cloud and all the major cloud providers. >> But they also have a different role. I mean, your role is not the security, the Nitro card or the Graviton chip, or is it? >> They are responsible for securing up to the operating system. We secure everything. >> They do a pretty good job of that. >> No, they do, certainly they have to. If they get bridged at that level, it's not just that one customer is going to suffer, the entire customer base. They have to spend a lot of time and money on it, and frankly, that's where they put their best security people. Securing the infrastructure, not building some cloud security feature. >> Absolutely. >> So Palo Alto Networks is, as we wrap here, on track to nearly double its revenues to nearly seven billion in FY '23, just compared to 2020, you were quoted in the press by saying, "We will be the first $100 billion cyber company." What is next for Palo Alto to achieve that? >> Yeah, so it was Nikesh, our CEO and chairman, that was quoted saying that, "We will double to a hundred billion." I don't think he gave it a timeframe, but what it takes is to double the sales, right? We're at 50 billion market cap right now, so we need to double sales. But in reality, you mentioned that we're growing the turn by doing more and more cybersecurity functions, and taking away pieces. Still, we have a relatively small, even though we're the largest cybersecurity vendor in the world, we have a very low market share that shows you how fragmented the market is. I would also like to point out something that is less known. Part of what we do with AI, is really take the part of the cybersecurity industry, which are service oriented, and that's about 50% of the cybersecurity industry services, and turn it into products. I mean, not all of it. But a good portion of what's provided today by people, and tens of billions of dollars are spent on that, can be done with products. And being one of the very, very few vendors that do that, I think we have a huge opportunity at turning those tens of billions of dollars in human services to AI. >> It's always been a good business taking human labor and translating into R and D, vendor R and D. >> Especially- >> It never fails if you do it well. >> Especially in difficult times, difficult economical times like we are probably experiencing right now around the world. We, not we, but we the world. >> Right, right. Well, congratulations. Coming up on the 18th anniversary. Tremendous amount of success. >> Thank you. >> Great vision, clear vision, STEM expansion strategy, really well underway. We are definitely going to continue to keep our eyes. >> Big company, a hundred billion, that's market capital, so that's a big company. You said you didn't want to work for a big company unless you founded it, is that... >> Unless it acts like a small company. >> There's the caveat. We'll keep our eye on that. >> Thank you very much. >> It's such a pleasure having you on. >> Thank you. >> Same here, thank you. >> All right, for our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live emerging and enterprise tech coverage. (upbeat music)

>>Hello, brilliant cloud community and welcome back to AWS Reinvent, where we are live all day every day. From the show floor here in Las Vegas, Nevada. I'm Savannah Peterson, joined by my beautiful cohost Lisa Martin here on the cube. Lisa, you're smiling. You're radiating Day three. You would think it was day one. How you doing? >>Amazing. I can't believe the energy that has been maintained omni show floor since Monday night at 4:00 PM >>I know. And I, I kind of thought today we might see some folks trickling out. It is packed as our, as our guests and I were, we were all just talking about right before the segment, almost two packed, which is a really great sign for aws. It is. We're >>Hearing worth of 55,000 people here. And of course we only get a, a little snapshot of which literally >>This corner, >>We don't get to see anything else around the strip that's going on. So it's massive. Yeah, >>It is a very massive, I'm super excited. We've got two guests from Infosys with us on this last segment from this stage today. David and Anant, welcome to the show. How you doing? >>Awesome. >>You're both smiling and I am really excited. We have our first prop of the show and it's a pretty flashy, sexy prop. Anant, what's going on here? >>Oh, so this is something that we are very proud of. Last year we won one award, which was very special for us because it was our first award with aws and that was the industry partner of the year award. And on the back of that, this year we won three awards. And this is super awesome for us because all of them are very special. One was in collaboration, second was in design, and third was in sustainability. So we are very proud and we thank AWS and it's a fantastic partnership. Yeah. And >>Congratulations. Yes. I mean that's >>Huge. Yes, it's absolutely huge. And the second one is we are the launch partner for msk, which again is a very proud thing for us. So I think those are the two things that we wanted to talk about. >>How many awards are you gonna win next year then? >>Do you want to target more than three? >>So we keep going up probably fine, >>Right? I >>Love, >>That's the odd numbers. 1, 3, 5, 7, 10. There you go. >>Yeah, >>I think you, we got that question last year and we said we get two and we ended up overdelivering with three. So who >>Knows? Hey, nothing. Nothing wrong with the setting the bar low and clearing it and I mean, not setting it low, setting it with one and clearing it with three is pretty fantastic. Yes, yes. We talk about it as an ego thing sometimes with awards and it feels great for internal culture. But David, what does it mean on the partnership side to win awards like that? So >>What's really important for us with our partners is to make sure that we're achieving their goals and when, when their goals are achieved in our partnership, it's just the byproduct that we're achieving our own with our clients. The awards are a great representation of that to see, you know, again, being recognized three in three different categories really shows that we've had success with AWS and in turn, you know, know and not, I can attest to it, we've been very successful with the partnership on our side. >>Yeah. And I bet it's really exciting for the team. Just speaking for energy, are your >>Team sponsor? Absolutely. There's celebration, you know, there's been a few cocktails being raised >>In Las Vegas >>Cocktail. Oh, >>I wouldn't mind one right now to be really be really honest. Let's dig into the, into the product a little bit. Infosys Cobalt, what's the scooping on? >>Yeah, so first of all, we were the first ones to actually launch a cloud brand called Cobalt. Right? We are the first ones in the world. In fact, one of our competitor followed us soon after. So essentially what we did was we brought all our cloud offerings into one brand called Cobalt. It becomes very clear to our customers on what our proposition is. It is very consistent to the market in terms of what our narrative is. And it's little easy for our customers to understand what we bring to the table. So is not one product or one platform. It's a set of services, solutions and platforms that we bring to accelerate customers journey where they're leveraging cloud. So that's what Cobalt is. >>Awesome. Everyone wants to do everything faster. Yes. And Booth was packed. I walked by earlier, it was absolutely buzzing. Yes. >>Yeah. Nobody wants to do it, you know, wants less data slower. Yes. Always more faster. More faster. And we're living in this explosion unlike anything, this swarm of data, unlike anything that we've ever seen before. Yes. Every company, regardless of industry, has to be a data company. Yes. But they have to be able to work with the right partners. Absolutely. To extract, to first of all, harness all that data. Yes. Extract insights in real time. Yes. Because of course, on the consumer side, we're not patient anymore. Yes. We expect a personalized, real time custom experience. Absolutely. How do you work with AWS to help deliver that and how do the partners help deliver that as well? >>Well, I'll start with on the partner side of it. You walk through the hallways here or down the aisles, you see partners like MongoDB, snowflake, data Bricks and and such. They're all attest their commitment and their strong partnership with aws. And coincidentally, they're also very good partners of our own. And as a result, what >>Big happy family here at AWS when you >>Met? Yes, and this, this is something that I'm, I'm calling coining the phrase sub ecosystems. These are partnerships where one is successful with each other and then the three come together and we go together with an integrated solution. And it's really taking off. It's something that's really powerful. The, the fun thing about, you know, reinvent here is it's just that we're having amazing discussions with our clients and aws, but we're also having it with the other partners here about how we can all work together. So, and data analytics is a big one. Security is another hot one. This is huge. >>Yeah. Optimization. >>The absolutely. And I, and Ruba was saying this, right? Ruba said like she was giving example of a marathon or Marathon is not a single man or a single woman sport. Right? So similarly cloud journey is a team's sort of, you know, team journey. Yeah. So that's why partners play a big role in that and that's exactly what we are trying to do. >>So you guys get to see a lot of different companies across a lot of different industries. We've, we're living in very interesting times. How do you see the cloud evolving? >>Oh yeah. So, so what we did when we launched Cobalt in 2020, we have now evolved our story, we call it Cobalt 2.0. And essentially what we want to do was to focus on industry clouds. So it's not just about taking a workload and doing it from point A to point B or moving data to cloud or getting out of data centers, but also being very specific to the industry that this specific customer belongs to. Right? So for example, if you go to banking, they would say, we want to better our security posture. If you go to a retailer, they want to basically have smart stores. If we go to a manufacturing customer, they want to have a smart factory. So we want to make sure that there are specific industry blueprints and specific reference architectures that we bring and start delivering outcomes. So we have, we call it something called, >>I know you're hot on business outcomes. Yes, yes. >>So we call it something called the link of life forces. So there are six technologies, cloud, data Edge, iot, 5g, and ai. They will come together to deliver business outcomes. So that's where we are heading with Cobalt 2.0. And that's essentially what we want to do with our customers. >>That's a lot to think about. Yes. And yeah, go for it. >>David. I just say from a partnering perspective, you know, prior to cloud we were talking about transactional type businesses where if you ask a technology company who their partner is, is generally a reseller where they're just basically taking one product and selling it to their, their client. What's happened with cloud now, it's not about the transaction up front, it's about the, the actual, you know, the consumption of the technology and the bringing together all of these to form an outcome. It changes the model dramatically. And, and quite honestly, you know, the global system integrators like emphasis are in a great position cuz we can pull that together to the benefit our of our partners put our own secret sauce around it and take these solutions to market and drive consumption. Cuz that's what the cloud's all about. >>Absolutely. Right. How are you helping customers really treat cloud as a strategic focus? You know, we, we often hear companies talk about we're we're cloud first. Well, not everything belongs in the cloud. So then we hear companies start talking about being cloud smart. Yes. How are you helping? And so we'll go with that. How are you helping enterprises really become cloud smart and where is the partner angle? So we'll start with you and then we'll bring the partner angle in. >>Sure. Oh yeah, big time. I think one of the things that we have been educating our customers is cloud is not about cost takeout. So cloud is about innovation, cloud is about growth. And I'll give two examples. One of one of the beauty products companies, they wanted to set up their shop in us and they said that, you know, we don't have time to basically buy the infrastructure, implement an er p platform and you know, or roll it out, test it, and go into production. We don't have so much time, time to market is very important for us. And they embarked on the cloud journey. So expanding into new market cloud can play a big role. That is one of the ways to expand and, you know, grow your business. Similarly, there is another company that they, they wanted to get into retail banking, right? And they didn't have years to launch a product. So they actually use AWS and it's a joint infos and AWS customer, a pretty big bank. They launched into, they launched retail banking and they did it in less than six months. So I think these are some of the examples of, wow, it's Snap Cloud not being cost takeout, but it's about innovation and growth. So that's what we are trying to tell >>Customers. Big impacts, big impact. >>Absolutely. And that's where the, the Cobalt assets come into play as well. We, you know, as as not mentioned, we have literally thousand of these industries specific, and they're derived in, in a lot of cases in, in, in partnership with the, the companies you see down the, the aisles here and, and aws. And it accelerates the, the, the deployments and ensures a accessible adoption more so than before. You know, we, we have clients that are coming to us now that used to buy, run their own procurement. You know, they, they would have literally, there was one bank that came to us with a over a hundred, >>The amount of work. Yeah. >>A list of a hundred products. Some they bought directly from a, a vendor, some they went through a distributor, something went through a, a seller and such. And they're, they're, now they're looking at this in a completely different way. And they're looking to rationalize those, those technologies, again, look for companies that will contract for a business outcome and leverage the cloud and get to that next era. And it's, it's a, it's a fun time. We're really excited. >>I can imagine you, you're really a part of the transformation process for a lot of these companies. Absolutely. And when we were chatting before we went live, you talked about your passion for business outcomes. Can you give us a couple examples of customers or business outcomes that really get you and the team excited? Same thing to you, David, after. Yeah, >>Well, absolutely. Even our contractual structures are now moving into business outcomes. So we are getting paid by the outcomes that we are delivering, right? So one of the insurance customers that we have, we actually get paid by the number of claims that we process, right? Similarly, there is a healthcare customer where we actually get paid by the number of customers that we cater to from a Medicare and Medicaid standpoint, right? >>Tangible results versus >>Projected forecast. Successful process of >>Claims. That's interesting. Exactly. Yeah. I love reality. Yeah, reality. What a novel idea. Yeah. >>One of the great examples you hear about airplane engines now that the model is you don't buy the engine. You basically pay for the hours that it's used and the maintenance and the downtime so that they, you take the risk away. You know, you put that in the context of a traditional business, you're taking away the risk of owning the individual asset, the maintenance, any, any of the issues, the bug fixes. And again, you're, you're partnering with a company like Emphasis will take on that based upon our knowledge and based upon our vast experience, we can confidently contract in that way that, you know, years ago that wasn't possible. >>It's kind of a sharing economy at scale style. >>Exactly. Absolutely. >>Yeah. Which is really exciting. So we have a new challenge here on the cube this year at ve We are looking for your 32nd Instagram real sizzle sound bite, your hot take your thought leadership on the, the biggest theme or most important thing coming out of this year's show. David, we'll start with you. We've been starting with it on, I'm to go to you. We're making eye contact right now, so you're in the hot seat. >>Well, let's, I I think there's a lot of time given to sustainability on the stage this week, and I think that, you know, every, every CEO that we talk to is bringing that up as a major priority and that's a very important element for us as a company and as a service >>Provider. I mean, you're obviously award-winning and the sustainability department. Exactly. >>Yes. Nice little plug there. You know, and I, I think the other things that have come up, we saw a lot about data analytics this week. You know, I think new offerings from aws, but also new partnerships that we're gonna take advantage of. And, and again, security has been a hot topic. >>Absolutely. And not, what's your hot take? >>Yeah. I think one, one very exciting thing for partners like us is the, the reimagining that is being done by rhu for the partners, right? The AWS marketplace. I think that is a big, big thing that I took out. Of course, sustainability is huge. Like Adam said, the fastest way to become sustainable is to move to cloud, right? So rather than overthinking and over-engineering this whole topic, just take your workloads and move it to cloud and you'll be sustainable. Right. So I think that's the second one. And third is of course cyber security. Zscaler, Palo Alto, CrowdStrike. These are some of the big companies that are at the event here. And we have been partnering with them many more. I'm just calling out three names, but many more. I think cyber security is the next one. So I think these are three on top of my mind. >>Just, just a few things you casually think about. That was great, great responses from both of you and David, such a pleasure to have you both with us. We hope to have you back again. You're doing such exciting things. I'm sure that everything we talked about is gonna be a hot topic for many years to come as, as people navigate the future, as well as continue their business transformations. It is always a joy to sit next to you on stage. Likewise. Thank you. And thank all of you wherever you're tuning in from. For joining us here at AWS Reinvent Live from Las Vegas, Nevada, with Lisa Martin. I'm Savannah Peterson. And for the last time today, this is the cube, the leader in high tech coverage.

(upbeat music) >> It's theCUBE live in Las Vegas, covering AWS re:Invent 2022. This is the first full day of coverage. We will be here tomorrow and Thursday but we started last night. So hopefully you've caught some of those interviews. Lisa Martin here in Vegas with Paul Gillin. Paul, it's great to be back. We just saw a tweet from a very reliable source saying that there are upwards of 70,000 people here at rei:Invent '22 >> I think there's 70,000 people just in that aisle right there. >> I think so. It's been great so far we've gotten, what are some of the things that you have been excited about today? >> Data, I just see data everywhere, which very much relates to our next guest. Companies realizing the value of data and the strategic value of data, beginning to treat it as an asset rather than just exhaust. I see a lot of focus on app development here and building scalable applications now. Developers have to get over that, have to sort of reorient themselves toward building around the set of cloud native primitives which I think we'll see some amazing applications come out of that. >> Absolutely, we will. We're pleased to welcome back one of our alumni to the program. Shinji Kim joins us, the CEO and founder of Select Star. Welcome back Shinji. It's great to have you. >> Thanks Lisa, great to be back. >> So for the audience who may not know much about Select Star before we start digging into all of the good stuff give us a little overview about what the company does and what differentiates you. >> Sure, so Select Star is an automated data discovery platform. We act like it's Google for data scientists, data analysts and data engineers to help find and understand their data better. Lot of companies today, like what you mentioned, Paul, have 100s and 1000s of database tables now swimming through large volumes of data and variety of data today and it's getting harder and harder for people that wants to utilize data make decisions around data and analyze data to truly have the full context of where this data came from, who do you think that's inside the company or what other analysis might have been done? So Select Star's role in this case is we connect different data warehouses BI tools, wherever the data is actually being used inside the company, bringing out all the usage analytics and the pipeline and the models in one place so anyone can search through what's available and how the data has been created, used and being analyzed within the company. So that's why we call it it's kind of like your Google for data. >> What are some of the biggest challenges to doing that? I mean you've got data squirreled away in lots of corners of the organization, Excel spreadsheets, thumb drives, cloud storage accounts. How granular do you get and what's the difficulty of finding all this data? >> So today we focus primarily on lot of cloud data warehouses and data lakes. So this includes data warehouses like Redshift, Snowflake (indistinct), Databricks, S3 buckets, where a lot of the data from different sources are arriving. Because this is a one area where a lot of analysis are now being done. This is a place where you can join other data sets within the same infrastructural umbrella. And so that is one portion that we always integrate with. The other part that we also integrate a lot with are the BI tools. So whether that's (indistinct) where you are running analysis, building reports, and dashboards. We will pull out how those are, which analysis has been done and which business stakeholders are consuming that data through those tools. So you also mentioned about the differentiation. I would say one of the biggest differentiation that we have in the market today is that we are more in the cloud. So it's very cloud native, fully managed SaaS service and it's really focused on user experience of how easily anyone can really search and understand data through Select Star. In the past, data catalogs as a sector has been primarily focused on inventorizing all your enterprise data which are in many disciplinary forces. So it was more focused on technical aspect of the metadata. At the same time now this enterprise data catalog is important and is needed for even smaller companies because they are dealing with ton of data. Another part that we also see is more of democratization of data. Many different types of users are utilizing data whether they are fully technical or not. So we had basically emphasis around how to make our user interface as intuitive as possible for business users or non-technical users but also bring out as much context as possible from the metadata and the laws that we have access to, to bring out these insights for our customers. >> Got it. What was the impetus or the catalyst to launch the business just a couple of years ago? >> Yeah, so prior to this I had another data startup called Concord Systems. We focused on distributed stream processing framework. I sold the company to Akamai which is now called ... and the product is now called IoT Edge Connect. Through Akamai I started working with a lot of enterprises in automotive and consumer electronics and this is where I saw lot of the issues starting to happen when enterprises are starting to try to use the data. Collection of data, storage of data, processing of data with the help of lot of cloud providers, scaling that is not going to be a challenge as much anymore. At the same time now lot of enterprises, what I realized is a lot of enterprises were sitting on top of ton of data that they may not know how to utilize it or know even how to give the access to because they are not 100% sure what's really inside. And more and more companies, as they are building up their cloud data warehouse infrastructure they're starting to run into the same issue. So this is a part that I felt like was missing gap in the market that I wanted to fulfill and that's why I started the company. >> I'm fascinated with some of the mechanics of doing that. In March of 2020 when lockdowns were happening worldwide you're starting new a company, you have to get funding, you have to hire people, you don't have a team in place presumably. So you have to build that as free to core. How did you do all that? (Shinji laughs) >> Yeah, that was definitely a lot of work just starting from scratch. But I've been brewing this idea, I would say three four months prior. I had a few other ideas. Basically after Akamai I took some time off and then when I decided I wanted to start another company there were a number of ideas that I was toying around with. And so late 2019 I was talking to a lot of different potential customers and users to learn a little bit more about whether my hypothesis around data discovery was true or not. And that kind of led into starting to build prototypes and designs and showing them around to see if there is an interest. So it's only after all those validations and conversations in place that I truly decided that I was going to start another company and it just happened to be at the timing of end of February, early March. So that's kind of how it happened. At the same time, I'm very lucky that I was able to have had number of investors that I kept in touch with and I kept them posted on how this process was going and that's why I think during the pandemic it was definitely not an easy thing to raise our initial seed round but we were able to close it and then move on to really start building the product in 2020. >> Now you were also entering a market that's there's quite a few competitors already in that market. What has been your strategy for getting a foot in the door, getting some name recognition for your company other than being on the queue? >> Yes, this is certainly part of it. So I think there are a few things. One is when I was doing my market research and even today there are a lot of customers out there looking for an easier, faster, time to value solution. >> Yes. >> In the market. Today, existing players and legacy players have a whole suite of platform. However, the implementation time for those platforms take six months or longer and they don't necessarily are built for lot of users to use. They are built for database administrators or more technical people to use so that they end up finding their data governance project not necessarily succeeding or getting as much value out of it as they were hoping for. So this is an area that we really try to fill the gaps in because for us from day one you will be able to see all the usage analysis, how your data models look like, and the analysis right up front. And this is one part that a lot of our customers really like and also some of those customers have moved from the legacy players to Select Star's floor. >> Interesting, so you're actually taking business from some of the legacy guys and girls that may not be able to move as fast and quickly as you can. But I'd love to hear, every company these days has to be a data company, whether it's a grocery store or obviously a bank or a car dealership, there's no choice anymore. As consumers, we have this expectation that we're going to be able to get what we want, self-service. So these companies have to figure out where all the data is, what's the insides, what does it say, how can they act on that quickly? And that's a big challenge to enable organizations to be able to see what it is that they have, where's the value, where's the liability as well. Give me a favorite customer story example that you think really highlights the value of what Select Star is delivering. >> Sure, so one customer that we helped and have been working with closely is Pitney Bowes. It's one of the oldest companies, 100 year old company in logistics and manufacturing. They have ton of IoT data they collect from parcels and all the tracking and all the manufacturing that they run. They have recently, I would say a couple years ago moved to a cloud data warehouse. And this is where their challenge around managing data have really started because they have many different teams accessing the data warehouses but maybe different teams creating different things that might have been created before and it's not clear to the other teams and there is no single source of truth that they could manage. So for them, as they were starting to look into implementing data mesh architecture they adopted Select Star. And they have a, as being a very large and also mature company they have considered a lot of other legacy solutions in the market as well. But they decided to give it a try with select Star mainly because all of the automated version of data modeling and the documentation that we were able to provide upfront. And with all that, with the implementation of Select Star now they claim that they save more than 30 hours a month of every person that they have in the data management team. And we have a case study about that. So this is like one place where we see it save a lot of time for the data team as well as all the consumers that data teams serve. >> I have to ask you this as a successful woman in technology, a field that has not been very inviting to women over the years, what do you think this industry has to do better in terms of bringing along girls and young women, particularly in secondary school to encourage them to pursue careers in science and technology? >> Like what could they do better? >> What could this industry do? What is this industry, these 70,000 people here need to do better? Of which maybe 15% are female. >> Yeah, so actually I do see a lot more women and minority in data analytics field which is always great to see, also like bridging the gap between technology and the business point of view. If anything as a takeaway I feel like just making more opportunities for everyone to participate is always great. I feel like there has been, or you know just like being in the industry, a lot of people tends to congregate with people that they know or more closed groups but having more inclusive open groups that is inviting regardless of the level or gender I think is definitely something that needs to be encouraged more just overall in the industry. >> I agree. I think the inclusivity is so important but it also needs to be intentional. We've done a lot of chatting with women in tech lately and we've been talking about this very topic and that they all talk about the inclusivity, diversity, equity but it needs to be intentional by companies to be able to do that. >> Right, and I think in a way if you were to put it as like women in tech then I feel like that's also making it more explosive. I think it's better when it's focused on the industry problem or like the subject matter, but then intentionally inviting more women and minority to participate so that there's more exchange with more diverse attendees in the AWS. >> That's a great point and I hope to your 0.1 day that we're able to get there, but we don't have to call out women in tech but it is just so much more even playing field. And I hope like you that we're on our way to doing that but it's amazing that Paul brought up that you started the company during the pandemic. Also as a female founder getting funding is incredibly difficult. So kudos to you. >> Thank you. >> For all the successes that you've had. Tell us what's next for Select Star before we get to that last question. >> Yeah, we have a lot of exciting features that have been recently released and also coming up. First and foremost we have an auto documentation feature that we recently released. We have a fairly sophisticated data lineage function that parses through activity log and sequel queries to give you what the data pipeline models look like. This allows you to tell what is the dependency of different tables and dashboards so you can plan what your migration or any changes that might happen in the data warehouse so that nothing breaks whenever these changes happen. We went one step further to that to understand how the data replication actually happens and based on that we are now able to detect which are the duplicated data sets and how each different field might have changed their data values. And if the data actually stays the same then we can also propagate the same documentation as well as tagging. So this is particularly useful if you are doing like a PII tagging, you just mark one thing once and based on the data model we will also have the rest of the PII that it's associated with. So that's one part. The second part is more on the security and data governance front. So we are really seeing policy based access control where you can define who can see what data in the catalog based on their team tags and how you want to define the model. So this allows more enterprises to be able to have different teams to work together. And last one at least we have more integrations that we are releasing. We have an upgraded integration now with Redshift so that there's an easy cloud formation template to get it set up, but we now have not added Databricks, and power BI as well. So there are lots of stuff coming up. >> Man, you have accomplished a lot in two and a half years Shinji, my goodness! Last question for you, describing Select Star in a bumper sticker, what would that bumper sticker say? >> So this is on our website, but yes, automated data catalog in 15 minutes would be what I would call. >> 15 minutes. That's awesome. Thank you so much for joining us back on the program reintroducing our audience to Select Star. And again, congratulations on the successes that you've had. You have to come back because what you're creating is a flywheel and I can't wait to see where it goes. >> Awesome, thanks so much for having me here. >> Oh, our pleasure. Shinji Kim and Paul Gillin, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (upbeat music)

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now, in this program, we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program, you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think, like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean, if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems. Certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean, commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away. Just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean, we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is, you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like, take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and, you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because, you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally, I would just say please, like watch in ice in Tim's sessions, Like these are two of our best and brightest. They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time, really hot area. As Brian said in a moment, I'll be right back with Anna East Dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't want to miss this.

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now in this program we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away, just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally I would just say please, like watch in ICE in Tim's sessions, like these are two of our best and brightest, They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time really hot area. As Brian said in a moment, I'll be right back with Anna East dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't wanna miss this >>Time series Data is everywhere. The number of sensors, systems and applications generating time series data increases every day. All these data sources producing so much data can cause analysis paralysis. Influx DB is an entire platform designed with everything you need to quickly build applications that generate value from time series data influx. DB Cloud is a serverless solution, which means you don't need to buy or manage your own servers. There's no need to worry about provisioning because you only pay for what you use. Influx DB Cloud is fully managed so you get the newest features and enhancements as they're added to the platform's code base. It also means you can spend time building solutions and delivering value to your users instead of wasting time and effort managing something else. Influx TVB Cloud offers a range of security features to protect your data, multiple layers of redundancy ensure you don't lose any data access controls ensure that only the people who should see your data can see it. >>And encryption protects your data at rest and in transit between any of our regions or cloud providers. InfluxDB uses a single API across the entire platform suite so you can build on open source, deploy to the cloud and then then easily query data in the cloud at the edge or on prem using the same scripts. And InfluxDB is schemaless automatically adjusting to changes in the shape of your data without requiring changes in your application. Logic. InfluxDB Cloud is production ready from day one. All it needs is your data and your imagination. Get started today@influxdata.com slash cloud. >>Okay, we're back. I'm Dave Valante with a Cube and you're watching evolving Influx DB into the smart data platform made possible by influx data. Anna ETOs Georgio is here, she's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into real-time analytics and is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IX is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory of course for speed. It's a kilo store, so it gives you a compression efficiency, it's gonna give you faster query speeds, you store files and object storage, so you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOx is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's live tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import super useful. Also broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so lot there. Now we talked to Brian about how you're using Rust and which is not a new programming language and of course we had some drama around Rust during the pandemic with the Mozilla layoffs, but the formation of the Rust Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, the adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Russ was chosen because of his exceptional performance and reliability. So while Russ is synt tactically similar to c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers. And dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on ality, for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fix race conditions, to protection against buffering overflows and to ensure thread safe async cashing structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learn about the, the new engine and, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It it's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data. And so much of the efficiency and performance of IOx comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of of illustrate why column or data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then enable each other and when they neighbor each other in the storage format, this provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the men and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one time stamp and do that for every single row. So you're scanning across a ton more data and that's why Rowe Oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, commoner data fit framework. So that's where a lot of the advantages come >>From. Okay. So you basically described like a traditional database, a row approach, but I've seen like a lot of traditional database say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native i, is it not as effective? Is the, is the foreman not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. Yeah. >>Got it. So let's talk about Arrow Data Fusion. What is data fusion? I know it's written in Rust, but what does it bring to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as it's in memory format. So the way that it helps in influx DB IOCs is that okay, it's great if you can write unlimited amount of cardinality into influx Cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So Data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PANDAS data frames as well and all of the machine learning tools associated with Pandas. >>Okay. You're also leveraging Par K in the platform cause we heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Parque and why is it important? >>Sure. So parque is the column oriented durable file format. So it's important because it'll enable bulk import, bulk export, it has compatibility with Python and Pandas, so it supports a broader ecosystem. Par K files also take very little disc disc space and they're faster to scan because again, they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and he's, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOx and I really encourage, if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and you just wanna learn more, then I would encourage you to go to the monthly Tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel look for the influx DDB unders IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about iacs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how INFLUX DB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and, and you guys super responsive, so really appreciate that. All right, thank you so much Anise for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yoakum, he's the director of engineering for Influx Data and we're gonna talk about how you update a SAS engine while the plane is flying at 30,000 feet. You don't wanna miss this. >>I'm really glad that we went with InfluxDB Cloud for our hosting because it has saved us a ton of time. It's helped us move faster, it's saved us money. And also InfluxDB has good support. My name's Alex Nada. I am CTO at Noble nine. Noble Nine is a platform to measure and manage service level objectives, which is a great way of measuring the reliability of your systems. You can essentially think of an slo, the product we're providing to our customers as a bunch of time series. So we need a way to store that data and the corresponding time series that are related to those. The main reason that we settled on InfluxDB as we were shopping around is that InfluxDB has a very flexible query language and as a general purpose time series database, it basically had the set of features we were looking for. >>As our platform has grown, we found InfluxDB Cloud to be a really scalable solution. We can quickly iterate on new features and functionality because Influx Cloud is entirely managed, it probably saved us at least a full additional person on our team. We also have the option of running InfluxDB Enterprise, which gives us the ability to even host off the cloud or in a private cloud if that's preferred by a customer. Influx data has been really flexible in adapting to the hosting requirements that we have. They listened to the challenges we were facing and they helped us solve it. As we've continued to grow, I'm really happy we have influx data by our side. >>Okay, we're back with Tim Yokum, who is the director of engineering at Influx Data. Tim, welcome. Good to see you. >>Good to see you. Thanks for having me. >>You're really welcome. Listen, we've been covering open source software in the cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem. The cloud has been being built out on open source, mobile, social platforms, key databases, and of course influx DB and influx data has been a big consumer and contributor of open source software. So my question to you is, where have you seen the biggest bang for the buck from open source software? >>So yeah, you know, influx really, we thrive at the intersection of commercial services and open, so open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services temping engines. Our, our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants and like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product influx db. >>You know, but I gotta ask you, Tim, because one of the challenge that that we've seen in particular, you saw this in the heyday of Hadoop, the, the innovations come so fast and furious and as a software company you gotta place bets, you gotta, you know, commit people and sometimes those bets can be risky and not pay off well, how have you managed this challenge? >>Oh, it moves fast. Yeah, that, that's a benefit though because it, the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we, what we tend to do is, is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example, that ecosystem is driven by thousands of intelligent developers, engineers, builders, they're adding value every day. So we have to really keep up with that. And as the stack changes, we, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's, it's something that we just do every day. >>So we have a survey partner down in New York City called Enterprise Technology Research etr, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity particularly, you know, along with cloud. But, but really Kubernetes is just, you know, still up until the right consistently even with, you know, the macro headwinds and all, all of the stuff that we're sick of talking about. But, so what are you doing with Kubernetes in the platform? >>Yeah, it, it's really central to our ability to run the product. When we first started out, we were just on AWS and, and the way we were running was, was a little bit like containers junior. Now we're running Kubernetes everywhere at aws, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code so our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google and figure out how to deliver services on those three clouds with all of their differences. >>Just to follow up on that, is it, no. So I presume it's sounds like there's a PAs layer there to allow you guys to have a consistent experience across clouds and out to the edge, you know, wherever is that, is that correct? >>Yeah, so we've basically built more or less platform engineering, This is the new hot phrase, you know, it, it's, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that, that just gets all of the underlying infrastructure out of the way and, and lets them focus on delivering influx cloud. >>Yeah, and I know I'm taking a little bit of a tangent, but is that, that, I'll call it a PAs layer if I can use that term. Is that, are there specific attributes to Influx db or is it kind of just generally off the shelf paths? You know, are there, is, is there any purpose built capability there that, that is, is value add or is it pretty much generic? >>So we really build, we, we look at things through, with a build versus buy through a, a build versus by lens. Some things we want to leverage cloud provider services, for instance, Postgres databases for metadata, perhaps we'll get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can, can deliver on that has consistency that is, is all generated from code that we can as a, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions and in no time. >>So how, so sometimes you build, sometimes you buy it. How do you make those decisions and and what does that mean for the, for the platform and for customers? >>Yeah, so what we're doing is, it's like everybody else will do, we're we're looking for trade offs that make sense. You know, we really want to protect our customers data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers you don't even see that, but we don't want to try to reinvent the wheel, like I had mentioned with SQL data stores for metadata, perhaps let's build on top of what of these three large cloud providers have already perfected. And we can then focus on our platform engineering and we can have our developers then focus on the influx data, software, influx, cloud software. >>So take it to the customer level, what does it mean for them? What's the value that they're gonna get out of all these innovations that we've been been talking about today and what can they expect in the future? >>So first of all, people who use the OSS product are really gonna be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across, over 4 billion series keys that people have stored. So there's a proven ability to scale now in terms of the open source, open source software and how we've developed the platform. You're getting highly available high cardinality time series platform. We manage it and, and really as, as I mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in real time. We deploy to our platform every day repeatedly all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change new features, better ways of doing deployments, safer ways of doing deployments. >>All of that happens behind the scenes. And like we had mentioned earlier, Kubernetes, I mean that, that allows us to get that done. We couldn't do it without having that platform as a, as a base layer for us to then put our software on. So we, we iterate quickly. When you're on the, the Influx cloud platform, you really are able to, to take advantage of new features immediately. We roll things out every day and as those things go into production, you have, you have the ability to, to use them. And so in the end we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let, let us do that for you. So, >>And that makes sense, but so is the, is the, are the innovations that we're talking about in the evolution of Influx db, do, do you see that as sort of a natural evolution for existing customers? I, is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >>Yeah, it really is it, it's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are, are really the hot thing. Iot, industrial iot especially, people want to just shove tons of data out there and be able to do queries immediately and they don't wanna manage infrastructure. What we've started to see are people that use the cloud service as their, their data store backbone and then they use edge computing with R OSS product to ingest data from say, multiple production lines and downsample that data, send the rest of that data off influx cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that and being in all sorts of different regions allows for people to really get out of the, the business of man trying to manage that big data, have us take care of that. And of course as we change the platform end users benefit from that immediately. And, >>And so obviously taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IOT and the Edge? How should we be thinking about the value that you bring from a security perspective? >>Yeah, we take, we take security super seriously. It, it's built into our dna. We do a lot of work to ensure that our platform is secure, that the data we store is, is kept private. It's of course always a concern. You see in the news all the time, companies being compromised, you know, that's something that you can have an entire team working on, which we do to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You know, you look at things like software, bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that, that's just part of our jobs to make sure that the platform that we're running it has, has fully vetted software and, and with open source especially, that's a lot of work. And so it's, it's definitely new territory. Supply chain attacks are, are definitely happening at a higher clip than they used to, but that is, that is really just part of a day in the, the life for folks like us that are, are building platforms. >>Yeah, and that's key. I mean especially when you start getting into the, the, you know, we talk about IOT and the operations technologies, the engineers running the, that infrastructure, you know, historically, as you know, Tim, they, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >>That >>Connected now, right? And so you've gotta have a partner that is again, take away that heavy lifting to r and d so you can focus on some of the other activities. Right. Give us the, the last word and the, the key takeaways from your perspective. >>Well, you know, from my perspective I see it as, as a a two lane approach with, with influx, with Anytime series data, you know, you've got a lot of stuff that you're gonna run on-prem, what you had mentioned, air gaping. Sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want torus their data to, to a company that's, that's got a full platform set up for them that they can build on, send that data over to the cloud, the cloud is not going away. I think more hybrid approach is, is where the future lives and that's what we're prepared for. >>Tim, really appreciate you coming to the program. Great stuff. Good to see you. >>Thanks very much. Appreciate it. >>Okay, in a moment I'll be back to wrap up. Today's session, you're watching The Cube. >>Are you looking for some help getting started with InfluxDB Telegraph or Flux Check >>Out Influx DB University >>Where you can find our entire catalog of free training that will help you make the most of your time series data >>Get >>Started for free@influxdbu.com. >>We'll see you in class. >>Okay, so we heard today from three experts on time series and data, how the Influx DB platform is evolving to support new ways of analyzing large data sets very efficiently and effectively in real time. And we learned that key open source components like Apache Arrow and the Rust Programming environment Data fusion par K are being leveraged to support realtime data analytics at scale. We also learned about the contributions in importance of open source software and how the Influx DB community is evolving the platform with minimal disruption to support new workloads, new use cases, and the future of realtime data analytics. Now remember these sessions, they're all available on demand. You can go to the cube.net to find those. Don't forget to check out silicon angle.com for all the news related to things enterprise and emerging tech. And you should also check out influx data.com. There you can learn about the company's products. You'll find developer resources like free courses. You could join the developer community and work with your peers to learn and solve problems. And there are plenty of other resources around use cases and customer stories on the website. This is Dave Valante. Thank you for watching Evolving Influx DB into the smart data platform, made possible by influx data and brought to you by the Cube, your leader in enterprise and emerging tech coverage.

>>Hi everyone. Welcome to the Cube special presentation here in Palo Alto, California. I'm John Foer, host of the Cube. We've got two great guests, one for calling in from Germany, our videoing in from Germany, one from Maryland. We've got VMware and aws. This is the customer successes with VMware cloud on AWS showcase, accelerating business transformation here in the showcase with Samir Candu Worldwide. VMware strategic alliance solution, architect leader with AWS Samir. Great to have you and Daniel Re Myer, principal architect global AWS synergy at VMware. Guys, you guys are, are working together. You're the key players in the re relationship as it rolls out and continues to grow. So welcome to the cube. >>Thank you. Greatly appreciate it. >>Great to have you guys both on, As you know, we've been covering this since 2016 when Pat Geling, then CEO and then then CEO AWS at Andy Chasy did this. It kind of got people by surprise, but it really kind of cleaned out the positioning in the enterprise for the success. OFM workloads in the cloud. VMware's had great success with it since, and you guys have the great partnerships. So this has been like a really strategic, successful partnership. Where are we right now? You know, years later we got this whole inflection point coming. You're starting to see, you know, this idea of higher level services, more performance are coming in at the infrastructure side. More automation, more serverless, I mean, and a, I mean it's just getting better and better every year in the cloud. Kinda a whole nother level. Where are we, Samir? Let's start with you on, on the relationship. >>Yeah, totally. So I mean, there's several things to keep in mind, right? So in 2016, right, that's when the partnership between AWS and VMware was announced, and then less than a year later, that's when we officially launched VMware cloud on aws. Years later, we've been driving innovation, working with our customers, jointly engineering this between AWS and VMware day in, day out. As far as advancing VMware cloud on aws. You know, even if you look at the innovation that takes place with a solution, things have modernized, things have changed, there's been advancements, you know, whether it's security focus, whether it's platform focus, whether it's networking focus, there's been modifications along the way, even storage, right? More recently, one of the things to keep in mind is we're looking to deliver value to our customers together. These are our joint customers. So there's hundreds of VMware and AWS engineers working together on this solution. >>And then factor in even our sales teams, right? We have VMware and AWS sales teams interacting with each other on a constant daily basis. We're working together with our customers at the end of the day too. Then we're looking to even offer and develop jointly engineered solutions specific to VMware cloud on aws, and even with VMware's, other platforms as well. Then the other thing comes down to is where we have dedicated teams around this at both AWS and VMware. So even from solutions architects, even to our sales specialists, even to our account teams, even to specific engineering teams within the organizations, they all come together to drive this innovation forward with VMware cloud on AWS and the jointly engineered solution partnership as well. And then I think one of the key things to keep in mind comes down to we have nearly 600 channel partners that have achieved VMware cloud on AWS service competency. So think about it from the standpoint there's 300 certified or validated technology solutions, they're now available to our customers. So that's even innovation right off the top as well. >>Great stuff. Daniel, I wanna get to you in a second. Upon this principal architect position you have in your title, you're the global a synergy person. Synergy means bringing things together, making it work. Take us through the architecture, because we heard a lot of folks at VMware explore this year, formerly world, talking about how the, the workloads on it has been completely transforming into cloud and hybrid, right? This is where the action is. Where are you? Is your customers taking advantage of that new shift? You got AI ops, you got it. Ops changing a lot, you got a lot more automation edges right around the corner. This is like a complete transformation from where we were just five years ago. What's your thoughts on the >>Relationship? So at at, at first, I would like to emphasize that our collaboration is not just that we have dedicated teams to help our customers get the most and the best benefits out of VMware cloud on aws. We are also enabling US mutually. So AWS learns from us about the VMware technology, where VMware people learn about the AWS technology. We are also enabling our channel partners and we are working together on customer projects. So we have regular assembled globally and also virtually on Slack and the usual suspect tools working together and listening to customers, that's, that's very important. Asking our customers where are their needs? And we are driving the solution into the direction that our customers get the, the best benefits out of VMware cloud on aws. And over the time we, we really have involved the solution. As Samia mentioned, we just added additional storage solutions to VMware cloud on aws. We now have three different instance types that cover a broad range of, of workload. So for example, we just added the I four I host, which is ideally for workloads that require a lot of CPU power, such as you mentioned it, AI workloads. >>Yeah. So I wanna guess just specifically on the customer journey and their transformation. You know, we've been reporting on Silicon angle in the queue in the past couple weeks in a big way that the OPS teams are now the new devs, right? I mean that sounds OP a little bit weird, but operation IT operations is now part of the, a lot more data ops, security writing code composing, you know, with open source, a lot of great things are changing. Can you share specifically what customers are looking for when you say, as you guys come in and assess their needs, what are they doing? What are some of the things that they're doing with VMware on AWS specifically that's a little bit different? Can you share some of and highlights there? >>That, that's a great point because originally VMware and AWS came from very different directions when it comes to speaking people at customers. So for example, aws very developer focused, whereas VMware has a very great footprint in the IT ops area. And usually these are very different, very different teams, groups, different cultures, but it's, it's getting together. However, we always try to address the customers, right? There are customers that want to build up a new application from the scratch and build resiliency, availability, recoverability, scalability into the application. But there are still a lot of customers that say, well we don't have all of the skills to redevelop everything to refactor an application to make it highly available. So we want to have all of that as a service, recoverability as a service, scalability as a service. We want to have this from the infrastructure. That was one of the unique selling points for VMware on premise and now we are bringing this into the cloud. >>Samir, talk about your perspective. I wanna get your thoughts, and not to take a tangent, but we had covered the AWS remar of, actually it was Amazon res machine learning automation, robotics and space. It was really kinda the confluence of industrial IOT software physical. And so when you look at like the IT operations piece becoming more software, you're seeing things about automation, but the skill gap is huge. So you're seeing low code, no code automation, you know, Hey Alexa, deploy a Kubernetes cluster. Yeah, I mean, I mean that's coming, right? So we're seeing this kind of operating automation meets higher level services meets workloads. Can you unpack that and share your opinion on, on what you see there from an Amazon perspective and how it relates to this? >>Yeah, totally. Right. And you know, look at it from the point of view where we said this is a jointly engineered solution, but it's not migrating to one option or the other option, right? It's more or less together. So even with VMware cloud on aws, yes it is utilizing AWS infrastructure, but your environment is connected to that AWS VPC in your AWS account. So if you wanna leverage any of the native AWS services, so any of the 200 plus AWS services, you have that option to do so. So that's gonna give you that power to do certain things, such as, for example, like how you mentioned with iot, even with utilizing Alexa or if there's any other service that you wanna utilize, that's the joining point between both of the offerings. Right off the top though, with digital transformation, right? You, you have to think about where it's not just about the technology, right? There's also where you want to drive growth in the underlying technology. Even in your business leaders are looking to reinvent their business. They're looking to take different steps as far as pursuing a new strategy. Maybe it's a process, maybe it's with the people, the culture, like how you said before, where people are coming in from a different background, right? They may not be used to the cloud, they may not be used to AWS services, but now you have that capability to mesh them together. Okay. Then also, Oh, >>Go ahead, finish >>Your thought. No, no, I was gonna say, what it also comes down to is you need to think about the operating model too, where it is a shift, right? Especially for that VS four admin that's used to their on-premises at environment. Now with VMware cloud on aws, you have that ability to leverage a cloud, but the investment that you made and certain things as far as automation, even with monitoring, even with logging, yeah. You still have that methodology where you can utilize that in VMware cloud on AWS two. >>Danielle, I wanna get your thoughts on this because at at explore and, and, and after the event, now as we prep for Cuban and reinvent coming up the big AWS show, I had a couple conversations with a lot of the VMware customers and operators and it's like hundreds of thousands of, of, of, of users and millions of people talking about and and peaked on VM we're interested in v VMware. The common thread was one's one, one person said, I'm trying to figure out where I'm gonna put my career in the next 10 to 15 years. And they've been very comfortable with VMware in the past, very loyal, and they're kind of talking about, I'm gonna be the next cloud, but there's no like role yet architects, is it Solution architect sre. So you're starting to see the psychology of the operators who now are gonna try to make these career decisions, like how, what am I gonna work on? And it's, and that was kind of fuzzy, but I wanna get your thoughts. How would you talk to that persona about the future of VMware on, say, cloud for instance? What should they be thinking about? What's the opportunity and what's gonna happen? >>So digital transformation definitely is a huge change for many organizations and leaders are perfectly aware of what that means. And that also means in, in to to some extent, concerns with your existing employees. Concerns about do I have to relearn everything? Do I have to acquire new skills? And, and trainings is everything worthless I learned over the last 15 years of my career? And the, the answer is to make digital transformation a success. We need not just to talk about technology, but also about process people and culture. And this is where VMware really can help because if you are applying VMware cloud on a, on AWS to your infrastructure, to your existing on-premise infrastructure, you do not need to change many things. You can use the same tools and skills, you can manage your virtual machines as you did in your on-premise environment. You can use the same managing and monitoring tools. If you have written, and many customers did this, if you have developed hundreds of, of scripts that automate tasks and if you know how to troubleshoot things, then you can use all of that in VMware cloud on aws. And that gives not just leaders, but but also the architects at customers, the operators at customers, the confidence in, in such a complex project, >>The consistency, very key point, gives them the confidence to go and, and then now that once they're confident they can start committing themselves to new things. Samir, you're reacting to this because you know, on your side you've got higher level services, you got more performance at the hardware level. I mean, lot improvement. So, okay, nothing's changed. I can still run my job now I got goodness on the other side. What's the upside? What's in it for the, for the, for the customer there? >>Yeah, so I think what it comes down to is they've already been so used to or entrenched with that VMware admin mentality, right? But now extending that to the cloud, that's where now you have that bridge between VMware cloud on AWS to bridge that VMware knowledge with that AWS knowledge. So I will look at it from the point of view where now one has that capability and that ability to just learn about the cloud, but if they're comfortable with certain aspects, no one's saying you have to change anything. You can still leverage that, right? But now if you wanna utilize any other AWS service in conjunction with that VM that resides maybe on premises or even in VMware cloud on aws, you have that option to do so. So think about it where you have that ability to be someone who's curious and wants to learn. And then if you wanna expand on the skills, you certainly have that capability to do so. >>Great stuff. I love, love that. Now that we're peeking behind the curtain here, I'd love to have you guys explain, cuz people wanna know what's goes on in behind the scenes. How does innovation get happen? How does it happen with the relationship? Can you take us through a day in the life of kind of what goes on to make innovation happen with the joint partnership? You guys just have a zoom meeting, Do you guys fly out, you write go do you ship thing? I mean I'm making it up, but you get the idea, what's the, what's, how does it work? What's going on behind the scenes? >>So we hope to get more frequently together in person, but of course we had some difficulties over the last two to three years. So we are very used to zoom conferences and and Slack meetings. You always have to have the time difference in mind if we are working globally together. But what we try, for example, we have reg regular assembled now also in person geo based. So for emia, for the Americas, for aj. And we are bringing up interesting customer situations, architectural bits and pieces together. We are discussing it always to share and to contribute to our community. >>What's interesting, you know, as, as events are coming back to here, before you get, you weigh in, I'll comment, as the cube's been going back out to events, we are hearing comments like what, what pandemic we were more productive in the pandemic. I mean, developers know how to work remotely and they've been on all the tools there, but then they get in person, they're happy to see people, but there's no one's, no one's really missed the beat. I mean it seems to be very productive, you know, workflow, not a lot of disruption. More if anything, productivity gains. >>Agreed, right? I think one of the key things to keep in mind is, you know, even if you look at AWS's and even Amazon's leadership principles, right? Customer obsession, that's key. VMware is carrying that forward as well. Where we are working with our customers, like how Daniel said met earlier, right? We might have meetings at different time zones, maybe it's in person, maybe it's virtual, but together we're working to listen to our customers. You know, we're taking and capturing that feedback to drive innovation and VMware cloud on AWS as well. But one of the key things to keep in mind is yes, there have been, there has been the pandemic, we might have been disconnected to a certain extent, but together through technology we've been able to still communicate work with our customers. Even with VMware in between, with AWS and whatnot. We had that flexibility to innovate and continue that innovation. So even if you look at it from the point of view, right? VMware cloud on AWS outposts, that was something that customers have been asking for. We've been been able to leverage the feedback and then continue to drive innovation even around VMware cloud on AWS outposts. So even with the on premises environment, if you're looking to handle maybe data sovereignty or compliance needs, maybe you have low latency requirements, that's where certain advancements come into play, right? So the key thing is always to maintain that communication track. >>And our last segment we did here on the, on this showcase, we listed the accomplishments and they were pretty significant. I mean go, you got the global rollouts of the relationship. It's just really been interesting and, and people can reference that. We won't get into it here, but I will ask you guys to comment on, as you guys continue to evolve the relationship, what's in it for the customer? What can they expect next? Cuz again, I think right now we're in at a, an inflection point more than ever. What can people expect from the relationship and what's coming up with reinvent? Can you share a little bit of kind of what's coming down the pike? >>So one of the most important things we have announced this year, and we will continue to evolve into that direction, is independent scale of storage. That absolutely was one of the most important items customer asked us for over the last years. Whenever, whenever you are requiring additional storage to host your virtual machines, you usually in VMware cloud on aws, you have to add additional notes. Now we have three different note types with different ratios of compute, storage and memory. But if you only require additional storage, you always have to get also additional compute and memory and you have to pay. And now with two solutions which offer choice for the customers, like FS six one, NetApp onap, and VMware cloud Flex Storage, you now have two cost effective opportunities to add storage to your virtual machines. And that offers opportunities for other instance types maybe that don't have local storage. We are also very, very keen looking forward to announcements, exciting announcements at the upcoming events. >>Samir, what's your, what's your reaction take on the, on what's coming down on your side? >>Yeah, I think one of the key things to keep in mind is, you know, we're looking to help our customers be agile and even scale with their needs, right? So with VMware cloud on aws, that's one of the key things that comes to mind, right? There are gonna be announcements, innovations and whatnot with outcoming events. But together we're able to leverage that to advance VMware cloud on AWS to Daniel's point storage, for example, even with host offerings. And then even with decoupling storage from compute and memory, right now you have the flexibility where you can do all of that. So to look at it from the standpoint where now with 21 regions where we have VMware cloud on AWS available as well, where customers can utilize that as needed when needed, right? So it comes down to, you know, transformation will be there. Yes, there's gonna be maybe where workloads have to be adapted where they're utilizing certain AWS services, but you have that flexibility and option to do so. And I think with the continuing events that's gonna give us the options to even advance our own services together. >>Well you guys are in the middle of it, you're in the trenches, you're making things happen, you've got a team of people working together. My final question is really more of a kind of a current situation, kind of future evolutionary thing that you haven't seen this before. I wanna get both of your reaction to it. And we've been bringing this up in, in the open conversations on the cube is in the old days it was going back this generation, you had ecosystems, you had VMware had an ecosystem they did best, had an ecosystem. You know, we have a product, you have a product, biz dev deals happen, people sign relationships and they do business together and they, they sell to each other's products or do some stuff. Now it's more about architecture cuz we're now in a distributed large scale environment where the role of ecosystems are intertwining. >>And this, you guys are in the middle of two big ecosystems. You mentioned channel partners, you both have a lot of partners on both sides. They come together. So you have this now almost a three dimensional or multidimensional ecosystem, you know, interplay. What's your thoughts on this? And, and, and because it's about the architecture, integration is a value, not so much. Innovation is only, you gotta do innovation, but when you do innovation, you gotta integrate it, you gotta connect it. So what is, how do you guys see this as a, as an architectural thing, start to see more technical business deals? >>So we are, we are removing dependencies from individual ecosystems and from individual vendors. So a customer no longer has to decide for one vendor and then it is a very expensive and high effort project to move away from that vendor, which ties customers even, even closer to specific vendors. We are removing these obstacles. So with VMware cloud on aws moving to the cloud, firstly it's, it's not a dead end. If you decide at one point in time because of latency requirements or maybe it's some compliance requirements, you need to move back into on-premise. You can do this if you decide you want to stay with some of your services on premise and just run a couple of dedicated services in the cloud, you can do this and you can mana manage it through a single pane of glass. That's quite important. So cloud is no longer a dead and it's no longer a binary decision, whether it's on premise or the cloud. It it is the cloud. And the second thing is you can choose the best of both works, right? If you are migrating virtual machines that have been running in your on-premise environment to VMware cloud on aws, by the way, in a very, very fast cost effective and safe way, then you can enrich later on enrich these virtual machines with services that are offered by aws. More than 200 different services ranging from object based storage, load balancing and so on. So it's an endless, endless possibility. >>We, we call that super cloud in, in a, in a way that we be generically defining it where everyone's innovating, but yet there's some common services. But the differentiation comes from innovation where the lock in is the value, not some spec, right? Samir, this is gonna where cloud is right now, you guys are, are not commodity. Amazon's completely differentiating, but there's some commodity things. Having got storage, you got compute, but then you got now advances in all areas. But partners innovate with you on their terms. Absolutely. And everybody wins. >>Yeah. And a hundred percent agree with you. I think one of the key things, you know, as Daniel mentioned before, is where it it, it's a cross education where there might be someone who's more proficient on the cloud side with aws, maybe more proficient with the viewers technology, but then for partners, right? They bridge that gap as well where they come in and they might have a specific niche or expertise where their background, where they can help our customers go through that transformation. So then that comes down to, hey, maybe I don't know how to connect to the cloud. Maybe I don't know what the networking constructs are. Maybe I can leverage that partner. That's one aspect to go about it. Now maybe you migrated that workload to VMware cloud on aws. Maybe you wanna leverage any of the native AWS services or even just off the top 200 plus AWS services, right? But it comes down to that skill, right? So again, solutions architecture at the back of, back of the day, end of the day, what it comes down to is being able to utilize the best of both worlds. That's what we're giving our customers at the end of the >>Day. I mean, I just think it's, it's a, it's a refactoring and innovation opportunity at all levels. I think now more than ever, you can take advantage of each other's ecosystems and partners and technologies and change how things get done with keeping the consistency. I mean, Daniel, you nailed that, right? I mean, you don't have to do anything. You still run the fear, the way you working on it and now do new things. This is kind of a cultural shift. >>Yeah, absolutely. And if, if you look, not every, not every customer, not every organization has the resources to refactor and re-platform everything. And we gave, we give them a very simple and easy way to move workloads to the cloud. Simply run them and at the same time they can free up resources to develop new innovations and, and grow their business. >>Awesome. Samir, thank you for coming on. Danielle, thank you for coming to Germany, Octoberfest, I know it's evening over there, your weekend's here. And thank you for spending the time. Samir final give you the final word, AWS reinvents coming up. Preparing. We're gonna have an exclusive with Adam, but Fry, we do a curtain raise, a dual preview. What's coming down on your side with the relationship and what can we expect to hear about what you got going on at reinvent this year? The big show? >>Yeah, so I think, you know, Daniel hit upon some of the key points, but what I will say is we do have, for example, specific sessions, both that VMware's driving and then also that AWS is driving. We do have even where we have what I call a chalk talks. So I would say, and then even with workshops, right? So even with the customers, the attendees who are there, whatnot, if they're looking for to sit and listen to a session, yes that's there. But if they wanna be hands on, that is also there too. So personally for me as an IT background, you know, been in CIS admin world and whatnot, being hands on, that's one of the key things that I personally am looking forward. But I think that's one of the key ways just to learn and get familiar with the technology. Yeah, >>Reinvents an amazing show for the in person. You guys nail it every year. We'll have three sets this year at the cube. It's becoming popular. We more and more content. You guys got live streams going on, a lot of content, a lot of media, so thanks, thanks for sharing that. Samir Daniel, thank you for coming on on this part of the showcase episode of really the customer successes with VMware Cloud Ons, really accelerating business transformation withs and VMware. I'm John Fur with the cube, thanks for watching. Hello everyone. Welcome to this cube showcase, accelerating business transformation with VMware cloud on it's a solution innovation conversation with two great guests, Fred and VP of commercial services at aws and NA Ryan Bard, who's the VP and general manager of cloud solutions at VMware. Gentlemen, thanks for joining me on this showcase. >>Great to be here. >>Hey, thanks for having us on. It's a great topic. You know, we, we've been covering this VMware cloud on abus since, since the launch going back and it's been amazing to watch the evolution from people saying, Oh, it's the worst thing I've ever seen. It's what's this mean? And depress work were, we're kind of not really on board with kind of the vision, but as it played out as you guys had announced together, it did work out great for VMware. It did work out great for a D and it continues two years later and I want just get an update from you guys on where you guys see this has been going. I'll see multiple years. Where is the evolution of the solution as we are right now coming off VMware explorer just recently and going in to reinvent, which is only a couple weeks away, feels like tomorrow. But you know, as we prepare a lot going on, where are we with the evolution of the solution? >>I mean, first thing I wanna say is, you know, PBO 2016 was a someon moment and the history of it, right? When Pat Gelsinger and Andy Jessey came together to announce this and I think John, you were there at the time I was there, it was a great, great moment. We launched the solution in 2017, the year after that at VM Word back when we called it Word, I think we have gone from strength to strength. One of the things that has really mattered to us is we have learned froms also in the processes, this notion of working backwards. So we really, really focused on customer feedback as we build a service offering now five years old, pretty remarkable journey. You know, in the first years we tried to get across all the regions, you know, that was a big focus because there was so much demand for it. >>In the second year we started going really on enterprise grade features. We invented this pretty awesome feature called Stretch clusters, where you could stretch a vSphere cluster using VSA and NSX across two AZs in the same region. Pretty phenomenal four nine s availability that applications start started to get with that particular feature. And we kept moving forward all kinds of integration with AWS direct connect transit gateways with our own advanced networking capabilities. You know, along the way, disaster recovery, we punched out two, two new services just focused on that. And then more recently we launched our outposts partnership. We were up on stage at Reinvent, again with Pat Andy announcing AWS outposts and the VMware flavor of that VMware cloud and AWS outposts. I think it's been significant growth in our federal sector as well with our federal and high certification more recently. So all in all, we are super excited. We're five years old. The customer momentum is really, really strong and we are scaling the service massively across all geos and industries. >>That's great, great update. And I think one of the things that you mentioned was how the advantages you guys got from that relationship. And, and this has kind of been the theme for AWS since I can remember from day one. Fred, you guys do the heavy lifting as as, as you always say for the customers here, VMware comes on board, takes advantage of the AWS and kind of just doesn't miss a beat, continues to move their workloads that everyone's using, you know, vSphere and these are, these are big workloads on aws. What's the AWS perspective on this? How do you see it? >>Yeah, it's pretty fascinating to watch how fast customers can actually transform and move when you take the, the skill set that they're familiar with and the advanced capabilities that they've been using on Preem and then overlay it on top of the AWS infrastructure that's, that's evolving quickly and, and building out new hardware and new instances we'll talk about. But that combined experience between both of us on a jointly engineered solution to bring the best security and the best features that really matter for those workloads drive a lot of efficiency and speed for the, for the customer. So it's been well received and the partnership is stronger than ever from an engineering standpoint, from a business standpoint. And obviously it's been very interesting to look at just how we stay day one in terms of looking at new features and work and, and responding to what customers want. So pretty, pretty excited about just seeing the transformation and the speed that which customers can move to bmc. Yeah, >>That's what great value publish. We've been talking about that in context too. Anyone building on top of the cloud, they can have their own supercloud as we call it. If you take advantage of all the CapEx and and investment Amazon's made and AWS has made and, and and continues to make in performance IAS and pass all great stuff. I have to ask you guys both as you guys see this going to the next level, what are some of the differentiations you see around the service compared to other options on the market? What makes it different? What's the combination? You mentioned jointly engineered, what are some of the key differentiators of the service compared to others? >>Yeah, I think one of the key things Fred talked about is this jointly engineered notion right from day one. We were the earlier doctors of AWS Nitro platform, right? The reinvention of E two back five years ago. And so we have been, you know, having a very, very strong engineering partnership at that level. I think from a VMware customer standpoint, you get the full software defined data center or compute storage networking on EC two, bare metal across all regions. You can scale that elastically up and down. It's pretty phenomenal just having that consistency globally, right on aws EC two global regions. Now the other thing that's a real differentiator for us that customers tell us about is this whole notion of a managed service, right? And this was somewhat new to VMware, but we took away the pain of this undifferentiated heavy lifting where customers had to provision rack, stack hardware, configure the software on top, and then upgrade the software and the security batches on top. >>So we took, took away all of that pain as customers transitioned to VMware cloud and aws. In fact, my favorite story from last year when we were all going through the lock for j debacle industry was just going through that, right? Favorite proof point from customers was before they put even race this issue to us, we sent them a notification saying we already patched all of your systems, no action from you. The customers were super thrilled. I mean these are large banks, many other customers around the world, super thrilled they had to take no action, but a pretty incredible industry challenge that we were all facing. >>Nora, that's a great, so that's a great point. You know, the whole managed service piece brings up the security, you kind of teasing at it, but you know, there's always vulnerabilities that emerge when you are doing complex logic. And as you grow your solutions, there's more bits. You know, Fred, we were commenting before we came on camera, there's more bits than ever before and, and at at the physics layer too, as well as the software. So you never know when there's gonna be a zero day vulnerability out there. Just, it happens. We saw one with fornet this week, this came outta the woodwork. But moving fast on those patches, it's huge. This brings up the whole support angle. I wanted to ask you about how you guys are doing that as well, because to me we see the value when we, when we talk to customers on the cube about this, you know, it was a real, real easy understanding of how, what the cloud means to them with VMware now with the aws. But the question that comes up that we wanna get more clarity on is how do you guys handle support together? >>Well, what's interesting about this is that it's, it's done mutually. We have dedicated support teams on both sides that work together pretty seamlessly to make sure that whether there's a issue at any layer, including all the way up into the app layer, as you think about some of the other workloads like sap, we'll go end to end and make sure that we support the customer regardless of where the particular issue might be for them. And on top of that, we look at where, where we're improving reliability in, in as a first order of, of principle between both companies. So from an availability and reliability standpoint, it's, it's top of mind and no matter where the particular item might land, we're gonna go help the customer resolve. That works really well >>On the VMware side. What's been the feedback there? What's the, what are some of the updates? >>Yeah, I think, look, I mean, VMware owns and operates the service, but we have a phenomenal backend relationship with aws. Customers call VMware for the service for any issues and, and then we have a awesome relationship with AWS on the backend for support issues or any hardware issues. The BASKE management that we jointly do, right? All of the hard problems that customers don't have to worry about. I think on the front end, we also have a really good group of solution architects across the companies that help to really explain the solution. Do complex things like cloud migration, which is much, much easier with VMware cloud aws, you know, we are presenting that easy button to the public cloud in many ways. And so we have a whole technical audience across the two companies that are working with customers every single day. >>You know, you had mentioned, I've got a list here, some of the innovations the, you mentioned the stretch clustering, you know, getting the GOs working, Advanced network, disaster recovery, you know, fed, Fed ramp, public sector certifications, outposts, all good. You guys are checking the boxes every year. You got a good, good accomplishments list there on the VMware AWS side here in this relationship. The question that I'm interested in is what's next? What recent innovations are you doing? Are you making investments in what's on the lists this year? What items will be next year? How do you see the, the new things, the list of accomplishments, people wanna know what's next. They don't wanna see stagnant growth here, they wanna see more action, you know, as as cloud kind of continues to scale and modern applications cloud native, you're seeing more and more containers, more and more, you know, more CF C I C D pipe pipelining with with modern apps, put more pressure on the system. What's new, what's the new innovations? >>Absolutely. And I think as a five yearold service offering innovation is top of mind for us every single day. So just to call out a few recent innovations that we announced in San Francisco at VMware Explorer. First of all, our new platform i four I dot metal, it's isolate based, it's pretty awesome. It's the latest and greatest, all the speeds and feeds that we would expect from VMware and aws. At this point in our relationship. We announced two different storage options. This notion of working from customer feedback, allowing customers even more price reductions, really take off that storage and park it externally, right? And you know, separate that from compute. So two different storage offerings there. One is with AWS Fsx, with NetApp on tap, which brings in our NetApp partnership as well into the equation and really get that NetApp based, really excited about this offering as well. >>And the second storage offering for VMware cloud Flex Storage, VMware's own managed storage offering. Beyond that, we have done a lot of other innovations as well. I really wanted to talk about VMware cloud Flex Compute, where previously customers could only scale by hosts and a host is 36 to 48 cores, give or take. But with VMware cloud Flex Compute, we are now allowing this notion of a resource defined compute model where customers can just get exactly the V C P memory and storage that maps to the applications, however small they might be. So this notion of granularity is really a big innovation that that we are launching in the market this year. And then last but not least, talk about ransomware. Of course it's a hot topic in industry. We are seeing many, many customers ask for this. We are happy to announce a new ransomware recovery with our VMware cloud DR solution. >>A lot of innovation there and the way we are able to do machine learning and make sure the workloads that are covered from snapshots and backups are actually safe to use. So there's a lot of differentiation on that front as well. A lot of networking innovations with Project Knot star for ability to have layer flow through layer seven, you know, new SaaS services in that area as well. Keep in mind that the service already supports managed Kubernetes for containers. It's built in to the same clusters that have virtual machines. And so this notion of a single service with a great TCO for VMs and containers and sort of at the heart of our office, >>The networking side certainly is a hot area to keep innovating on. Every year it's the same, same conversation, get better, faster networking, more, more options there. The flex computes. Interesting. If you don't mind me getting a quick clarification, could you explain the Drew screen resource defined versus hardware defined? Because this is kind of what we had saw at Explore coming out, that notion of resource defined versus hardware defined. What's the, what does that mean? >>Yeah, I mean I think we have been super successful in this hardware defined notion. We we're scaling by the hardware unit that we present as software defined data centers, right? And so that's been super successful. But we, you know, customers wanted more, especially customers in different parts of the world wanted to start even smaller and grow even more incrementally, right? Lower their costs even more. And so this is the part where resource defined starts to be very, very interesting as a way to think about, you know, here's my bag of resources exactly based on what the customers request for fiber machines, five containers, its size exactly for that. And then as utilization grows, we elastically behind the scenes, we're able to grow it through policies. So that's a whole different dimension. It's a whole different service offering that adds value and customers are comfortable. They can go from one to the other, they can go back to that post based model if they so choose to. And there's a jump off point across these two different economic models. >>It's kind of cloud of flexibility right there. I like the name Fred. Let's get into some of the examples of customers, if you don't mind. Let's get into some of the ex, we have some time. I wanna unpack a little bit of what's going on with the customer deployments. One of the things we've heard again on the cube is from customers is they like the clarity of the relationship, they love the cloud positioning of it. And then what happens is they lift and shift the workloads and it's like, feels great. It's just like we're running VMware on AWS and then they would start consuming higher level services, kind of that adoption next level happens and because it it's in the cloud, so, So can you guys take us through some recent examples of customer wins or deployments where they're using VMware cloud on AWS on getting started, and then how do they progress once they're there? How does it evolve? Can you just walk us through a couple of use cases? >>Sure. There's a, well there's a couple. One, it's pretty interesting that, you know, like you said, as there's more and more bits you need better and better hardware and networking. And we're super excited about the I four and the capabilities there in terms of doubling and or tripling what we're doing around a lower variability on latency and just improving all the speeds. But what customers are doing with it, like the college in New Jersey, they're accelerating their deployment on a, on onboarding over like 7,400 students over a six to eight month period. And they've really realized a ton of savings. But what's interesting is where and how they can actually grow onto additional native services too. So connectivity to any other services is available as they start to move and migrate into this. The, the options there obviously are tied to all the innovation that we have across any services, whether it's containerized and with what they're doing with Tanu or with any other container and or services within aws. >>So there's, there's some pretty interesting scenarios where that data and or the processing, which is moved quickly with full compliance, whether it's in like healthcare or regulatory business is, is allowed to then consume and use things, for example, with tech extract or any other really cool service that has, you know, monthly and quarterly innovations. So there's things that you just can't, could not do before that are coming out and saving customers money and building innovative applications on top of their, their current app base in, in a rapid fashion. So pretty excited about it. There's a lot of examples. I think I probably don't have time to go into too, too many here. Yeah. But that's actually the best part is listening to customers and seeing how many net new services and new applications are they actually building on top of this platform. >>Nora, what's your perspective from the VMware sy? So, you know, you guys have now a lot of headroom to offer customers with Amazon's, you know, higher level services and or whatever's homegrown where's being rolled out? Cuz you now have a lot of hybrid too, so, so what's your, what's your take on what, what's happening in with customers? >>I mean, it's been phenomenal, the, the customer adoption of this and you know, banks and many other highly sensitive verticals are running production grade applications, tier one applications on the service over the last five years. And so, you know, I have a couple of really good examples. S and p Global is one of my favorite examples. Large bank, they merge with IHS market, big sort of conglomeration. Now both customers were using VMware cloud and AWS in different ways. And with the, with the use case, one of their use cases was how do I just respond to these global opportunities without having to invest in physical data centers? And then how do I migrate and consolidate all my data centers across the global, which there were many. And so one specific example for this company was how they migrated thousand 1000 workloads to VMware cloud AWS in just six weeks. Pretty phenomenal. If you think about everything that goes into a cloud migration process, people process technology and the beauty of the technology going from VMware point A to VMware point B, the the lowest cost, lowest risk approach to adopting VMware, VMware cloud, and aws. So that's, you know, one of my favorite examples. There are many other examples across other verticals that we continue to see. The good thing is we are seeing rapid expansion across the globe that constantly entering new markets with the limited number of regions and progressing our roadmap there. >>Yeah, it's great to see, I mean the data center migrations go from months, many, many months to weeks. It's interesting to see some of those success stories. So congratulations. One >>Of other, one of the other interesting fascinating benefits is the sustainability improvement in terms of being green. So the efficiency gains that we have both in current generation and new generation processors and everything that we're doing to make sure that when a customer can be elastic, they're also saving power, which is really critical in a lot of regions worldwide at this point in time. They're, they're seeing those benefits. If you're running really inefficiently in your own data center, that is just a, not a great use of power. So the actual calculators and the benefits to these workloads is, are pretty phenomenal just in being more green, which I like. We just all need to do our part there. And, and this is a big part of it here. >>It's a huge, it's a huge point about the sustainability. Fred, I'm glad you called that out. The other one I would say is supply chain issues. Another one you see that constrains, I can't buy hardware. And the third one is really obvious, but no one really talks about it. It's security, right? I mean, I remember interviewing Stephen Schmidt with that AWS and many years ago, this is like 2013, and you know, at that time people were saying the cloud's not secure. And he's like, listen, it's more secure in the cloud on premise. And if you look at the security breaches, it's all about the on-premise data center vulnerabilities, not so much hardware. So there's a lot you gotta to stay current on, on the isolation there is is hard. So I think, I think the security and supply chain, Fred is, is another one. Do you agree? >>I I absolutely agree. It's, it's hard to manage supply chain nowadays. We put a lot of effort into that and I think we have a great ability to forecast and make sure that we can lean in and, and have the resources that are available and run them, run them more efficiently. Yeah, and then like you said on the security point, security is job one. It is, it is the only P one. And if you think of how we build our infrastructure from Nitro all the way up and how we respond and work with our partners and our customers, there's nothing more important. >>And naron your point earlier about the managed service patching and being on top of things, it's really gonna get better. All right, final question. I really wanna thank you for your time on this showcase. It's really been a great conversation. Fred, you had made a comment earlier. I wanna kind of end with kind of a curve ball and put you eyes on the spot. We're talking about a modern, a new modern shift. It's another, we're seeing another inflection point, we've been documenting it, it's almost like cloud hitting another inflection point with application and open source growth significantly at the app layer. Continue to put a lot of pressure and, and innovation in the infrastructure side. So the question is for you guys each to answer is what's the same and what's different in today's market? So it's kind of like we want more of the same here, but also things have changed radically and better here. What are the, what's, what's changed for the better and where, what's still the same kind of thing hanging around that people are focused on? Can you share your perspective? >>I'll, I'll, I'll, I'll tackle it. You know, businesses are complex and they're often unique that that's the same. What's changed is how fast you can innovate. The ability to combine manage services and new innovative services and build new applications is so much faster today. Leveraging world class hardware that you don't have to worry about that's elastic. You, you could not do that even five, 10 years ago to the degree you can today, especially with innovation. So innovation is accelerating at a, at a rate that most people can't even comprehend and understand the, the set of services that are available to them. It's really fascinating to see what a one pizza team of of engineers can go actually develop in a week. It is phenomenal. So super excited about this space and it's only gonna continue to accelerate that. That's my take. All right. >>You got a lot of platform to compete on with, got a lot to build on then you're Ryan, your side, What's your, what's your answer to that question? >>I think we are seeing a lot of innovation with new applications that customers are constant. I think what we see is this whole notion of how do you go from desktop to production to the secure supply chain and how can we truly, you know, build on the agility that developers desire and build all the security and the pipelines to energize that motor production quickly and efficiently. I think we, we are seeing, you know, we are at the very start of that sort of of journey. Of course we have invested in Kubernetes the means to an end, but there's so much more beyond that's happening in industry. And I think we're at the very, very beginning of this transformations, enterprise transformation that many of our customers are going through and we are inherently part of it. >>Yeah. Well gentlemen, I really appreciate that we're seeing the same thing. It's more the same here on, you know, solving these complexities with distractions. Whether it's, you know, higher level services with large scale infrastructure at, at your fingertips. Infrastructures, code, infrastructure to be provisioned, serverless, all the good stuff happen in Fred with AWS on your side. And we're seeing customers resonate with this idea of being an operator, again, being a cloud operator and developer. So the developer ops is kind of, DevOps is kind of changing too. So all for the better. Thank you for spending the time and we're seeing again, that traction with the VMware customer base and of us getting, getting along great together. So thanks for sharing your perspectives, >>I appreciate it. Thank you so >>Much. Okay, thank you John. Okay, this is the Cube and AWS VMware showcase, accelerating business transformation. VMware cloud on aws, jointly engineered solution, bringing innovation to the VMware customer base, going to the cloud and beyond. I'm John Fur, your host. Thanks for watching. Hello everyone. Welcome to the special cube presentation of accelerating business transformation on vmc on aws. I'm John Furrier, host of the Cube. We have dawan director of global sales and go to market for VMware cloud on adb. This is a great showcase and should be a lot of fun. Ashish, thanks for coming on. >>Hi John. Thank you so much. >>So VMware cloud on AWS has been well documented as this big success for VMware and aws. As customers move their workloads into the cloud, IT operations of VMware customers has signaling a lot of change. This is changing the landscape globally is on cloud migration and beyond. What's your take on this? Can you open this up with the most important story around VMC on aws? >>Yes, John. The most important thing for our customers today is the how they can safely and swiftly move their ID infrastructure and applications through cloud. Now, VMware cloud AWS is a service that allows all vSphere based workloads to move to cloud safely, swiftly and reliably. Banks can move their core, core banking platforms, insurance companies move their core insurance platforms, telcos move their goss, bss, PLA platforms, government organizations are moving their citizen engagement platforms using VMC on aws because this is one platform that allows you to move it, move their VMware based platforms very fast. Migrations can happen in a matter of days instead of months. Extremely securely. It's a VMware manage service. It's very secure and highly reliably. It gets the, the reliability of the underlyings infrastructure along with it. So win-win from our customers perspective. >>You know, we reported on this big news in 2016 with Andy Chas, the, and Pat Geling at the time, a lot of people said it was a bad deal. It turned out to be a great deal because not only could VMware customers actually have a cloud migrate to the cloud, do it safely, which was their number one concern. They didn't want to have disruption to their operations, but also position themselves for what's beyond just shifting to the cloud. So I have to ask you, since you got the finger on the pulse here, what are we seeing in the market when it comes to migrating and modern modernizing in the cloud? Because that's the next step. They go to the cloud, you guys have done that, doing it, then they go, I gotta modernize, which means kind of upgrading or refactoring. What's your take on that? >>Yeah, absolutely. Look, the first step is to help our customers assess their infrastructure and licensing and entire ID operations. Once we've done the assessment, we then create their migration plans. A lot of our customers are at that inflection point. They're, they're looking at their real estate, ex data center, real estate. They're looking at their contracts with colocation vendors. They really want to exit their data centers, right? And VMware cloud and AWS is a perfect solution for customers who wanna exit their data centers, migrate these applications onto the AWS platform using VMC on aws, get rid of additional real estate overheads, power overheads, be socially and environmentally conscious by doing that as well, right? So that's the migration story, but to your point, it doesn't end there, right? Modernization is a critical aspect of the entire customer journey as as well customers, once they've migrated their ID applications and infrastructure on cloud get access to all the modernization services that AWS has. They can correct easily to our data lake services, to our AIML services, to custom databases, right? They can decide which applications they want to keep and which applications they want to refactor. They want to take decisions on containerization, make decisions on service computing once they've come to the cloud. But the most important thing is to take that first step. You know, exit data centers, come to AWS using vmc or aws, and then a whole host of modernization options available to them. >>Yeah, I gotta say, we had this right on this, on this story, because you just pointed out a big thing, which was first order of business is to make sure to leverage the on-prem investments that those customers made and then migrate to the cloud where they can maintain their applications, their data, their infrastructure operations that they're used to, and then be in position to start getting modern. So I have to ask you, how are you guys specifically, or how is VMware cloud on s addressing these needs of the customers? Because what happens next is something that needs to happen faster. And sometimes the skills might not be there because if they're running old school, IT ops now they gotta come in and jump in. They're gonna use a data cloud, they're gonna want to use all kinds of machine learning, and there's a lot of great goodness going on above the stack there. So as you move with the higher level services, you know, it's a no brainer, obviously, but they're not, it's not yesterday's higher level services in the cloud. So how are, how is this being addressed? >>Absolutely. I think you hit up on a very important point, and that is skills, right? When our customers are operating, some of the most critical applications I just mentioned, core banking, core insurance, et cetera, they're most of the core applications that our customers have across industries, like even, even large scale ERP systems, they're actually sitting on VMware's vSphere platform right now. When the customer wants to migrate these to cloud, one of the key bottlenecks they face is skill sets. They have the trained manpower for these core applications, but for these high level services, they may not, right? So the first order of business is to help them ease this migration pain as much as possible by not wanting them to, to upscale immediately. And we VMware cloud and AWS exactly does that. I mean, you don't have to do anything. You don't have to create new skill set for doing this, right? Their existing skill sets suffice, but at the same time, it gives them that, that leeway to build that skills roadmap for their team. DNS is invested in that, right? Yes. We want to help them build those skills in the high level services, be it aml, be it, be it i t be it data lake and analytics. We want to invest in them, and we help our customers through that. So that ultimately the ultimate goal of making them drop data is, is, is a front and center. >>I wanna get into some of the use cases and success stories, but I want to just reiterate, hit back your point on the skill thing. Because if you look at what you guys have done at aws, you've essentially, and Andy Chassey used to talk about this all the time when I would interview him, and now last year Adam was saying the same thing. You guys do all the heavy lifting, but if you're a VMware customer user or operator, you are used to things. You don't have to be relearn to be a cloud architect. Now you're already in the game. So this is like almost like a instant path to cloud skills for the VMware. There's hundreds of thousands of, of VMware architects and operators that now instantly become cloud architects, literally overnight. Can you respond to that? Do you agree with that? And then give an example. >>Yes, absolutely. You know, if you have skills on the VMware platform, you know, know, migrating to AWS using via by cloud and AWS is absolutely possible. You don't have to really change the skills. The operations are exactly the same. The management systems are exactly the same. So you don't really have to change anything but the advantages that you get access to all the other AWS services. So you are instantly able to integrate with other AWS services and you become a cloud architect immediately, right? You are able to solve some of the critical problems that your underlying IT infrastructure has immediately using this. And I think that's a great value proposition for our customers to use this service. >>And just one more point, I want just get into something that's really kind of inside baseball or nuanced VMC or VMware cloud on AWS means something. Could you take a minute to explain what on AWS means? Just because you're like hosting and using Amazon as a, as a work workload? Being on AWS means something specific in your world, being VMC on AWS mean? >>Yes. This is a great question, by the way, You know, on AWS means that, you know, VMware's vse platform is, is a, is an iconic enterprise virtualization software, you know, a disproportionately high market share across industries. So when we wanted to create a cloud product along with them, obviously our aim was for them, for the, for this platform to have the goodness of the AWS underlying infrastructure, right? And, and therefore, when we created this VMware cloud solution, it it literally use the AWS platform under the eighth, right? And that's why it's called a VMs VMware cloud on AWS using, using the, the, the wide portfolio of our regions across the world and the strength of the underlying infrastructure, the reliability and, and, and sustainability that it offers. And therefore this product is called VMC on aws. >>It's a distinction I think is worth noting, and it does reflect engineering and some levels of integration that go well beyond just having a SaaS app and, and basically platform as a service or past services. So I just wanna make sure that now super cloud, we'll talk about that a little bit in another interview, but I gotta get one more question in before we get into the use cases and customer success stories is in, in most of the VM world, VMware world, in that IT world, it used to, when you heard migration, people would go, Oh my God, that's gonna take months. And when I hear about moving stuff around and doing cloud native, the first reaction people might have is complexity. So two questions for you before we move on to the next talk. Track complexity. How are you addressing the complexity issue and how long these migrations take? Is it easy? Is it it hard? I mean, you know, the knee jerk reaction is month, You're very used to that. If they're dealing with Oracle or other old school vendors, like, they're, like the old guard would be like, takes a year to move stuff around. So can you comment on complexity and speed? >>Yeah. So the first, first thing is complexity. And you know, what makes what makes anything complex is if you're, if you're required to acquire new skill sets or you've gotta, if you're required to manage something differently, and as far as VMware cloud and AWS on both these aspects, you don't have to do anything, right? You don't have to acquire new skill sets. Your existing idea operation skill sets on, on VMware's platforms are absolutely fine and you don't have to manage it any differently like, than what you're managing your, your ID infrastructure today. So in both these aspects, it's exactly the same and therefore it is absolutely not complex as far as, as far as, as far as we cloud and AWS is concerned. And the other thing is speed. This is where the huge differentiation is. You have seen that, you know, large banks and large telcos have now moved their workloads, you know, literally in days instead of months. >>Because because of VMware cloud and aws, a lot of time customers come to us with specific deadlines because they want to exit their data centers on a particular date. And what happens, VMware cloud and AWS is called upon to do that migration, right? So speed is absolutely critical. The reason is also exactly the same because you are using the exactly the same platform, the same management systems, people are available to you, you're able to migrate quickly, right? I would just reference recently we got an award from President Zelensky of Ukraine for, you know, migrating their entire ID digital infrastructure and, and that that happened because they were using VMware cloud database and happened very swiftly. >>That's been a great example. I mean, that's one political, but the economic advantage of getting outta the data center could be national security. You mentioned Ukraine, I mean Oscar see bombing and death over there. So clearly that's a critical crown jewel for their running their operations, which is, you know, you know, world mission critical. So great stuff. I love the speed thing. I think that's a huge one. Let's get into some of the use cases. One of them is, the first one I wanted to talk about was we just hit on data, data center migration. It could be financial reasons on a downturn or our, or market growth. People can make money by shifting to the cloud, either saving money or making money. You win on both sides. It's a, it's a, it's almost a recession proof, if you will. Cloud is so use case for number one data center migration. Take us through what that looks like. Give an example of a success. Take us through a day, day in the life of a data center migration in, in a couple minutes. >>Yeah. You know, I can give you an example of a, of a, of a large bank who decided to migrate, you know, their, all their data centers outside their existing infrastructure. And they had, they had a set timeline, right? They had a set timeline to migrate the, the, they were coming up on a renewal and they wanted to make sure that this set timeline is met. We did a, a complete assessment of their infrastructure. We did a complete assessment of their IT applications, more than 80% of their IT applications, underlying v vSphere platform. And we, we thought that the right solution for them in the timeline that they wanted, right, is VMware cloud ands. And obviously it was a large bank, it wanted to do it safely and securely. It wanted to have it completely managed, and therefore VMware cloud and aws, you know, ticked all the boxes as far as that is concerned. >>I'll be happy to report that the large bank has moved to most of their applications on AWS exiting three of their data centers, and they'll be exiting 12 more very soon. So that's a great example of, of, of the large bank exiting data centers. There's another Corolla to that. Not only did they manage to manage to exit their data centers and of course use and be more agile, but they also met their sustainability goals. Their board of directors had given them goals to be carbon neutral by 2025. They found out that 35% of all their carbon foot footprint was in their data centers. And if they moved their, their ID infrastructure to cloud, they would severely reduce the, the carbon footprint, which is 35% down to 17 to 18%. Right? And that meant their, their, their, their sustainability targets and their commitment to the go to being carbon neutral as well. >>And that they, and they shift that to you guys. Would you guys take that burden? A heavy lifting there and you guys have a sustainability story, which is a whole nother showcase in and of itself. We >>Can Exactly. And, and cause of the scale of our, of our operations, we are able to, we are able to work on that really well as >>Well. All right. So love the data migration. I think that's got real proof points. You got, I can save money, I can, I can then move and position my applications into the cloud for that reason and other reasons as a lot of other reasons to do that. But now it gets into what you mentioned earlier was, okay, data migration, clearly a use case and you laid out some successes. I'm sure there's a zillion others. But then the next step comes, now you got cloud architects becoming minted every, and you got managed services and higher level services. What happens next? Can you give us an example of the use case of the modernization around the NextGen workloads, NextGen applications? We're starting to see, you know, things like data clouds, not data warehouses. We're not gonna data clouds, it's gonna be all kinds of clouds. These NextGen apps are pure digital transformation in action. Take us through a use case of how you guys make that happen with a success story. >>Yes, absolutely. And this is, this is an amazing success story and the customer here is s and p global ratings. As you know, s and p global ratings is, is the world leader as far as global ratings, global credit ratings is concerned. And for them, you know, the last couple of years have been tough as far as hardware procurement is concerned, right? The pandemic has really upended the, the supply chain. And it was taking a lot of time to procure hardware, you know, configure it in time, make sure that that's reliable and then, you know, distribute it in the wide variety of, of, of offices and locations that they have. And they came to us. We, we did, again, a, a, a alar, a fairly large comprehensive assessment of their ID infrastructure and their licensing contracts. And we also found out that VMware cloud and AWS is the right solution for them. >>So we worked there, migrated all their applications, and as soon as we migrated all their applications, they got, they got access to, you know, our high level services be our analytics services, our machine learning services, our, our, our, our artificial intelligence services that have been critical for them, for their growth. And, and that really is helping them, you know, get towards their next level of modern applications. Right Now, obviously going forward, they will have, they will have the choice to, you know, really think about which applications they want to, you know, refactor or which applications they want to go ahead with. That is really a choice in front of them. And, but you know, the, we VMware cloud and AWS really gave them the opportunity to first migrate and then, you know, move towards modernization with speed. >>You know, the speed of a startup is always the kind of the Silicon Valley story where you're, you know, people can make massive changes in 18 months, whether that's a pivot or a new product. You see that in startup world. Now, in the enterprise, you can see the same thing. I noticed behind you on your whiteboard, you got a slogan that says, are you thinking big? I know Amazon likes to think big, but also you work back from the customers and, and I think this modern application thing's a big deal because I think the mindset has always been constrained because back before they moved to the cloud, most IT, and, and, and on-premise data center shops, it's slow. You gotta get the hardware, you gotta configure it, you gotta, you gotta stand it up, make sure all the software is validated on it, and loading a database and loading oss, I mean, mean, yeah, it got easier and with scripting and whatnot, but when you move to the cloud, you have more scale, which means more speed, which means it opens up their capability to think differently and build product. What are you seeing there? Can you share your opinion on that epiphany of, wow, things are going fast, I got more time to actually think about maybe doing a cloud native app or transforming this or that. What's your, what's your reaction to that? Can you share your opinion? >>Well, ultimately we, we want our customers to utilize, you know, most of our modern services, you know, applications should be microservices based. When desired, they should use serverless applic. So list technology, they should not have monolithic, you know, relational database contracts. They should use custom databases, they should use containers when needed, right? So ultimately, we want our customers to use these modern technologies to make sure that their IT infrastructure, their licensing, their, their entire IT spend is completely native to cloud technologies. They work with the speed of a startup, but it's important for them to, to, to get to the first step, right? So that's why we create this journey for our customers, where you help them migrate, give them time to build the skills, they'll help them mo modernize, take our partners along with their, along with us to, to make sure that they can address the need for our customers. That's, that's what our customers need today, and that's what we are working backwards from. >>Yeah, and I think that opens up some big ideas. I'll just say that the, you know, we're joking, I was joking the other night with someone here in, in Palo Alto around serverless, and I said, you know, soon you're gonna hear words like architectural list. And that's a criticism on one hand, but you might say, Hey, you know, if you don't really need an architecture, you know, storage lists, I mean, at the end of the day, infrastructure is code means developers can do all the it in the coding cycles and then make the operations cloud based. And I think this is kind of where I see the dots connecting. Final thought here, take us through what you're thinking around how this new world is evolving. I mean, architecturals kind of a joke, but the point is, you know, you have to some sort of architecture, but you don't have to overthink it. >>Totally. No, that's a great thought, by the way. I know it's a joke, but it's a great thought because at the end of the day, you know, what do the customers really want? They want outcomes, right? Why did service technology come? It was because there was an outcome that they needed. They didn't want to get stuck with, you know, the, the, the real estate of, of a, of a server. They wanted to use compute when they needed to, right? Similarly, what you're talking about is, you know, outcome based, you know, desire of our customers and, and, and that's exactly where the word is going to, Right? Cloud really enforces that, right? We are actually, you know, working backwards from a customer's outcome and using, using our area the breadth and depth of our services to, to deliver those outcomes, right? And, and most of our services are in that path, right? When we use VMware cloud and aws, the outcome is a, to migrate then to modernize, but doesn't stop there, use our native services, you know, get the business outcomes using this. So I think that's, that's exactly what we are going through >>Actually, should actually, you're the director of global sales and go to market for VMware cloud on Aus. I wanna thank you for coming on, but I'll give you the final minute. Give a plug, explain what is the VMware cloud on Aus, Why is it great? Why should people engage with you and, and the team, and what ultimately is this path look like for them going forward? >>Yeah. At the end of the day, we want our customers to have the best paths to the cloud, right? The, the best path to the cloud is making sure that they migrate safely, reliably, and securely as well as with speed, right? And then, you know, use that cloud platform to, to utilize AWS's native services to make sure that they modernize their IT infrastructure and applications, right? We want, ultimately that our customers, customers, customer get the best out of, you know, utilizing the, that whole application experience is enhanced tremendously by using our services. And I think that's, that's exactly what we are working towards VMware cloud AWS is, is helping our customers in that journey towards migrating, modernizing, whether they wanna exit a data center or whether they wanna modernize their applications. It's a essential first step that we wanna help our customers with >>One director of global sales and go to market with VMware cloud on neighbors. He's with aws sharing his thoughts on accelerating business transformation on aws. This is a showcase. We're talking about the future path. We're talking about use cases with success stories from customers as she's thank you for spending time today on this showcase. >>Thank you, John. I appreciate it. >>Okay. This is the cube, special coverage, special presentation of the AWS Showcase. I'm John Furrier, thanks for watching.

(upbeat music) >> Hello, brilliant humans and welcome back to theCUBE. We're live from Detroit, Michigan. My name is Savannah Peterson. Joined here with John Furrier, John, so exciting, day three. >> Day three, cranking along, doing great, final day of KubeCon, it wraps up. This next segment's going to be great. It's about WebAssembly, the hottest trend here, at KubeCon that nobody knows about cause they just got some funding and it's got some great traction. Multiple players in here. People are really interested in this and they're really discovering it. They're digging into it. So, we're going to hear from one of the founders of the company that's involved. So, it'll be great. >> Yeah, I think we're right at the tip of the iceberg really. We started off the show with Scott from Docker talking about this, but we have a thought leader in this space. Please welcome Matt Butcher the CEO and co-founder of Fermyon Thank you for being here. Welcome. >> Yeah, thanks so much for having me. Favorite thing to talk about is WebAssembly after that is coffee but WebAssembly first. >> Hey, it's the morning. We can talk about both those on the show. (all chuckles) >> It might get confusing, but I'm willing to try. >> If you can use coffee as a metaphor to teach everyone about WebAssembly throughout the rest of the show. >> All right. That would be awesome. >> All right I'll keep that in mind. >> So when we were talking before we got on here I thought it was really fun because I think the hype is just starting in the WebAssembly space. Very excited about it. Where do you think we're at, set the stage? >> Honestly, we were really excited to come here and see that kind of first wave of hype. We came here expecting to have to answer the question you know, what is WebAssembly and why is anybody looking at it in the cloud space, and instead people have been coming up to us and saying, you know this WebAssembly thing, we're hearing about it. What are the problems it's solving? >> Savannah: Yeah. >> We're really excited to hear about it. So, people literally have been stopping us in restaurants and walking down the street, hey, "You're at KubeCon, you're the WebAssembly people. Tell us more about what's going on." >> You're like awesome celeb. I love this. >> Yeah, and I, >> This is great >> You know the, the description I used was I expected to come here shouting into the void. Hey, you know anybody, somebody, let me tell you about WebAssembly. Instead it's been people coming to us and saying "We've heard about it. Get us excited about it," and I think that's a great place to be. >> You know, one of the things that's exciting too is that this kind of big trend with this whole extraction layer conversation, multicloud, it reminds me of the old app server days where, you know there was a separation between the back end and front end, and then we're kind of seeing that now with this WebAssembly Wasm trend where the developers just want to have the apps run everywhere and the coding to kind of fall in, take a minute to explain what this is, why it's important, why are people jazzed about there's other companies like Cosmonic is in there. There's a lot of open source movement behind it. You guys are out there, >> Savannah: Docker. >> 20 million in fresh funding. Why is this important? What is it and why is it relevant right now? Why are people talking about it? >> I mean, we can't... There is no penasia in the tech world much for the good of all of us, right? To keep us employed. But WebAssembly seems to be that technology that just sort of arose at the right time to solve a number of problems that were really feeling intractable not very long ago. You know, at the core of what is WebAssembly? Well it's a binary format, right? But there's, you know, built on the same, strain of development that Java was built on in the 90's and then the .net run time. But with a couple of little fundamental changes that are what have made it compelling today. So when we think about the cloud world, we think about, okay well security's a big deal to us. Virtual machines are a way for us to run other people's untrusted operating systems on our hardware. Containers come along, they're a... The virtual machine is really the heavyweight class. This is the big thing. The workhorse of the cloud. Then along come Containers, they're a little slimmer. They're kind of the middleweight class. They provide us this great way to sort of package up just the application, not the entire operating system just the application and the bits we care about and then be able to execute those in a trusted environment. Well you know, serverless was the buzzword a few years ago. But one thing that serverless really identified for us is that we didn't actually have the kind of cloud side architecture that was the compute layer that was going to be able to fulfill the promise of serverless. >> Yeah. >> And you know, at that time I was at Microsoft we got to see behind the curtain and see how Azure operates and see the frustration with going, okay how do we get this faster? How do we get this startup time down from seconds to hundreds of milliseconds, WebAssembly comes along and we're able to execute these things in sub one millisecond, which means there is almost no cost to starting up one of these. >> Sub one millisecond. I just want to let everyone rest on that for a second. We've talked a lot about velocity and scale on the show. I mean everyone here is trying to do things faster >> Yep >> Obviously, but that is a real linchpin that makes a very big difference when we're talking about deploying things. Yeah. >> Yeah, and I mean when you think about the ecological and the cost impact of what we're building with the cloud. When we leave a bunch of things running in idle we're consuming electricity if nothing else. The electricity bill keeps going up and we're paying for it via cloud service charges. If you can start something in sub one millisecond then there's no reason you have to leave it running when nobody's using it. >> Savannah: Doesn't need to be in the background. >> That's right. >> So the lightweight is awesome. So, this new class comes up. So, like Java was a great metaphor there. This is kind of like that for the modern era of apps. >> Yeah. >> Where is this going to apply most, do you think? Where's it going to impact most? >> Well, you know, I think there are really four big categories. I think there's the kind of thing I was just talking about I think serverless and edge computing and kind of the server class of problem space. I think IOT is going to benefit, Amazon, Disney Plus, >> Savannah: Yes, edge. >> And PBS, sorry BBC, they all use WebAssembly for the players because they need to run the same player on thousands of different devices. >> I didn't even think about that use case. What a good example. >> It's a brilliant way to apply it. IOT is a hard space period and to be able to have that kind of layer of abstraction. So, that's another good use case >> Savannah: Yeah. >> And then I think this kind of plugin model is another one. You see it was Envoy proxy using this as a way to extend the core features. And I think that one's going to be very, very promising as well. I'm forgetting one, but you know. (all chuckles) I think you end up with these kind of discreet compartments where you can easily fit WebAssembly in here and it's solving a problem that we didn't have the technology that was really adequately solving it before. >> No, I love that. One of the things I thought was interesting we were all at dinner, we were together on Tuesday. I was chatting with Paris who runs Deliveroo at Apple and I can't say I've heard this about too many tools but when we were talking about WebAssembly she said "This is good for everybody" And, it's really nice when technologies come along that will raise the water level across the board. And I love that you're leading this. Speaking of you just announced a huge series aid, 20 million dollars just a few days ago. What does that mean for you and the team? >> I mean there's a little bit of economic uncertainty and it's always nice, >> Savannah: Just a little bit. >> Little bit. >> Savannah: It's come up on the show a little bit this week >> Just smidge. and it's nice to know that we're at a critical time developing this kind of infrastructure layer developing this kind of developer experience where they can go from, you know, blinking cursor to deployed application in two minutes or less. It would be a tragedy if that got forestalled merely because you can't achieve the velocity you need to carry it out. So, what's very exciting about being able to raise around like that at this critical time is that gives us the ability to grow strategically, be able to continue releasing products, building a community around WebAssembly as a whole and of course around our products at Fermyon is a little smaller circle in the bigger circle, and that's why we are so excited about having closed around, that's the perfect one to extend a runway like that. >> Well I'm super excited by this because one I love the concept. I think it's very relevant, like how you progress heavyweight, middleweight, maybe this is lightweight class. >> I know, I'm here for the analogy. No, it's great, its great. >> Maybe it's a lightweight class. >> And we're slimming, which not many of us can say in these times so that's awesome. >> Maybe it's more like the tractor trailer, the van, now you got the sports car. >> Matt: Yeah, I can go.. >> Now you're getting Detroit on us. >> I was trying for a coffee, when I just couldn't figure it out. (all chuckles) >> So, you got 20 million. I noticed the investors amplify very good technical VC and early stage firm. >> Amazing, yeah. >> Insight, they do early stage, big early stage like this. Also they're on the board of Docker. Docker was intent to put a tool out there. There's other competition out there. Cosmonic is out there. They're funded. So you got VC funded companies like yourselves and Cosmonic and others. What's that mean? Different tool chains, is it going to create fragmentation? Is there a common mission? How do you look at the competition as you get into the market >> When you see an ecosystem form. So, here we are at KubeCon, the cloud native ecosystem at this point I like to think of them as like concentric rings. You have the kind of core and then networking and storage and you build these rings out and the farther out you get then the easier it is to begin talking about competition and differentiation. But, when you're looking at that core piece everybody's got to be in there together working on the same stuff, because we want interoperability, we want standards based solutions. We want common ways of building things. More than anything, we want the developers and operators and users who come into the ecosystem to be able to like instantly feel like, okay I don't have to learn. Like you said, you know, 50 different tools for 50 different companies. "I see how this works", and they're doing this and they're doing this. >> Are you guys all contributing into the same open source? >> Yep, yeah, so... >> All the funding happens. >> Both CNCF and the ByteCode Alliance are organizations that are really kind of pushing forward that core technology. You know, you mentioned Cosmonic, Microsoft, SOSA, Red Hat, VMware, they're all in here too. All contributing and again, with all of us knowing this is that nascent stage where we got to execute it. >> How? >> Do it together. >> How are you guys differentiating? Because you know, open source is a great thing. Rising Tide floats all boats. This is a hot area. Is there a differentiation discussion or is it more let's see how it goes, kind of thing? >> Well for us, we came into it knowing very specifically what the problem was we wanted to solve. We wanted this serverless architecture that executed in sub one millisecond to solve, to really create a new wave of microservices. >> KubeCon loves performance. They want to run their stuff on the fastest platform possible. >> Yeah, and it shouldn't be a roadblock, you know, yeah. >> And you look at someone like SingleStore who's a database company and they're in it because they want to be able to run web assemblies close to the data. Instead of doing a sequel select and pulling it way out here and munging it and then pushing it back in. They move the code in there and it's executing in there. So everybody's kind of finding a neat little niche. You know, Cosmonic has really gone more for an enterprise play where they're able to provide a lot of high level security guarantees. Whereas we've been more interested in saying, "Hey, this your first foray into WebAssembly and you're interested in serverless we'll get you going in like a couple of minutes". >> I want to ask you because we had Scott Johnston on earlier opening keynote so we kind of chatted one-on-one and I went off form cause I really wanted to talk to him because Docker is one of the most important companies since their pivot, when they did their little reset after the first Docker kind of then they sold the enterprise off to Mirantis they've been doing really, really well. What's your relationship to Docker? He was very bullish with you guys. Insights, joint investor. Is there a relationship? You guys talk, what's going on there? >> I mean, I'm going to have to admit a little bit of hero worship on my part. I think Scott is brilliant. I just do, and having come from the Kubernetes world the Fermyon team, we've always kind of kept an eye on Docker communicated with a lot of them. We've known Justin Cormack for years. Chris Cornett. (indistinct) I mean yeah, and so it has been a very natural >> Probably have been accused of every Docker Con and we've did the last three years on the virtual side with them. So, we know them really well. >> You've always got your finger on the pulse for them. >> Do you have a relationship besides a formal relationship or is it more of pass shoot score together in the industry? >> Yeah. No, I think it is kind of the multi-level one. You come in knowing people. You've worked together before and you like working with each other and then it sort of naturally extends onto saying, "Hey, what can we do together?" And also how do we start building this ecosystem around us with Docker? They've done an excellent job of articulating why WebAssembly is a complimentary technology with Containers. Which is something I believe very wholeheartedly. You need all three of the heavyweight, middleweight, lightweight. You can't do all the with just one, and to have someone like that sort of with a voice profoundly be able to express, look we're going to start integrating it to show you how it works this way and prevent this sort of like needless drama where people are going, oh Dockers dead, now everything's WebAssembly, and that's been a great.. >> This fight that's been going on. I mean, Docker, Kubernetes, WebAssembly, Containers. >> Yeah. >> We've seen on the show and we both know this hybrid is the future. We're all going to be using a variety of different tools to achieve our goals and I think that you are obviously one of them. I'm curious because just as we were going on you mentioned that you have a PhD in philosophy. (Matt chuckles) >> Matt: Yeah. >> Which is a wild card. You're actually our second PhD in philosophy working in a very technical role on the show this week, which is kind of cool. So, how does that translate into the culture at Fermyon? What's it like on the team? >> Well, you know, a philosophy degree if nothing else teaches you to think in systems and both human systems and formal systems. So that helps and when you approach the process of building a company, you need to be thinking both in terms of how are we organizing this? How are we organizing the product? How do we organize the team? We have really learned that culture is a major deal and culture philosophy, >> Savannah: Why I'm bringing it up. >> We like that, you know, we've been very forward. We have our chip values, curiosity, humility inclusivity and passion, and those are kind of the four things that we feel like that each of us every day should strive to be exhibiting these kinds of things. Curiosity, because you can't push the envelope if you don't ask the hard questions. Humility, because you know, it's easy to get cocky and talk about things as if you knew all the answers. We know we don't and that means we can learn from Docker and Microsoft >> Savannah: That's why you're curious. >> And the person who stops by the booth that we've never met before and says, "hey" and inclusivity, of course, building a community if you don't execute on that well you can't build a good community. The diversity of the community is what makes it stronger than a singular.. >> You have to come in and be cohesive with the community. >> Matt: Yeah. >> The app focus is a really, I think, relevant right now. The timing of this is right online. I think Scott had a good answer I thought on the relationship and how he sees it. I think it's going to be a nice extension to not a extension that way, but like. >> It probably will be as well. >> Almost a pun there John, almost a pun. >> There actually might be an extension, but evolution what we're going to get to which I think is going to be pure application server, like. >> Yep, yep. Like performance for new class of developer. Then now the question comes up and we've been watching developer productivity. That is a big theme and our belief is that if you take digital transformation to its conclusion IT and developers aren't a department serving the business they are the business. That means the developer workflows will have to be radically rebuilt to handle the velocity and new tech for just coding. I call it architectural list. >> I like that. I might steal that. >> It's a pun, but it's also brings up the provocative question. You shouldn't have to need an architecture to code. I mean, Java was great for that reason in many ways. So, if that happens if the developers are running the business that means more apps. The apps is the business. You got to have tool chains and productivity. You can't have fragmentation. Some people are saying WebAssembly might, fork tool chains, might challenge the developer productivity. what's your answer to that? How would you address that objection? >> I mean the threat of forking is always lurking in the corner in open source. In a way it's probably a positive threat because it keeps us honest it keeps us wanting to be inclusive again and keep people involved. Honestly though, I'm not particularly worried about it. I know that the W-3 as a standards body, of course, one of the most respected standards bodies on the planet. They do html, they do cascading style sheets. WebAssembly is in that camp and those of us in the core are really very interested in saying, you know, come on in, let's build something that's going to be where the core is solid and you know what you got and then you can go into the resurgence of the application server. I mean, I wholeheartedly agree with you on that, and we can only get there if we say, all right, here are the common paradigms that we're all going to agree to use, now let's go build stuff. >> And as we've been saying, developers are setting, I think are going to set the standards and they're going to vote with their code and their feet, if you will. >> Savannah: A hundred percent. >> They will decide if you're not aligning with what they want to do. okay. On how they want to self-serve and or work, you'll figure that out. >> Yep, yep. >> You'll get instant feedback. >> Yeah. >> Well, you know, again, I tell you a huge fan of Docker. One of the things that Docker understood at the very outset, is that they had an infrastructure tool and developers were the way to get adoption, and if you look at how fast they got adoption versus many, many other technologies that are profoundly impacted. >> Savannah: Wild. >> Yeah. >> Savannah: It's a cool story. >> It's because they got the developers to go, "This is amazing, hey infrastructure folks, here's an infrastructure tool that we like" and the infrastructure folks are used to code being tossed over the wall are going, "Are you for real?" I mean, and that was a brilliant way to do it and I think that what.. >> John: Yeah, yeah. >> We want to replay in the WebAssembly world is making it developer friendly and you know the kind of infrastructure that we can actually operate. >> Well congratulations to the entire community. We're huge fans of the concept. I kind of see where it's going with connect the dots. You guys getting a lot of buzz. I have to ask you, my final question is the hype is beyond all recognition at this point. People are super pumped and enthusiastic about it and people are looking at it maybe some challenging it, but that's all good things. How do you get to the next level where people are confident that this is actually going to go the next step? Hype to confidence. We've seen great hype. Envoy was hyped up big time before it came in, then it became great. That was one of my favorite examples. Hype is okay, but now you got to put some meat on the bone. The sizzle on the stake so to speak. So what's going to be the stake for you guys as you see this going forward? What's the need? >> Yeah, you know, I talk about our first guiding story was, you know, blinking cursor to deployed application in two minutes. That's what you need to win developers initially. So, what's the next story after that? It's got to be, Fermyon can run real world applications that solve real world problems. That's where hype often fails. If you can build something that's neat but nobody's quite sure what to do with it, to use it, maybe somebody will discover a good use. But, if you take that gambling asset, >> Savannah: It's that ending answer that makes the difference. >> Yeah, yeah. So we say, all right, what are developers trying to build with our platform and then relentlessly focus on making that easier and solving the real world problem that way. That's the crucial thing that's going to drive us out of that sort of early hype stage into a well adopted technology and I talk from Fermyon point of view but really that's for all of us in the WebAssembly. >> John: Absolutely. >> Very well stated Matt, just to wrap us up when we're interviewing you here on theCUBE next year, what do you hope to be able to say then that you can't say today? >> All this stuff about coffee we didn't cover today, but also.. (all chuckles) >> Savannah: Here for the coffee show. Only analogies, that's a great analogy. >> I want to walk here and say, you know last time we talked about being able to achieve density in servers that was, you know, 10 times Kubernetes. Next year I want to say no, we're actually thousands of times beyond Kubernetes that we're lowering people's electricity bill by making these servers more efficient and the developers love it. >> That your commitment to the environment is something I want to do an entirely different show on. We learned that 7-8% of all the world's powers actually used on data centers through the show this week which is jarring quite frankly. >> Yeah, yeah. Tragic would be a better way of saying that. >> Yeah, I'm holding back so that we don't go over time here quite frankly. But anyways, Matt Butcher thank you so much for being here with us. >> Thank you so much for having me it was pleasure.. >> You are worth the hype you are getting. I am grateful to have you as our WebAssembly thought leader. In addition to Scott today from Docker earlier in the show. John Furrier, thanks for being my co-host and thank all of you for tuning into theCUBE here, live from Detroit. I'm Savannah Peterson and we'll be back with more soon. (ambient music)

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now in this program we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away, just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally I would just say please, like watch in ICE in Tim's sessions, like these are two of our best and brightest, They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time really hot area. As Brian said in a moment, I'll be right back with Anna East dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't wanna miss this >>Time series Data is everywhere. The number of sensors, systems and applications generating time series data increases every day. All these data sources producing so much data can cause analysis paralysis. Influx DB is an entire platform designed with everything you need to quickly build applications that generate value from time series data influx. DB Cloud is a serverless solution, which means you don't need to buy or manage your own servers. There's no need to worry about provisioning because you only pay for what you use. Influx DB Cloud is fully managed so you get the newest features and enhancements as they're added to the platform's code base. It also means you can spend time building solutions and delivering value to your users instead of wasting time and effort managing something else. Influx TVB Cloud offers a range of security features to protect your data, multiple layers of redundancy ensure you don't lose any data access controls ensure that only the people who should see your data can see it. >>And encryption protects your data at rest and in transit between any of our regions or cloud providers. InfluxDB uses a single API across the entire platform suite so you can build on open source, deploy to the cloud and then then easily query data in the cloud at the edge or on prem using the same scripts. And InfluxDB is schemaless automatically adjusting to changes in the shape of your data without requiring changes in your application. Logic. InfluxDB Cloud is production ready from day one. All it needs is your data and your imagination. Get started today@influxdata.com slash cloud. >>Okay, we're back. I'm Dave Valante with a Cube and you're watching evolving Influx DB into the smart data platform made possible by influx data. Anna ETOs Georgio is here, she's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into real-time analytics and is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IX is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory of course for speed. It's a kilo store, so it gives you a compression efficiency, it's gonna give you faster query speeds, you store files and object storage, so you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOx is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's live tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import super useful. Also broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so lot there. Now we talked to Brian about how you're using Rust and which is not a new programming language and of course we had some drama around Rust during the pandemic with the Mozilla layoffs, but the formation of the Rust Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, the adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Russ was chosen because of his exceptional performance and reliability. So while Russ is synt tactically similar to c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers. And dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on ality, for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fix race conditions, to protection against buffering overflows and to ensure thread safe async cashing structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learn about the, the new engine and, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It it's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data. And so much of the efficiency and performance of IOx comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of of illustrate why column or data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then enable each other and when they neighbor each other in the storage format, this provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the men and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one time stamp and do that for every single row. So you're scanning across a ton more data and that's why Rowe Oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, commoner data fit framework. So that's where a lot of the advantages come >>From. Okay. So you basically described like a traditional database, a row approach, but I've seen like a lot of traditional database say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native i, is it not as effective? Is the, is the foreman not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. Yeah. >>Got it. So let's talk about Arrow Data Fusion. What is data fusion? I know it's written in Rust, but what does it bring to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as it's in memory format. So the way that it helps in influx DB IOCs is that okay, it's great if you can write unlimited amount of cardinality into influx Cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So Data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PANDAS data frames as well and all of the machine learning tools associated with Pandas. >>Okay. You're also leveraging Par K in the platform cause we heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Parque and why is it important? >>Sure. So parque is the column oriented durable file format. So it's important because it'll enable bulk import, bulk export, it has compatibility with Python and Pandas, so it supports a broader ecosystem. Par K files also take very little disc disc space and they're faster to scan because again, they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and he's, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOx and I really encourage, if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and you just wanna learn more, then I would encourage you to go to the monthly Tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel look for the influx DDB unders IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about iacs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how INFLUX DB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and, and you guys super responsive, so really appreciate that. All right, thank you so much Anise for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yoakum, he's the director of engineering for Influx Data and we're gonna talk about how you update a SAS engine while the plane is flying at 30,000 feet. You don't wanna miss this. >>I'm really glad that we went with InfluxDB Cloud for our hosting because it has saved us a ton of time. It's helped us move faster, it's saved us money. And also InfluxDB has good support. My name's Alex Nada. I am CTO at Noble nine. Noble Nine is a platform to measure and manage service level objectives, which is a great way of measuring the reliability of your systems. You can essentially think of an slo, the product we're providing to our customers as a bunch of time series. So we need a way to store that data and the corresponding time series that are related to those. The main reason that we settled on InfluxDB as we were shopping around is that InfluxDB has a very flexible query language and as a general purpose time series database, it basically had the set of features we were looking for. >>As our platform has grown, we found InfluxDB Cloud to be a really scalable solution. We can quickly iterate on new features and functionality because Influx Cloud is entirely managed, it probably saved us at least a full additional person on our team. We also have the option of running InfluxDB Enterprise, which gives us the ability to even host off the cloud or in a private cloud if that's preferred by a customer. Influx data has been really flexible in adapting to the hosting requirements that we have. They listened to the challenges we were facing and they helped us solve it. As we've continued to grow, I'm really happy we have influx data by our side. >>Okay, we're back with Tim Yokum, who is the director of engineering at Influx Data. Tim, welcome. Good to see you. >>Good to see you. Thanks for having me. >>You're really welcome. Listen, we've been covering open source software in the cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem. The cloud has been being built out on open source, mobile, social platforms, key databases, and of course influx DB and influx data has been a big consumer and contributor of open source software. So my question to you is, where have you seen the biggest bang for the buck from open source software? >>So yeah, you know, influx really, we thrive at the intersection of commercial services and open, so open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services temping engines. Our, our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants and like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product influx db. >>You know, but I gotta ask you, Tim, because one of the challenge that that we've seen in particular, you saw this in the heyday of Hadoop, the, the innovations come so fast and furious and as a software company you gotta place bets, you gotta, you know, commit people and sometimes those bets can be risky and not pay off well, how have you managed this challenge? >>Oh, it moves fast. Yeah, that, that's a benefit though because it, the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we, what we tend to do is, is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example, that ecosystem is driven by thousands of intelligent developers, engineers, builders, they're adding value every day. So we have to really keep up with that. And as the stack changes, we, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's, it's something that we just do every day. >>So we have a survey partner down in New York City called Enterprise Technology Research etr, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity particularly, you know, along with cloud. But, but really Kubernetes is just, you know, still up until the right consistently even with, you know, the macro headwinds and all, all of the stuff that we're sick of talking about. But, so what are you doing with Kubernetes in the platform? >>Yeah, it, it's really central to our ability to run the product. When we first started out, we were just on AWS and, and the way we were running was, was a little bit like containers junior. Now we're running Kubernetes everywhere at aws, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code so our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google and figure out how to deliver services on those three clouds with all of their differences. >>Just to follow up on that, is it, no. So I presume it's sounds like there's a PAs layer there to allow you guys to have a consistent experience across clouds and out to the edge, you know, wherever is that, is that correct? >>Yeah, so we've basically built more or less platform engineering, This is the new hot phrase, you know, it, it's, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that, that just gets all of the underlying infrastructure out of the way and, and lets them focus on delivering influx cloud. >>Yeah, and I know I'm taking a little bit of a tangent, but is that, that, I'll call it a PAs layer if I can use that term. Is that, are there specific attributes to Influx db or is it kind of just generally off the shelf paths? You know, are there, is, is there any purpose built capability there that, that is, is value add or is it pretty much generic? >>So we really build, we, we look at things through, with a build versus buy through a, a build versus by lens. Some things we want to leverage cloud provider services, for instance, Postgres databases for metadata, perhaps we'll get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can, can deliver on that has consistency that is, is all generated from code that we can as a, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions and in no time. >>So how, so sometimes you build, sometimes you buy it. How do you make those decisions and and what does that mean for the, for the platform and for customers? >>Yeah, so what we're doing is, it's like everybody else will do, we're we're looking for trade offs that make sense. You know, we really want to protect our customers data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers you don't even see that, but we don't want to try to reinvent the wheel, like I had mentioned with SQL data stores for metadata, perhaps let's build on top of what of these three large cloud providers have already perfected. And we can then focus on our platform engineering and we can have our developers then focus on the influx data, software, influx, cloud software. >>So take it to the customer level, what does it mean for them? What's the value that they're gonna get out of all these innovations that we've been been talking about today and what can they expect in the future? >>So first of all, people who use the OSS product are really gonna be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across, over 4 billion series keys that people have stored. So there's a proven ability to scale now in terms of the open source, open source software and how we've developed the platform. You're getting highly available high cardinality time series platform. We manage it and, and really as, as I mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in real time. We deploy to our platform every day repeatedly all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change new features, better ways of doing deployments, safer ways of doing deployments. >>All of that happens behind the scenes. And like we had mentioned earlier, Kubernetes, I mean that, that allows us to get that done. We couldn't do it without having that platform as a, as a base layer for us to then put our software on. So we, we iterate quickly. When you're on the, the Influx cloud platform, you really are able to, to take advantage of new features immediately. We roll things out every day and as those things go into production, you have, you have the ability to, to use them. And so in the end we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let, let us do that for you. So, >>And that makes sense, but so is the, is the, are the innovations that we're talking about in the evolution of Influx db, do, do you see that as sort of a natural evolution for existing customers? I, is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >>Yeah, it really is it, it's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are, are really the hot thing. Iot, industrial iot especially, people want to just shove tons of data out there and be able to do queries immediately and they don't wanna manage infrastructure. What we've started to see are people that use the cloud service as their, their data store backbone and then they use edge computing with R OSS product to ingest data from say, multiple production lines and downsample that data, send the rest of that data off influx cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that and being in all sorts of different regions allows for people to really get out of the, the business of man trying to manage that big data, have us take care of that. And of course as we change the platform end users benefit from that immediately. And, >>And so obviously taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IOT and the Edge? How should we be thinking about the value that you bring from a security perspective? >>Yeah, we take, we take security super seriously. It, it's built into our dna. We do a lot of work to ensure that our platform is secure, that the data we store is, is kept private. It's of course always a concern. You see in the news all the time, companies being compromised, you know, that's something that you can have an entire team working on, which we do to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You know, you look at things like software, bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that, that's just part of our jobs to make sure that the platform that we're running it has, has fully vetted software and, and with open source especially, that's a lot of work. And so it's, it's definitely new territory. Supply chain attacks are, are definitely happening at a higher clip than they used to, but that is, that is really just part of a day in the, the life for folks like us that are, are building platforms. >>Yeah, and that's key. I mean especially when you start getting into the, the, you know, we talk about IOT and the operations technologies, the engineers running the, that infrastructure, you know, historically, as you know, Tim, they, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >>That >>Connected now, right? And so you've gotta have a partner that is again, take away that heavy lifting to r and d so you can focus on some of the other activities. Right. Give us the, the last word and the, the key takeaways from your perspective. >>Well, you know, from my perspective I see it as, as a a two lane approach with, with influx, with Anytime series data, you know, you've got a lot of stuff that you're gonna run on-prem, what you had mentioned, air gaping. Sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want torus their data to, to a company that's, that's got a full platform set up for them that they can build on, send that data over to the cloud, the cloud is not going away. I think more hybrid approach is, is where the future lives and that's what we're prepared for. >>Tim, really appreciate you coming to the program. Great stuff. Good to see you. >>Thanks very much. Appreciate it. >>Okay, in a moment I'll be back to wrap up. Today's session, you're watching The Cube. >>Are you looking for some help getting started with InfluxDB Telegraph or Flux Check >>Out Influx DB University >>Where you can find our entire catalog of free training that will help you make the most of your time series data >>Get >>Started for free@influxdbu.com. >>We'll see you in class. >>Okay, so we heard today from three experts on time series and data, how the Influx DB platform is evolving to support new ways of analyzing large data sets very efficiently and effectively in real time. And we learned that key open source components like Apache Arrow and the Rust Programming environment Data fusion par K are being leveraged to support realtime data analytics at scale. We also learned about the contributions in importance of open source software and how the Influx DB community is evolving the platform with minimal disruption to support new workloads, new use cases, and the future of realtime data analytics. Now remember these sessions, they're all available on demand. You can go to the cube.net to find those. Don't forget to check out silicon angle.com for all the news related to things enterprise and emerging tech. And you should also check out influx data.com. There you can learn about the company's products. You'll find developer resources like free courses. You could join the developer community and work with your peers to learn and solve problems. And there are plenty of other resources around use cases and customer stories on the website. This is Dave Valante. Thank you for watching Evolving Influx DB into the smart data platform, made possible by influx data and brought to you by the Cube, your leader in enterprise and emerging tech coverage.

>> This past May, the Cube, in collaboration with Influx Data shared with you the latest innovations in Time series databases. We talked at length about why a purpose-built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember that time series data is any data that's stamped in time and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community we talked about how in theory those time slices could be taken, you know every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors, and other devices and IOT equipment. Time series databases have had to evolve to efficiently support realtime data in emerging use, use cases in IOT and other use cases. And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the Smart Data platform, made possible by influx data and produced by the cube. My name is Dave Vellante, and I'll be your host today. Now, in this program, we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're going to hear from Brian Gilmore who is the director of IOT and emerging technologies at Influx Data. And we're going to talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program, you're going to hear a lot about things like rust implementation of Apache Arrow, the use of Parquet and tooling such as data fusion, which are powering a new engine for Influx db. Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices if you will, from, for example minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're going to hear from Anais Dotis-Georgiou who is a developer advocate at Influx Data. And we're going to get into the "why's" of these open source capabilities, and how they contribute to the evolution of the Influx DB platform. And then we're going to close the program with Tim Yocum. He's the director of engineering at Influx Data, and he's going to explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started.

(intro music) >> Hi everybody. Dave Vellante, back with Day Two coverage, we're live at the ARIA Hotel in Las Vegas for fal.con '22. Several thousand people here today. The keynote was, it was a little light. I think people were out late last night, but the keynote was outstanding and it's still going on. We had to break early because we have to strike early today, but we're really excited to have Stephan Goldberg here, Vice President of Technology Alliances at Claroty. And we're going to talk about an extremely important topic, which is the internet of things, the edge, we talk about it a lot. We haven't covered securing the edge here at theCUBE this week. And so Stephan really excited to have you on. >> Thank you for having me. >> You're very welcome. Tell us more about Claroty, C-L-A-R-O-T-Y, a very interesting spelling, but what's it all about? >> Claroty is cybersecurity company that specializes in cyber physical systems, also known as operational technology systems and the extended internet of things. The difference between the traditional IoT and what what everyone calls an IoT in the cyber physical system is that an IoT device has anything connected on the network that traditionally cannot carry an agent, a security camera, a card reader. A cyber physical system is a system that has influence and operates in the physical world but is controlled from the cyberspace. An example would be a controller, a turbine, a robotic arm, or an MRI machine. >> Yeah, so those are really high-end systems, run, are looked after by engineers, not necessarily consumers. So what's what's happening in that world? I mean, we've talked a lot on theCUBE about the schism between OT and IT, they haven't really talked a lot, but in the last several years, they've started to talk more. You look at the ecosystem of IoT providers. I mean, it's companies like Hitachi and PTC and Siemens. I mean, it's the different names than we're used to in IT. What are the big trends that you're seeing the macro? >> So, first of all, traditionally, most manufacturers and environments that were heavy on operations, operational technology, they had the networks air-gapped, completely separated. You had your IT network for business administration, you had the OT network to actually build stuff. Today with emerging technologies and even modern switching architecture everything is being converged. You have the same physical infrastructure in terms of networking, that carries both networks. Sometimes a human error, sometimes a business logic that needs to interconnect these networks to transmit data from the OT side of the house, to the IT side of the house, exposes the OT environment to cyber threats. >> Was that air-gap by design or was it just that there wasn't connectivity? >> It was air-gap by design, due to security and operational reasons, and also ownership in these organizations. The IT-managed space was completely separate from the OT-managed space. So whoever built a network for the controllers to build a car, for example, was an automation engineer and the vendors, that have built these networks, were automation vendors, unlike the traditional Ciscos of the world, that we're specializing in IT. Today we're seeing the IT vendors on the OT side, and the OT vendors, they're worried about the IT side. >> But I mean, tradition, I mean, engineers are control freaks. No offense, but, I'm glad they are, I'm thankful for that. So there must have been some initial reticence to them connecting up these air-gap systems. They went wanted to make sure that they were secure, that they did it right, and presumably that's where you guys come in. What are the exposures and risks of these, of this critical infrastructure that we should be aware of? >> So you're completely right. And from an operational perspective let let's call it change control is very rigorous. So they did not want to go on the internet and just, we're seeing it with adoption of cloud technologies, for example. Cloud as in industry four ago, five ago, cloud as in cyber security. We all heard Amol's keynote from this morning talking about critical infrastructures and we'll touch upon our partnership in a second, but CrowdStrike, CrowdStrike being considered and deployed within these environments is a new thing. It's a new thing because the OT operation managers and the chief information security officers, they understand that air-gap is no longer a valid strategy. From a business perspective, these networks are already connected. We're seeing the trends of cyber attacks, IT cyber attacks, like not Patreon, I'm not talking about the Stoxnet, the targeted OT. I'm talking about WannaCry, EternalBlue, IT vulnerabilities that did not target OT, but due to the outdated and the specification of OT posture on the networks, they hit healthcare, they hit OT much harder than they did IT. >> Was Log4J, did that sleep into OT, or any IT that. >> So, absolutely. >> So Log4J right, which was so pervasive, like so many of these malwares. >> All these vulnerabilities that, it's a windows vulnerability, it has nothing to do with OT. But then when you stop and you say, hold on, my human machine interface workstation, although it has some proprietary software by Rockwell or Siemens running on it, what is the underlying operating system? Oh, hold on, it's Windows. We haven't updated that for like eight years. We were focused on updating the software but not the underlying operating system. The vulnerabilities exist to a greater extent on the OT side of the house because of the same characteristic of operational technology environments. >> So the brute force air-gap approach was no longer viable because the business imperative came in and said, no, we have to connect these systems to digitally transform, or advance our business, there's opportunities to monetize, whatever it was. The business laid that out as an imperative. So now OT engineers have to rethink how they secure it. So what are the steps that they're taking and how does Claroty help? Is there a sort of a playbook, a sequential playbook? >> Absolutely, so before we discussed the maturity curve of adopting an CPS security, or OT security technology, let's touch upon the characteristic of the space and what it led vendors like Claroty to build. So you have the rigorous chain control. You have the security in mind, operations, lowered the risk state of mind. That led vendors, likes of Claroty, to build a solution. And I'm talking about seven, eight years ago, to be passive, mostly passive or passive only to inspect network and to analyze network and focus on detection rather than taking action like response or preventative maintenance. >> Um-hmm. >> It made vendors to build on-prem solutions because of the cloud-averse state of mind of this industry. And because OT is very specific, it led vendors to focus only on OT devices, overlooking what we discussed as IoT, Unfortunately, besides HMI and PLC, the controller in the plant, you also have the security camera. So when you install an OT security solution I'm talking about the traditional ones, they traditionally overlook the security camera or anything that is not considered traditional OT. These three observations, although they were necessary in the beginning, you understand the shortcomings of it today. >> Um-hmm. >> So cloud-averse led to on-prem which leads to war security. It's like comparing CrowdStrike and one of its traditional competitors in the antivirus space. What CrowdStrike innovated is the SaaS first, cloud-native solution that is continuously being updated and provide the best in cloud security, right? And that is very much like what Claroty's building. We decided to go SaaS first and cloud-native solution. >> So, because of cloud-aversion, the industry shows somewhat outdated deployment models, on-prem, which limited scale and created greater diversity, more stovepipes, all the problems that we always talk about. Okay, and so is the answer to that, just becoming more cloud, having more of an affinity to cloud? That was a starting point, right. >> This is exactly it. Air-gap is perceived as secured, but you don't get updates and you don't really know what's going on in your network. If you have a Claroty or a crosswork installer, you have much higher probability detecting fast and responding fast. If you don't have it, you are just blind. You will be bridged, that's the. >> I was going to say, plus, air-gap, it's true, but people can get through air-gaps, too. I mean, it's harder, but Stoxnet. Yeah, look at Stoxnet right, oh, it's mopping the floor, boom, or however it happened, but so yeah. >> Correct. >> So, but the point being, you know, assume that breach, even though I know CrowdStrike thinks that the unstoppable breach is a myth, but you know, you talk to people like Kevin Mandia, it's like, we assume you're going to get breached, right? Let's make that assumption. Yeah, okay, and so that means you've got to have visibility into the network. So what are those steps that you would, what's that maturity model that you referenced before? >> So on top of these underlying principles, which is cloud-native, comprehensive, not OT only, but XIoT, and then bring that the verticalization and OT specificity. On top of that, you're exactly right. There is a maturity curve. You cannot boil the ocean, deploy protections, and change the environment within one day. It starts with discovering everything that is connected to your network. Everything from the traditional workstations to the cameras, and of course ending up with the cyber physical systems on the network. That discovery cannot be only a high level profile, it needs to be in depth to the level you need to know application versions of these devices. If you cannot tell the application version you cannot correlate it to a vulnerability, right? Just knowing that's an HMI or that's a PLC by Siemens is insufficient. You need to know the app version, then you can correlate to vulnerability, then you can correlate to risk. This is the next step, risk assessment. You need to put up a score basically, on each one of these devices. A vulnerability score, risk score, in order to prioritize action. >> Um-hmm. >> These two steps are discovery and thinking about the environment. The next two steps are taking action. After we have the prioritized devices discovered on your network, our approach is that you need to ladle in and deploy protections from a preventative perspective. Claroty delivers recommended policies in the form of access control lists or rules. >> Right. >> That can leverage existing infrastructure without touching a device without patching it, just to protect it. The next step would be detection and response. Once you have these policies deployed you also can leverage them to spot policy deviations. >> And that's where CrowdStrike comes in. So talk about how you guys partner with CrowdStrike, what that integration looks like and what the differentiation is. >> So actually the integration with CrowdStrike crosses the the entire customer journey. It starts with visibility. CrowdStrike and us exchange data on the asset level. With the announcement during FalCon, with Falcon Discover for IoT, we are really, really proud working on that with CrowdStrike. Traditionally CrowdStrike discovered and provided data about the IT assets. And we did the same thing with CPS and OT. Today with Falcon Discover for IoT, and us expanding to the XIoT space, both of us look at all devices but we can discover different things. When you merge these data sets you have an unparalleled visibility into any environment, and specifically OT. The integrations continue, and maybe the second spotlight I'll put, but without diminishing the other ones, is detection and response. It's the XDR Alliance. Claroty is very proud to be one of the first partners, XDR Alliance partners, for CrowdStrike, fitting in to the XDR, to CrowdStrike's XDR, the data that is needed to mitigate and respond and get more context about breaches in these OT environments, but also take action. Also trigger action, via Claroty and leverage Claroty's network-centric capabilities to respond. >> We hear a lot. We heard a lot in today's keynote note about the data, the importance of data, of the graph database. How unique is this Stephan, in the industry, in your view? >> The uniqueness of what exactly? >> Of this joint solution, if you will, this capability. >> I told my counterparts from CrowdStrike yesterday, the go-to market ones and the product management ones. If we are successful with Falcon Discover for IoT, and that product matures, as we plan for it to mature, it will change the industry, the OT security industry, for all of us. Not only for Claroty, for all players in this space. And this is why it's so important for us to stay coordinated and support this amazing company to enter this space and provide better security to organizations that really support our lives. >> We got to leave it there, but this is such an important topic. We're seeing in the war in Ukraine, there's a cyber component in the future of war. >> Yes. >> Today. And what do they do? They go after critical infrastructure. So protecting that critical infrastructure is so important, especially for a country like the United States, which has so much critical infrastructure and a lot to lose. So Stephan, thanks so much. >> Thank you. >> For the work that you're doing. It was great to have you on theCUBE. >> Thank you. >> All right, keep it right there. Dave Vellante for theCUBE. We'll be right back from fal.con '22. We're live from the ARIA in Las Vegas. (techno music)

foreign okay we're back at Falcon 2022 crowdstrike's big user conference first time in a couple of years obviously because of kova this is thecube's coverage Dave vellante and Dave Nicholson wall-to-wall coverage two days in a row Michael Rogers the series the newly minted vice president of global alliances at crowdstrike Michael first of all congratulations on the new appointment and welcome to the cube thank you very much it's an honor to be here so dial back just a bit like think about your first hundred days in this new role what was it like who'd you talk to what'd you learn wow well the first hundred days were filled with uh excitement uh I would say 18 plus hours a day getting to know the team across the globe a wonderful team across all of the partner types that we cover and um just digging in and spending time with people and understanding uh what the partner needs were and and and and it was just a it was a blur but a blast I agree with any common patterns that you heard that you could sort of coalesce around yeah I mean I think that uh really what a common thing that we hear at crowdstrike whether it's internal is extra external is getting to the market as fast as possible there's so much opportunity and every time we open a door the resource investment we need we continue to invest in resources and that was an area that we identified and quickly pivoted and started making some of those new investments in a structure of the organization how we cover Partners uh how we optimize uh the different routes to Market with our partners and yeah just a just a it's been a wonderful experience and in my 25 years of cyber security uh actually 24 and a half as of Saturday uh I can tell you that I have never felt and had a better experience in terms of culture people and a greater mission for our customers and our partners you'll Max funny a lot of times Dave we talk about this is we you know we learned a lot from Amazon AWS with the cloud you know taking something you did internally pointing it externally to Pizza teams there's shared responsibility model we talk about that and and one of the things is blockers you know Amazon uses that term blocker so were there any blockers that you identified that you're you're sort of working with the partner ecosystem to knock down to accelerate that go to market well I mean if I think about what we had put in place prior and I had the benefit of being vice president of America's prior to the appointment um and had the pleasure of succeeding my dear friend and Mentor Matthew Pauley um a lot of that groundwork was put in place and we work collectively as a leadership team to knock down a lot of those blockers and I think it really as I came into the opportunity and we made new Investments going into the fiscal year it's really getting to Market as fast as possible it's a massive Target addressable market and identifying the right routes and how to how to harness that power of we to drive the most value to the marketplace yeah what is it what does that look like in terms of alliances alliances can take a lot of shape we've we've talked to uh service providers today as an example um our Global Systems integrators in that group also what what is what does the range look like yeah I mean alliances at crowdstrike and it's a great question because a lot of times people think alliances and they only think of Technology alliances and for us it spans really any and all routes to Market it could be your traditional solution providers which might be regionally focused it could be nationally focused larger solution providers or Lars as you noted service providers and telcos global system integrators mssps iot Partners OEM Partners um and store crouchstrike store Partners so you look across that broad spectrum and we cover it all so the mssps we heard a lot about that on the recent earnings call we've heard this is a consistent theme we've interviewed a couple here today what's driving that I mean is it the fact that csos are just you know drowning for talent um and why crowdstrike why is there such an affinity between mssps and crowdstrike yeah a great question we um and you noted that uh succinctly that csos today are faced with the number one challenge is lack of resources and cyber security the last that I heard was you know in the hundreds of thousands like 350 000 and that's an old stat so I would venture to Guess that the open positions in cyber security are north of a half a million uh as we sit here today and um service providers and mssps are focused on providing service to those customers that are understaffed and have that Personnel need and they are harnessing the crowdstrike platform to bring a cloud native best of breed solution to their customers to augment and enhance the services that they bring to those customers so partner survey what tell us about the I love surveys I love data you know this what was the Genesis of the survey who took it give us the breakdown yeah that's a great question no uh nothing is more important than the feedback that we get from our partners so every single year we do a partner survey it reaches all partner types in the uh in the ecosystem and we use the net promoter score model and so we look at ourselves in terms of how we how we uh rate against other SAS solution providers and then we look at how we did last year and in the next year and so I'm happy to say that we increased our net promoter score by 16 percent year over year but my philosophy is there's always room for improvement so the feedback from our partners on the positive side they love the Falcon platform they love the crowdstrike technology they love the people that they work with at crowdstrike and they like our enablement programs the areas that they like us to see more investment in is the partner program uh better and enhanced enablement making it easier to work with crowdstrike and more opportunities to offer services enhance services to their customers dramatic differences between the types of Partners and and if so you know why do you think those were I mean like you mentioned you know iot Partners that's kind of a new area you know so maybe maybe there was less awareness there were there any sort of differences that you noticed by type of partner I would say that you know the areas or the part the partners that identified areas for improvement were the partners that that uh either were new to crowdstrike or they're areas that we're just investing in uh as as we expand as a company and a demand from the market is you know pull this thing into these new routes to Market um not not one in particular I mean iot is something that we're looking to really blow up in the next uh 12 to 18 months um but no no Common Thread uh consistent feedback across the partner base speaking of iot he brought it up before it's is it in a you see it as an adjacency to i-team it seems like it and OT used to never talk to each other and now they're increasingly doing so but they're still it still seems like different worlds what have you found and learned in that iot partner space yeah I mean I think the key and we the way we look at the journey is it starts with um Discovery discovering the assets that are in the OT environment um it then uh transitions to uh detection and response and really prevention and once you can solve that and you build that trust through certifications in the industry um you know it really is a game changer anytime you have Global in your job title first word that comes to mind for me anyway is sovereignty issues is that something that you deal with in this space uh in terms of partners that you're working with uh focusing on Partners in certain regions so that they can comply with any governance or sovereignty yeah that's that's a great question Dave I mean we have a fantastic and deep bench on our compliance team and there are certain uh you know parameters and processes that have been put in place to make sure that we have a solid understanding in all markets in terms of sovereignty and and uh where we're able to play and how that were you North America before or Americas uh Americas America so you're familiar with the sovereignty issue yeah a little already Latin America is certainly uh exposed me plenty of plenty of that yes 100 so you mentioned uh uh Tam before I think it was total available Market you had a different word for the t uh total addressable Mark still addressable Market okay fine so I'm hearing Global that's a tam expansion opportunity iot is definitely you know the OT piece and then just working better um you know better Groove swing with the partners for higher velocity when you think about the total available total addressable market and and accelerating penetration and growing your Tam I've seen the the charts in your investor presentation and you know starts out small and then grows to you know I think it could be 100 billion I do a lot of Tam analysis but just my back a napkin had you guys approaching 100 billion anyway how do you think about the Tam and what role do Partners play in terms of uh increasing your team yeah that's a great question I mean if you think about it today uh George announced on the day after our 11th anniversary as a company uh 20 000 customers and and if you look at that addressable Market just in the SMB space it's north of 50 million companies that are running on Legacy on-prem Solutions and it really provides us an opportunity to provide those customers with uh Next Generation uh threat protection and and detection and and response partners are the route to get there there is no doubt that we cannot cover 50 50 million companies requires a span of of uh of of of a number of service providers and mssps to get to that market and that's where we're making our bets what what's an SMB that is a candidate for crowdstrike like employee size or how do you look at that like what's the sort of minimum range yeah the way we segment out the SMB space it's 250 seats or endpoints and below 250 endpoints yes right and so it's going to be fairly significant so math changes with xdr with the X and xdr being extended the greater number of endpoints means that a customer today when you talk about total addressable Market that market can expand even without expanding the number of net new customers is that a fair yeah Fair assessment yep yeah you got that way in that way but but map that to like company size can you roughly what's the what's the smallest s that would do business with crowdstrike yeah I mean we have uh companies as small as five employees that will leverage crowd strike yeah 100 and they've got hundreds of endpoints oh no I'm sorry five uh five endpoints is oh okay so it's kind of 250 endpoints as well like the app that's the sweets that's it's that's kind of the Top Line we look at and then we focus oh okay when we Define SMB it's below so five to 250 endpoints right yes and so roughly so you're talking to companies with less than 100 employees right yeah yeah so I mean this is what I was talking about before I say I look around the the ecosystem myself it kind of reminds me of service now in 2013 but servicenow never had a SMB play right and and you know very kind of proprietary closed platform not that you don't have a lot of propriety in your platform you do but you they were never going to get down Market there and their Tam is not as big in my view but I mean your team is when you start bringing an iot it's it's mind-boggling it's endless how large it could be yeah all right so what's your vision for the Elevate program partner program well I I look at uh a couple things that we've we've have in place today one is um one is we've we've established for the first time ever at crowdstrike the Alliance program management office apmo and that team is focused on building out our next Generation partner program and that's you know processes it's you know uh it's it's ring fencing but it's most important importantly identifying capabilities for partners to expand to reduce friction and uh grow their business together with crowdstrike we also look at uh what we call program Harmony and that's taking all of the partner types or the majority of the partner types and starting to look at it with the customer in the middle and so multiple partners can play a role on the journey to bringing a customer on board initially to supporting that customer going forward and they can all participate and be rewarded for their contribution to that opportunity so it's really a key area for us going forward Hub and spoke model with the center of the that model is the customer you're saying that's good okay so you're not like necessarily fighting each other for for a sort of ownership of that model but uh cool Michael Rogers thanks so much for coming on thecube it was great to have you my pleasure thank you for having me you're welcome all right keep it right there Dave Nicholson and Dave vellante we'll be right back to Falcon 22 from the Aria in Las Vegas you're watching thecube foreign [Music]