Ray Wang, Constellation & Pascal Bornet, Best-selling Author | UiPath FORWARD 5
>>The Cube Presents UI Path Forward five. Brought to you by UI Path, >>Everybody. We're back in Las Vegas. The cube's coverage we're day one at UI Path forward. Five. Pascal Borne is here. He's an expert and bestselling author in the topic of AI and automation and the book Intelligent Automation. Welcome to the world of Hyper Automation, the first book on the topic. And of course, Ray Wong is back on the cube. He's the founder, chairman and principal analyst, Constellation Reese, also bestselling author of Everybody Wants To Rule the World. Guys, thanks so much for coming on The Cubes. Always a pleasure. Ray Pascal, First time on the Cube, I believe. >>Yes, thank you. Thanks for the invitation. Thank you. >>So what is artificial about artificial intelligence, >>For sure, not people. >>So, okay, so you guys are both speaking at the conference, Ray today. I think you're interviewing the co CEOs. What do you make of that? What's, what are you gonna, what are you gonna probe with these guys? Like, how they're gonna divide their divide and conquer, and why do you think the, the company Danielle in particular, decided to bring in Rob Sland? >>Well, you know what I mean, Like, you know, these companies are now at a different stage of growth, right? There's that early battle between RPA vendors. Now we're actually talking something different, right? We're talking about where does automation go? How do we get the decisioning? What's the next best action? That's gonna be the next step. And to take where UI path is today to somewhere else, You really want someone with that enterprise cred and experience the sales motions, the packages, the partnership capabilities, and who else better than Roblin? He, that's, he's done, he can do that in his sleep, but now he's gotta do that in a new space, taking whole category to another level. Now, Daniel on the other hand, right, I mean, he's the visionary founder. He put this thing from nothing to where he is today, right? I mean, at that point you want your founder thinking about the next set of ideas, right? So you get this interesting dynamic that we've seen for a while with co CEOs, those that are doing the operations, getting the stuff out the door, and then letting the founders get a chance to go back and rethink, take a look at the perspective, and hopefully get a chance to build the next idea or take the next idea back into the organization. >>Right? Very well said. Pascal, why did you write your book on intelligent automation and, and hyper automation, and what's changed since you've written that book? >>So, I, I wrote this book, An Intelligent Automation, two years ago. At that time, it was really a new topic. It was really about the key, the, the key, the key content of the, of the book is really about combining different technologies to automate the most complex end to end business processes in companies. And when I say capabilities, it's, we, we hear a lot about up here, especially here, robotic process automation. But up here alone, if you just trying to transform a company with only up here, you just fall short. Okay? A lot of those processes need more than execution. They need language, they need the capacity to view, to see, they need the capacity to understand and to, and to create insights. So by combining process automation with ai, natural language processing, computer vision, you give this capability to create impact by automating end to end processes in companies. >>I, I like the test, what I hear in the keynote with independent experts like yourself. So we're hearing that that intelligent automation or automation is a fundamental component of digital transformation. Is it? Or is it more sort of a back office sort of hidden in inside plumbing Ray? What do you think? >>Well, you start by understanding what's going on in the process phase. And that's where you see discover become very important in that keynote, right? And that's where process mining's playing a role. Then you gotta automate stuff. But when you get to operations, that's really where the change is going to happen, right? We actually think that, you know, when you're doing the digital transformation pieces, right? Analytics, automation and AI are coming together to create a concept we call decision velocity. You and I make a quick decision, boom, how long does it take to get out? Management committee could free forever, right? A week, two months, never. But if you're thinking about competing with the automation, right? These decisions are actually being done a hundred times per second by machine, even a thousand times per second. That asymmetry is really what people are facing at the moment. >>And the companies that are gonna be able to do that and start automating decisions are gonna be operating at another level. Back to what Pascal's book talking about, right? And there are four questions everyone has to ask you, like, when do you fully intelligently automate? And that happens right in the background when you augment the machine with a human. So we can find why did you make an exception? Why did you break a roll? Why didn't you follow this protocol so we can get it down to a higher level confidence? When do you augment the human with the machine so we can give you the information so you can act quickly. And the last one is, when do you wanna insert a human in the process? That's gonna be the biggest question. Order to cash, incident or resolution, Hire to retire, procure to pay. It doesn't matter. When do you want to put a human in the process? When do you want a man in the middle, person in the middle? And more importantly, when do you want insert friction? >>So Pascal, you wrote your book in the middle of the, the pandemic. Yes. And, and so, you know, pre pandemic digital transformation was kind of a buzzword. A lot of people gave it lip service, eh, not on my watch, I don't have to worry about that. But then it became sort of, you're not a digital business, you're out of business. So, so what have you seen as the catalyst for adoption of automation? Was it the, the pandemic? Was it sort of good runway before that? What's changed? You know, pre isolation, post isolation economy. >>You, you make me think about a joke. Who, who did your best digital transformation over the last years? The ceo, C H R O, the Covid. >>It's a big record ball, right? Yeah. >>Right. And that's exactly true. You know, before pandemic digital transformation was a competitive advantage. >>Companies that went into it had an opportunity to get a bit better than their, their competitors during the pandemic. Things have changed completely. Companies that were not digitalized and automated could not survive. And we've seen so many companies just burning out and, and, and those companies that have been able to capitalize on intelligent automation, digital transformations during the pandemic have been able not only to survive, but to, to thrive, to really create their place on the market. So that's, that has been a catalyst, definitely a catalyst for that. That explains the success of the book, basically. Yeah. >>Okay. Okay. >>So you're familiar with the concept of Stew the food, right? So Stew by definition is something that's delicious to eat. Stew isn't simply taking one of every ingredient from the pantry and throwing it in the pot and stirring it around. When we start talking about intelligent automation, artificial intelligence, augmented intelligence, it starts getting a bit overwhelming. My spy sense goes off and I start thinking, this sounds like mush. It doesn't sound like Stew. So I wanna hear from each of you, what is the methodical process that, that people need to go through when they're going through digital trans transmission, digital transformation, so that you get delicious stew instead of a mush that's just confused everything in your business. So you, Ray, you want, you want to, you wanna answer that first? >>Yeah. You know, I mean, we've been talking about digital transformation since 2010, right? And part of it was really getting the business model, right? What are you trying to achieve? Is that a new type of offering? Are you changing the way you monetize something? Are you taking existing process and applying it to a new set of technologies? And what do you wanna accomplish, right? Once you start there, then it becomes a whole lot of operational stuff. And it's more than st right? I mean, it, it could be like, well, I can't use those words there. But the point being is it could be a complete like, operational exercise. It could be a complete revenue exercise, it could be a regulatory exercise, it could be something about where you want to take growth into the next level. And each one of those processes, some of it is automation, right? There's a big component of it today. But most of it is really rethinking about what you want things to do, right? How do you actually make things to be successful, right? Do I reorganize a process? Do I insert a place to do monetization? Where do I put engagement in place? How do I collect data along the way so I can build better feedback loop? What can I do to build the business graph so that I have that knowledge for the future so I can go forward doing that so I can be successful. >>The Pascal should, should, should the directive be first ia, then ai? Or are these, are these things going to happen in parallel naturally? What's your position on that? Is it first, >>So it, so, >>So AI is part of IA because that's, it's, it's part of the big umbrella. And very often I got the question. So how do you differentiate AI in, I a, I like to say that AI is only the brain. So think of ai cuz I'm consider, I consider AI as machine learning, Okay? Think of AI in a, like a brain near jar that only can think, create, insight, learn, but doesn't do anything, doesn't have any arms, doesn't have any eyes, doesn't not have any mouth and ears can't talk, can't understand with ia, you, you give those capabilities to ai. You, you basically, you create a cap, the capability, technological capability that is able to do more than just thinking, learning and, and create insight, but also acting, speaking, understanding the environment, viewing it, interacting with it. So basically performing these, those end to end processes that are performed currently by people in companies. >>Yeah, we're gonna get to a point where we get to what we call a dynamic scenario generation. You're talking to me, you get excited, well, I changed the story because something else shows up, or you're talking to me and you're really upset. We're gonna have to actually ch, you know, address that issue right away. Well, we want the ability to have that sense and respond capability so that the next best action is served. So your data, your process, the journey, all the analytics on the top end, that's all gonna be served up and changed along the way. As we go from 2D journeys to 3D scenarios in the metaverse, if we think about what happens from a decentralized world to decentralized, and we think about what's happening from web two to web three, we're gonna make those types of shifts so that things are moving along. Everything's a choose your end venture journey. >>So I hope I remember this correctly from your book. You talked about disruption scenarios within industries and within companies. And I go back to the early days of, of our industry and East coast Prime, Wang, dg, they're all gone. And then, but, but you look at companies like Microsoft, you know, they were, they were able to, you know, get through that novel. Yeah. Ibm, you know, I call it survived. Intel is now going through their, you know, their challenge. So, so maybe it's inevitable, but how do you see the future in terms of disruption with an industry, Forget our industry for a second, all industry across, whether it's healthcare, financial services, manufacturing, automobiles, et cetera. How do you see the disruption scenario? I'm pretty sure you talked about this in your book, it's been a while since I read it, but I wonder if you could talk about that disruption scenario and, and the role that automation is going to play, either as the disruptor or as the protector of the incumbents. >>Let's take healthcare and auto as an example. Healthcare is a great example. If we think about what's going on, not enough nurses, massive shortage, right? What are we doing at the moment? We're setting five foot nine robots to do non-patient care. We're trying to capture enough information off, you know, patient analytics like this watch is gonna capture vitals from a going forward. We're doing a lot what we can do in the ambient level so that information and data is automatically captured and decisions are being rendered against that. Maybe you're gonna change your diet along the way, maybe you're gonna walk an extra 10 minutes. All those things are gonna be provided in that level of automation. Take the car business. It's not about selling cars. Tesla's a great example. We talk about this all the time. What Tesla's doing, they're basically gonna be an insurance company with all the data they have. They have better data than the insurance companies. They can do better underwriting, they've got better mapping information and insights they can actually suggest next best action do collision avoidance, right? Those are all the things that are actually happening today. And automation plays a big role, not just in the collection of that, that information insight, but also in the ability to make recommendations, to do predictions and to help you prevent things from going wrong. >>So, you know, it's interesting. It's like you talk about Tesla as the, the disrupting the insurance companies. It's almost like the over the top vendors have all the data relative to the telcos and mopped them up for lunch. Pascal, I wanna ask you, you know, the topic of future of work kind of was a bromide before, but, but now I feel like, you know, post pandemic, it, it actually has substance. How do you see the future of work? Can you even summarize what it's gonna look like? It's, it's, Or are we here? >>It's, yeah, it's, and definitely it's, it's more and more important topic currently. And you, you all heard about the great resignation and how employee experience is more and more important for companies according to have a business review. The companies that take care of their employee experience are four times more profitable that those that don't. So it's a, it's a, it's an issue for CEOs and, and shareholders. Now, how do we get there? How, how do we, how do we improve the, the quality of the employee experience, understanding the people, getting information from them, educating them. I'm talking about educating them on those new technologies and how they can benefit from those empowering them. And, and I think we've talked a lot about this, about the democratization local type of, of technologies that democratize the access to those technologies. Everyone can be empowered today to change their work, improve their work, and finally, incentivization. I think it's a very important point where companies that, yeah, I >>Give that. What's gonna be the key message of your talk tomorrow. Give us the bumper sticker, >>If you will. Oh, I'm gonna talk, It's a little bit different. I'm gonna talk for the IT community in this, in the context of the IT summit. And I'm gonna talk about the future of intelligent automation. So basically how new technologies will impact beyond what we see today, The future of work. >>Well, I always love having you on the cube, so articulate and, and and crisp. What's, what's exciting you these days, you know, in your world, I know you're traveling around a lot, but what's, what's hot? >>Yeah, I think one of the coolest thing that's going on right now is the fact that we're trying to figure out do we go to work or do we not go to work? Back to your other point, I mean, I don't know, work, work is, I mean, for me, work has been everywhere, right? And we're starting to figure out what that means. I think the second thing though is this notion around mission and purpose. And everyone's trying to figure out what does that mean for themselves? And that's really, I don't know if it's a great, great resignation. We call it great refactoring, right? Where you work, when you work, how we work, why you work, that's changing. But more importantly, the business models are changing. The monetization models are changing macro dynamics that are happening. Us versus China, G seven versus bricks, right? War on the dollar. All these things are happening around us at this moment and, and I think it's gonna really reshape us the way that we came out of the seventies into the eighties. >>Guys, always a pleasure having folks like yourself on, Thank you, Pascal. Been great to see you again. All right, Dave Nicholson, Dave Ante, keep it right there. Forward five from Las Vegas. You're watching the cue.
SUMMARY :
Brought to you by And of course, Ray Wong is back on the cube. Thanks for the invitation. What's, what are you gonna, what are you gonna probe with these guys? I mean, at that point you want your founder thinking about the next set Pascal, why did you write your book on intelligent automation and, the key, the key content of the, of the book is really about combining different technologies to automate What do you think? And that's where you see discover become very important And that happens right in the background when you augment So Pascal, you wrote your book in the middle of the, the pandemic. You, you make me think about a joke. It's a big record ball, right? And that's exactly true. That explains the success of the book, basically. you want, you want to, you wanna answer that first? And what do you wanna accomplish, right? So how do you differentiate AI in, I a, I We're gonna have to actually ch, you know, address that issue right away. about that disruption scenario and, and the role that automation is going to play, either as the disruptor to do predictions and to help you prevent things from going wrong. How do you see the future of work? is more and more important for companies according to have a business review. What's gonna be the key message of your talk tomorrow. And I'm gonna talk about the future of intelligent automation. what's exciting you these days, you know, in your world, I know you're traveling around a lot, when you work, how we work, why you work, that's changing. Been great to see you again.
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R "Ray" Wang, Constellation Research | Nutanix .NEXT EU
>> Announcer: Live, from Copenhagen, Denmark, it's theCUBE! Covering Nutanix.NEXT 2019. Brought to you by Nutanix. >> Welcome back everyone to theCUBE's live coverage of Nutanix.NEXT. We are at the Bella Center in Copenhagen, Denmark. I'm your host, Rebecca Knight, alongside of Stu Miniman, of course. We are joined by a good friend of theCUBE, Ray Wang, principal analyst and CEO of Constellation Research. Thank you so much for returning to theCUBE. >> Hey, how you doing? Good morning! >> Good morning, good morning! >> Good morning! (laughing) >> Good morning! >> I don't know. I get all my accents wrong out here. >> (laughing) So, you got a shout out on the main stage this morning, from Monica Kumar, congratulations on that. She talked about you and your research on the infinite role of computing. You also do a lot with the future of work. I know that that is really right in your wheelhouse right now. What are you hearing, what are you seeing, what kinds of conversations are you having that are interesting you? >> Yeah, so, this infinite computing option, it's one of the that we're talking about, the fact that you can scale out forever, right? And the problem that's holding us back has been technical debt, right? So all that legacy that everyone's got to figure out. It's like, my connections, my server, my disk-rack recovery, my disaster recovery, my backup, everything. It's a pain in the butt. And I'm still trying to get onto the cloud. So on that end, we're like, okay, all this stuff is holding us back, how do we get there? Now, the future of work is a little bit different. We're seeing a very very different set of work. People have talked about where we are the gig economy, but that's just one aspect of it. Everything is being decomposed into microservices. Large processes are becoming smaller and smaller microservices, they're being reusable, well our work and tasks are following the same way. We're getting smaller and smaller tasks, some are more repetitive, some are going to be automated, and it's really about where we actually find the difference between augmentation of humanity, and full automation, and that's where the next battle's going to be. >> Yeah, Ray, some of the discussions we've been having this week, is how do we really simplify the environment? The balance I hear from customers, on the one hand, they're always like, I don't have enough money, I don't have enough personnel, on the other hand, oh my gosh, that full automation sounds like you're going to put me out of a job. We know we're not putting everybody out of work in the next couple of years. There are challenges; we worry about the hollowing out of the center of the economy, but here, what Nutanix is trying to do, of course, is, I don't want to have to thrive in that complexity anymore, I want to be able to drive innovation, keep up with that, take advantage of that unlimited resources out there, so, where do you see, you've been here at the show, what are you hearing from the customers here? Anything different in Europe versus back in North America that you'd share about that journey onto the changing roles? >> Oh it's a great point. It's about simplifying everything where you can, it's about areas of automation where they make sense. Here in Europe it's slightly different because a lot of the focus in Europe has been about cost and efficiency, followed by of course regulatory. Those have been the two drivers. And they've been battling that in order to be, even they will look at some level of innovation. Where in the US, people are head on doing innovation, regulatory and operational efficiency at the same time. So that creates a very very different environment. But what we have noticed are some patterns, especially when we look at automation and AI; there are four areas out of seven where we see a lot more automation that's happening. The first one is massively repetitive tasks, those are things, yeah, got to get that out of the way, we don't do this very very well. The second one is really thinking about massive nodes of interaction. When you're connected to multiple places, multiple organizations, multiple instances, that's something where we start to get overwhelmed, and then of course, there's lots of volume. If you've got lots of volume or requests that are coming through, you can't possibly handle that, and that's a place where we see a lot of machine scale. And the last piece is really when you have to scale, humans don't scale very well. However, it's actually not a hollowing out of the middle; it's actually a hollowing out of the ends in a very, very real end, because really really simple tasks go away, super complex tasks go away, and the middle actually remains, and the middle is things that are complex that cannot be recreated by math, they're also areas that require a lot of creativity, humans make the rules, we break the rules, and then the last part is really fine motor skills and presence, the machines still aren't as good. So we still have some hope. So the middle stays, it's the hollowing out of the ends, the high end jobs and the low end jobs are the ones where we're going to see a lot of risk. >> So what does that mean? So we have, leaving the middle there, and as you said, the high end jobs and the low end jobs go away, but what does that mean in terms of the skills? In terms of what employers are looking for, in terms of what they need in their prospective applicants and hirees. >> That's a great point. Soft skills are important; it's the qualitative skills that become even more important, it's also being able to manage and orchestrate the hard skills; because you don't necessarily have to know how to do the calculation, you have to just know which algorithm to apply. >> Okay, and then also, these soft skills of managing people, I'm assuming too? Because computers are not so good at that either. >> Yes. Soft skills are managing people, but also manage the human and machine equation that's going to happen. Because we have to train the machines, the machines aren't going to know that level of intuition, and there's a large amount of training that's going to happen over time. >> All right. So, Ray, one of the things Nutanix is doing is, as they've been transforming to not only subscription, software's always been at their core, but they're starting to do not just infrastructure software, but application software. I know you live in that world quite a lot, so when you hear Nutanix talking about building databases, delivering these services, it's something that I look at, Amazon does some of that, but for the most part they're infrastructure and build on top of us. How do you think, how is Nutanix doing, what are some of the challenges for them, going up against some of the bellwethers out there in tech, and all the open source projects that are out there. >> So the challenge is always going to be, there is a one dominant player in every market. And what they're providing is an alternative to allow the orchestration of not having that, not only that dominant player, but a choice. So in every single market, they're focused on giving users choice, and giving the ability to aggregate, and bring everything into one single plane. That is tough to do, right? And the fact that they see that as their big hairy audacious goal, that's impressive. If you said they were going to do this three years ago, I wouldn't have believed them. >> Well yeah, I think back to, remember almost 10 years ago, VMware tried to get into applications, they bought Zimbra, they bought a few others. Cisco did like 26 adjacencies, they were going to take over video and do all these things, and we've seen lots of failures over the years. They refocused on their core, was a big thing that I heard, that the users seem to be excited about. Are there areas that you're find especially interesting as to where Nutanix is poking? >> So, I would say that Nutanix three years ago was a little bit sleepy. They got comfortable, they did the stuff that they did really well, and it feels like, maybe about 12 months ago, Dheeraj had a different vision. Like something snapped, something hit, he said this isn't working, we're going to change things, and we've seen a whole bunch of new talent come into play. We've also seen a huge expansion of what they're trying to do, and a cleanup of all those side projects that were all going on before. So I think they've actually honed in on, okay, if we can simplify this piece, this is a money-winning business for some time, and they're talking about 80% margins last quarter, I mean that's huge, and that's just trying to save customers money, and make their lives simpler. >> Do you think that they have the messaging right? Because, I mean, they're going to this Thoreauvian/Emersonian idea of simplify, simplify, simplify, and it does resonate, of course! What customer doesn't want a simpler computing experience? But do you think that they are reaching the right people, and they have obviously very passionate customers, but are they getting into new businesses. >> I think they're getting to the businesses that their customers are asking them to, those adjacencies are huge, I think and when you think about cleaning up technical debt, all that legacy debt that you actually have to fix, I mean, this is where you begin. It's so hard to make that cloud journey to begin with, it's even harder to carry all that legacy with you. And we're going to see a lot more of this going forward. >> All right. So, Ray, talk a little bit about, I loved an event you did last year, the people's centered digital future. Help explain to our audience what this is about, and where you're taking it again this year. >> So that event was a one-time event. We were celebrating the 70th anniversary of the United Nations founding, we were celebrating almost 50 years of the internet, and 50% of the world being connected to the internet. And part of the reason that was an important event was, we really felt that there was a need to get back to the roots of where the internet had begun, and more importantly, talk about where we are today in the world of privacy. One of the biggest challenges we have in the a digital world is that your personal data, your genomics, all this information about you is being brokered for free. And what we have to do is take that back. And by taking that back, what I mean is, we've got to make all these rights, property right. If we can make that a property right, we can leverage the existing rules and legislation that's there, and we can actually start paying people for that data through consent, and giving people that ability, on consent to data, could create lots of things, from universal basic income, to a brand new set of data economy that equalizes the playing field, while keeping the large tech giants. >> There's some of those big journeys that we went on, you talk about the internet, this year's 50th anniversary of the first walking on the moon, and you look at how entire countries rallied together, so much technology was-- >> Yeah, look at India. >> Spun off of what they've done there, it's like we need some rallying cries in today's day and age to solve some of these big day and age. Is that AI? Where are some of the big areas that you see tech needing to drive forward in the next decade? >> I think the big area's going to be around decentralization, giving individuals more empowerment. We've got large, big tech companies, that are, I'd say, imbalanced. We start companies right away, building monopolies on day one, and we don't open up those markets. And the question is, how do we create a level playing field for the individual to be to compete, to bring a new idea, and to innovate, if that's continuously stifled by big technology companies without an opportunity, we're in trouble. And so that starts by making data a property right, to the personal data. It starts by also creating marketplaces for that data, and those marketplaces have to have regulations, similar to capital market flows. The way treat exchanges, we treat marketplaces, we need to do the same thing with the way we do with data, and then the third piece, there has to be some level of a tax, that goes to all these data economies, so that they can fund the infrastructure and the watch dogs that are there. Now this is coming from a free market, I'm a free market capitalist, okay? I can't stand regulation, but I also realize that it's so important that we have a fair market. >> But do you, we know so much about how Americans are so much more cavalier about their privacy than even Europeans, what will it take to galvanize Americans to care about those little crumbs that they're leaving on the internet, that is the data that you say should be a property right, that we should be paid for? >> I think it's going to start with companies actually take, and do the right thing, where they actually give them that opportunity to monetize that information. >> Will they do that? >> I think the new set of startups are starting to do that, because they're looking at the risk that's being posed, at Facebook and Google and Amazon, on the anti-trust, DOJ, FCC, they're all coming in at the same time, the FTC, they're all wondering, do we break these companies up or not? The short answer is, I don't think they're going to, because we're competing with China, and when you're looking at that scale of data, where Amazon's transactions are only 1/10 of Ali Baba's? That's huge. So the consolidation has to happen, but we need to create a layer that actually democratizes and creates a fair trading play. >> And those startups, you think, can compete with established players? >> I think once we set the roles, and the ground rules, I think people are going to be able to do that, but once you free that data, what are we competing on now? You have to pay for my consent, you have to earn my business, you can't trade it for free, or just say, "Hey look, you are the product." That changes everything. >> Rebecca: Yeah, that's a good point. >> Ray, I know you spend a lot of time talking to, and giving advice to some of the leaders in technology, you're welcome to get into some specifics about Nutanix, or some of the cloud players, but what are some of the key themes, what are people getting right, and what are they still doing wrong? >> Okay, so theme number one, this is going to be a multicloud hybrid world for a long time. Anybody that's bucking the multicloud trend, they've missed the point, right? Because we want portability in data, there's only two or three players in every single market, if I can't move my data, my workloads, and my IO in and out, then you've actually created vendor lock-in from hell. And I think customers are going to protest against that. The second one, and you guys are probably following this trend a lot, is really about AI ethics and design principles for AI. So what is ethical AI? We've got five things that are important: The first one is make sure it's transparent. See the algorithms, see what they write. Second one, make sure it's explainable. Hey, bias is not a bad thing, so if I'm discriminating against redheads, with, left-handed, and that happened to like, I don't know, Oracle, fine. But, if that was unintended, and you're discriminating against that, then we have to get rid of that, right? And so we have to figure out how to reduce that kind of bias, if it's unwanted bias. If you discover that you're discriminating, and not being inclusive, you've got to make sure that you address that. So then the next part is, it's got to be reversible. And once you have that reversibility, we also make sure that we can train these systems over time. And then the last piece is, Musk could be right! Musk could be right, the machines might take over, but if you insert a human at the beginning of the process, and at the end of the process, you won't get taken over. >> I want to hear about what the future of work looks like for Ray Wang. You are on the road constantly, you are (laughs) you are moving your data from one place, you are everywhere, all the time. So what do you have on next, what's exciting you about your professional life? >> I think the challenge's that we are living in a world where there's too much information, too much content. And you guys say this all the time, right? Separating the signal from the noise. And people are willing to pay for that signal. But that is a very very tough job, right? It's about the analysis, the insights, and when you have that, people don't want to read through your reports. They don't want to watch through the videos. They just want to call you up and say, "Hey, what's going on?" And get the short version of it. And that's what's making it very interesting, because you would expect this would be in a chat bot, it'd be in a robo advisor, doesn't work that way. People still want the human connection, especially given all that data out there, they want the analysis and insights that you guys provide, that's very very important, but even more important right now, it's really about getting back to those relationships. I think people are very careful about the relationships they're keeping, they're also curating those relationships, and coming back to spending more time. And so we're seeing a lot more of in-person meetings, in-person events, very very small, curated conversations, and I think that's coming back. I mean that's why we do our conference every year, as well, we try to keep 200 to 300 people intimately together. >> Those human connections, not going away. (laughs) >> Nope, not going away, in an automated, AI, digital world! This is our post-digital future. >> That's excellent. Well Ray, thanks you so much for coming on theCUBE, it's always so much fun to talk to you. >> Hey, thanks a lot. >> High energy guy (laughs). >> Low energy. >> I'm Rebecca Knight for Stu Miniman, we will have more from the Bella Center at Nutanix.NEXT coming up in just a little bit. (upbeat music)
SUMMARY :
Brought to you by Nutanix. We are at the Bella Center in Copenhagen, Denmark. I get all my accents wrong out here. what kinds of conversations are you having So all that legacy that everyone's got to figure out. I don't have enough personnel, on the other hand, And the last piece is really when you have to scale, So we have, leaving the middle there, and as you said, how to do the calculation, you have to just know Because computers are not so good at that either. the machines aren't going to know that level of intuition, and all the open source projects that are out there. So the challenge is always going to be, that the users seem to be excited about. and they're talking about 80% margins last quarter, But do you think that they are reaching the right people, I mean, this is where you begin. I loved an event you did last year, One of the biggest challenges we have in the a digital world Where are some of the big areas that you see tech for the individual to be to compete, to bring a new idea, and do the right thing, where they actually So the consolidation has to happen, I think people are going to be able to do that, and at the end of the process, you won't get taken over. You are on the road constantly, you are (laughs) and when you have that, Those human connections, not going away. Nope, not going away, in an automated, AI, digital world! it's always so much fun to talk to you. we will have more from the Bella Center at Nutanix
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R "Ray" Wang, Constellation Research & Churchill Club | The Churchills 2019
>> from Santa Clara in the heart of Silicon Valley. It's the Q covering the Churchills 2019 brought to you by Silicon Angle Media. >> Hey, welcome back, everybody. Jefe Rick here with the Cube. We're in Santa Clara, California At the Churchills. It's the ninth annual kind of awards banquet at the Church O Club. It's on, and the theme this year is all about leadership. And we're excited to have not one of the winners, but one of the newest board members of the church, Oh, club. And someone is going to be interviewing some of the winners at a very many time. Cuba LEM Ray Wong, You know, from Constellation Research of founder, chief analyst >> and also >> a new board member for the Churchill Club Brigade, is >> also being back here. I love this event. There's one my favorite ones. You get to see all the cool interviews, >> right? So you're interviewing Grandstand from Pallet on for the life changer award. >> Yeah, so this is really incredible. I mean, this company has pretty much converge right. We're talking, It's media, It's sports, It's fitness. It's like social at the same time. And it's completely changed. So many people they've got more writers than soul cycle. Can you believe that? >> Yeah. I like to ride my bike outside, so I'm just not part of this whole thing. But I guess I guess on those bikes you can write anywhere >> you can write anywhere, anywhere with anyone. But it's not that. It's the classes, right? You basically hop on. You see the classes. People are actually pumping you up there. Okay, Go, go, go. You can see all the other riders are in the space. It's kind >> of >> addictive. Let's let's shift gears. Talk about leadership more generally, because things were a little rough right here in the Valley right now. And people are taking some hits and black eyes. You talk to a lot of leaders. She go to a tonic, shows you got more shows. A. We go to talk to a lot of CEOs when you kind of take a step back about what makes a good leader, what doesn't make a good leader? What are some of the things that jump into your head? >> You know, we really think about a dynamic leadership model. It's something conceit on my Twitter handle. It's basically the fact that you got a balance. All these different traits. Leaders have to perform in different ways in different situation. Something like Oh, wow, that's a general. They've done a great job commanding leadership. Other times we had individuals, a wonderful, empathetic leader, right? There's a balance between those types of traits that have to happen, and they curve like seven different dimensions and each of these dimensions. It's like sometimes you're gonna have to be more empathetic. Sometimes you got to be more realistic. Sometimes you're going to be harder. And I think right now we have this challenge because there's a certain style that's being imposed on all the leaders that might not be correct >> theater thing. The hypothesis for you to think about is, you know, when a lot of these people start the Silicon Valley companies the classic. It's not like they went to P and G and work their way up through the ranks. You know, they started a company, it was cool. And suddenly boom. You know, they get hundreds of millions of dollars, the I po and now you've got platforms that are impacting geopolitical things all over the world. They didn't necessarily sign up for that. That's not necessarily what they wanted to do, and they might not be qualified. So, you know, Is it? Is it fair to expect the leader of a tech company that just built some cool app that suddenly grew into, ah, ubiquitous platform over the world that many, many types of people are using for good and bad to suddenly be responsible? That's really interesting situation for these people. >> Well, that's what we talked about the need for responsive and responsible leadership. Those are two different types of traits. Look, the founding individual might not be the right person to do that, but they can surround themselves with team members that can do that. That could make sure that they're being responsive or responsible, depending on what's required for each of those traits. You know, great examples like that Black Mirror episode where you see the guru of, like, some slasher meet a guy. Some guys like Colin is like, you know, he wants to make sure that you know someone's paying attention to him. Well, the thing is like a lot of times, at least folks are surrounded by people that don't have that empathetic You might not have had what a founder is looking at, or it could be the flip side. The founder might not be empathetic. They're just gung ho, right, ready to build out the next set of features and capabilities that they wanted to d'oh! And they need that empathy that's around there. So I think we're going to start to see that mix and blend. But it's hard, right? I mean, going through a start up as a CEO and founder is very, very different than coming in through the corporate ranks. There's a >> very good running a company, you know. It's funny again. You go to a lot of shows. We get a lot of shows, a lot of key, knows a lot of CEO keynotes, and it's just interesting. Some people just seem to have that It factor one that jumps off the top is Dobie. You know, some people just seemed >> like the have it >> where they can get people to follow, and it's it's really weird. We just said John W. Thompson, on talking about Sathya changing the culture at Microsoft, with hundreds and hundreds of thousands of employees distributed all over the world. What a creative and amazing job to be able to turn that ship. >> Oh, it is. I mean, I can turn on the charm and just, like, get your view Lee excited about something just like that, right? And it's also about making sure you bring in the input and make people feel that they're inclusive. But you gotta make decisions at some point, too. Sometimes you have to make the tough choices. You cut out products, you cut out certain types of policies, or sometimes you gotta be much more responsive to customers. Right? Might look like you're eating crow. But you know what? At the inn today, cos they're really built around customers or state Kohler's stay close air bigger today than just shareholders. >> Right. Last question. Churchill Club. How'd you get involved? What makes you excited to jump on board? >> You know, this is like an institution for the valley, right? This is you know, if you think about like the top interviews, right? If you think about the top conversations, the interesting moments in the Valley, they've all happened here. And it's really about making sure that you know, the people that I know the people that you know there's an opportunity to re create that for the next set of generations. I remember coming here when it's like I go back, I think give Hey, just I don't hear anybody in 96 right? And just thinking like, Hey, what were the cool activities? What were the interesting conversations and the church? The club was definitely one of those, and it's time to give back. >> Very good. All right, well, congrats on that on that new assignment. And good luck with the interview tonight. Hey, thanks a lot. All right. He's Ray. I'm Jeff. You wanted the Cube with that? Churchill's in Santa Clara, California. Thanks for watching. We'll see you next time.
SUMMARY :
covering the Churchills 2019 brought to you by Silicon Angle It's the ninth annual kind of awards banquet at the Church O Club. You get to see all the cool interviews, So you're interviewing Grandstand from Pallet on for the It's like social at the same time. But I guess I guess on those bikes you can write anywhere You can see all the other riders are in the space. She go to a tonic, shows you got more shows. It's basically the fact that you got a balance. The hypothesis for you to think about is, you know, when a lot of these people start You know, great examples like that Black Mirror episode where you see the guru of, like, You go to a lot of shows. changing the culture at Microsoft, with hundreds and hundreds of thousands of employees distributed And it's also about making sure you bring in the input and make people feel that they're inclusive. What makes you excited to jump on And it's really about making sure that you know, the people that I know the people that you know there's an opportunity to re create We'll see you next time.
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Ray Krug, NETSCOUT | Unified Communications
>> From the SiliconANGLE media office in Boston, Massachusetts, it's theCUBE. Now, here's your host Dave Vellante. >> Hi everybody, welcome to this CUBE conversation. I'm Dave Vellante.\ We're going to talk about unified communications and its role in digital transformations. Ray Krug is here. He's a solutions architect at NETSCOUT. Ray, good to see you, thanks for coming on. >> Hi Dave, good to be here. >> So talk a little bit about NETSCOUT. You guys are into a lot of different things, but give us the overview. >> Yeah, NETSCOUT, what they're primarily focused on is providing the visibility to assure digital business initiatives, to provide availability assurance, performance assurance, as well as security assurance as well and we do this using our smart data and smart analytics platform. We kind of do this for, okay, got a huge customer base, we do this for over 90% of the Fortune 500, 95% of the carrier service providers, so we scale to these large enterprises, sophisticated service providers, providing the visibility they need to assure their services. >> So as a solution architect what specifically is your role? >> Probably worthwhile giving a bit of history because I know we're talking about unified communications. So I have been with NETSCOUT now for about eight years it's been and I came from an acquisition. The acquisition was from a British company, a spin-out of British telecom called Psytechnics and we specialized, this is eight years, well 10 years ago, in analyzing the IP network for voice and video traffic and actually being able to understand how we can take impoundments in the network and how that translates to impoundments in voice quality over a voiceover IP. So that was the original data transformation project, the so-called digital transformation from TDM networks to IP. So yeah, we took those analytics and basically figured out how to do that. >> So deep understanding of actually what's going on in the network? >> Yeah absolutely, and what was exciting, and back to NETSCOUT, is when they acquired Psytechnics, they took this technology and put that into their pro-technology, they did that within three or four months. Our technology was in their probe monitoring the voice, both voiceover IP networks, and then what was interesting, within 12 months, all our workflows that we created for insured performance of voiceover IP networks got embedded into the NETSCOUT portfolio of products. And since then, eight years, winding on forward, we've been embedding more and more technology into our InGenuis One platform to give you better and better voice, video, and unified communication analytics. >> I love that story, Ray, because the vast majority of mergers and acquisitions fail to meet their original objectives, they take too long to integrate so some companies are good at it, some not so good at it, so it must've been pleasing to see that happen, and see your baby actually scale like that. All right, lets talk about big picture. What are the big trends that you see sort of driving unified communications today? >> Yeah, unified communications is getting more and more complex, and perhaps on one accord, sophisticated, but you kind of think, okay, most common used case for us is to be a contact center because at the end of the day, contact center, the customers are demanding more and more ways to interact with the business, traditionally it was voice but now they want web, web chats, video, whatever it might be, so contact centers a big consumer of unified communications. And then there's the different technology trends like, of course, Microsoft Skype for business, evolving into Microsoft Teams, or Cisco Jabber, unified communications and all that sort of thing. A whole bunch of other topics going on, again, part of digital transformation initiatives, SIP trunking, we're still seeing that going on. So I was talking about TDM to IP, so that was back in my day in Psytechnics, now it's taking those and transferring IP to SIP trunking to save costs, that's the main thing, but it is a change and it is more, not instrumentation, but more appliances on a network, like session border controllers in order to add your SIP trunking, and of course there's also other technology, migration to the cloud as well, which ends up, from our perspective what we're seeing is in very hybrid environments. So now you've got a lot of on-prem stuff and some cloud stuff, it's all going to work together in order to make voice, video, unified communications successful. >> Isn't another sort of challenge, I'll call it give the people what they want, you talk about contact centers being a primary source, people want to communicate in different ways. Young people maybe want to use chat, some people like me want to pick up and talk to a human. Is that part of the challenge, is bringing all those together to service all these different constituents? >> Yeah, absolutely, because at the end of the day, it's a contact center, you want to make sure you provide an engaging experience to your customers, however that might be. Omnichannel or whatever word you want to do it. The longer and happier the customer is dealing with your business, perhaps the more money they'll spend with your business, perhaps the better brand awareness they have of your business as well. >> So double click into some of the challenges of actually bringing this stuff together, making it work, is it cost, you mentioned complexity before, is it understanding the analytics, who's using what, predicting, double click on that. >> That's a big topic, but we talked about new features and immersive experience from unified communications, so that's all brilliant. The trouble is, high quality is key. You got to make sure that it's successful, so any migration project, you need to be successful to make sure that you've succeeded. Okay, so that's number one. Quality is key, but also in terms of cost, sometimes these initiatives about cost savings, so SIP trunking is a good example of that. I want to make my service the same as it was before, have some sort of future upgrade capability, but kind of make it cheaper, that's what SIP trunking does for you as well. So those are some of the reasons for doing it, but then that introduces more components in your infrastructure to make all that stuff work and it's not just about voice and video, it's all about the other backend servers as well to make it all happen whether that's mail or chat or presence or whatever it might be. Lots of components now that have to work together, stuff that you control but also stuff that you don't control like SIP trunks is a good example, or gateways out to the PSTs, things that you don't control, and that makes it kind of really tricky to deal with. There's a bunch of other stuff as well that's important, network convergence, you've got all these applications converging onto that one network infrastructure, how do you manage that? >> Quick tangent. So you mentioned SIP trunking, explain what that is for our audience so they don't have to google it. (Ray laughs) >> Yeah, so SIP trunking, basically, if you think about gatewaying out to the PSTN in terms of making your plain old telephone calls, dialing a number and sending out, SIP trunking does that all from an IP perspective. So the idea is, you don't necessarily do a conversion to TDM, traditional phone systems, it all goes IP. So basically, you then send everything out, IP, over the network, it gets to the other end, and the whole purpose of that, it's a service that you buy from your service provider and it's cheap. >> Okay, you talked about these challenges. Generally, how does the industry approach solving these problems and specifically how does NETSCOUT solve them? >> Great question. So traditionally, let's sort of rewind a little bit, I talked about a lot of components that need to work together to make your unified communications experience. Lots of servers, lots of network infrastructure, firewalls, session boarder controllers and all that. Traditionally, what you do is monitor each of those devices. Take a look at their CPU utilization, or take a look at how the servers are performing, and often, very little is taken into account about the network and how that's behaving, because again, I've said it's a converged network. So you end up with a picture saying, all my servers are working fine, but then you end up with the problem, but users are complaining because they can't dial, users are complaining because the quality is bad. So that's kind of the problem with trying to bring all those together using the different metrics and coming up with some sort of conclusion. >> And then it's finger pointing, right? >> Oh yeah, classic. >> Which mole to whack. >> Yeah, in constant use cases, war rooms, okay, all my lights are green for every person in that war room but the people are still complaining, absolutely. >> Okay, so talk more about how NETSCOUT approaches this. >> So, the name gives it away, really. We always focus on what's going on in the network, wherever that network may be, so we're taking a look at that, we call it Y data, it's packet data, and we're able to translate that. Whatever's going over the wires, whether it be an application going over the wires or whether it be unified communications going over the wire like voiceover IP, RTP, or signaling, SIP as an example of those. So we're able to get that picture of how everything is communicating with each other, and we're being able to raise that level. So packets are notoriously hard to interpret, but we've cracked it, we've got a sort of technology, it's a patented technology called ASI, adaptive service intelligence, we call it smart data, but it's converting that Y data into meaningful keeper points metrics. So you name it, you name the application, we've got performance metrics. So whether that be voice, voice quality, mean opinion score, we're taking that from the Y data. Whether it be application performance from a database that might be running, or a mail server that might be running, we have performance. Whether it's this signaling that goes on to get data and all that, we have performance metrics about that. So we're using the same data set, the Y data, bringing it up to our analytics, our ASI layer, and then we have an understanding of what component's failing. Is it the voice that's failing? Is it this part of the network that's failing? And then, for voice, there's a whole topic on how we understand that, remembering my background and the analytics behind that. >> So, your secret sauce is you've got this deep probe into the network, you've got this ASI, this patented technology, and you've got an architecture to leverage that capability, and that is really your big differentiator from a technical perspective? Is that right? >> Well, from a technical perspective, absolutely. And from an obvious perspective, we solve, in the easiest way, the most complex problems. It's kind of where it's coming, 'cause these are tricky problems to do, they sometimes go unseen for ages, but because we've got that overall visibility, we get to that root cause very quickly. >> Okay, let's talk about the business impact. Maybe you can give us some examples, customer examples, and how it affected their business? >> Yeah, so that's important. A couple of things, let's imagine you're contact center, a service company, so I've got one in mind, and the one that I have in mind, six contact centers, they take up to about 100,000 calls in a day. So it's important. They're a service company so people phone them up to have their service. If you can't make contact with your service company, maybe the impact of that is, okay, that service is rubbish, I'm going to go to a competitor, as an example. Or you don't get your service that you require. So there's huge implications. In this example, we've found that calls were dropping, as an example, so people are connecting with their agent, calls are dropping, okay, hopeless. It's really problematic. And it's interesting that you pointed out about war rooms and finger pointing, and that's exactly what happened. What they'd done, they'd engaged in a SIP trunking project to deploy SIP trunking they were going to save a million dollars a month by implementing this SIP trunk. So that's huge, okay yet, when they deployed this, they were having a bad experience, so that's critical, so they needed to achieve that successful migration, so they had tours but nothing that could spot what was going on with these calls dropping. So along come NETSCOUT, we deployed our probe, and very quickly, it's just amazing, very quickly we were to able to analyze the reason for the call dropping. Turned out it was a firewall issue, complex network so it's kind of difficult to know where the traffic is routing. We were able to figure that out, give it the evidence to say the signaling, the SIP, was dropping, and we were able to pinpoint that and they got that fixed very quickly. >> Which meant that they were able to realize that million dollar a month savings. >> Precisely, yes exactly. Let alone that any business that might've been affected by the fact that people couldn't call in. >> Any other examples you can share? >> Yeah, I've got a really great one, probably closer to a lot of people's hearts, and relates to a hospital, and they were going through a digital migration project. It's as simple as changing their phone handsets from one vendor to another in some respect, about 2,000 phones that they were replacing, so it's kind of interesting. So I've now got a nice new shiny phone on my desk, when I pick up the phone I get very bad quality and stuff like that, and just blame the phone and all that sort of thing. Sometimes that's change, people don't like change, they like all the buttons on their old phone, and sometimes it's real, but in a way, the business impact for that one is, if I'm a customer, a patient, I'm phoning up my doctor for some records, and the phone quality is bad, then I'm not going to have that much confidence that the doctor's going to be able to cope with my ailment that I might have. So it's really important to have quality, and when it's about your health, then it's really important that it's there. >> Awesome. Let's end on some advice that you would give to customers. So you got people trying to do digital transformations, they're trying to pull all these different communication systems together, trying to understand where the exposures are, the performance issues. What advice would you give to people that are struggling with these problems, where should they start, and what should their journey look like? >> In some respects, I think visibility is key, both before pre-migration, during migration and afterwards. So in my example before, having visibility of the performance of the phones before, in this migration issue, and then as I go through the migration, being able to just check that when they deployed the new phones, everything's working. And then of course, once, if there were any problems, so in my example, it was QOS problem. QOS, quality of service, so that's a networking problem and it goes back to, because we're in the network, we're looking at the network, as much as that's the most complex problem to solve, and it's everywhere, QOS problems are everywhere, it's the simplest thing for us to fix. So monitoring during migration, seeing what the behavior of the phones are, during that process, correcting everything quickly, so that the migration project is successful, and then post-migration, business as usual, monitoring, so if there are any problems you can quickly react to it. >> Got it, okay, so you're going to through a business case, you're going to make this part of your digital transformation, you're going to bring together all the stakeholders but I think your point is, if you don't have visibility on what's going on in the network, there are going to be some blind spots that you potentially run into. If you have visibility in the network, you're going to be able to remediate those, and the example you gave of the services company, you're going to be able to achieve your expectations and your ROI results and have confidence that you're going to be around for the next project. So Ray, thanks very much for coming on and sharing with us. And thank you for watching everybody, we'll see you next time. This is Dave Vellante with theCUBE. (bright synth music)
SUMMARY :
From the SiliconANGLE media office We're going to talk about unified communications So talk a little bit about NETSCOUT. 95% of the carrier service providers, and actually being able to understand how we can take and back to NETSCOUT, is when they acquired Psytechnics, What are the big trends that you see sort of driving and some cloud stuff, it's all going to work together Is that part of the challenge, an engaging experience to your customers, So double click into some of the challenges Lots of components now that have to work together, so they don't have to google it. and the whole purpose of that, it's a service that you buy Generally, how does the industry approach So that's kind of the problem with trying to but the people are still complaining, absolutely. and the analytics behind that. in the easiest way, the most complex problems. Okay, let's talk about the business impact. give it the evidence to say the signaling, Which meant that they were able to realize by the fact that people couldn't call in. that the doctor's going to be able to cope Let's end on some advice that you would give to customers. as much as that's the most complex problem to solve, and the example you gave of the services company,
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Ray Krug, NETSCOUT | Cloud Migration
from the silicon angle media office in Boston Massachusetts it's the queue now here's your host David on tape hi everybody welcome to this cube conversation I'm Dave Volante and you're cloud and cloud migrations are a major challenge for customers they move things into the cloud and variably they've got things that they want to maintain on Prem they've got to figure out what to move how to move it how to maintain performance and how to maintain the experience from on Prem into the cloud rate Krueger's here is a solution architect at net scout rate thanks for coming on nice to be here thank you so tell us a little bit about nesco yeah I mean net sky I mean primarily it helps you provide the visibility required to protect your digital digital business transformation we give you availability information performance information and security insights into what's going on in your environment we do this for 90% of the fortune 500 we do this for 95% of service provider so we're kind of Carrio class service provider and enterprise sophistication and we basically give you that visibility without borders and the visibility without borders is all about saying wherever you deploy your application whether it's being on Prem in your private data center software-defined data center or West en or whatever it might be or whether you migrate some or all of that into the public cloud AWS or as your we give you that same visibility same metrics wherever you host your application even in this hybrid world or this multi cloud world of today okay so top level one of those discussions like you heard my sort of intro and some of the challenges but what a customer is telling you about their cloud migrations well ok that's interesting so so that's kind of been around for eight years we're in like as I said thousands of customers and and these guys have been tasked they've been tasked with going to the cloud for business agility reasons and the idea of business agility is can you sort of create new services quicker new business initiatives new projects new application new ways that customers can we communicate with the business and they it's all about wrapping this and delivering these applications very quickly so the guys that we're talking to us said are being our task to move it to the cloud for various reasons it's not necessarily cost reasons as well it's LT and the the view is of the businesses the cloud will give them that agility maybe easier to manage maybe it's quicker to deploy applications quickly and all that sort of thing so they mean tasks to do that and that's a challenge because you know providing that visibility on premon in the cloud has been historically true well the other thing about the cloud is it's it's easy to test you know you test things you experiment you fail fast try the next one and it's relatively inexpensive to do that versus you know buying infrastructure but now so you see that but so talk a little bit more about some of the the real challenges that customers are facing you know when they start that migration as I said before they've got on-prem they've got workloads in the cloud they want a consistent experience but what are some of the problems yeah I mean yeah yeah yeah that's that shadow IT if you thought it has been a big problem but that's business utility isn't it okay because it's taken so long to deploy stuff on Prem ok to take four days before I have a new host ready for you to do that application so no wonder they've done that shadow whitey right but but anyway okay so on task to migrate this application so okay so I got to understand what that application looks like what are the components what it's what is it talking to because if I miss something right if I don't migrate all the components and don't forget these application it's not just one server or one component of the application it's maybe ten components might be whatever it is I need to know what that is and I can't just go to the documentation team to actually see all the protocols it's talking to all the dependencies whether it's one app tier talking to a database tier or whatever it might be the documentation just doesn't exist and the developers who developed that application no longer are part of the company they've long gone if ever they wrote any documentation so to understand right what you need to migrate is one of the biggest challenges and as it happens it's one of the challenges that we can help in netscape well this is a huge problem because you mentioned dependencies so if as you say an application talking to a database and maybe an ancillary application downstream those are going to affect business processes and unless you understand those dependencies if you effect one it's going to have a ripple effect on others and it could affect the business process so so that is a critical problem okay well so how do you nets go solve that problem I mean I have a question how does the industry generally solve it and I want to understand how you're different yeah okay so there's a couple of problems there is what one is understanding the components the dependencies and then one is understanding the performance so you can migrate successfully and all that sort of thing yeah so the industry typically will actually try and use some rudimentary network data to try and take a look at one application communicating to another and trying to get that from some devices various devices around the network because what they'll try to do to do that looking for connections is ok looking for connections and how they're doing that and in terms of performance they're they're resorting to looking at the different logs or the different infrastructure information like CPU utilization or those sort of things or developers are looking at instrumenting code into the applications which give them that performance information trouble with those they only see what the developers put into them rather than the whole picture of all those dependencies so while a bespoke data a lot of bespoke data trying to bring that together and come up with a conclusion that they this is all the components and this is how it's performing it is it's tricky ok so how do you guys do so yeah ok so as you know we use the network the wire data in order to understand what's going on so think about it if an application if I'm talking to my CRM application I might have a web browser it's talking to a web server talking to an app server to talking to a micro survey database or whatever it might be but all of those are interactions in a network different protocols HTTP HWS database Active Directory DNS so because we look at the network we can see it all so we can see all the traffic on the network we can see how things are communicating in reality so you don't necessarily need the documentation because we're documenting what's going on right now and that's kind of where we really score big in terms of understanding those dependencies and it's the it's the secret sauce that we've always known about the that that net Scout has your ability to to probe the network your your layer that analyzes that data the architecture that you've created right that's your IT yeah that's our secret sauce so we translate why data trauma is why data there's a lot of it and it's hard to interpret so that's one thing so we we've cured that problem by creating a patented technology called ASI adaptive service intelligent which translates that wire data into meaningful key performance metrics so you name the application it's all the applications going on your network translate them into performance metrics let's say application performance metrics and then differentiating that's a application latency from Network latency so we can see whether it was a network problem slowing things down or the application server slowing things down but also errors we can see all of that in that that wire data so that's that next layer up and then we have the analytics platform which we call ingenious one which actually takes that metadata and then allows us to display okay it's service dependency map so this is how your application is communicating all the nooks and cranny's the things that you didn't expect and not only does it do the dependency it does the performance as well the metadata oh it always comes back to the metadata one of the challenges that customers tell us they have is just creating the experience between on Prem and cloud you know the so called hybrid a lot of times it's it's different and they want to take that cloud experience and bring it to wherever they are cloud a cloud be on Prem are you able to maintain that experience in in this hybrid model yeah so to multi cloud or or not to multi class yeah no that's the beauty of number one why data and what we do why data is everywhere ok so if your applications communicate communicating in the cloud it's still communicating over IP and so we can actually instrument into the cloud collecting that wire data and then doing the same analytics asi in the same taking the same meta data and actually bring together a view of now the dependencies across the multi-cloud so whatever the cloud were able to get at that wire data and translate it into a si all uniform it's the same metrics okay so let's say we're out in a bar and you meet me and I'm an IT guy and I start chatting and I say hey I got this I'm doing this big project I'm really you know get this important it's got visibility at the board level and we're moving to the cloud and it gets your attention say whoo that's interesting and you start asking me to what advice would you would you give me I'm open to that okay obviously it's a talk to Nets character but the important thing is is this is that the question is that I've got a migrate this to the cloud and all that something and it's like sort of quite scary because I don't necessarily understand the cloud I don't realize that it's either the same or it's it's it's it's different or how its performing and it's I'm losing that visibility so you want to give that guy confidence you also want to give that guy the ability to say okay I understand the cloud and when things aren't the cloud I can continue to monitor it because that's after all the important thing so we've given them that confidence by saying hey we can instrument that application when it goes to the cloud and we can instrument beforehand so it goes it goes in the view understand what you're going to migrate all the components because you don't want to miss something migrate it and still have that visibility when it goes into the cloud we can give you that we give you this is interesting we give you access to that wire data when there are no wires that's to say the magic of nets carrots because we can instrument inside the workloads and get access to the traffic that's going in and out of those virtual machines those ec2 instances those virtual machines in in different clouds get access to that wire data and translate it into those key performance metrics and that's unique to Nets code like how do you do that well okay so the ASI is unique and the our agent technology is also unique to us to actually translate in the virtual machine in the cloud that wire data into metrics and then doing that all on the workload itself is very powerful if we can't instrument in the workload then there's another solution as well to get access to that wire data and that's what recently people like Amazon web services and as you I have announced the ability to tap in to that traffic so as you offer V tap which allows you to copy packets from VM to a destination which would be one of our probe technologies in the cloud Amazon have V PC traffic mirroring to actually get access to that data as well and we do the same thing the point is whether their workloads in the cloud workloads in the private cloud or the data center it's the same metrics and we get that visibility end-to-end visibility is the key ray thanks so much for coming on the cube and explaining so that your approach to a cloud and multi clouds great have you thank you very much you're welcome Eric thanks for watching everybody this is Dave Volante thanks for watching this cubed conversation [Music]
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Ray O'Farrell, VMware | VMware Radio 2019
>> Narrator: From San Francisco, its theCUBE. Covering VMware Radio 2019. Brought to you by VMware. >> Welcome to theCUBE, from San Francisco at the VMware Radio 2019 Event. I am Lisa Martin with John Furrier, welcoming back one of our distinguished CUBE alumni, VMware CTO Ray O'Farrell. Ray, welcome back to theCUBE. >> Thank you, very happy to be here. >> This is the fifteenth annual Radio R and D. >> Yes >> Innovation offsite. >> Correct. >> Really competitive there's about eighteen-hundred engineers here, Over a thousand different projects submitted. >> Yes. >> Only about 15 to 20 percent may be selected to be featured here. >> Correct. >> This is the third day or so, talk to us about some of the projects that really caught your attention as really innovative, that really kind of embody the VMware culture of innovation. >> Okay, so the event is an internal event, and, so, but are treated very much in the same way as you would, you know, a more formal, people submitting papers, being peer reviewed and then as you say. A small number of them make it through to the poster sessions or the presentations here. If you look at the broad swat that come in initially, they are very broad, covering everything from technologies that VMware has a lot of focus on whether that's kubernetes virtualization and so on, but also some that are, you know, for the field like virtual reality, augmented reality. You also get quite a few projects which are, fall into how can we be better as a company, So better ways of, if we developed our software using this technology or this approach we'd see better efficiency or ways of testing in new and interesting ways. And I also, for the first time, I think saw a few projects which were more around, less around the technology and more around ways of working together. How can we build teams that work better globally. There's quite a few poster sessions around here about even how to manage and increase the inclusiveness of your team, right? So you're seeing it beyond the technology and instead, how do we as a company become more successful. >> I think that Virtual first is an interesting dynamic, we call it Virtual first because no one has actually built technology for fully virtual teams. >> Yes. >> It's always been kind of collaboration bolted onto pre-existing on premise activity. >> Yeah yeah its a, so our R&D teams, as most R&D teams you're going to see these days are going to be pretty distributed, you are going to have people working from home, you're going to have people in remote sites, you're going to have many project teams, where the actual project itself, right down to the smallest team of ten to fifteen people may well be distributed, so what you got is very core pieces of code being done by teams who are acting remotely. Now, when you think about it, as we work with more and more open source, you're seeing the exact same thing and like the open source community has worked very well in terms of how do you run those projects and so we get to learn from that and we've actually created an office for open source, open source program office, and a lot of what we're trying to figure is how do we make sure to be able to build and leverage that innovation across multiple teams. >> Well Ray, we want to thank you because I know Ray has been around for a while but this is the second year where presses select presses. >> Correct. >> To be invited to get access to some of the projects so we really appreciate that. >> Great idea. >> As CTO of VMware you got to look at the landscape and just look at the organic innovation inside, bring the acquisitions in, and then bring it through to the company architecture. What's, Where's the intersection point on the organic to the CTO, architecture map because you got a lot of great business model going on now, the cloud's looking good. Cloud foundation, yet the Telco business booming, where is the action on the business side, where is this come in, where's the action happening? on the technical business side. >> Yeah on the technical side, what we're seeing is well you've mentioned two questions in there, one is about the innovation and what we will do, how do we fit acquisitions and so on into that mix, we have a fairly formal, I guess, innovation program if I could put it that way. Which basically focuses on what do we do to make sure that we have a really strong culture of innovation as the company, and this event is one of those things. It's not just a few days event, that lead up to it, the lead off from it and so on, that really is focus on make sure we have a culture of innovation in the company. We can create new products, new features as needed, from that. But we also recognize that some of those innovations are going to come from partnerships and from acquisitions, either from partnerships with an Open-source community or in the case of, you saw yesterday, we made an acquisition of a company, Bitnami, which is part of the broader story of us being focused on cloud native applications, what is the best way to be able to, you know, manage that new type of development, container based, kubernetes based and so on. So we're open to wherever that innovation comes from, In fact, that's one of the things I really like about the company. You know, we will look at all the possibilities. And sometimes, you know, as you saw with even some of the partnerships we struck in the last year, you got to be creative. >> So I got to ask you about 5G, one of the things that we're seeing is a lot of hype around 5G, I mean, I was in Vegas, they said 5G LTAE. (laughs) >> Yes. >> E 5GE evolution it wasn't even real 5G. So there's some skepticism, but certainly it's a catalyst. How is 5G impacting your business opportunity in the industry? >> So the Telco industry in general was not particularly virtualized if you go back you know, about two years or three years ago. So one of the key things as people as Telco's are building out, you know, to deal with the 5G infrastructure, there're also saying okay what do I need to build, do I use the way I used to do it? And more and more are saying hey, I should be able to use virtualization, why can I not leverage that same technology which revolutionized Cloud in the data center. So we're seeing some very good business in that space, much of it is what you call the Telco Core, the you know, core infrastructure before you get to the radio networks themselves, but we're also beginning to see even some of that beginning to move out to the radio networks. >> John: Virtualization or software, or both? >> Well virtualization even in fact, that MobileWorld Congress in February I guess, we did some demos of some pretty advanced technologies around network slicing where you're essentially beginning to virtualize the network all the way from the radio network back into the data center itself. >> And the Telco's are certainly from a business that already have been struggling for decades, trying to figure out what that over the top, what their business model could be, will this help them? >> Yeah, well any time, our experience is that anytime you turn something into a flexible software model within that agility within that flexibility you get to do a whole ton of advantages, because you're able to update, you're able to modify, so it's all around flexibility. And everybody talks about you know, how agile you need to be, well, virtualization software, moving into a more software defined model really helps with that. >> Let's talk about, back to Radio 2019, the R and D innovation offsite Radio. Let's talk about customers, how do customer influence projects say from last year to what some of the engineers put together, are these engineers that are having a lot of interactions with customers, what is that influence that customers deliver to VMware's culture of innovation? >> Yeah, it's rather interesting, with more and more we have customers who come to us and actually are asking the question, not necessarily about products, but about the culture of innovation, a question around how do they repeat that or can they learn something from us, and we learn from them too, but it's interesting that the question that has begin to come up more and more as these customers realize we must be agile, we must innovate or else someone is going to get them, from a competitive point of view. They're trying to understand what we do in that space, so that's one aspect of it. In terms of the projects, and what you see here, we do have our professional services organization here, we do have our customer support organization, we do have a lot of our CTO's, a lot of these projects come from offices of the CTO, they all spend a ton of time with customers. We also do make sure for the most part that we get our senior engineers to have an opportunity to go out and visit customers or when customers come on site, that we will have those discussions. So there's a lot of customer input into the mix, where you actually see it showing out or where you should see it begin to appearing more and more, there's a lot of projects here that are deeply systems projects. You'll also find a lot though around pretty basic customer satisfaction things, like user interfaces, ease of licensing all those types of things. So there's a good balance between the two. >> You know one of the things you guys are really doing well in the market place, obviously with the cloud decision with AWS that was a great message to both your field, customer base, how cloud is going to evolve, then cloud foundation, now you got the edge of the network developing, but the software defined data center NSX is doing well. As you start to get into the networking side because the pitch we heard at Dell technologies world was, don't look down, look up the stack. That's where kubernetes is and where the action is on the abstraction layer. There's still a lot of work to do with the networking and security piece of it. >> Correct. >> Where's the innovation angle there, what are the dots to connect on the networking and security side. >> I think probably the biggest focus is on security, almost every customer as they're becoming completely dependent on digital infrastructure just to get there work done. You know like, everything from a farmer to a hospital, they're all digital now, right? Security pops up over and over again. The key products we have in that space are things like, obviously NSX has a large security component to it, but also app defense and some of the projects we do there. So I think security is probably one of the key areas we see that focus. In some ways, what we're seeing is customers coming to us and saying, I want to be able to worry about my applications, can you somehow figure out how to make the IS and the virtualized infrastructure and the security as policy-driven, as automated as possible. And that's where we're focusing. >> You know, one of the things I see as a trend, obviously a student would love, any man would love to be also talking about is the hyper-converged HDI (mumbles) infrastructure really was a tell sign to what customers want, they want to converge everything into an abstraction. >> Correct. >> Into software model, Cloud's hyper-converging. Cloud's is also another. >> Correct. >> Multi-cloud kind of objective. So this notion of consolidating. >> Yeah. >> And kind of creating abstraction is a trend. >> It is, I actually think its really a decision by most customers to say where do I need to focus all of my bandwidth to be successful, and they're saying I want to focus on the layer which is specific to my company. The applications, my customer relations, please somebody help me with all the other stuff. And that's cloud hyper-converge infrastructure VMware. >> John: Do you feel you got like VMware's positioned well in that area? >> I do, I think that in the end, I think we have an interesting blend of what I sometimes use the word agnost or enterprise pragmatic innovation, we know you want to leverage the latest technologies, we know you want to be able to advance in those spaces, but we also know in the end, you know, you are a bank or a hospital, and you need to manage that transition in a fashion which allows you to keep your business going, I think we've been very good at helping companies do that-- >> If I took you on a sales call and I was say, a VMware sales rep or a competitor, obviously the competitors will try to counter what you guys are doing, as we know, we see Cisco out there and others where there's competition, this industry is evolving but you guys have an advantage, what is that pitch to the customer, why VMware over the competition, because they're certainly saying that they can do things better than you guys (mumbles) and vice versa. >> Yeah, I think there's a few advantages, one of them is our enterprise history, our enterprise readiness, some of our competitors obviously have that as well, but you know we are very very strong across all the global, the worlds global enterprises. The other part that you are going to see of course is in some ways we've got the ability to be a little bit of a Switzerland in many cases, our job is effectively to say abstract virtualize your infrastructure, make it easy to manage and optimize and we don't necessarily care what that infrastructure is, is it a public cloud, is it a private cloud, is it a hyper converge infrastructure. So we're able to offer that unified or essential kind of digital infrastructure that goes across all of those things. And within that you're giving choice and flexibility. If you want to move that work load because you think you'll get a better deal on a different cloud, we will help you to do that, or at least make that easier to do. >> Along the spirit of competitive advantage, besides innovation, which we talked about, this very rich history, twenty plus years of innovation at VMware. What are some of the other elements in your opinion that companies like VMware need to have, to be disruptors, couple that come to mind when I think of VMware are partnerships and diversity, what are some of those core elements that really are essential to drive disruption. >> So I often use the phrase which sounds maybe a little bit opposite to disruption, which is resilience, right? Is your company in a position to be able to take either blows from an economy, from competition, and so on. And actually take advantage of those in some ways. And the other part of that is leveraging that innovation as you're trying to say I want to be able to grow and be successful can I do so in a way which that innovation is, I use that word again, pragmatic it fits well with everything you do. I think VMware in my view has a very strong culture, which leads to that, and sometimes we use the phrase of VMware, as a bit of a why culture, people ask why all the time, right? So if I say we're going to do something with project X, some senior engineer is going to say why, now what's even more important, is that often becomes a why not, so you look at some of the partnerships we've done, some of these, where we get into those conversations and you know, the natural thing, well we're not going to partner there, but then somebody says why not, we could partner there, and after that you get some very interesting-- >> We can integrate this into theCUBE Q and A, so right, why block chain? >> Right. (John laughs) Yeah so, the key area where we look at block chain is what actually part has, made some comments on block chain around it being this key almost like the IP story for the future of financial services, right, IP networking so from networking point of view. So what we see is that this is essentially a foundation layer for applications to be built on, not just for financial services, but we see it also showing up more and more in things like supply chain. That's a hard problem, its a distributed problem, its a problem where you get a bunch of customers saying we want to operate as some sort of a group together but one wants to go on prem, the other wants to go on cloud. And that's what, where we've got a unique-- >> The IP metaphor is interesting, I mean, if you look at what IP networking did. >> This is pre-web, this is internet. >> Correct yeah. >> I mean what happened after that was just an amazing shift in our world. So you guys see block chain as a similar paradigm? >> We do see that, well we see, its a layer for which it's be, kind of somewhat ubiquitous layer that then you build these trust applications on top of, right. And so its almost like a platform layer at that stage. That's why when we look at it, its almost becoming kind of a software infrastructure story. >> Well, you know we'd love block chain. We (mumbles) time We're going to talk more in depth. >> You do I saw some of your stuff on block chain online. >> Yeah, great Thanks. >> Yeah. >> So I saw a tweet from you the other day, that of all these poster presentations behind us you were really trying, with all these stickers and things. How is you sticker collection coming along? >> It's coming pretty well, its kind of funny, what you're, me seeing here is a bunch of engineers who are really passionate about the thing they are presenting. So when I find someone and built little LEGO characters, there's little stickers that they build and so on all trying to push to some degree their passion about what they're doing, right? So yesterday I come in here at 7a.m, thought the place would be empty but there was actually a bunch of engineers here. But I was getting all these stickers, right, it was just surprising to me, wow, people put a lot of even artwork into these projects as they try and describe them. >> Well, and what I think about that is it shows creativity and its one of those, you might call it a softer skill, which I don't know why its called softer skills, but thats essential is, is the ability to express that creativity. And also some of the other skills like collaboration and learning how to present even better, which are also elements that the folks that attend Radio get to work on. >> Correct, many of the engineers who present here, this will be their first maybe or their second time presenting to a large group, now they are presenting in front of two thousand people, and in many cases, two thousand of their peers, who know exactly what technology they're talking about, so you can't just give some high-level, oh it might be better kind of thing. Someone will say, where will it be better, how fast will I be, and so on. So, we make sure that if any engineers are looking for training, or want to get some help to do those presentations, we spend quite a bit of time making sure they can get that because that's part of growing them as engineers and as future professionals or business leaders. >> Absolutely, well Ray thank you so much for joining John and me on theCUBE at Radio 2019, great to talk to you, and excited to hear some exciting things to come out of VMworld 2019 which is just around the corner. >> That's right just coming up, Thank you. >> Absolutely. For John Furrier I am Lisa Martin, you're watching theCUBE from VMware Radio 2019 from San Francisco. Thanks for watching. (upbeat music)
SUMMARY :
Brought to you by VMware. at the VMware Radio 2019 Event. This is the fifteenth annual Radio there's about eighteen-hundred engineers here, may be selected to be featured here. This is the third day or so, but also some that are, you know, we call it Virtual first because no one has actually It's always been kind of collaboration bolted onto may well be distributed, so what you got is Well Ray, we want to thank you To be invited to get access to some of the projects on the organic to the CTO, architecture map or in the case of, you saw yesterday, So I got to ask you about 5G, one of the things in the industry? the you know, core infrastructure back into the data center itself. our experience is that anytime you turn something that customers deliver to VMware's culture of innovation? In terms of the projects, and what you see here, You know one of the things you guys are really on the networking and security side. but also app defense and some of the projects we do there. You know, one of the things I see as a trend, Cloud's is also another. So this notion all of my bandwidth to be successful, that they can do things better than you guys (mumbles) on a different cloud, we will help you to do that, that really are essential to drive disruption. and after that you get some very interesting-- its a problem where you get a bunch of customers saying if you look at what IP networking did. This is pre-web, So you guys see block chain as a similar paradigm? that then you build these Well, you know we'd love block chain. So I saw a tweet from you the other day, that of all really passionate about the thing they are presenting. that the folks that attend Radio get to work on. so you can't just give some high-level, and excited to hear some exciting things to come out of you're watching theCUBE
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Ray Wang, Constellation Research | IBM Think 2019
>> Live, from San Francisco. It's theCUBE. Covering IBM Think 2019. Brought to you by IBM. >> Welcome back to theCUBE's coverage of IBM Think 2019. Here in Moscone, we're talking so much multi clouds. It's been raining all day, really windy. To help us wrap up our third day, what we call theCUBE Insights, I have our co-CEO, Dave Vellante. I'm Stu Miniman and happy to welcome back to the program. It's been at least 15 times on the program, I think our counter is breaking as to how many you've been on, Ray Wang, who is the founder, chairman and analyst with Constellation Research, also the host of dDsrupTV who was gracious enough to have me on the podcast earlier this year, Ray. >> Little reciprocity there, Stu. >> Hey, we got to get you back on, this is awesome! Day three is wrap-up and this is going to be fun. >> Ray, as we say, theCUBE is everywhere, except it's really a subset of what you and the Constellation Research team do, we see you all over the place so thanks for taking time to join us. Alright, so tell us what's going on in your world, Ray. >> So what we're seeing here is actually really interesting, we've got a set of data-driven business models that are being lit up, and you see IBM everywhere in that network. And it's not about Cloud, it's not about AI, it's not about security, it's not about Blockchain. It's really about companies are actually building these digital networks, these business models, and they're lighting them up. IBM-Maersk, we saw things with insurance companies, you see it with food trust, you see it with healthcare. It's happening, and it's the top customers that are doing this. And so it's like we see a flicker of hope here at IBM that they're turning around, they're not just selling services, they're not just selling software, they're actually delivering these business models to executives and companies, and the early adopters are getting it. >> Ray that was one of the questions we had, is what's the theme of the show and-- >> There is no theme! >> You're giving us the theme here of what it should be because we talk digital, we talk cognitive, we talk all these other big thought-y words because we need to think while we're here, right? >> We need to think, we need to think! No, but the thing is this is a theme-less show, people can't figure it out but the main thing is, look, I've got a problem, this digital disruption is happening, my business models are changing. Help me be part of that shift, or I may go away! And people realize that and that's what they're starting to get, and you see that in all the reference customers the people that were on stage. The science slams were also really great. I don't know if you had a chance to catch those but the science slams were kind of a flicker into research, IBM research which is the heart of IBM, is coming up. They're going from concept to commercialization so much faster than they used to be, used to be research would do a project people are like, that's kind of cool, maybe I'll adopt it. They're now saying hey, let's get this into the market, let's get into academia, let's get early adopters on board. >> So Ray, what do you make of the Red Hat deal? What does it say about IBM's strategy? Do you like the deal? What does it say about the industry at large? >> It's a great question. The Red Hat deal to me was overpaid, however, at 20x multiples, that's what PE firms are paying. So every vendor is now competing with PE firms for assets. Red Hat, at about 9x, 10x? Makes a lot of sense, at 20x? It's kind of like, okay, is this the Hail Mary or is this the future strategy or is this basically what the new company is? I would have rather taken that money and put it into venture funds to continue what they're doing with these network models. That would have been a better strategy to me but Red Hat's a great company, you get a great team, you get great COs you get great tooling. >> So you would've rather seen tuck-ins to actually build that network effect that you've been alluding to. Of course that would have taken longer you know, wouldn't have solidified Ginni's legacy. So, it's a big move, a big move on the chessboard. >> Well the legacy's interesting, last year the stock was down some 20-some percent, it's up 20% since January so we're going to see what happens, but it's a doubt component. >> Well I've always said she inherited a bag of rocks from Palmisano at the peak of 2012 and then it got hit hard and she had to architect the transformation. It took, I don't know, five years plus, so, you know, she was dealt a tough hand, in my opinion. >> She had a bad hand, but we've had seven years to play this. I think that's what the market's saying. >> So it's on her, is what you're saying. >> It's now on her. She's got to turn this around, finish the legacy, but you've got a great CEO in waiting with the Red Hat guy. >> Jim Whitehurst you're saying? >> Yeah, he's good >> So she's what, Ginni is 60, 61? Is that about right? >> She's past the retirement age. Normally IBM CEOs would have gone through. >> 61 to 63 I think, is that range maybe, hey, women live longer so maybe they live longer as the CEO of IBM, I don't know. >> She did get a bad hand, but I think when you execute the strategy that money, here's the tough part. Investors are saying, hey, we'd rather take your money, back away from you through stock buybacks, dividends and mergers and acquisitions, and we don't trust you to do the innovation. That's happening to every company, including all of IBM's customers. The problem is if you do that, they're hedging against those companies too. The same investors are taking 50, 100 million, giving it to three kids in a start-up anywhere in the world and saying, hey, go disrupt these guys, so they're betting against their own investments and hedging. So that's the challenge she's up against. >> We talked about in our open for the show here. It's developers, though, that's the business model. We saw IBM struggle for years to get any real traction there, there's little pockets there, they've got great legacy in open source, but Red Hat's got developers. Ray, you go and see a lot of shows, who's doing well with developers out there? >> Microsoft redid their developer network by going younger with GitHub, whole bunch of other acquisitions, this is a great developer buy in that percent. But the other piece that we noticed here was it's the partner developers that are coming in in force. It's not your average developer. I'm going to build a coding and do a mobile app, it's people that work for large system integrators, large networks, small midsize VARs, those are where the developers are coming from and now they have a reason, right? Now they have a reason to build and I think that's been a good turnaround. >> How about Salesforce with the developer angle, what's your radar say there? >> It's not about the developer angle on the Salesforce side, what's interesting about the Salesforce side is Trailhead. This is, like, learning management meets gamification meets a whole LinkedIn training program in the back end. This is the way to actually take out LinkedIn without going after LinkedIn, by giving everyone a badge. There's a couple of million people actually on this thing. Think about this, all getting badges, all training each other, all doing customer support and experience, that's amazing! They crowd-source customer experience and learning right there. And they're building a community and they're building a movement. That's the thing, Salesforce is about a movement. >> Couple of others, SAP and Oracle, give us your update there. >> I think SAP's in the middle of trying to figure out what they have to do to make those investments. We see a lot of partnerships with Microsoft and IBM as they're doing the Cloud upgrades, that's an area. The acquisition of Qualtrics is another great example, 20x. 20x is the number people are now paying for for acquisitions and for assets on that end. And Oracle's going to be interesting to watch, post-Kurian to see how they come at it. They have a lot of the assets, they've got to put them together to get there, and then we've got all these interesting things like ServiceNow and Adobe on the other end. Like, ServiceNow is like, great platform! Awesome, people are building and extending the Cloud in ServiceNow, but no leadership! Right? I mean, you've got a consumer CEO trying to figure out enterprise, a consumer CMO trying to figure out enterprise, and they don't know if am I a platform or am I an app? You've got to figure that out now! People want to work with you! >> Well it is a company in transition at the top, for sure. >> But they can do nothing and still make a ton of money on the way out. >> And they've kicked butt since Donahoe came on, I mean just from a performance standpoint, amazing. >> Oh yeah, performance? You can do nothing and I think it's still going to coast but the thing is at some point it's going to come bite you, you got to figure that out. >> How do you think that Kurian will fit at Google, what's your take there? >> You know, early reactions on Kurian at Google is good, right? The developers are embracing him, he understands what the problems are. Let's be honest, I've said this many times to you guys in private and also in public, you know. It was a mess, it was a cluster before. I mean, you had three years, and you lost traction in the market, right? And it's because you didn't get enterprise, you couldn't figure out partners and, I mean, you paid sales people on consumption! Who does that? You're a sales rep, you're like, I'm not going to do this on consumption! Makes no sense! >> Ray, Kurian had been quoted that no acquisition is off the table, you know, they didn't buy GitHub, they didn't buy Red Hat, do you see them making a 10, 20 million dollar acquisition to get them into the enterprise space? >> Billion. >> Yeah, sorry, 20 billion. >> I think there's a lot that they go after. I know there's rumors about ServiceNow, there's a couple of other things. I think the first acquisition, if I were to make it would be Looker. I mean I love that thing that's on there and buy Snowflake too while you're at it. But we'll see what they do. I think the strategy is they've got to win back the trust of enterprises. People need to know, I'm buying your relationship, I have a relationship, I can count on you to be successful as opposed to, hey, you know, you can get this feature for less and if you do this on a sustained unit or, I want to know I can trust you and build that relationship and I think that's what they're going to focus on. >> Well, come on, isn't Google's business still ads? I mean, that's still where all their revenue is. >> It is, but the other category is $10 billion. That other category of devices and Cloud and all that? That's still a big category and that's where all the growth is. I mean look at this, it's a full frontal assault between Amazon and Google, Amazon Alexa versus Google Home, right? Amazon in ads, $10 billion in ads, going after Google's ad business. Amazon doing an AWS versus Google Cloud. Google's under assault right now! >> Give us the update on Constellation, your conference is really taking off, you've got great buzz in the industry, and congratulations on getting that off the ground. >> And the Tech for Good stuff, loved it. >> Thank you. We had great event, December 10th, talking about the future of the Internet. What it means in terms of, you know, digital rights, human rights in a digital age, was really that conference. Our big flagship conference is November 4th through 7th, it's at Half Moon Bay. We get about 250 CXOs together, about 100 vendors and tech folks that are visionaries and bring them together, that's doing well, and we do our healthcare summits. We brought on a new analyst, David Chou. David Chou, and if you've seen him before, he's like one of the top analysts for CIOs and chief data officers in the healthcare space, he's at HIMSS right now. >> He's awesome, we know him from Twitter. He's been on, he's great. >> Yeah, so we do healthcare summits twice a year and that's been picking up, some of the top thinkers in healthcare. We bring them in to Las Vegas, we do a brainstorming session, we work with them. They think about ideas and then we meet again, so. >> Alright, Ray, we want to give you the final word. We're halfway through IBM Think, what have you been thinking about this and any final musings on the industry? >> So I was very upset last year at how it was run. And I think this has run much better than last year. I think they did a good job. February in San Francisco? Never again, don't do that. I know it's May next year, is when this event's going to be. But I think the main thing is IBM's got to do more events than once a year. If you get enterprise marketing you realize it's at the beginning of the year, it's still sales kick-off and partners. March? March is like closing the quarter, so you do an event in April or May, and you do it in April or May but you have multiple events that are more targeted. This theme-less approach is not working. Right, partners are a little confused but they're here because it's once a year. But more importantly, build that pipeline over the quarters, don't just stop at a certain set of events, and I think they'll get very successful if they do that. >> Alright well, Ray, next time you come on the program, can you please bring a little bit of energy? We'll try to get you on early in the show when you're not so worn down. >> I know. >> Thanks as always. >> Appreciate you coming back on, man. >> Hey thanks, man, it's theCUBE! I love being on this thing.. >> Always a pleasure. >> Alright and, yeah, we always love helping you extract the signal from the noise. We're Dave Vellante, John Furrier, Lisa Martin. I'm Stu Miniman. Thanks for watching day three of theCUBE at IBM Think. Join us tomorrow, thanks for watching. (light music)
SUMMARY :
Brought to you by IBM. I'm Stu Miniman and happy to Hey, we got to get you except it's really a subset of what you and you see IBM everywhere and you see that in all to continue what they're doing move on the chessboard. Well the legacy's interesting, from Palmisano at the I think that's what the market's saying. around, finish the legacy, She's past the retirement age. as the CEO of IBM, I don't know. and we don't trust you that's the business model. But the other piece that we noticed here It's not about the developer angle Couple of others, SAP and Oracle, They have a lot of the assets, Well it is a company in money on the way out. I mean just from a performance but the thing is at some point to you guys in private and I can count on you to be I mean, that's still where It is, but the other getting that off the ground. What it means in terms of, you know, He's awesome, we know him from Twitter. some of the top thinkers in healthcare. and any final musings on the industry? and you do it in April or May time you come on the program, I love being on this thing.. extract the signal from the noise.
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Prakash Rajamani & Ronnie Ray, Cisco | Cisco Live EU 2019
(upbeat music) >> Live from Barcelona, Spain. It's theCUBE covering Cisco Live Europe, brought to you by Cisco and its ecosystem partners. >> Hello everyone welcome back to theCUBE's live coverage here in Barcelona, Spain for Cisco Live Europe 2019. I'm John Furrier with theCUBE, with Stu Miniman and Dave Alonte also here doing interviews. Our next guests, two guests from the DNA center platform, Cisco, the agent platform team, Prakash Rajamani, director of product management, Cisco and Ronnie Ray, vice president of product management, Cisco, the DNA center platform growing 70% of the use cases, software distractions, API automation. Congratulations. Great success. Thanks for joining us. >> Thanks John. >> Big Fan of the DNA center. You guys have made great progress. Take a step through us. The positioning, how things are rolling, what's some of the feedback? Where's the DNA center platform at right now for Cisco? >> Yup. >> So DNA center was launched about 80 months back and it's probably one of the products in Cisco that has completely started to transform how we do the selling motions. So this is one of the key drivers of Cisco moving into light sensing mode switch, more software like. Now as part of how we do management Typically and traditionally it has been very much a manual driven process there's some reporting but it is a lot of expert light capabilities that you need to have to do management of the infrastructure then it's kind of moving that access to where you can now do machine-lift management. Of course it doesn't solve all the use cases absolutely as you mentioned, more than 70% but there's a whole host of new capabilities that you have to put on top and that's where developers come in because this is a platform that's built for developers to be able to extend it's capabilities to really look at solving problems for our customers. >> I think you know, after listening to all the announcements in temp based networking, ACI anywhere, hyperflex anywhere, data at the center of the value, data centered as you guys say, it's clever but I think it highlights what you guys are doing because you're talking about programmability of the network as two worlds collide actually three worlds collide, Cloud, On Premises and Edge into one network, you have a network, the network is key it's getting bigger, to cross domains is a big theme here, these are hard problems that are being solved by Cisco more complex cause there's more moving parts but it still has to operate as one network. This is essentially highlights the success of the DNA platform, am I kind of getting it right or is that kind of in line with how you guys see it? >> Sure, I mean I think Cisco DNA centered I mean if you look at the evolution we started in the network domain. You're absolutely right we have kind of extended to the brand change, there's nine integrations that are happening with the data center integrations, happening with the cloud, so yeah absolutely looking at the fabric that we launched about 18 months back now extending and stretching to all of those domains and wherever users connect and wherever users go to and that's of Cisco data center but think about that as we kind of do that, yes there is a change that also required not just in the product but also in the IT process because earlier companies had silos of things and now those silos will be forced to work together and CI was one that our network folks that support us because really they want to see cross domain bring power to the organizations but we are the enabler of making that happen. >> No brainer. >> Prakash, I'd love for you to take us inside ya know, we love looking at the product management piece here because you've had a lot of constituencies. You've got the internal product teams that all I'm sure want to get in and mature and expand their used cases. You've got all your partners that are building the platform. You've got the customers asking for feedback You've got a - ya know, a lot of options to choose from which is a good thing but you've obviously got limited resources. So take us inside that, what you've learned over the last year and how you helped prioritize and move this product forward so fast over the last 18 months. >> So one of the main things we did when we started with Data Center is to start thinking and having the vision to get a data center platform. With that in mind, every feature, every capability that we built in the product was built API first before we built a UI around it. Right? That has helped us immensely in the last couple releases we've started delivering features as APIs even before it had a face to it, and I think that has helped us prioritize and make sure that we are able to meet the demands going demands of customer or partner we had a customer who was like "I need this feature now" and we were hands strapped, we had a big back log, we couldn't get things done but the fact that we were able to get the APIs we were able to work with the customer and say "Hey here you can wire these three APIs and you can get what you're looking for" and he was like "Wow, that's so simple and I'm on my own" he was happy, we are happy we are able to manage our back log better. So I think the main strategy for us that's working is going API first on a pragmatic basis. This is us moving completely software driven as Ronnie was highlighting earlier in that relevant process that is helping us get there and that's part of it >> Well, it's customers a lot I mean they get to roll their own if you will without having be customized, it's still standardized with the APIs >> That's right, right? I mean the benefit is as you start getting into the 30% used case where "Hey, what's coming out of the box is not meeting exactly what I do today" we provide very grander APIs to very business driven, simplified interned APIs. The grander APIs allows the customer who wants to say I want A, B and then D and E to move forward compared to intern based API who is using the pride in the simplicity in driving that formula. >> Yeah, Ronnie I'm wondering if we can up level for a second here cause feedback I've gotten over the last year. Ya know, a year ago we heard Cisco is moving heavily towards software. When I talked to a lot of the partners both technology partners and channel partners they said this had a ripple effect inside Cisco it's not so much okay here's the skews and here's the new boards and here's the products but I need to sell a solution and therefore that's platforms that I have to have and therefore everything needs to work together and I have to think API first and like it does significant changes to how Cisco is, the joke I used to have is Cisco is like 100 companies and some people were like "Well, maybe it's 100, maybe it's 200." But today it's now something like platform is a unifying place, is that what is your solution set part of that drive and is that something you're seeing more broadly inside Cisco? >> Certainly, I think you're absolutely right that is does have a unifying effect if I might put it that way. >> Yeah Right? Because there's so many different capabilities that existed in different tools that are coalescing on Cisco data central and which is becoming part of the platform which is now customizable by our entire development community but think how fast that happens in a now within the sales force, within Cisco as a company there is no more cross domain knowledge that'll be required because now it operates different parts it can tune different things, that also means that is supposed to change the business model because going into software and kind of bringing it together and is increasing Cisco is obviously ya know foyering into softer subscriptions, this is a key product that's kind of supporting that, so in many ways it's not just the technology, it's not just APIs but also as a business process that's changing Cisco just like it'll change customers. >> One of the things we're seeing is a lot of design thinking principles this year. Love the new positioning bridged to the future bridged to tomorrow, wherever it goes but it's clean. Connecting the worlds are connecting together through the network get that. What has been some of the challenges and opportunities you guys are seeing around simplicity? Love this API, exposing API allows for customization, I love the broader intent based templates are great but it's hard to make things simple. Can you just elaborate on how you guys are thinking about the product short, medium, long term in terms of continuing to work the back log, I'm sure the feature list is growing like crazy but you got a challenge to make it simpler. >> Absolutely >> How hard is it? What does it entail? Share some insight there. >> So lets take the question in two parts and Prakash can talk to the product simplicity because that is a certainly something that we've got to manage very very carefully but think about also when simple doesn't just mean usable product, it also means a product that can fit into the ecosystem and make the process simpler. So there's a lot of deeper understanding that we are developing through the learning as we work with customers and how do we embed how do we make customers life easier how do we make the process easier and then after goal is how do we make their operational expenses lower? Because we want them to go faster, we want them to go faster at a lower cost and so there's a certainly both learning and investment that's happening there and the product side Prakash. >> On the product side it's about how we used to build to how we are building right now the way we used to do was a new feature comes in it goes to the device layer first the device team builds it puts CLI around it ships it off, sends it to the management team and the management team says "Oh, I got to support this feature" They go, they wrap a UI around it to support the feature, ships. Now we have flipped it turn completely around we start with like what is a customer's work field? What do they need to do and how can we do it in the minimal steps? Once we identify that we push that down to saying "Here is what the user interface looks like here are the three steps that they need to do. That trickles down to saying what we need as an APA on the device layer to develop the feature so we've gone down from going a bottom up way to build a product to a top down, customer driven, used case driven way to build a product. That means we are addressing the customer head on from a simplicity perspective and that's basically what has made us successful in moving the ball forward on this one. >> What has been some of the customer feedback? Can you share some anecdotes around some of the early customers you started rolling this out and what are the ones receiving on the receiving end today saying? >> So when you see from a simplicity feedback perspective I have a large retail store rolling out like maybe 60 APs in a single store over night and they've gone from having that be done over three nights to one person spending 20 minutes putting all the APs up going to the tool and the tool recognizing everything that's come up and deployed. So it's a night and day transformation on how it used to be to how it is right now. So the simplicity >> Sounds like the old way was >> Sounds like you saved a night in a day >> Manually configure, go put a wireless ping to it >> Yep, the old way was yeah you go you plugged the AP, you come back you look at the tool, the AP is there >> Check the channel, stuff is there. >> Map it to the right controller, do all the mappings Now you don't have to do anything just plug the APs and upload preloaded to say these APs are going to the store. The tool takes care of the rest of the stuff that's how simple it is become >> It's almost like old way new way What why are we doing that? And it's good when they have consistent environments with policies there's definitely more expansion. I get that, what about other used cases? Wireless is one hot one, I could see that branch off it's deployments what are some of the popular used cases that you're seeing in the customer base I know you got a broad base but what are the ones what are the patterns that are emerging out of this? >> So let me start another then have Ronnie chime in on the used cases he's seen. Some of the ones that are probably very transformational is that on the policy based used case, we have companies turning around and creating small subdivisions within their organizations. We have a large government in Yasha who is deploying that, they have 20 divisions. Earlier to do that it's extremely complex. They have to go in, they have to understand what division, who is using on which device, which ports mapped to them, just planning that it says it's so huge. For the new policy different approach that we have going, they don't have to know about anything they just need to know Prakash works for division A, Ronnie works for division B assign me to respective divisions, as I come in my policy gets right over to the network. I deploy the network as is, as I speak that is basically the level of simplicity that has changed and that all ties back to doing your network from a policy perspective not a networking from a feature perspective. >> Got it, Ronnie any comments on used case on your end? >> Yeah absolutely so think about we've talked about assurance we launched segmentation that's doing very very well of course even with when all of the public acknowledgement that goes with it but an interesting used case that's come up which is in fact in the keynote this week at Cisco live is about IUT extensions. So Data seto owa is extending to the factory floor, the production equipment and transportation and these are tremendous neo opportunities that are both for companies to kind of look at IT and OT and how this comes together, again going back to the unification simplification theme that do many more things at the same time they try to make it in a rationally much more operable. >> Okay so lot of progress in 18 months give us the road map going forward. We're at the beginning of 2019 what you'll be looking for, can a high level show show us what we should expect to see down the road >> K so from a road map perspective it's in a think about that we've been very focused on getting the customer value. Now the lens is kind of shifting to how do we deal with large enterprise capabilities? So both the hardening of the system itself, how do we look at, for example multiple clusters opening up in diverse locations will give us geo diversity and support there from that perspective and high availability. So these are enterprise class features every large customer requires it and as they move from smaller deployments to full scale deployments that is something that the labs look to need >> Yeah, Prakash when I heard you talking about things I need to think a little bit differently. It's like okay I'm used to going into the deploy and it's going to take me three days wait how do I learn about the fact that I can do it now in a couple of hours? What kind of training or retraining or education is that part of what you're doing in your team or where does that happen? >> It's part of the education, part of the videos we double up and publish to customers so that they don't think about this as I'm going to approach my same 20 steps and think that I'm going do that through data center except that I'm going to do that through a user interface. The first thing that we tell them is like "You're going to do 20" You're going to do two. Right? So the immediate feedback is oh does it address everything I want to do? And so that's the 70% used case more would rather say yes it addresses only thing is we have simplified it, we have compressed it so you don't have to go and go through all these 20 steps but instead get it done in two, so the watts have helped some of the trainings that you have done has helped even talking to from a sales process the customer to know "Hey this is what I'm embracing" so when they come in they don't come in with I'm going to run my network the same way but no no I'm going to run it differently has helped us immensely to make the transition >> Well guys, congratulations on a great successful product, big fan I love that thing, I think it's going to be the future there's a lot more head room there that's cause we're looking at automations the devnet zone we're in is showing massive growth. The appetite for automation the appetite for configuration and scale and managing the complexity is a sweet spot I think that you guys had a nice formally hear looking forward to it. Final question for these guys Ronnie and Prakash are going to both answer it. Say something about DNA center platform that people should pay attention to that they might not hear in the mainstream chatter that's important that they should maybe want to kick the tires or understand it further, an area that they should know about that they might not hear about or they should know about what's the most important feature. Share some, share some insight. >> So again just looking at a little bit into the future of Cisco data center platform, right now we're kind of talking of APIs, there's capability that's coming in the future that will also deal with work flows and the work flows will be built on something which is machine built so there will be a lot of analytics in fact in a data center not only does automation but also extends data analytics so a lot of cool stuff that'll come there and again we'll talk about it more as we get to the next Cisco live. >> Prakash anything? >> I'm going to go a little more ground level people tend to talk about simplicity, talk about how we can do things way differently with data center and people tend to forget that we have not forgotten the network engineer who has been managing the network. We have APIs for you to do the same things you've done all along, create articles create re-lance, do some of the basic networking stuff so that it's not about this just as simple we also have the more detailed breakdown of the API so that you can still continue to know the nuts and the bolts and other things as well as much as the simple stuff so it's the >> It's an empowering all personas in the network from network engineer low level getting down and dirty to large scale automations, whatever the use case is you got the empowerment. >> Yep that's basically what I would like to >> That's awesome, well congratulations Again big fan, DNA center takeover here in the Devnet zone I'm John Furrier with Stu Miniman Cube coverage day two of three days stay with us for more after this short break. (electronic music plays)
SUMMARY :
brought to you by Cisco and its ecosystem partners. growing 70% of the use cases, software distractions, Big Fan of the DNA center. and it's probably one of the products in Cisco of the network as two worlds collide looking at the fabric that we launched over the last year and how you helped So one of the main things we did when we the benefit is as you start getting into the 30% and here's the new boards and here's the products absolutely right that is does have that also means that is supposed to change Love the new positioning bridged to the future How hard is it? and the product side Prakash. as an APA on the device layer to develop the feature having that be done over three nights to Map it to the right controller, do all the mappings Wireless is one hot one, I could see that For the new policy different approach that we So Data seto owa is extending to the factory floor, We're at the beginning of 2019 that the labs look to need and it's going to take me three days wait some of the trainings that you have done has helped I think it's going to be the future and the work flows will be built on and people tend to forget that It's an empowering all personas in the network in the Devnet zone
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Ray O’Farrell, VMware | VMworld 2018
(soft music) - [Narrator] Live CUBE coverage at VMworld 2018 continues in a moment. Live from Las Vegas, it's theCUBE! Covering VMworld 2018. Brought to you by VMware and its ecosystem partners. - Hello everyone, and welcome to back to theCUBE, live coverage here in Las Vegas with VMworld 2018. This is our three days of exclusive wall-to-wall coverage, two sets, it's our ninth year covering VMworld, when Dave and I started theCUBE nine years ago, Paul Maritz was the CEO, he actually got referenced on stage by Pat Gelsinger. I'm John Furrier with Dave Vellante, our next guest Ray O'Farrel, CTO, Chief Technology Officer at VMware, keynote today on stage with Pat; great to see you again, thanks for coming on. - Really good to see you guys again. - So, reaction from the keynote was very positive. Probably, from a content standpoint, probably one of the most meatiest content pieces I've seen, mega news, serious announcement with Amazon, with Andy Jassy coming on stage releasing the Relational Database Service, RDS, on VMware, on-premises. Monster news. That is like, I don't think the world has yet felt the reverb for this thing yet. - But that was only one of the many stories. - [John] That was just one, that was just like (makes explosion noise). And then the CloudHealth acquisition, and you had tons of demos, pretty intense. - [Ray] Well, it's been- - Summarize what you did (laughs) in ten seconds. - Summarize all of that. So, you know, the key thing that we wanted to achieve with the keynote was obviously to make sure Pat drives the the vision that VMware has and, a lot of focus on that was focused on multi-cloud, this view of the world that you've now got multiple clouds emerging. And you know one of our key rules is to make sure that enterprises are able to work across all of those, networking, how we do management, how we work across all of these, and CloudHealth is a key part of that, making it easier to use cloud, in particular multi-cloud. You know as the CTO I get the fun part of tryna you know let our customers know all the cool work that the engineering teams are doing, so one of the things we want to do is make sure we put a lot of good demos in there. The feedback we get from our customers at VMworld over and over again is they want to see demos, they want to know that stuff is real. You can take a look for instance at the hands on labs. I came in here on Saturday night, walked down there about 6:30 am on Sunday morning, and there was people lining up to go in there and use those labs. So what did we talk through? Broadly speaking we spoke to how you can use VMC on AWS, and the easy way it is to migrate vSphere applications onto vSphere on AWS; we had some new features there around live migration. The next thing we spoke about was around RDS itself and what this project is about. Broadly speaking, at its most basic, it allows you to take the RDS components from Amazon but run them in your data center. With all of the implications of that in terms of how your developers work and they build those applications. We spoke about Project Dimension, which is also around a now delivery as a service, a cloud experience, but again, at your infrastructure, whether it's at the edge or whether it's your data center. And, you know we spoke about what we're doing in blockchain, some opensource components that we're doing over there. New features of Workspace ONE, particularly around the relationship with Dell, and how that will now be combined with some of their laptops And, oh and of course, what we did with some of the Nvidia GPUs, demonstrating the ability to be able to run the most sophisticated AI workloads on a vSphere environment. And I suspect I forgot something in that list, but- - [John] You're going to have to hit the pillow tonight and have a good nap, and crash. - [Dave] Project Magma. - [Ray] Project Magma which is a very future looking concept around basically where we think AI and ML is going to be used to drive a lot of the automation moving forward. - [Dave] Self-driving data center, - Self-driving data center. - [Dave] I think you'd call it (John laughs) Are they coinin' a new term there? - No it's great, we can reuse an old term, and you know rebrand it. - Auto pilot. Put your data center on auto pilot. I want to just drill down on one, on the Amazon relationship, because that was obviously the height, big news in there what you're talking about is the depth of the relationship is deep on the partnership side. I want to, and you guys, you pointed that out, I want to amplify that, but I also want to ask you around the RDS demand. You know, talkin' to some of the Amazon sources, they tell me that the demand for this was very strong, over multiple years. So, first on the RDS, the demand, some of the customer feedback, this is not just you guys in a room goin' hey, let's just do this; it makes sense, but it's customer driven. - Yeah, when you look at what VMC on AWS actually is, it's creating this bridge between the on-prem and the private cloud, sorry, and the public cloud on Amazon. But, initially most of that is really an I as relationship, yes we can move workloads, yes we can move VMs, yes we can manage networking, but one of the key things you want from a public cloud or from cloud in general is access to services. So, as we went down that first part of saying we'll give you this basic infrastructure, very quickly customers began to ask for some other things, some other aspects of that, and that of course was services. So after lots of discussions around what are services, one, that are appropriate to be able to put into this new type environment, but which had to demand RDS certainly rode very quickly to the top of that. In the end almost everybody has some form of database in their application, and so it's a very likely start for us to make them. - So I remember when customers first started wanting to run, to virtualize Oracle, with of course VMware; and Oracle, didn't really embrace that early on. They would say things, their sales guys would scare the customers, we're not going to certify it, but then some of the customers said "Dam the torpedoes, we're going to do it." it actually worked great. - [Ray] Right. - Now, I don't know if that's 'cause, just that's the inherent nature of VMware, or you guys had to do some work, so my question is: two fold, was that just the inherent nature of VMware, and what did you have to do or will you have to do to get RDS running the way that customers want it, trust it on AWS, I mean on VMware? - So, in the case of the Oracle situation, we didn't have to do a whole lot to make that happen, we were virtualizing in x86, Oracle runs on x86, and so you got that basic pattern and mix. In the case of RDS, the actual database that you're running on your VMware infrastructure, our database is such as my SQL, we run an enormous amount of those databases already, so that core aspect of getting the database running is not something that's fundamentally difficult for us to do The challenging part is, how do bridge all the management aspects of that? The RDS components, the APIs, that a developer wants to use, and which are used to using over on, with RDS on AWS, so that's where the work is involved. Now by the way, you're implying that maybe this is a future thing, right? A lot of that work has already occurred, in fact, you know the demo you're seeing is not based on this is what we could do at some possible time in the future, it is actually tied to some very close future releases. - [Dave] So recovery, I'm going to be co-, that's future release of recovery and all the things, if something goes wrong, I'm going to be comfortable as a customer that - [Ray] Correct, correct. - You're going to be backed. - Some of those things we still need to work true, because there's tons of features that you can begin to add onto this, disaster recovery, backup, all of those sort of things, and they're not all going to be there on day one, but you can expect us to continue to add all of that. - [Dave] And you'll have all of those? - Correct. - Now the other question I got to ask you is about migration. When I hear the term migration I go, ugh, you know IT practitioners they tighten up, but what I heard on stage today is we're going to make this really easy. But moving data, help me square that circle, Ray, because, you know data, people say data has gravity, speed of light, network bandwidth, proximity. What's the secret sauce that enables you guys to solve those problems? - So the core secret sauce there is if you're virtualized on VMware on-premise, and you're using VMC on AWS, the basic unit of execution is still that virtual machine, and that virtual machine encapsulates the storage, the networking, everything associated with that box, right? So virtual machines have that very core strength of encapsulating not just the application, or some aspect of the, even some aspect of a minimal piece of the operating system, it encapsulates everything which is tied into that box almost on a physical level. So when you say I'm going to move a virtual machine, you're moving the disk, you're moving the storage, you're doing all of those things. So now think of a database running in a virtual machine, it might not even be the applications, just the database, we're able to capture that and represent that as we moved the virtual machine, you're moving all of that as well. Now there's two aspects of that, one of them is moving the underlying storage, the disk, which might well be even a a virtual disk on NFS or something like that, that's slower task, and that's why we leverage vSphere replication for that. And then the final live part which is, it's always the cool part, but is in fact in this stage maybe not the most difficult part, and what we're describing here is moving the actual memory contents of a given VM and flipping it over to VMC on AWS. - [Dave] Okay, so the key there, you've got the replication piece, and then you just unhook the original and then you're up and running. - Correct. Traditional vMotion relies that both servers access the same disk, so I don't need to move the disk, in this case I need to actually move the disk, and that's what the replication does. - [John] Ray, I want to ask you about something that Pat Gelsinger kind of cheesed out on the keynote. You could tell he had so much confidence, he wanted to expand on this one section but he got a couple digs in on it but, he did point out that the telco piece was very big; and only, he had a percent, I think 10% or 20% is virtualized when enterprises are like 80, I forget what now, I forget the exact numbers but his point was: huge opportunity in telco. What was he referring to there? - So, broadly speaking, if you look across most of you know where workloads run, you look at your IT infrastructure, you look at most of the public clouds and private clouds, they're virtualized to an enormous extent. Now when you go into the telco side of things and begin to look at what's happening at the edge, what's happening in the large telco infrastructure, both, a little bit from a cloud point of view, but also from everything to do from all the services and so on that the run; much of that is not virtualized. Now we actually made a very distinct focus on that over the last few years, we created a, basically a product line and a mini business unit, focused on telco, and that's where you see products like the virtual network functions, all of those technologies coming from. But actually the key product from that area is actually VIO, VMware Incubated Openstack, that's because the telco providers, to a large degree, attempted to leverage Openstack, had some challenges of getting the reliability, the stability you need on that, so what we did was merged the hypervisor, the infrastructure of VMware, with the Openstack management APIs, produced VMware Incubated Openstack, and the telco providers are very aggressively taking that on - [Dave] Now, I got to ask ya, whaddya got against capex? (Dave and Ray laugh) Pat said "You should never spend capex for DR again." it was basically- - [Ray] Yep. So I mean, I think the key part of that solution is it is now so, I will use the word easy, the technology behind it is not easy, but it easy for an end user to be able to say: "I can connect my application from a private infrastructure to a public infrastructure, in a way which is very highly connected using NSX, which is easily replicated, which is easily moved; therefore, I now have a ready ability to be able to create DR scenarios leveraging the public cloud." It is easier than it's ever been before, so instead of building another data center to do that, leverage VMC on AWS, leverage those type of technologies to be able to do that. - Ray, can you clarify, or amplify the VMware Cloud Foundations, how does, trials and tribulations over the years has evolved, it's now front and center in the conversation. How has that evolved from a product standpoint, tech, is it integration layer, how are you guys looking at that, what is the role of VMware Cloud Foundation, and what does it mean for your partners and customers? - Yeah, so I think that, your comments about it having a a kind of an early mixed reaction or so on is actually partially because a naming challenge that we called right? VMware Cloud Foundation is a unified story where we basically take the core elements of the SDDC and we combine in management infrastructure with that, which is actually called SDDC Manager, we don't necessarily spell that out but it's combined into that. But that's the key aspect of this, and then we build architectures based on that; so VxRack is based on VMware Cloud Foundation. The infrastructure which runs in Amazon which we manage as part of the VMC on AWS is built on VMware Cloud Foundation. So it's an architectural and, it's an architectural statement as opposed to a product statement. Where the confusion arises, we also have products that people call VMware Cloud Foundation. One of the ones they're with now as an instance of that is for instance VxRack, right? Which is basically a rack of infrastructure, think of it as a really big VxRail, but it's got all of this management software combined with it as well. And actually, you know your comment about that having some mixed reaction, some of that is because of our renaming that - [John] Renaming. - we've done along the way. But that is actually growing, and quite successful product at this stage, so. - It's been getting a lot of good buzz. - It's getting a lot of good buzz, yes. - [John] And the value is what? Times in market on, on solution building, or pull out, what's the main value? - In some way it goes back to the core value of hyper-converged infrastructure, somebody else is taking care of making sure that the software components all blend together; somebody else is making sure that there's any easy way to update and manage all of these things together, and in many cases, making sure it's well integrated with underlying hardware. So it's all around making it easy to get that basic SDDC up and running. - [Dave] So I got to question on your architecture, and I honestly don't even know how to ask it, but, maybe you can help me as a technologist; you've got, you know the VMware architecture which was developed initially decades ago, and now you've got all this microservices, and Kubernetes, and containers comin' into the fore, and you see the quote unquote modern architectures, speed of deployment, software release is much faster, much more cloud-like, cloud first. How do you go from you know the historical architecture to that level, how do you bridge the two worlds? - So, as with any company, as these transitions have taken place, we've had to be able to make sure we invest in those new techniques and new technologies as well. So you see for instance VMC on AWS, you see for instance Project Tango the cloud-based VR realms product. All of those are cloud-based infrastructure using, you know those more, well I guess they're described new or modern ways of developing applications, microservices, containerized, leveraging Kubernetes and so on in the mix. So just like the rest of the industry, we've been doing the same as part of that broader sorry, that broader industry momentum. There isn't a conflict that you, I think might think is there. The bottom line is our primary purpose is to deliver enterprise software which is solid, stable, secure, easily connected to the rest of the infrastructure. And that might sound a little bit boring, but it is the thing that keeps most of the data centers running and safe. VMware's ESX architecture, VMware's VC architecture has been at the very heart of that. And while they've matured over the years, right, they're still at the very heart of that virtualization part of what we do, but all of these other things we do, what we do in terms of cloud monitoring, what we do in terms of Wavefront, what we do in terms of VMC on AWS, they're new code, new architectures, broadly expanding that story, leveraging microservices and the things you would expect in that space. - Well, and VMware has proven to the gold standard in that regard. Maybe it is boring, but it's super important. - [John] So you got some compliments on theCUBE today, for the work you guys are doing, Andy Bechtolsheim was on earlier, a well-documented career he's had he knows a thing or two about networks. He said "VMware as NSX is ..." this is a quote from today, "... is the best solution that's available today that I can use for a use case of the large numbers I have between smooth connection between on-premise and off-premise public cloud, into the future, to edge, and telco, and all other things cloud." - Yeah, I'm not going to argue with that quote. (laughs) - [John] So, instant testimonial. Okay, NSX has become really this, and Pat was giddy about this last year, he's all like, you watch more NSX, you know more goodness coming; it seems to be the center piece to the a lot of the VMware's connection strategies to cloud and other things including manageability. What's the big thing about NSX, what should people know about NSX? - I think the single biggest thing is software-defined networking had a promise, and the promise is this highly flexible, easily configured, and in many ways, automated, or policy-driven in some cases; networking infrastructure. So it's all around that flexibility and fluidity of software-defined networking. The key strength that NSX does, it delivers on that promise, so it's easy to say software-defined networking, it's not easy to build it, right? And that's where I think NSX is proving all of its strength, it is a very strong implementation; I would argue, obviously, the best implementation of software-defined networking. So that testimonial is an echo of that, it's delivering on all the things you expect from a software-defined network. - [John] And what is NSX enabling? - In terms of the cloud connectivity story which you just described a second ago, what it enables is, really in some ways, because it is not tied to a specific infrastructure, I'm able to run NSX on a public cloud infrastructure and on a private cloud infrastructure, or on a hyper-converged infrastructure, but it's essentially the same NSX. It's the same control plane, it's managed in the same way, all of those different instances know how to interoperate with each other. So what it's enabling is this massive ability to have these networks very quickly brought up, connect to each other, and reliably communicate with each other, and be managed in a unified fashion. - [John] And it's targeting one of the hardest things people are working on which is interoperability. - [Ray] Correct, it's also targeting security. I mean one of the things when we think about networking that you should never forget is this key aspect of security, and NSX is clearly targeting that as well. So some of the things, even the features you see around app defense, a combination of app defense and NSX gives you enormous power. Pat's made a good presentation today where he was talkin' about the adaptive micro-segmentation. You can only do that because you have a great NSX underlying that network. - What's interesting about the NSX, just want to get your reaction to is that that the people are talking about here on theCUBE and also in the industry is that by having the security at the application portion of it, when NSX plays, takes the pressure of the network teams; security teams can have comfort in their piece, and then, (laughs) you don't intertwine them. Is that true, or is that ...? - So I'm reluctant to say it's true because the bottom line is, everybody needs to be paranoid, right? (John laughs) So- - Well from a segmentation standpoint, form a cohesiveness, not this finger pointings, there's not a lot of, it's not thorny. - [Ray] Because it moves the networking layer up a level, and that level is closer to the application. But, when I really I looked at, I think the key strength there is because it's software-defined, because it's flexible, where you get a lot of the problems is when applications change, there's a new version of the application, or we're now popping up a new instance of the application; now because NSX is this software layer beneath that, it is able to react to that. So instead of, you know the finger pointing back to the security or networking person saying you didn't reconfigure the network to deal with my new application; instead, the application and the network are intimately bound together. Actually Pat used some phrase today where he said "I think the app is the network" and so, or something like that, he was talking a little bit differently about it, but broadly speaking that's what's going on there. It's all around the flexibility and the fluidity that you get from NSX. - [John] The application is a network! - [Ray] Correct, that's what he said, yes. - Was his word. - [Ray] Yep, yep. - Which I love, to think he's right on the money. Complex and if some services evolve, the service measure are right around the corner. - [Ray] Yeah, highly interconnected, you know what app, think of any application on your iPhone or your Android device, which doesn't rely on about 20 other applications or databases or cloud services. - [John] Well, Ray, we'll have to get you on a white board sometime, and have you do a deeper dive, love this conversation, congratulations. Final word I'm going to ask you, what is this VMworld all about on stage, if you could knot down the technical engineering successes that you've had this year, what's it about this year, what's the scene from your perspective? - So I think one of the key things is, we've got a lot of products, a lot of technologies under development for the last few years, a lot of them are now starting to see fruition and the light of day; you know, you know you spoke about NSX, NSX is now reaching a real strength right? But that's work we've had to start two and three and four years ago. So to me, that's probably the strongest thing here, products, ideas, research that we've done over the years, development we've done over the years is now becoming real, is getting out and making available to customers; and in the end, that's what we're about, tryna get those technologies to hand to customers. - [John] And we're going to do our job to share that, and we're going to be tracking the successes; and also thank you for inviting us to your radio event where you had your top scientists. - Oh yeah it was great, very good to see you guys there, thank you. - [John] Great to see the energy, and the engineering prowess of VMware continuing strong, technical team, community, and customer base. This is theCUBE, bringing you our hardcore tech coverage here at VMworld 2018, three days, we're in day one, stay with us for more after this short break. (bubbly music)
SUMMARY :
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Ronnie Ray & Prakash Rajamani, Cisco | Cisco Live US 2018
>> Live from Orlando, Florida, it's theCUBE, covering Cisco Live 2018 brought to you by Cisco, NetApp, and theCUBE's ecosystem partners. >> Welcome back everyone. This is theCUBE's live coverage here in Orlando, Florida, for Cisco Live 2018. I'm John Furrier with theCUBE. Stu Miniman, my co-host, for the next two more days. We're in three days of coverage. Our next two guests here from Cisco Ronnie Ray, Vice President of Cisco, and Prakash Rajamani, Director of Project Management at Cisco. Guys, welcome to theCube. Thanks for coming on. >> Thank you, John. >> So all the buzz is about the DevNet developer aspect, the rise of the network engineer moving up to the stack while taking care of business in the software-defined data center, software-defined service provider. Everything is software-defined. You guys are involved in the DNA Center Platform. We talked about the DNA Center, the product. This is a real innovation environment for you guys, so take a minute to explain, what is the DNA Center Platform? And how does that compare from the DNA Center? How should customers think about this? What is it? what's the offering? >> Absolutely. So if we just walk back about a year. A year ago we launched DNA Center. DNA Center is the product, and that supported things, like SD-Access, which is absolutely a new innovation about Software-Defined campuses. Through the year, we've launched showrooms, through the year we've launched Enterprise Network Functions Virtualization, we have capabilities in automation, and these are all product capabilities that DNA Center has. What we're doing today and this week in Cisco live and in the DevNet area right now is that we have launched DNA Center platform, which is the ability to open up and expose all of the APIs and the STKs that now makes DNA Center a product that our customers, our partners and developers out there can now work on and create new value. It could be apps, it could be integrations, it could be new devices, third-party devices that Cisco's never supported before, but they can now make that supportable in DNA Center because we're giving them the tools to do that. >> So this is not so much a customer thing, it's more of a partner or app, is that kind of how this goes? So if I'm a partner, makes sense. is this kind of where it's different? I mean, where's the line here, or is it open for everybody? >> It is for everybody. If you are a networking expert and you've done CLI in the past, what we are doing is making API simpler, we are making them intent-based, which means that they can achieve a lot more and this is open to you as a networking expert, you as an application developer, you as a partner that is providing, creating your services for your end customer or client. All of you can now use DNA Center platform to create new value. >> This is great, it's for everyone. So this is where, if I get this right, we love this notion of DevOps on cloud, Susie and you guys have been talking about network programmability. Is this kind of where it is? We're talking about network programmability, is this where the APIs shine, and what's our vision? >> This is truly network programmability, in fact in the past what we've talked about is device programmability, but now what you're doing in DNA Center platform is really expressing intent and using APIs that apply across the whole network. Prakash can probably give you some examples of what these intent APIs look like. >> I think as Ronnie said, we like to call it Network DevOps, I think Susie calls it that too. And this is the way in which Network DevOps is conductible. There are two kinds of target market that we look at. One is the network engineer who understands everything network-centric, who knows all the nuances, and are very comfortable with those, but then being able to achieve those through a programmable API, that's one market. The way we want to go with the intent API is for the software engineers who want to be able to say, I want to prioritize YouTube traffic less than my network, and I want to prioritize my custom-built app as the most critical for my enterprise, as the most critical on my network. And I want to express that as an intent through an API, and then let the DNA Center platform take care of making that real on the network without having to worry about all the technologies and all the, >> How to provision it, what's going on under the hood, essentially to them it's a call. >> To them it's a call, and it's taken care of. >> That is actually seamless to the software developer, by the way, who doesn't want to get in the weeds of networking. The networking guys who are under the hood, what does it mean for them? They get to provide services to the developers, so it sounds like everyone's winning here. What's the benefit to the network engineers? They get scalability? I see the benefits to the software developer, that's awesome, but where's the network engineer, what are they getting out of it? >> They can achieve more things faster, they can get deeper, and this is absolutely making it simpler for them operationally to run their network. So they can basically free up time to do other tasks, like design and architecture that typically is, very hard to explain. >> Cooler tasks. (laughs) Not boring, mundane, cut and paste the scripts, CLI scripts, to another device. >> Absolutely and that's one part. The other part is about the cool new apps that they can create because there are use cases, even if you look at all the show floor, the companies that are here in Cisco Live and that they come every year, there are use cases out there that even collectively as an industry we cannot solve, that needs to be solved in the context of the company and the environment that you're in and so the network expert that's sitting in a customer environment can say, "Okay, I have this problem, let me solve it, "let me go build-" >> But they're gettable problems to solve now. Because now you're taking off more time, but also cloud and some of the software-defined things are now at the disposal to create that creativity. Is that what you're getting at, this is the new opportunity. Is that what Chuck was kind of referring to in his keynote around getting at these new use cases? >> Certainly, this opens up a new use case because this is a new way to program across the entire network in a much more simpler fashion than it's ever been done before. >> So when I hear a new way to program, I want to understand, what's the learning curve for this? If somebody understands the rocky APIs, is this a short learning curve, if they don't, is it a longer learning curve? >> So what we have done from a learning curve perspective, we have worked with a development team, we have learning labs where somebody who's not familiar with programming completely can start with the basics of, okay, how do I get started with DNA Center platform APIs and get started and go through a sequence of learning labs to get them completely familiarized with everything. Somebody like what you said, like a Meraki person, who's already using the Meraki API, for them, anybody who understands REST XML APIs can just turn around and there's a bunch of new APIs available that they can understand, program, try within the product, and then get sample codes and then build on top of that. So it's that easy as that. >> It was interesting, I was walking through the show floor, talking to some of the customers here, and for some of them, what's off the shelf is good, but I hear them griping about, not about Cisco, some of the partners, like "I can't customize what I need." One of the challenges we've always had in IT is, it's great if you can take the off the shelf, but everybody needs to tweak and adjust what they have. How's that addressed with this solution? >> From a customer's perspective, because we provide in our product we provide a specific set of capabilities, but when it comes to API, we make it much, much, much richer and granular so that people can create any workflow that they want. The workflows that we create in the API context is in three formats. We have what we call as tasks, which are individual operations that we perform, and then we group the tasks and offer them as workflows. And we group the workflows and offer them as an intent. So as a user, based on what level of granular they need, you can go to the lowest level task, or you can go all the way up to the intent based on your skillset and then use them and customize them as it fits your needs. >> So they can get up and running pretty quickly, sounds like, and if you know APIs then it's just JSON, it's all the same XML, all the great stuff, but I gotta ask where this goes from here because one of the things we were talking about before we came on camera is, we've been covering all the Linux Foundation, the Cloud Native Computing Foundation, CNCF, you've got Docker Containers, and containers now have been a great thing. Pretty much check, standard, everyone's using containers. And it's great, put a container around it, a lot of great things could happen. Kubernetes and then microservices around Service Meshes, Diane Greene mentioned in her keynote with Chuck Robbins, Istio was a big hot, one of the hottest projects in the Linux foundation, so that's kind of microservices, this sounds like it's got a lot of levels of granularity. I love that word because now when you get to that point, you can really make the software targeted and strong and bullet-proof. How is that on the road map, where does someone who's actually looking at microservices as a North Star, what does your offering mean for them? Is it right in line? What's the progression, what's the road map? >> So, from a microservice perspective, DNA Center as a product itself is completely microservice-based architecture. There's 110 microservices today that make up what is DNA Center. This gives us a flexibility to really update every single service, every single capability, and make it almost like giving customers ability to do this every two weeks or every four weeks, new changes, new announcements, in a very simple fashion. That's kind of how the part is being built. What we eventually want to do is extend the platform as an ability for partners and others to build microservices that can be built and deployed within DNA Center over time. That's further down the road, but given that solution and given the strategy where we are as a product architecture that lends us to extend that to them. >> It's natural extension, so basically you're cloudified. You've got all the APIs, so if a customer wants to sling APIs, customers want to integrate in, like you mentioned, ServiceNow, they can do that easily today, and then you've got some extensibility in the road map to be kind of Cloud Native when things start growing. Timing's everything, it's kind of evolving right now heavily at the Cloud Native. >> I mean that's the benefit of this architecture, that you can really pick and choose where you want to run over time. We are right now on a box, an appliance that helps us solve the solution, but there's nothing that stops us from going anywhere. >> So Ronnie, I want you to talk about the significance, this is an open platform. I've watched Cisco my entire career, and always Cisco's been heavily involved in standards, but takes arrows from people as to how they do this. This is open, what does that mean? And what's that mean to your customers? >> Absolutely, this is basically opening up Cisco to industry-wide innovation. So until now, if you look at everything that we've done on DNA Center and on some of the other Cisco platforms that Cisco developed, but we are now getting to a point where with DevNet, now with 500,000 developers registered, we have the critical mass to basically say the industry can come and develop on top of Cisco platforms. And so this is completely new kinds of innovation that we will see, use cases that we've never thought of, and this will happen. And of course we will continue to contribute to all whether it's IETF or whether it's OpenConfig, all of these in with the YANG models that we are doing across the industry, those will continue, the open source confirmations that we do, but this is really saying, okay, let's provide our best customers and our partners and of course the individual developer that's out there a way to today build new creations and maybe tomorrow there's a part to monetize that. >> It's interesting you bring that up, I love the open. We love open, we're open content. You guys are now open networking, for lack of a better description. Chuck Robbins talked about in his keynote, one of the things I was really impressed on, he highlighted something that we've been talking about, is that the geo-political, the geo-technical world, is a huge factor, you look at just cloud computing, you've got Regis, you've got GDPR, I mean all these things going on, you mentioned assurances off camera, this is like a huge deal, right. You've got a global tech landscape, you've got global tech compliance issues, so you got this now open source and it's whatever fourth generation where it's part of the entrepreneurial fabric. So Ronnie, I've got to ask you, you've been an entrepreneur before. With bringing entrepreneurship into networking, what's the guiding principles, what's your inspirational view on this because this is really, not only save time for engineers, it makes them part of an open collaborative culture, like open source which you're used to, bringing an entrepreneurial vibe to it. >> Absolutely. >> This is a big dynamic, what's your view on this? >> It's a huge dynamic and I can talk from personal experience, you know when I've done start-ups and I've raised money or put my own money into it, 70% of your calories go in building a platform. So you're just looking at how do I store data, how do I process data, how to I look at availability of systems, and 30% of it really goes into building a use case. What we are doing with DNA Center platform is basically saying forget about the 70%. We will give you normalized data, whether it's for Cisco equipment or whether it's for third-party equipment. So the STK will allow you to bring in Juniper or Huawei or Aruba or whoever that's out there and you can bring that into DNA Center, so now you have a view of the entire network, Cisco and Non-Cisco. You have normalized data for all of those and you can configure all of those, you can image update all of those. It's very very powerful. Just from an ISV standpoint, individual available standpoint now you are kind of unlocking, making this almost democratic. >> You've done the heavy-lifting. >> Yep, absolutely. >> That's what Cloud is all about, but talk about the creativity because you mentioned that entrepreneurial, a lot of the energy goes into trying to find the fatal flaw, is the product gonna be product-market fit, you do all that heavy-lifting and bootstrap it, right now it's simply, okay, I can sling some APIs together, get a prototype, then the creativity starts. Talk about the creativity impact. How do you see that impacting some of these new use cases, these hard problems. This is gonna come from, not some guy coming out of business school saying, "Hey, I'm gonna go hire "some engineers and solve that big, hard problem." It's gonna come organically, this is a huge deal. >> This is a huge deal, and because we're making it simpler it can come from any quarters, it doesn't have to be an established company, it can be an individual person that can't solve any use case, and then we ask Cisco, not only do we have, and of course the majority share in the market, but will also we have the platforms, like DevNet, and DevNet now has an equal system exchange, so if something that's cool can float up in the exchange can be voted on, can become something that becomes an absolutely easy part to monetization for somebody, that basically saying, "Okay, how do I marry business "and how do I take network and bring them together." >> This is awesome and it's also external to Cisco, but talk about the global impact. Just outside North America, massive growth, you're seeing things going on in Europe, but really in the Asia and China, huge growth markets going on. When you go to China, talk about mobility, they have mobility nailed down. India is absolutely on fire, growing like crazy. The talent, this is a melting pot of tech talent. How do you make all that work from a Cisco standpoint because what you want to do is bring the goods to everybody, that's open source. >> Absolutely, so think about any of the logical place that people go to with, given the way that the platform is already built, which is, it is Cloud Native. We've not in the cloud yet, but at some point the platform will go to cloud. And we are looking at harnessing the creative talent worldwide, whether it be in Asia or whether it be in Europe, or whether it be in the Americas, really doing that new value creation and taking that to the masses. And Cisco has the right to claim this market, we are absolutely in support of folks that want to do that. That's why DevNet has all of the learning labs and the sandboxes and everything else that's there in support, these are free to use. We want people to come and learn and co-create on the platform. >> And making it open and collaborative, the community aspect of it. >> Absolutely. >> Alright, final question while you guys are here, obviously you're at the Cisco perspective, but put your industry landscape hat on, people who couldn't make Cisco Live this year here in Orlando, they might be watching this video either live or on demand when it goes up to YouTube. What's the big story, I mean obviously what you guys are doing, across the whole show, what's the most important stories that are developing here this week that people should pay attention to deeply? >> So in terms of looking at the openness of the platform, Cisco is an open platform, API is really the new CLI because that's the way that you'll talk to the network. And think about what Chuck said at the opening keynote, this starts from the user, the things that you want to do to the applications, wherever they live, whether it be in a cloud, in a multi-cloud environment, Cisco is bringing all of that together. >> Prakash, what's your thoughts? >> Adding on to Ronnie's point, the openness and something that new that we are doing, not just from campus perspective, but campus, branch, data center, and making it open across everything, which is what Dave Goeckeler covered today in his keynote, I think that's something that Cisco is not just looking at one infrastructure, but across all of his portfolio and making it unique is really something that people should take away from this one. >> That's awesome. Great stuff, well guys, thanks for sharing. Thanks for co-sharing, co-developing content with us. I gotta say just from the hallway conversations, people are impressed that you guys are taking a very practical approach, not trying to boil over the ocean here with all these capabilities and announcements, focusing on the network value, where it fits in, and being Cloud Native from day one with microservices is a good start, so congratulations. >> Thank you. >> Thanks for sharing. Live coverage here in theCUBER. Day two of Cisco Live, I'm John Furrier with Stu Miniman. More live coverage, stay with us here at day two as we start winding down day two here at Cisco Live in Orlando, Florida, be right back.
SUMMARY :
covering Cisco Live 2018 brought to you by Cisco, NetApp, Stu Miniman, my co-host, for the next two more days. And how does that compare from the DNA Center? is that we have launched DNA Center platform, is that kind of how this goes? and this is open to you as a networking expert, Susie and you guys have been talking about in fact in the past what we've talked about One is the network engineer who understands How to provision it, what's going on under the hood, I see the benefits to the software developer, and this is absolutely making it simpler for them Not boring, mundane, cut and paste the scripts, in the context of the company and the environment are now at the disposal to create that creativity. across the entire network in a much more simpler fashion Somebody like what you said, like a Meraki person, some of the partners, like "I can't customize what I need." all the way up to the intent based on your skillset How is that on the road map, and given the strategy where we are as a product some extensibility in the road map to be kind of I mean that's the benefit of this architecture, So Ronnie, I want you to talk about the significance, and of course the individual developer that's out there is that the geo-political, the geo-technical world, So the STK will allow you to bring in Juniper is all about, but talk about the creativity share in the market, but will also we have the platforms, This is awesome and it's also external to Cisco, And Cisco has the right to claim this market, the community aspect of it. What's the big story, I mean obviously Cisco is an open platform, API is really the new CLI and something that new that we are doing, focusing on the network value, where it fits in, as we start winding down day two here at Cisco Live
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Ray O'Farrell, VMware | VMware Radio 2018
>> From San Francisco, it's theCUBE. Covering Radio 2018. Brought to you by VMware. (lively electronic music) Hello everyone and welcome to special Cube coverage here in San Francisco, California. We're at VMware's Radio 2018 event. This is their annual R&D event where all the best people, smartest people, come together to collaborate on new projects, new innovations. Not imitation, innovation. Had great speakers up there. They had Steve Herrick, Cube alumni, now a venture capitalist, formerly CTO of VMware. And our first guest here today is Ray O'Farrell, executive vice president and CTO of VMware, been on theCUBE before. Great to see you, thanks for joining us. >> Great to see you, good morning. >> So I love this event 'cause it's, like you mentioned before we came on camera, Steve Herrick said it's like a sales kick-off for engineers. >> Correct yeah, yep. >> Which is like a rah, rah but also, you know, really motivating, but also putting out the north star. >> [Ray] Yep. >> Which is the innovation message. >> [Ray] Correct. >> So take minute to talk about what this event is. Explain to the folks, what is Radio 2018? There's a lot of history involved here. >> [Ray] Yep. >> Behind us is a t-shirt row of, you know, key milestones of VMware history. You know, think inside the box, now it's, think inside the cloud. What's this event about? >> So, um, the event has quite a few years. This is like the 14th year we've done this, right? And when it started, what it was really focused on was, in some ways, a recognition that, as the company begins to grow, as you begin to build new products and engage in new partnerships, In order to keep innovation alive, you almost need to manage it. The problem is you can't manage innovation. Almost by, you know, by definition it's something chaotic. It's an inspirational idea. It's something that was not expected. That's what makes it innovation. But what you can do, is you can create a culture which promotes that innovation or creates opportunities for those ideas to emerge. Or when those ideas do emerge, make sure there's a place for them to be heard and there's an opportunity for a network to build around them. And Radio is a part of that. We have lots of other programs in VMware to help keep driving that culture of innovation, but Radio is probably the primary one. >> Talk about some of the history from this event. What has come out of these events? 'Cause I wanna get into some of the specific questions around how R&D works these days vis-a-vis how it used to work. But, specifically, what has come out of these events? Can you point to any things that kind of popped out? Because R&D, I won't say hit or miss, but it's the idea is to experiment and try new things and nail it. What has come out of VMware's Radio years of history? >> Yeah, so, very practically, we get a lot of patents out of Radio. That's just a very practical sense. As people are building up the papers, as they're looking at the ideas they want to drive, as they work with different teams to build prototypes. Quite a few times people do that at Radio when they're making a presentation. They'll generate ideas, invention disclosures, which generate new patents. This show alone, even though we just actually entering the show at this stage, has already generated about nearly 240 IDFs. A lot of those have the potential to become patents. So it's very, very practical and pragmatic about the generation of patents and new ideas. When you look at the products side of things, quite often what you see at Radio is not necessarily a new product in a whole new area. What you tend to see is, we have existing technologies bubbling in different spaces and now, because you're able to bring these teams together, somebody gets an idea that says, Oh, I can combine machine learning with what we're doing in terms of logging and now I've got an interesting product to help support our customers, you know, deal with real world problems. >> So, it's not take that hill, build me a blockchain product, it's more of, take a step back, zoom back, look at the big picture, understand the fusion of where things are coming together, look at architecture. Is that kind of the-- >> Yeah, actually, sometimes there is the, take that hill, take the blockchain product, but quite often, it starts as something small. You have a Radio event where somebody will say blockchain is cool and interesting. Here's how you run it in a more efficient fashion on vSphere, something like that. And that would be a poster session. And it's only then when somebody sees that that says, I can really run blockchain on vSphere? Can I do it better even now it's physical in some way? And that's when the story emerges. So you don't necessarily see the product announcement coming from Radio itself. What you see is the core of that idea and then a few months later, or the next major VMWorld, or two VMWorlds out, you begin to see these things emerging. >> It's like you're creating sparks of innovation, throw onto the fire, create some action. >> That's exactly the way it works. You know, things like, a lot of stuff what we do in containers. You know, the VMware integrated containers, the combination of containers and VMs from a security point of view. You can trace a lot of that back to ideas that were generated for Radio. And it's pretty rigorous. People have to go through, submit papers, there's a submit ideas. And, you know, our most senior engineers crawl all over those and critique them and so, you know-- >> So it's competitive? >> Oh, it's very competitive. That it is, in many ways, it's a mark of honor to be invited to Radio or to present a paper and so people fight very hard to do so. >> Built in gamifications called just be smart and show some good papers. >> Yes, it's a little bit tougher today. >> How much goes into the prep for this? Because obviously that's a great bar. You guys set a high bar, high is great. And it's a great place here for people to stretch and flex their technical muscle. >> Yep. >> What's the process? How do people get to that bar? Do they collaborate? Is there meet-ups? Is there organic processes of top-down? How do you guys handle it? >> So we've a lot of different processes or programs around driving innovation, but when you look at Radio itself, and it leverages some of those others, but when you look at Radio itself, basically we create a Radio committee. The one for next year will be starting somewhere in the next couple of weeks, right? We create a Radio committee. It is typically driven by members of the office of the CTO, but works and pulls in our fellows, our principle engineers, and we form a committee which really splits into two different directions. One of which is all around the technical papers, the presentations which are gonna be presented later here today. And another one which focuses around how do you do the keynotes? How do you get invited speakers? How do you create this inspirational, you know pervasive sense of innovation. And so you have those two groups working, while cooperating somewhat independently of each other. And it takes a long time. So for instance, only about 15% of the papers which are actually submitted are presented here. So there's a lot of work going through, scanning those, combining those. One of the most exciting things you can do at VMware is, if you go back somewhere in around the February timeframe, all of our most senior engineers sit in one of our largest conference rooms with a bunch of engineers submitting papers and so on, and there is a lively debate working through paper after paper, idea after idea, and saying is this a good thing for Radio? Is this original? Hey, nobody else thought of that. What we gonna be able to do to do that? Or, in some cases, saying these two people, one from Bangalore, one from Bulgaria, we've earned these sites all over the world, these ideas look similar. Can we get those guys to talk to each other? And see what comes out of that. >> So it's kind of a team-building exercise. At the same time, pre-innovation, but it's interesting. You've mentioned you've got the challenge of the papers, which is, you know, get the accuracy on the facts, original content, original ideas. >> [Ray] Correct. >> And then the content program for the event has to be inspiring and motivating at the same time Two different things, but two design standards for you guys. >> Yeah. And, you know, we need to combine them both and, 80% to 90% of the people who are here are hardcore R&D engineers. Their day job is to write code, produce product, archetype product, right? And, you know, if you haven't worked with a group of senior engineers, they are not going to be tolerant of presentations which, oh, we saw that before-- >> [John] Or fluff. >> Or fluff, right. They want to get hardcore into the meat. In fact, the presentations that you see that get some of the highest ratings, tend to be those that are deeply technical in nature. You know VMware's software base is primarily systems software, systems engineering. They expect to see deeply technical solutions to how to attack some real world problems. >> You guys do have some smart people. It's great to have you on theCUBE. This is our ninth year doing VMWorld. Great to start coming in to the more technical events. It's fantastic. The question I gotta ask for you is, Pat Gelsinger always says on theCUBE, he's says on theCUBE a few times, but consistent theme, you gotta get out in front of that next wave or you're driftwood. To the point of, don't just take that point product at view, jump on the wave. And the wave is all about the next 10 years or 20 years. What is the wave that you guys are, that you would categorize, obviously Cloud is key, but as you have the hyper-convergence and the on-premise private cloud boom and VSAN's great. We've seen great results from that. The cloud's right there. You've got Amazon, you got Microsoft, kicking butt on the numbers. As the R&D tries not to get caught up into the fashionable day to day, you can have the long view. >> [Ray] Yeah. >> What's the wave for the long view? >> So I think there's two waves we're looking at. One of them is you need to spend a lot of time with customers and understand what their agenda is. What their innovation agenda is. You look at that, you see, you know, products popping up. How will I leverage AI in a new and interesting way? How will I do something with Blockchain? You know, I want to run AI algorithms, I need different hardware and different management software to do that. So we focus on those and make sure we're doing that. But perhaps, more importantly, I think when you begin to look at what's happening with the industry right now, you know, you saw private cloud, you saw public cloud, you see how you connect these together. It's actually that connectivity is going to be important. You know, I believe you're going to see the emergence of Edge infrastructure, but isolated? That's not powerful. Now combine that Edge infrastructure with how you can leverage what's going into the public cloud or how you're going to be able to secure all these in a way that falls back into, you know, even Teleco in some way. You're now beginning to see this synergy across all of those things. And I think, you know, that's where our sweet spot is. We know how to deal with those hard, how do I connect things together? How do I manage complex different piece of systems software? So that's where we're gonna see it. >> Well, it's great stuff. One of the benefits of being so close to VMware over the past nine years, and I was showing you some of our online data analysis. When I look at the VMware ecosystem, the interesting see the evolution and kind of the journey, 14 years. And looking at the milestones. Clearly, infrastructure, on-premise data center. And then you saw that emergence of clouds. You start to see these markets emerge. Cloud, big data comes on the scene. Data warehouse in the infrastructure. Now, that's AI, cloud is bigger. All kind of taking a little bit off the infrastructure, kind of squeezing that down, but it moves up into the Cloud. And now you've got that, over the top, Blockchain, cryptocurrency, decentralized applications. In the middle of these circles, is security, IOT, and data. >> Correct. >> You guys are right there, so I have to ask you, because they're all, the confluence of all of those are coming together. You're not up here playing Blockchain, although there's some stuff we can get into. You got some AI influencing. So, in the center of infrastructure, Cloud, AI, and Blockchain, etc. is security data, IOT. How is that coming together? What's the R&D task? >> So, actually, I think the key word you used there was confluence. You cannot really look at these as independent things. And, you know, so our focus is what does it mean to be, essentially, the infrastructure. The infrastructure management story for that new form of multi-Cloud, Edge, IOT type of narrative. So our role there is, we believe security is one of the key things to focus on. And we believe that, in that new world, connectivity is a key part of what goes on. The Edge was taught to the Cloud. The Cloud was taught to the Teleco. The Teleco was taught to the IOT. >> [John] They need power. >> Right. They need power, they need communication. They need those things. So a lot of the time, a lot of where we focus comes back to intersects. We do believe that software-defined networking is a key way of being able to deliver a new fluidity of when you get that confluence. And intersects very quickly brings you into security. That's how you begin to understand how you isolate those components, understand what you need to do to detect. When that Edge IOT device is not even the device you think it is. Somebody might have replaced it. That's where you begin to be able to see the communications as a result sort of from that. So security is key, interconnectivity is key, and you know, when we speak about IOT itself, I've got kind of a dual role at VMware. While I'm the CTO at VMware, I also focus on IOT for Dell Technologies. And when we look at that, you know, today many of the examples of IOT are very narrow, almost point, solutions. The real power will come when you begin to combine across those solutions. You know, the thing that tells you the weather, the thing that tells you the traffic, and then the thing that tells you, you know, what's the best way to get there in your car, or whatever it is. Combine those things, now you gotta secure all that. 'Cause you're sharing information. >> [John] It's super exciting. It's probably the best time to be doing R&D because Dave Vellante and I always talk about on theCUBE all the time, that, you know, if everything was Cloud operations, because the confluence is happening, what is IOT? >> [Ray] Yep. >> You have a thin Edge, could be a windfarm, traffic signal, sensor network, or it could be a data center. The data center could be an Edge. I mean, you could look at it any way, it depends on how you look at it. >> One of the biggest questions that comes up all the time is what exactly is the edge, right? And I think, you know, it means different things within different industries. It's very clear on the extreme edge. That's a device, it's a windfarm, it's measuring the behavior of a robot, or something like that. And it's very clear on the other side. That's a Cloud, I run a bunch of analytics over there. It's the interesting piece in the middle where it is both, you know, a lot of opportunity and a lot of, you know, difficulty defining it. Is the SD1 server inside of an office, is that edge? Yeah, that looks like edge, it's at the edge of the network. But it's not controlling something physical. But that SD1 server inside in a retail store, may well also be doing something with the refrigerators or the cold chain or something in that store. And now you begin to see it more as kind of an IOT device. >> That's awesome, and it's great conversation. Certainly fodder for more R&D and more innovation and the management site's key. And, I think the holy grail on all this is programmable networks, right? Come on, we've been waiting. How fast is that coming, pedal harder, come on. I know you've got to go thanks for coming on. >> But I do wanna ask you, you guys are, I wanna give you some props and just get your thoughts on obviously Blockchain. We see things like Filecoin had a very huge ICO on the IPFs side, but, you know, they didn't really have a product, but they're promising, hey, store using decentralized, we have them in the Blockchain. Obviously, it's a network storage infrastructure, it's not so much selling tokens with token economics, although it does have a piece of it. That's gonna impact you guys on the horizon. What's the current state of you guys view, your view, the team's view of Blockchain-- >> Of Blockchain? Obviously, a lot of the hype and even some of the valuations and things you see are tied to what's happening on the financial side. Bitcoin, and so on. We're not focused on that at all. What we're saying is Blockchain, or more specifically, a distributed hyper-ledger, forms the basis of a community of companies or organizations being able to, essentially, look at trust as a service. I've got a contract with you, we're now able to look across a group of companies and say we all agree, that contract is valid because of our leverage of this blockchain. That then becomes an application story. How do I run it more efficiently? How do I make sure I run it securely? How do I make sure that that community is able to leverage that service in a shared fashion? And that's what we're focused on. In fact, one of the more interesting things is when you look at things like Blockchain, when it's used in the context of something like Bitcoin, there's a degree what people value is an anonymity. We don't know who bought it, but somebody bought it. But when you look at it from a trust point of view, we actually want to be able to see who exactly did the contract. I agree that you put the contract, we worked the contract together, and we're all agreeing with that. So you see these changes when you begin to bring these technologies into enterprise. >> Efficiencies come, big time-- >> Correct. >> On supply chain. >> Exactly. Actually, we've put a lot of focus on efficiencies. We've got a research team whose job has been very focused on, given Blockchain, how do I improve the core algorithms? How do I make them more applicable to something that'd be run by a typical enterprise, or by a group of enterprises? And, you know, that's a little bit unusual for us because we're entering a kind of an application space, but what's good about this application space, it is hard systems engineering. And that's what we know how to do and that's why we think this is a great application space for us to be able to deliver real value. >> And the key word is engineering, you also mentioned earlier, community. Open Source has brought this community dynamic together where there's no middle men. This is the beautiful thing of the future infrastructure. How do you manage it? How do you make it secure trust as a service. >> Yes. >> You guys are doing a great job. Based on our data, you are on the ecosystem. You guys have all the waves covered. >> Okay. >> Ray thanks for coming on. >> Great, thank you very much. >> I appreciate the conversation. I'm John Furrier, here in San Francisco for VMware's Radio 2018. 14th year of their annual engineering kick-off, motivation, hardcore engineering critique, and also collaboration where the sparks of innovation are happening. Be right back with more. Thanks for watching. (lively electronic music)
SUMMARY :
Brought to you by VMware. like you mentioned before we came on camera, Which is like a rah, rah but also, you know, So take minute to talk about what this event is. Behind us is a t-shirt row of, you know, But what you can do, is you can create a culture but it's the idea is to experiment to help support our customers, you know, So, it's not take that hill, So you don't necessarily see the product announcement It's like you're creating sparks of innovation, And, you know, our most senior engineers it's a mark of honor to be invited to Radio or to and show some good papers. And it's a great place here for people to stretch One of the most exciting things you can do at VMware is, which is, you know, get the accuracy on the facts, Two different things, but two design standards for you guys. And, you know, if you haven't worked with In fact, the presentations that you see What is the wave that you guys are, And I think, you know, that's where our sweet spot is. One of the benefits of being so close to VMware So, in the center of infrastructure, Cloud, AI, one of the key things to focus on. You know, the thing that tells you the weather, all the time, that, you know, it depends on how you look at it. And I think, you know, it means different things and the management site's key. on the IPFs side, but, you know, even some of the valuations and things you see And, you know, that's a little bit unusual for us How do you manage it? Based on our data, you are on the ecosystem. I appreciate the conversation.
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Ray Zhu & Roger Barga, AWS | Splunk .conf 2017
>> Narrator: Live from Washington D.C., it's theCUBE covering .conf2017 Brought to you by Splunk. (techno music) >> Well, welcome back to Washington D.C. We're at the Walter Washington Convention Center as we wrap up our coverage here of .conf2017. As Dave Vellante joins me, I'm John Walls here at theCUBE, coming to you live from our nation's capital. Joined by Team AWS here. With us we have rather, Ray Zhu rather, who is a senior product manager at AWS. And Roger Barga, who is the general manager of Amazon Kinesis Services. So gentlemen, thanks for being with us, we appreciate the time. >> Absolutely, thank you for the invitation. >> Dave: Oh, you're welcome. >> You bet. Alright, so let's just jump in. The streaming data thing, right? It's just blowing up. What's inspiring that popularity of the Cloud? What's kind of lit that fire and what's going to keep it burning? >> Yeah, I think over time, I think customers really do realize the value that you can get out of by collecting, analyzing, and reacting to data in real time. Cause that really provides a very differentiated experience to their customers, you know, for example you're able to analyze your user behavior data in real time, provide them with a much more engaging experience, much more relevant content. You're able to diagnosis your service, understand your law of data issues in real time, so that when you have an issue, you can fix that right away. So that really provides a very different customer experience. So I think our customers are realizing the value of real time processing, which is why we think streaming data is gaining more and more popularity. And this is why Cloud is all the good stuff that Cloud can offer and tell the customers. It's highly scalable, so you don't need to worry about if it's going to scale later on when I scale my business. It's a matter of sort of like click of a button. We scale the infrastructure for you and we got all the resource ready for you to go on streaming data. We got super, it's very cost effective, right? So that cause we price at very low. As we keep improving the efficiency of running the service, we reduce our cost structure, we return that back to our customers as a price cut. The third thing which I think is super important is agility, right cause you don't need to set up an infrastructure, install any software, make all the configurations. Starting up a Kinesis Stream is like 15 seconds on the average console, you're done. And it really allows the developers, the customers, to move fast and purely focus their resources and effort on the things that really differentiate their customer experience. >> So very AWS like, we love AWS, we're a customer, it's our favorite Cloud. We'll go on record of saying that, you know? (laughs) We're loyal to you guys. Crowd, our Crowd Chat App runs on it, basically run our whole company on Amazon, where we can. >> Roger: Great. >> In 2013, we got the preview of Kinesis. It was a lot of buzz. It was kind of before the whole streaming meme took over. We were talkin' about real time at the time, but so maybe you can take us through the evolution of Kinesis and where we are today. >> I'd be happy to. You know, when we first built Kinesis Stream, what the company was trying to do, is we had all of the AWS billing and metering records coming from all of our services, our EC2 incidences. This was a lot of data that had to be captured. And the way we were doing it was in batch. We were storing this data in S3 buckets. We were starting large EMR jobs up at the end of day actually to aggregate them by the customer account. So say this was your bill for the end of the day. But we had customers that said actually I'd like to know what I'm spending every hour, every few minutes. And frankly that batch processing wasn't scaling. So we had to innovate and create Kinesis Streams as a real time system that was constantly aggregating all of the billing and metering records that were coming in from our customer's accounts. Totalling them in near real time and we presented our customers with a new experience of billing and insights into their billing and even forecasts of what they were spending at any given time. But we had other teams that immediately looked at Kinesis and said hey, we're dealing with real time streaming data and our customers want it delivered and aggregated and provided, so Cloud watch logs and Cloud watch metrics built on top of us. And this was the start of something which continues to this day. Other services are looking at, and even customers, are looking at a Kinesis Stream and saying, that's a really useful abstraction that we can build a new service, a new experience for our customers. And today we have over a dozen AWS and Amazon retail services that build on top of Kinesis Streams as a fundamental abstraction to offer new experiences and new insights as three events. Cloud watch events, there's a host of services, which underneath Kinesis is running, but they're offering unique value building on top of it. Which is why Kinesis today is considered a foundational service and we can't build an AWS region without Kinesis being there for all these other services to build on top of. So that's been exciting to see that kind of adoption, different uses for this fundamental abstraction called a Kinesis Stream. And you know, it's also, and we can talk later about how it's transforming analytics, which is really exciting as well. >> Well, that's a great topic. I mean, why don't we talk about that. And one of the things that we've noted about AWS, and other Cloud providers, is obviously simplicity and delivering as a service is critical. We all know about the complexity of, for instance, the Hadoop Ecosystem And the challenges that a lot of customers have. Delivering that as a service has dramatically simplified their lives. That's why you see so many people going to the Cloud. We've always predicted that is what happened. Maybe talk about that a little bit. And then we can get into the analytics discussion. >> Yeah, so again, customers are always looking at ways to actually get insights into their data to better support their customers, to better understand what's going on in their business. And of course, Hadoop had managed EMR, had been a great benefit, cause customers could move their developers into the analytics that they want to do and not worry about this undifferentiated heavy lifting of operating these services. And the same is true for Kinesis Streams. But we're seeing customers, and if you stop for a moment and think about this, data never loses it's value. It always has it's historical value for machine learning, for understanding trends over time, but the insights that data has are actually very, very perishable and they can actually turn to zero within an hour if you can't extract those insights. That's the unique area where Kinesis Streams has kept adding value to our customers. Giving 'em the ability to get instant insights into what's going on in their business, their customers, their business processes, so they can take action and improve a customer experience, or capitalize on an opportunity. So what we're seeing and the role, I believe, that streaming data, at large, plays is about giving customers real time insights and then business opportunity to improve how they run their business. >> So. >> Go ahead, please. So who's using it? I mean or what's the if there's a sweet spot or a sweet spot for an industry or vertical to use that, I mean, in terms of whether it's in a minute, an hour, or whatever, what would that be? >> Yeah, so today, I'm really pleased to see, because we have watched this evolution since 2014, but today in virtually every market segment, where data is being continuously generated, we have customers that are actually taking advantage of the real time insights that they can get out of that data virtually every market segment. I'll pick a couple of examples which are kind of fun. One is Amazon Game Studios, near and dear to our heart. Now typically games are written, they're completely developed end to end. They're shipped in a box, made available to customers, and they hope that game and the engagement has the outcome that they want. Amazon Games Studios is actually writing that game in near real time ahead of their customers, so they release a new level of the game. They will actually watch the engagement. They'll look at how customers are dying, surviving, how long they're playing. And is it traveling in the direction they want? They stream all of the multi, all of the game data from their players in real time. And they build dashboards so they can see exactly how game play is going. And if they don't like it or they think they can make an improvement, they'll get right online, change the game itself, and re-deploy the game, so the customer experience is actually, within minutes it's being evolved. Another customer I like to talk about is Hertz Publishing. We all like to read. When Hertz started making the transition of their magazines, Cosmopolitan, Car and Driver, from print to digital form, they instrumented it so they could actually watch how long was a customer reading an article, how were their comments trending in Twitter and in Facebook. So they could actually get a sense of engagement with an article. Whether the article should be rebroadcast to other digital channels, other magazines. Should they change the article? Double down and write a new one. So again, they're engagement and then the business metrics by which they measure engagement and readers, readership have all increased because they have that intimate understanding of what's happening in real time. So again, every market segment, where there's data continuously generated, customers are using this to provide a better experience. >> That phrase undifferentiated heavy lifting we first heard it widely in the tech community in 2012 in Andy Jassy's keynote at Reinvent and it's become sort of a mantra. It probably was one well before that inside of AWS. And often times AWS doesn't talk about TCL but it's not the main reason why people go to the Cloud. You emphasized that a lot. And there's all this debate. Oh a cheaper on prem, oh no, Cloud is cheaper. But this idea of essentially eliminating labor that is doing that non-differentiated heavy lifting is something that you guys have really lived and popularized. We see that labor cost shifting from provisioning luns into other areas, up the stack, if you will. Application, digital business, analytics, et cetera. What are you guys seeing, in terms of how organizations, I mean, there's two types of organizations, right, the Cloud native guys who obviously didn't have the resources, but then enterprises that are bringing their business to the Cloud. Where are they shifting that undifferentiated heavy lifting labor towards? >> To. And they are in fact moving it up stream. We think about it very abstractly. You know, operating servers doesn't really bring any special IP that that company possesses to bear. It is about, you know, just managing servers, managing the software on it, figuring our how to scale. These are problems which we are able to take away. And we've often worked with customers and showed them the value of moving to our managed servers. And the excitement from the leadership, from their customers, is like wonderful. That project we couldn't, we aren't able to fund, if we can just onboard here, onto Kinesis for example, or any one of our managed services, then we can immediately move and get that fund project that we really wanted to fund, it would actually be unique value as move them over to that. So they're actually moving upstream as you said. And they're actually leveraging their unique understanding of their industry, their customer, to go ahead and add value there. So it is a distribution and I think in a very productive way. >> I want to ask about the data pipeline. So one of the values that AWS brings is simplification. When I look, however, at the data pipeline, it's very rich. If I look at the number of data services, Kinesis, Aurora, DYNAMO dv, EBS, S3, Glacier, each of these has a programming interface that is, I use the word primitive not in pejorative way but >> Roger: Yes, yes. >> But a deep level, low level. And so the data pipeline gets increasingly complex. There's probably a benefit of that, because I get access to the primitives, but it increases complexity. First of all, is that a fair assertion on my part? And how are your customers dealing with that? >> Be happy to take that one, yeah? >> Sure. >> Okay. >> Yep, so I think from our perspective all these different capabilities and technologies by customer choice. We build these services because our customers ask for them. And we order a wide variety so that people can choose for the developers who want to have full control over the entire staff, they have access to these lower level services. You know as you mentioned a few, DYNAMO dv, Kinesis Stream, S3, but we also build an abstraction layer on top of these different services. We also have a different set of customers asking for simplicity, just doing a specific type of things. I want you guys to take care of all the complexities, I just want that functionality. The example would be services like Kinesis Files, Kinesis Analytics, which is the abstraction layer we put on top. So for customers who are looking for simplicity, we also have these kind of capability for them. So I think at the end of the day, it's customer choice and demand. That's why we have this rich functionality and capabilities at AWS. >> So you guys have already solved that problem essentially, the one that I was sort of putting forth. >> So I won't say, I like Ray's answer. It's about listening to the customer. Cause in many cases if we would have, if we said, hey, we're going to go build a monolithic service that simplifies this, we would potentially disappoint many other customers. Say actually I really do want to have that low level control. >> Right. >> I'm used to having that. But when we hear customers asking for something which we can then translate to a service, we'll build a new service. And we will actually up level it and actually build a simpler abstraction for a targeted audience. So for us it's all about listening to the customers, build what they want, and if it means that we're going to actually bring two or three of our services together to work in concert for our customer, we'd do that in a heartbeat. >> Yeah that low level control also allows you to be presumably maybe not more agile but more responsive to the market demand. Because if you did build that monolithic service, you would essentially be locking yourselves in to a fossilized set of functions and services that you can't easily respond to market conditions. Is that a fair way to think about it? >> That is a fair statement, because basically our customers can look at these API's and together for these various services, realize how to use these API's in concert to get an end and done. And should we have precise feedback on a specific service, we can add a new API or tailor it over time. So it does give us a great deal of agility in working on these individual services. >> So Ray, you're a product guy and you're talking about listening to customers, right? And coming up with products, it's what you do. What are you hearing now? Where do people want to go now? Because I assume you've been in the market place for four years now with this, evolution is (clears throat), excuse me, perpetual, constant, so where do you want to take it? What's the next level or what's percolating in the back of your mind right now? >> Yeah, I think people always looking for different type of tools that they're familiar with or they want to use to analyze these data in real time and provide a differentiated customer experience. A concrete example I want to give is actually why we're here. At the Splunk Conference is at Kinesis we have a service called Kinesis Firehose. Based on customer demand when we launched Kinesis Streams, customers wanted to make sure they had access to data sooner than they used to do, but they want to use the tools they're familiar with. And apparently there's a diverse set of tools different customers want to use. We started with S3 for data lay, kind of storage, we used Reshift as a data warehouse. And overtime we heard from customers say, hey, we want you to use Splunk analyze the data. But we would like to use Kinesis Firehose and suggest a solution. Can you guys do something about it? So actually the two teams got together. We thought it's a strong customer value proposition, great capability for other customers. So we start this partnership. We're here actually earlier this day, today, we made the announcement actually, Kinesis Firehose is going to support Splunk as data of redestinations. And this integration is not in beta program. It's open for public sign up. Just go to the Kinesis Files website. You can sign up, get early access. So basically from today, you can use Kinesis Firehose in real time streaming (mumbles) service to get real data into your Splunk cluster. We're super excited about it. >> And okay, and I can access those Splunk services through the market place or what's the way in which I bring Splunk to? >> Good question. For this integration actually we're just a different version of Splunk. You can run Splunk on AWS using ECT extensions. You can access through the market place. You can have your, you can use native Splunk Cloud, which manage all the servers for you. You can also use Splunk on print in that regard. >> Okay. What have you guys learned since the orig, the first reinvent? I mean, I think, and again, I don't mean this as a pejorative but AWS is pretty dogmatic in its view of the world as you you are very strict (laughs) about your philosophy. But at the same time, as you learn about the enterprise, you've evolved. What have you learned about enterprise customers in that five, seven year journey of really getting intense with the enterprise? >> Yeah, that's a good question. But again, we're dogmatic about we always listen to our customers. We will never deviate from that. It's part of our culture. And the customers need to tell us where they want to go. And I'll tell you when we first started with Kinesis, just to answer your question, it was about low latency. We want to get that answer really fast, cause our ad tech customers are some of our very early customers, so it really was about that that extremely low latency response. As even our customers have started to look at Kinesis as a fundamental abstraction on which to put all of their business data in and now they're telling their customers well you should, if their IT customers within their company, if you want any business data, attach to the stream and pull it out. So now we're seeing less emphasis on low latency and to end processing, but increase request I want to be able to attach a dozen consumers, because this stream is actually supporting my entire enterprise. I want to have security. So we recently released encryption at rest. Our customers are asking for support for a VPC flow logs, which we hope to be talking with you about very soon. So now it's becoming actually very mainstream to actually, for the enterprise, and they want all the enterprise ready features, all the certifications, Fed Rep, Hippa, et cetera. So now we're actually seeing the Kinesis Stream itself being put into the enterprise as a fundamental building block for how they're going to run their business and how they're going to build their applications within the business. >> So that philosophy, I mean, you are customer driven first and there's a lot a, Andy Jassy says, there's a lot of ways to compete. You can be competitive oriented, but we're customer oriented. And I, it's clear, you guys do that. At the same time, customers sometimes don't know what they want, so you have to be good at decoding. >> Roger: Yes. >> If you listen to all your customers, you know, five years ago, they say, well we're not going to put any data in there. Sensitive data in the Cloud. Now everybody has sort of gotten over that. You said, alright, well we have to make it more secure. We have to get, you know, whatever certified, et cetera, et cetera. There's an art to this, listening to customers, isn't there? >> It gets back to one of our leadership principles of we always work customer backwards. We need to understand what they want, what experience they'd like to have. We have to anchor everything on that. But there is this element of invent and simplify. Because our customers may guess at what a solution is, but let's make sure we really understand what they want, what they need, the constraints under which that solution must offer. Then we go back to our engineering teams and other teams and we invent and simplify on their behalf. And we're not done there. We actually then bring these back to customers and in fact, why we're here today, we've spent two days talking to customers but even before this collaboration with Splunk began, we actually brought customers in and it turned out, their customers were often our customers. So we started talking, what is the problem? And we started with the very clear problem stain. And once both of our teams, we've loved working with Splunk, they work very customer backwards, like we do. And together once we understood this is the problem we are trying to address, and we had no preconception about how we're going to do it, but we worked backwards on what it would take to actually get that experience for our customers. And we're actually here beta testing it. And we're going to have a very aggressive two or three month beta test with customers, did we get it right? And we'll refine as well before we actually release it to the customer. So again, that working with the customer, work customer backwards. But invent and simplify on their behalf. Because many Splunk customers weren't aware of Firehose until we explained it to them as a potential solution. They're like ah, that will do it, thank you. >> So very outcome driven. I mean, I know you guys write press releases before you sometimes launch products. Sort of as you say, that's what you mean by working backwards, right? >> Roger: Yes, yes it is. It really is. >> Ray: You're good listeners. >> So far it's worked. (laughter) >> It's always fun at the company, when somebody says I have a customer, the entire room gets quiet and we all start listening. It's actually fun to see that, because that's the magic word. I have a customer and we all want to listen. What do they want? What are they challenged with? Cause that's where the innovation starts from which is exciting to be part of that. >> It's been a great formula, no doubt about that. >> It has, it has. >> Thank you both for being here. Didn't realize it was a big day. So congratulations >> Thank you. >> on your announcement as well. >> Absolutely. >> Ray, Roger, good to see you. >> It's great talking with you. >> Alright, you're watching theCUBE live here from Washington D.C. .conf2017. (techno music)
SUMMARY :
Brought to you by Splunk. coming to you live from our nation's capital. What's inspiring that popularity of the Cloud? and we got all the resource ready for you So very AWS like, we love AWS, we're a customer, In 2013, we got the preview of Kinesis. And the way we were doing it was in batch. And then we can get into the analytics discussion. Giving 'em the ability to get instant insights So who's using it? Cosmopolitan, Car and Driver, from print to digital form, is something that you guys have really lived managing the software on it, figuring our how to scale. So one of the values that AWS brings is simplification. And so the data pipeline gets increasingly complex. And we order a wide variety so that people can choose So you guys have already solved that problem essentially, that simplifies this, we would potentially disappoint And we will actually up level it Yeah that low level control also allows you to be And should we have precise feedback on a specific service, And coming up with products, it's what you do. hey, we want you to use Splunk analyze the data. You can have your, you can use native Splunk Cloud, What have you guys learned since the orig, And the customers need to tell us where they want to go. So that philosophy, I mean, you are customer driven first We have to get, you know, and we had no preconception about how we're going to do it, I mean, I know you guys write press releases before It really is. So far it's worked. the entire room gets quiet and we all start listening. Thank you both for being here. from Washington D.C. .conf2017.
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Ray Smith, Mississippi Community College Board | Pure Accelerate 2017
>> Announcer: Live from San Francisco it's The Cube covering Pure Accelerate 2017. Brought to you by Pure Storage. >> Welcome back to Pier 70 in San Francisco everybody. This is The Cube, the leader in live tech coverage. I'm Dave Vellante with my cohost Stu Miniman. Ray Smith is here. He is the Assistant Director for Technology at the Mississippi Community College Board. Ray, thanks for coming to The Cube, it's good to see you. >> Glad to be here. >> We were having a good conversation off camera. Tell us a little bit about the college board. >> Well, Mississippi Community College Board is... We are the board that coordinates with the 15 community colleges in the state of Mississippi. Part of our job is to make sure that enrollment figures are taken care of. We look at budgets, we work with the legislature, and more importantly we work with the community colleges in helping to develop good outcomes for our students. >> Okay, so it's obviously a public institution, public funded, you've got a responsibility to report to the public. Do you also have responsibility for, well what services do you have responsibility for? You said enrollment, but.. >> I am, for instance, I'm responsible for a statewide network. The community colleges are a little different than some entities in that we have a shared network. In which all 15 community colleges they are connected back to the board office. We act as the ISP for the colleges. The colleges submit data to us. We also have in place a longitudinal data system in the state of Mississippi in which we collect information and we report that information up the line for our longitudinal data. But more importantly what we do is that we count students and we pay based upon enrollment. >> Community colleges play such a critical role today in education. Which we all know, anybody who has kids know how expensive it is to educate. And the colleges are way more open these days about accepting community college student transfers, allowing students to take summer classes at community colleges. My son, for instance, goes to GW, he's taking some math classes at community college. It really helps address the cost. It helps people who aren't ready to go to college. Talk a little bit about the mission and the role that your college plays. >> Our system, or the board office, what we do again is that we coordinate each community college as a separate entity amongst themselves, governed by a local board. But from the state level, we administer the payment based upon students. And one of the things that we do is we're heavily involved in the workforce. That's a real big issue in our system right now. To train more people for the jobs that we're trying to bring in to Mississippi. In addition to that, we have strong academics in which our students take two year academic courses that transfer to our universities. But more than anything our purpose is to try to make a better Mississippi, in providing our services, education and training to the people of Mississippi. >> You're a feeder system, in essence. It's a fast turnover, it's a two year cycle. So your job of enrollment has a lot of pressure on it. Now what kind of pressure does that put on the technology infrastructure. >> Well, a couple of things. Number one, community colleges are education based institutions but at the same time, people come there because of the lifestyle. Because coming out of high school, a lot of students aren't quite ready for the big universities. So, they come to the community colleges looking for a lot of the things that they have at home. Internet, fast internet, for instance, and also the ability to.. (laughs) that's the big one, and also the ability to have online classes where they don't have to come on campus or so forth. But our students want everything that the major universities have and they want everything they're used to as home as well as within coming out of K-12. >> Okay, so, let's get into the relationship with Pure Accelerate, let's talk about it. What led you to them? Talk about your journey, give us the before and after. >> Well, first of all, I have a real small staff at our agency, and we have a lot of big things to do. >> Dave: What's small? >> Small, three people including myself. >> Oh wow, for 15 colleges? >> 15 colleges for a statewide network, etc, etc. What we were looking for was a system that would allow us to bring all of our technical resources into a smaller unit. We looked at the converge systems of some other competitors to Pure early on. And what we were really wanting to see and what we needed help with was more of a technical infrastructure more than anything. But what we found, it was way too complex. And it actually required all of the additional services that you received in terms of technical support. When we moved to Pure, we looked at the Pure Storage, and one of the main reasons we did that was our current system was coming up for renewal. The renewal itself was triple what it was the year before. >> Dave: The maintenance renewal? >> The maintenance renewal. And it was the traditional forklift. We weren't ready to forklift. So looking at Pure, what we were looking for was number one, simplicity, we were looking for more speed, we were looking for all of those things that would make life easier for us. What we ended up getting was a situation where we were able to purchase the Pure array for the cost of maintenance of what we were looking at before. >> Dave: Wow. >> The cost of mainenance. We got the Pure array with three year maintenance on it. So it was a no brainer from our standpoint. >> And let me just put a point on that. When you say simplicity a lot of people what they say, "Oh well, give you more time to work, "but you're going to pay for it more upfront." But you're saying from a capital expense standpoint this was now a savings for you compared to keeping your old gear. >> Understand this, the Pure array is the first piece of technology equipment that I've ever purchased that would not be classified as an expense. It's an investment, simple as that. Because what we purchased, we will not have to throw it out when we upgrade. We simply, as we saw today in the presentation, we upgrade our software, we get same pieces and parts in place. It is, it's an investment. >> Can you walk us through that a little bit? Because you've got the full converged infrastructure solution. Were you using Cisco before or was that something you added? >> I was using Cisco from a UCS standpoint. But I was using another manufacturer's storage. We actually, we moved to the flat stack on our first conversion we kept our UCS, but we removed the storage and our converted it all to a flat stack. Then we subsequently purchased an additional flat stack. But what it has bought us is exactly what you mentioned earlier. We now have time to do things as opposed to just being a technology person. >> Ray, one thing when you talk about upgrades. You've got your computer, your storage, and your network. Storage sounds like you can upgrade it and move there, with converge you can upgrade it. Your network, too? Because network tends to be install it and don't breathe on it because I don't want to mess it up. So, does the full solution get upgraded or how do you manage it? Do you manage it as a stack or as the individual components? >> We manage our stack itself. Now from the infrastructure standpoint of what we do with internet service, that's handled with another piece of equipment. But we were able to number one, shut down two full racks of storage equipment down to four U, roughly. And it's changed our whole costing structure inside of our data center. The data center is much cooler. And of course, the whole support piece of it is just unbelievable. There's no one coming in to replace blades every other week. >> I was going to say, too. It had to have an IT labor impact. So what would you have done? You've got a small staff. It's yourself plus three individuals, correct? >> Ray: That's correct. >> What would you have done if you didn't get there? Would you just have to work more nights and weekends? >> That's what we would have done. We would have continued to do that. >> Dave: And you were doing that? >> That's what we were doing. >> Is it fair to say you got a lot of your nights and weekends back? >> Absolutely. >> So, presumably, people are more productive during the day. They're happier because they have more time with their families. >> Absolutely, and access to our data is a lot quicker than it was before. >> So, working less, you get more done. >> Correct. >> That's a good do more with less story, right? Because usually do more with less means you figure out how to work nights and weekends. I mean you remember that cycle of 20, ten, 15 years of hell after the dot come burst. It was like do more with less, do more with less, do more with less. And all it meant was more hours for IT people. I guess we hit the breaking point, and now technology's got us into this problem. Is technology finally getting us out of this problem? >> From our standpoint, it is solved. At least 50% of man hours that we have been using just to keep our systems up and running. Now I work it all from one pane of glass or from my cell phone. >> And here's the thing. What value did that really provide, that extra nights and weekends, to the organization? I guess the value was, if it didn't get done, IT would fail, was the value. But it wasn't incremental value, right? >> Well, what we've been able to do is move more into the job responsibilities that are actually there more along with the technical side. >> Dave: So the strategic stuff? >> Absolutely, I have a developer now that can spend his whole time developing as opposed to responding to some error message on a hard drive or whatever. >> I'll make a prediction. I think it was, it might have been Greenspan, but he said during the 80's, we all went to PC's, they said you see the productivity numbers aren't up ticking. But we're spending all of this money on technology, but you don't see it in the productivity numbers. And of course in the 90's we had this productivity boom. You're kind of seeing some flatness in productivity, but the stories that we get like this, I think we're going to have another boom. Do you feel that way as a technology practitioner? >> Absolutely. Even myself, I deal more with the infrastructure so far as our servers and so forth. I have time to do a whole bunch of things. We're redesigning, for instance, our websites. We're doing a lot of other things now that honestly we didn't have time to do. >> And I think that's a big factor in the flash. It's not just speed. >> Yeah, and Dave, it's something we've been talking about for years, some of the MIT guys. As automation and tools and platforms are actually going to free us up to do more. Stories like your developer wasn't developing and now they are. So, yeah, what are you seeing that's going to enable you to do even more? Is there anything you're asking for from the community that, either some announcements you've seen this week or other things you're looking for? >> Believe it or not, the announcement that I just heard today about the active active scenario, that's it. I have two data centers. >> The multi site replication? >> Absolutely. >> You used to work at EMC in the heyday and they referenced it today. SRDF was kind of the gold standard, expensive, complicated... >> Stu: In 1994 >> Dave: But it changed the business. What I heard, and maybe you alpha geeks can help me, but what I heard is that we're going to dramatically simplify that whole process. So, that's what you heard, but add some color to that. What does that mean for you? >> What that means for me is now my two sites will operate as one. And that I actually have a real active active configuration that I'm not afraid if something goes down that the other one's not going to be there. I don't have to go through the process of rebuilding on the other side because it's all automatic. There are a number of things that were said that if you understood what we have gone through in the past couple of years in working, trying to get together an active active environment, it was just like the creation of fire, as far as I'm concerned. >> It's something we've had in storage forever. The reason we over provision and get such low utilization is because if I have a failure or something goes wrong. If something's a little slow, I have trouble. If I go down, I'm out of a job. >> The traditional vendors weren't able to solve this problem for you. I mean they've been trying for a while, right? But you didn't see anything from those guys. >> If you attempted to do that using hardware base, using software base, it's more than just a notion. I have reasonable assurances, based on what I've seen with Pure that it is going to be as straightforward and as simple as they have described. >> That's great. Alright, Ray, give you the last word. Pure Accelerate, where you here last year? >> Ray: I was not here last year. >> So this is your first year? >> This is my first year, and it's great, it's wonderful. >> Are there things you are seeing that are interesting to you? >> Absolutely, everything, everything. >> Why do you come to these shows? >> Well, number one, I come to learn something new. I like to hear about the announcements number one. And I like to be able to have the opportunity to meet some of the people who actually building, designing, writing the source code for this stuff. It's amazing. >> I got to ask you a personal question. You shared with me you like to funkify, you're a bass player, do you play in a band? >> My band is getting back together for kind of a short reunion here. We have some roots that go back to hip-hop. And it'll be interesting to see Snoop tomorrow night. >> That's awesome, fantastic. Well, Ray, thank you so much for coming to The Cube. >> Appreciate it, appreciate it. >> Alright, keep right there and we'll be back with our next guest. Right after this short break, this is The Cube, we're live from Pure Accelerate 2017 in San Francisco. We'll be right back. (exciting music)
SUMMARY :
Brought to you by Pure Storage. This is The Cube, the leader in live tech coverage. We were having a good conversation off camera. We are the board that coordinates with well what services do you have responsibility for? is that we count students and we pay based upon enrollment. and the role that your college plays. And one of the things that we do is put on the technology infrastructure. and also the ability to have online classes What led you to them? at our agency, and we have a lot of big things to do. and one of the main reasons we did that for the cost of maintenance of what We got the Pure array with three year maintenance on it. what they say, "Oh well, give you more time to work, We simply, as we saw today in the presentation, Were you using Cisco before or was that something you added? We now have time to do things as opposed and move there, with converge you can upgrade it. And of course, the whole support piece of it So what would you have done? That's what we would have done. So, presumably, people are more productive during the day. Absolutely, and access to our data I mean you remember that cycle of 20, At least 50% of man hours that we have been using I guess the value was, if it didn't get done, is move more into the job responsibilities that as opposed to responding to some error message And of course in the 90's we had this productivity boom. I have time to do a whole bunch of things. And I think that's a big factor in the flash. going to enable you to do even more? Believe it or not, the announcement and they referenced it today. So, that's what you heard, but add some color to that. that the other one's not going to be there. The reason we over provision and get But you didn't see anything from those guys. If you attempted to do that using hardware base, Alright, Ray, give you the last word. And I like to be able to have the opportunity I got to ask you a personal question. We have some roots that go back to hip-hop. Well, Ray, thank you so much for coming to The Cube. with our next guest.
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Ray Wang, Constellation Research - Zuora Subscribed 2017 (old)
>> Hey, welcome back everybody! Jeff Frick here with theCUBE. We're at Zuora Subscribe at downtown San Francisco, and every time we go out to conferences, there's a pretty high probability we're going to run into this Cube alumni. Sure enough, here he is, Ray Wang. He's the founder and principal of Constellation Research. Ray, always great to see you. >> Hey Jeff, this is awesome, thanks for having me. >> And close to your hometown, what a thrill! >> This is, it's a local conference! What else can I ask for? >> So what do you think? Subscription economy, these guys have been at it for a while, 1200 people here, I'm a big Spotify fan, Amazon Prime, go back to Costco if you want to go back that far. But it seems to really be taking off. >> It is. About three years ago, digital transformation became a hot topic. And because it became a hot topic, it's really about how do I get products to be more like services. How do I get services to get into insights, and how do I make insights more like experiences and outcomes? And that natural transition as companies make a shift in business models is what's driving and fueling the subscription economy. >> It's interesting. Do you think they had to put the two and two together, that once the products become services now you can tap into that service, you can pull all kinds of data after that thing, you can have analytics, as opposed to shipping that product out the door it goes and maybe you see it every 15,000 miles for a checkup? >> You know what it is? It's basically, about three years ago, people started to realize this. Tien's been talking about this for ages, right? He's been talking about everything's a subscription economy, everything is going to be SAS-ified. And in tech world, everybody got that. But it was when companies like GE, which we saw together, a Caterpillar or a Ford, started to realize, "Hey we can do remote monitoring and sensing "with IOT on our cars, "and I can now figure out what's going on "and monitor them or give an upgrade, "or give a company an upgrade on their appliance, "or give an upgrade on their vehicle, "or do safety and compliance." Then people started realizing, "Oh, wow. "We're not just selling products. "We're in the services business." >> Right. It's funny, if you read the Elon Musk book, how the model years of Teslas, there's no such thing as a model year. It's what firmware version are you on, and then they upgrade. >> Oh, no, that's what we do all the time. You click on a little T, and it's like, boom, firmware. Oh, I get a new upgrade. Only the other day, you touch your head seat, there's like a lumbar support thing, the software popped up for headrest! I never knew I could change the headrest! It literally showed up two months ago. It's unbelievable. >> So, the cool thing, I think, that doesn't get enough play is the difference in the relationship when now you have a subscription-based relationship. That's a monthly recurring or annual recurring, you got to keep delivering value. You got to keep surprising you every morning, when you come out and get in your car, as opposed to that one time purchase. "Adios, we'll see you in however many years "until you get your next vehicle." >> Oh, that's a great example. And the Tesla, we got the Easter eggs over Christmas, right? So the Christmas holiday thing with the Model X that actually did Trans-Siberian Express to the Bellagio fountains with the doors that popped up. You're like, "Hey, what is this thing?" It's just an upgrade that shows up. You're like, "Okay." But you do. You do have to delight customers, you're always capturing their attention, and the fact is, hey, I might buy a toaster. And in that toaster, I might get an upgrade two to three years out. Or maybe, I just buy toasters, and I subscribe to them. And every three years, I get a new toaster. And I can choose between a model L or I can go upsell, get a different color, or I can change out a different set of features, but we're starting to see that. Or maybe, I get a hotel room or a vacation. And that hotel room is at level X, and if I get a couple more members of my family, I get to level Z, and I get to another level, where I lose all the kids, I go back to level A. But the point being is I'm buying a subscription to having an awesome vacation. And that is the type of things that we're talking about here. It's that freedom that Tien was talking about. >> Because he talked about the freedom from obsolescence, freedom from maintenance. There's a whole bunch of benefits that aren't necessarily surfaced when you consume stuff as a service versus consuming it as a product. >> It does. And sometimes it may cost more, but you're trading the convenience, you're trading the velocity of innovation, right? For some people, they just want to own the same thing, they're not going to make the move, but for other people, it's about getting the newest thing, getting delighted, having a new feature. And in some cases, it's about safety, right? This is regulatory compliant or I'm actually doing rev rec correctly, as they were talking about, ASC606. >> Alright, so you're getting out on the road a lot, it's June 6, and I won't tell anyone on air how many miles you already have, because Tamara is probably watching, and she'll be jealous, but biggest surprise is you see here or recently as this digital transformation just continues to gain speed. I'm doing a little research now, and maybe you can help me out. Looking back at digital photography, because it's like, "No, no, no, no, no." for the film, and then it's like, boom. I think these really steep inflection points, or up if you're on the right side, are coming. >> Let's stick to digital photography, that was a great one. There was the point, remember, where we actually had all those disposable cameras at parties that'd get developed, one hour developing. Then we get to back to the point where you just showed up at Costco, dropped something off, you'd get the disk and the photo. Then we had O-Photo, and now we have nothing. Everything just went away because of the phones. These things changed everything, right? I mean, they changed the way we look at photography to the point where, do we even have an album? I was breaking out albums basically three weeks ago, showing my kids, like "Hey, this is what a photo album looks like." And they were completely mystified. "Oh, you print these, how do they get printed?" I mean, they're asking the basic questions. That transformation is what we're having right now. "You own a car?" "You actually buy a PC?" I'm buying compute power. Kilowatts per hour for artificial intelligence in the next year. It's not going to be, I bought the server, I loaded it up, I got it tuned, I got it ready. So yeah, we are in the middle of that shift. But it's the fact that companies are willing to change their business models, and they're willing to break free in the post ERP era. A lot of this is just, my old ERP does not do billing, it doesn't understand the smallest unit of something I sell, and I've got to fix that. And more importantly, my customers, they want to buy it today. The want to buy it in pieces. They want to buy it even smaller pieces. They might buy it every other week, they might buy it-- we have no idea. Yeah, I've got to make sure I can do that. >> It's just interesting too that this is happening now. We're talking about autonomous cars. We see the Waymo cars all the time. The guy from Caterpillar, he's got to a whole autonomous fleet of mining vehicles that are operating today. >> 500,000! He's got 500,000 little trucks. Well, they're not little trucks, they can't fit in this building. >> They're big trucks. Apparently, they tried. >> But they're trying to get these trucks in. We used to think about, like "Hey, these are agricultural vehicles that can be remotely controlled by GPS, they also work for tanks." These are things that are actually doing runs. Now, it's a great reason. Think Australia. Out in Perth, it's about $150,000 to hire a driver. Just to go back and forth. So they figured, "This is just getting ridiculous. "We don't have enough people out here. "We can't convince enough people "to come drive these trucks. "Let's go automate that." That's a lot of the story of where a lot of this came from. >> Or he had a bad night, or broke up with his girlfriend, or distracted about this or that. The whole autonomous vehicle versus regular people driver-- all you've got to do is ride around on your bicycle in your neighborhood, and watch how many people stop at stop signs. Should we answer that question real fast? >> Oh, I do that in California. That's kind of bad, actually. >> Alright Ray. Well, thanks for taking a few minutes. I'm glad you get a weekend at home. Where you off to next, I should ask? >> Oh, it's going to be a crazy next few weeks. I'm going to be in London and Paris and Boston all next week. >> Oh, you're going to eat well. >> I'll try. >> Alright, he's Ray Wang. I'm Jeff Frick. You're watching the Cube from Zuora Subscribe. Thanks for watching.
SUMMARY :
Ray, always great to see you. go back to Costco if you want to go back that far. How do I get services to get into insights, that once the products become services now you can everything is going to be SAS-ified. It's what firmware version are you on, I never knew I could change the headrest! You got to keep surprising you every morning, And that is the type of things when you consume stuff as a service they're not going to make the move, and maybe you can help me out. and I've got to fix that. he's got to a whole autonomous fleet they can't fit in this building. Apparently, they tried. Out in Perth, it's about $150,000 to hire a driver. and watch how many people stop at stop signs. Oh, I do that in California. I'm glad you get a weekend at home. Oh, it's going to be a crazy next few weeks. I'm Jeff Frick.
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R "Ray" Wang, Constellation Research | ServiceNow Knowledge16
>> Good Live from Las Vegas. It's the cute covering knowledge sixteen Brought to you by service. Now carry your host David, Dante and Jeffrey. >> Oh, >> welcome back to knowledge. Sixteen everybody, this is the cubicle wall to wall coverage. We got the events. We extract the signal from the noise. This is Day one for us will be going Three days of knowledge extraction from knowledge. Sixteen. Ray Wong is here. He's the founder and principal analyst and chairman of Consolation Research. Up and coming Smoking Hot research company. Ray, Always a pleasure to see you. Thanks for coming >> on. Excited to be here, man. It's been a world one week of events, so >> I'LL say So you were You were down. It's Sapphire, right? You were over it on Tampa Amplify And >> I'm Austin s Wait after this race, a >> normal week for you >> It's a normal week for all of us. >> So impressed You were telling us off camera that you were at one of the earlier knowledge events down in San Diego. So you've got a lot of experience with this company, >> you know, it was in a tent. It was outside they had detected. I think it's like a park. I'm not even sure what it was. I just But remember, there's one one hundred fifty people next. There's like five hundred people. Three years later, it's pretty wild. >> So they've come out of the blue and really, you know, escalated a lot of momentum. The latest billion dollar software company with a plan to get to four billion. So stepping back a second just looking at the software landscape, one has to be impressed with the progress that service now is made. What's your take on the industry and service now in particular? >> Well, I think what people don't understand this service now is a platform, right? There's a business model platform or the way that we used to look at paga or the way we used to look at a lot of those companies that were actually sit in the middle. That orchestration what's changes? Because everything's in the cloud. What we now have the ability to abstract orchestrating doing away that we've never seen before, right so you can take specific business problems. Take the heart of what's actually happened on the idle piece. Use it to not just manage the process, but also do the analytics and the monitoring. So when we get the things like Coyote coyotes really about having a set of smart services and being well. To put that in the construct is a lot of the opportunity that we see going forward >> so high. So I said three years ago in the Cube after I saw the platform capabilities and said, Wow, this is a collision course with sales force Investor's Business Daily wrote a article today. Collision course of sales for so glad they caught up with Theo. But But, I mean, it's you could kind of see it coming together. And now you Frank lays out this vision this morning. Have you got the AARP estate, the C R M estate and tea or a service management Rather kind of bridging those two. How do you see it? >> No, we definitely see this as a platform play. Now here's what's interesting is the lots of the developments, and you see this all the time has been happening in the APP to have side of the House package. APS have kind of been at a standstill for innovation compared to what's going on on the customs side. And so every so often we see that flip on platforms. This is the beginning of that flip, more than one person said. I it is going to be the end of the affair, right, because we're going to put all the intelligence into the interaction. You don't have to go to the specific app. No. And the fact is, what becomes important is the ability, the orchestration, the intelligence, the recommendation and what you want to build to get to the part where I'm making the right set of recommendations to augment the next set of processes. That's what gets really powerful and these platforms that are emerging on, What's the next set of clouds? That's going to be where we're going to see a lot of this advancement. >> So the FBI essentially becomes the product. Is that kind of? >> It's the orchestration of the AP eyes, the way the context was delivered against those AP eyes and more importantly, how we actually pulled together those journeys, like a couple things that we talk about time mass personalization of scale, lots of context, right, so rolls relationship, identity weather, location, time, all important, Then choose your own adventure journeys the ability to actually abstract different processes from different places and bring them together, and the more importantly, we call intention driven design, which is. I'm gonna give you three or four choices. Learn over time. Take that machine learning. Then apply that the next set of recommendations and then start building against that. And that power sits on the network. That power sits in these new platforms. >> So you're here speaking to the service now customers about customer experience, right? It's something we hear a lot about. Your an expert in that in that space. What did you say? What was the reaction? What was the feedback? >> Well, I think the important thing is we're seeing new business models and you hear me say this before it's we're in a post sale on demand, attention, economy. And what that means is everything after the sale is what's happening right now. That's the service. That's the experience. Peace. The on demand pieces were accessing smaller, smaller slices of a product. Maybe not even a product, a service, maybe not even a service, and incite maybe not even insight and experience. And then, more importantly, it's an attention to come. If you're not capturing my time and attention, which is mind share, or if you're not saving me time and money, I don't care. And That's what we're in. We're in. These business moms are built around. This is interesting. Came out of the Oracle Marketing cloud shows Well, same thing. Just smaller and smaller slices of attention based on the way you interact with all the other applications you have. You don't have time to give somebody the big story. You've got to get him when you can. They could be standing in line. They look at their phones, are in the middle of their kids, switching innings at the baseball game. And you got to get in that little tiny video that in between time is so important because you don't close there. You lose him, right? And it's not for something really big. It's move them along the needle down. The journal. Correct. >> What do you make of this, Dave? Dave Wright was just not talking about the new state of work. IBM has been talking about a new way to work in. He is kind of running the collaboration, you know, group. Now you you talk about millennials and how they work. What are you seeing in state of work? >> Well, a lot of the research we're looking on the future of work is by one of my colleagues, Alain Le Pastilles, and what he's been really looking as this shift in terms of conversations as a service. He's been looking at the shift in terms of intelligent collaboration. Right, and all this stuff is actually leading the point where we're actually using technology to augment ability. Teo do decisions had a lot more automation than we had him before. But then cognitive assistance pop up right and they help make a smarter. And they learned from our different our actions and all that's starting to come into the workplace, which is exciting and a little bit creepy and scary at the same time. >> So what's the What's the What's new with Constellation? You guys are growing. Bring it on. New analyst Cranking out Want to research? Your event keeps growing? Give us the update on Constellation. >> You know, I think the big thing is this digital transformation story we've been talking about for the last five to six years is huge. The next set is really not about transformation. It's about finding growth in times where there is no growth. That's where we're going to talk about the next five years at our conference. Really? Talking about what are those factors, right? We gotta jump start growth. Global GDP is growing two to three percent at best. Every company has a target of like five to ten. Someone's gonna lose, and it's gonna be very interesting. >> So you think that growth is going to come through productivity improvements or investments in technology? Actually, Dr sort of new productivity levels were taking away from >> someone else. I think we're taking me for someone else. That's what I'm really scared about, that they're smart growth that's sustainable and helps people with the jobs and the job transitions. And there's what we've been doing, which a lot of destructive Cross, which is actually limiting all of the jobs and actually making it harder to grow in the long run. >> Well, so yeah, we've talked about this on the Cuba lot machines replacing humans, which they've always replaced humans. But it seems to be now happening at the cognitive level. That's scary. I know you guys to the valley, wags. You know the seasonal nervous right now, You guys, you more sanguine? Then the VCs air >> well with these three big areas where we see a lot of investment. Deep learning happens to be one of them, right? We see a lot of medicine going off. Some of the smartest people I know are all focused and on deep learning. Very interesting thing. If you look at that university, California, Irvine there's a whole department around. This artificial intelligence that just lifted itself up became a private corporation. So there's very unfeeling things there. There's nanotech, which is also some erasers, things on the material science piece that's also playing a big role. And then, of course, there's stem cells in the biotech piece. Those three things are converging, and you know it's more than just building out the Star Trek roadmap that Apple's been doing. It's a lot bigger than that. There's some big societal shift that are happening. >> What, what's next for you? You say you're heading Teo. That's sweet, but we're So we work. We find Ray Juan. I'm >> off this sweet world, Max. There's a monetary it next week. There's a whole bunch of other events picking up in June as well as you. You're going to be at them, but I think we do our retreat every year at the end of the year. May June, we're going to be at Stanford, the faculty club. All the constellation folks get together on. Then we go back out into the field and it's a crazy summer as well. I don't know when this stops making, so yeah, you could always find him on Twitter That that's but I looked for you guys when I'm where you're at is where the events are. >> Well, hopefully our past will continue to cross. We love having you in the Cube was a great guests. Really appreciate your time. Thanks for coming on. >> You know, Thanks for having have a >> great conference. All right. They've travelled, right, everybody. We'LL be back after this short break. This's the Cube or live from knowledge. Sixteen, right? >> Service now is the
SUMMARY :
covering knowledge sixteen Brought to you by service. We extract the signal from the noise. on. Excited to be here, man. I'LL say So you were You were down. So impressed You were telling us off camera that you were at one of the earlier knowledge you know, it was in a tent. at the software landscape, one has to be impressed with the progress that service now is made. To put that in the construct is a lot of the opportunity that we see going forward But But, I mean, it's you could kind of see it coming together. the orchestration, the intelligence, the recommendation and what you want to build to get to the part where I'm making the So the FBI essentially becomes the product. And that power sits on the network. What did you say? the way you interact with all the other applications you have. He is kind of running the collaboration, you know, Well, a lot of the research we're looking on the future of work is by one of my colleagues, Alain Le Pastilles, and what he's been really looking as this So what's the What's the What's new with Constellation? You know, I think the big thing is this digital transformation story we've been talking about for the last five to six years is huge. And there's what we've been doing, which a lot of destructive Cross, I know you guys to the valley, wags. Some of the smartest people I know are all focused and on deep learning. That's sweet, but we're So we work. so yeah, you could always find him on Twitter That that's but I looked for you guys when I'm where you're at is where the events We love having you in the Cube was a great guests. This's the Cube or live from knowledge.
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R "Ray" Wang, Constellation Research - IBM Information on Demand 2013 - #IBMIOD #theCUBE
okay we're back here live ending up day one of IBM's information on demand exclusive coverage for SiliconANGLE and Wikibon and constellation research breaking down the day one analysis I'm John furrier and join my co-host E on the cube Dave vellante of course as usual and for this closing wrap up segment of day one we have analyst and founder of constellation research ray Wang former analyst big data guru software heading up the partner pavilion kicking off all the flying around the world your own event this month past month things going great how are you how are you doing we're going to great man there's a lot of energy in q3 q4 we've been watching people look at trying to spend down their budgets and I think people are just like worried that there's going to be nothing in 2014 right so they're just bending down we're seeing these big orders like tonight I've got to fly out to New York to close out a deal and help someone else that's basically it was a big day to deal that's going down this is how crazy it's going on and so it's been like this pretty much like for the last four or five weeks so flows budget flush I just wash this budget lunchtime what are you seeing for the deals out there give us some of the examples of some of the sizes and magnitude is it you know you know how are you up and run to get get some cash into secure what size scopes are you seeing up yeah i mean what we're seeing I mean it's anything from a quarter million into like five million dollar deals some of our platform we sing at all levels the one that's really hot we were talking about this that the tableau conference was the date of is right dative is is still really really hot but on the back end we're saying data quality pop-up we're seeing the integration piece play a role we also saw a little bit of content management but not the traditional content management that's coming in more about the text mining text analytics to kind of drive that I mean I'm not sure what are you guys seeing alone yeah so what we're seeing a lot of energy I've seen the budget flush we're not involved in the deals like you are Dave is but for me what I'm seeing is IT the cloud is being accepted I'll you know those has not talked about publicly is kind of a public secret is amazon is just destroying the value proposition of many folks out there with cloud they're just winning the developers hand over fist and you know i'm not sure pivotal with cloud family even catch up even OpenStack has really got some consume energy around we're following that so it opens stack yet amazon on the public cloud winning everything no money's pouring into the enterprise saying hey we got to build the infrastructure under the hood so you can't have the application edge if you don't have the engine so the 100 x price advantage and that's really a scary thing but I think softlayer gives IBM a shot here yeah we were talking about self leyva so you are seeing more I'm seeing it aight aight figure deals and big data right and it's starting to get up there so softly I'd love to get your take on soft layers we've been having a debate all day Oh softlayer jaws mckenna what do you what's your take you're saying it's a hosting I've been a look at first of all yeah I love putting a huge gap 9 million dollars per lock event data center hosting now if that's a footprint they can shave that and kind of give their customers some comfort I think that's the way i see it i mean just I haven't gone inside the numbers to see where it's going to be where this energy is but like we're software virtualization is going on where everyone's going on with virtualization the data center I'll give them a cloud play I just don't see ya didn't have one before I mean happy cloud I mean whistling private club Wow is their software involved I think it provides them with an option to actually deliver cloud services with a compression ratio on storage and a speed that they need to do to deliver mobile mobile data analytics right there's things that are there that are required so it gives them an option to be playing the cloud well I just saw I mean in the news coverage and the small inspection that we did I did was I just didn't reek of software innovation it's simply a data center large hosting big on you agree they didn't really have a northern wobblin driving him before this was brilliant on your Sun setting their previous all these chairs deal kind of musical chairs me for the music stops get something it was that kind of the deal no I think they are feel more like customers asking for something and they wanted IBM to have it yeah IBM works it's an irr play for IBM they're gonna make money on this team not a tuck under deal 900 million no I know but they'll make money on it that's IBM almost always does with it I'll leave it up to you guys to rip on I was your conference oh thanks hey constellation connecting enterprise was awesome we were at the half moon bay Ritz we had 220 folks that were there senior level individuals one of the shocking things for me was the fact that when we pulled the audience on day one two things happen that I would never imagine first thing as ninety percent of the folks downloaded our mobile app which was like awesome right so the network was with them the knowledge is with them when they leave the event and all the relationships the second thing that really shocked me we knew we had really good ratios but it was seventy-five percent of the audience that was line of business execs and twenty-five percent IT it was like we were we didn't have to preach to the choir it was amazing and the IT folks that were they were very very innovative on that end so it was awesome in that way so a lot like the mix the mix here is much more line of business execs the last week at hadoop world loose you know the t-shirt crowd right a lot of practitioners you know scoop I've flume hey we got the earth animals ever right oh but no this event is actually interesting IBM iod for me is like I didn't realize this when I didn't I looked at numbers when we're doing a partner event yesterday and there are thirteen thousand attendees here that actually makes that the biggest big data and analytics conference bigger than strata bigger than a whole bunch of other ones and so I mean this is pretty much the Nexus of what about open world big data over there but this is a big opera you see world any world cloud big data yeah hey the between no but so IBM's done a fantastic job of really transitioning this conference from sort of an eclectic swix db2 informix right I'm management routine fest right yeah and now it's like what are the business things I mean what are we trying to save around the world are they telling the story effectively it's a hard story to tell you got big data analytics cloud mobile in the middle and you got social business but then you got all this use case they have success stories if customers that creating business outcomes they telling the story effectively is it not enough speeds and fees is it too what's your take the stories are there we've seen like 122 case studies from the business partner side we just haven't seen them percolate out and I think they've got to do a better job evangelizing stories but what's interesting is like there's that remember we talked about this data to decision level there's that data level that was IBM right here's the database here's the structure here's the content management here's the unstructured stuff this is where it sets then there was that information management level which that they started to do which is really about cleaning the data connecting that data connecting to upstream and downstream systems getting into CRM and payroll and then they got to this level about insights which was all the Cognos stuff right so they've been building up the stat from data decisions so they got data information information to insight and then we're getting to this decision-making level which they haven't made a lot of the assets or acquisitions there but that's the predictive analytics that's the cognitive computing you can see how they're wrapping around there I mean there's a lot of vendors to buy there's a lot of opportunity out there's a lot to connect and they've been working on it for a while but I guess I got to ask you how they doing what's your report card from last year this year better better storytelling better messaging I think the stories are getting better but we're seeing them in more deals now right before we'd see a lot more SI p traditional SI p oracle you know kind of competes and a little bit of IBM Cognos now we're seeing them in a lot of end-to-end deals and what we're talking about it's not like I T deals these are line of business folks that say look I really need to change my shopping experience what do you guys have we see other things like you know the fraud examples that any was talking about those are hilarious I mean those are real I see em in every place right I mean even with Obamacare right there's gonna be massive amounts of fraud there any places that people going to want to go in and figure out how to connect or correct those kind of things yeah so so seeing the use cases emerge yeah and in particular me last week in a dupe world it was financial services you're talking risk you talk a marketing you're talking fraud protection to forecasting yep the big three and then underneath that is predicted predictive analytics so you know that's all sort of interesting what's your take on on Amazon these days you know they are crushing it on so many different unbelievable right on more billion this year maybe it's when you build a whole company which is basically on the premise of hey let's get people to offset our cost structure from November 15th to january first I mean it's pretty amazing what you can do it's like everyone's covering for it and even more funny it's like they're doing in the physical world with distribution centers I know if we talked about this before but what's really interesting is they've got last mile delivery UPS FedEx DHL can't cat can't handle their capacity so now the ability from digital to physical goods they've got that and beezus goes out and buys the post so he can make the post for example a national paper overnight again he can do home delivery things that they couldn't do before they can take digital ads bring that back in and so basically what they're doing on the cloud side they're also doing on the physical distribution side amazing isn't it they're almost the pushing towards sunday delivery right US Postal Service go into five day deliveries sort of the different directions amazon I'm Amazon's going to be the postal service by the time they're done we're all going to subsidize it so so I gotta get you take on the the Oracle early statement Larry Ellison said were the iphone for the data center that's his metaphor a couple of couple or global enrolls ago now you got open stack and though we kind of laugh at that but but amazon is like the iPhone you know it's disruptive its new its emerging like Apple was reading out of the ashes with Steve Jobs Oracle I think trying to shoehorn in an iphone positioning but if OpenStack if everyone's open and you got amazon here there is a plausible strategy scenario that says hey these guys can continue to to put the naysayers at the side of the road as they march forward to the enterprise and be the iphone they've turned the data center into an API so so we got the date as their lock in right so this sim lock in Apple has lock in so is that lock in what's your take of that scenario you think it's video in the open ecosystem world they're all false open because a walk-in also applies but but you've been even to this for a long time right and probably one of the things that you're seeing is that it's not about open versus closed it's about ubiquity right Microsoft was a closed evil empire back ten years ago now it's like oh the standard right it's like ok they're harmless Google was like open and now they're the evil empire right it just depends on the perception and the really is ubiquity Amazon's got ubiquity on it so i did is pushing their winning the developers the winning the developers they got the ecosystem they got ubiquity they've got a cost structure I mean I don't know what else could go wrong I think they could get s la's maybe and once that had I don't know what is Amazon's blind spot I mean s la's I think well a lumpy performance no one wants lumpy right they want the big Dayton who's got ever who's got better public as public cloud SL is denied well I think about what he just said us everybody no but here's think that's a public road statement not an amazon said let's crunch big data computation December fifteenth you tell me what this is all I want to know well I think I think an easy move is I mean this day you've got to do that on premise I just I just don't I just don't think that people are forecasting amazon the enterprise properly and you just set out the Washington Post that is a left-field move we can now look back and say okay I said makes sense amazon can continue to commoditize and disrupt and be innovative then shift and having some sort of on prem playing oh then it's over right then and then gets the stir days surrounded the castle but they really don't have a great arm tremblay have no on print but they could they could get one good I think they want to see well think they want to but I think with them what they figured out was let's go build some cool public service get everyone else to subsidize our main offerings right it's basically ultimate shared service everyone's subsidizing Amazon's destruction of their business right so if you're Macy is why the heck are you on amazon right you know if you're competing with them why the heck are you on Amazon you're basically digging your own grave I'm paying them to do it it's amazing I mean that's that's the brilliance of this goes invade they brag about it yeah digging your own brave like it's a you know put the compute power is great okay great but you're subsidizing Amazon's for the you know compute power so r a great shot great to have you here congratulations on your event constellation research awesome successful venues ahead last month top folks in you're doing a great job with your company and the end the day out today in the last word tell the folks what's happening with IBM what do you expect to hear from them tomorrow I know you're going to be another thing you had to fly to but what does IBM what's a trajectory coming out of the show for IBM what's your analysis I think the executives have figured out that the important audience here is really the line of business leaders and to figure out how to do couple things one democratize decision-making the second thing figure out how they can actually make it easy to consume IBM at different entry points and I think the third thing is really how can we focus on improving data visualization graphics I think you'll see something about that ray Wang on the cube cube alumni tech athlete entrepreneur new for his new firm not new anymore it's a couple years on his belt doing a great job but three years old congratulations we'll be back day two tomorrow stay with us here exclusive coverage of IBM information I'm John prairie with Dave vellante this is the cube will see you tomorrow the queue
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Robert Nishihara, Anyscale | AWS Startup Showcase S3 E1
(upbeat music) >> Hello everyone. Welcome to theCube's presentation of the "AWS Startup Showcase." The topic this episode is AI and machine learning, top startups building foundational model infrastructure. This is season three, episode one of the ongoing series covering exciting startups from the AWS ecosystem. And this time we're talking about AI and machine learning. I'm your host, John Furrier. I'm excited I'm joined today by Robert Nishihara, who's the co-founder and CEO of a hot startup called Anyscale. He's here to talk about Ray, the open source project, Anyscale's infrastructure for foundation as well. Robert, thank you for joining us today. >> Yeah, thanks so much as well. >> I've been following your company since the founding pre pandemic and you guys really had a great vision scaled up and in a perfect position for this big wave that we all see with ChatGPT and OpenAI that's gone mainstream. Finally, AI has broken out through the ropes and now gone mainstream, so I think you guys are really well positioned. I'm looking forward to to talking with you today. But before we get into it, introduce the core mission for Anyscale. Why do you guys exist? What is the North Star for Anyscale? >> Yeah, like you mentioned, there's a tremendous amount of excitement about AI right now. You know, I think a lot of us believe that AI can transform just every different industry. So one of the things that was clear to us when we started this company was that the amount of compute needed to do AI was just exploding. Like to actually succeed with AI, companies like OpenAI or Google or you know, these companies getting a lot of value from AI, were not just running these machine learning models on their laptops or on a single machine. They were scaling these applications across hundreds or thousands or more machines and GPUs and other resources in the Cloud. And so to actually succeed with AI, and this has been one of the biggest trends in computing, maybe the biggest trend in computing in, you know, in recent history, the amount of compute has been exploding. And so to actually succeed with that AI, to actually build these scalable applications and scale the AI applications, there's a tremendous software engineering lift to build the infrastructure to actually run these scalable applications. And that's very hard to do. So one of the reasons many AI projects and initiatives fail is that, or don't make it to production, is the need for this scale, the infrastructure lift, to actually make it happen. So our goal here with Anyscale and Ray, is to make that easy, is to make scalable computing easy. So that as a developer or as a business, if you want to do AI, if you want to get value out of AI, all you need to know is how to program on your laptop. Like, all you need to know is how to program in Python. And if you can do that, then you're good to go. Then you can do what companies like OpenAI or Google do and get value out of machine learning. >> That programming example of how easy it is with Python reminds me of the early days of Cloud, when infrastructure as code was talked about was, it was just code the infrastructure programmable. That's super important. That's what AI people wanted, first program AI. That's the new trend. And I want to understand, if you don't mind explaining, the relationship that Anyscale has to these foundational models and particular the large language models, also called LLMs, was seen with like OpenAI and ChatGPT. Before you get into the relationship that you have with them, can you explain why the hype around foundational models? Why are people going crazy over foundational models? What is it and why is it so important? >> Yeah, so foundational models and foundation models are incredibly important because they enable businesses and developers to get value out of machine learning, to use machine learning off the shelf with these large models that have been trained on tons of data and that are useful out of the box. And then, of course, you know, as a business or as a developer, you can take those foundational models and repurpose them or fine tune them or adapt them to your specific use case and what you want to achieve. But it's much easier to do that than to train them from scratch. And I think there are three, for people to actually use foundation models, there are three main types of workloads or problems that need to be solved. One is training these foundation models in the first place, like actually creating them. The second is fine tuning them and adapting them to your use case. And the third is serving them and actually deploying them. Okay, so Ray and Anyscale are used for all of these three different workloads. Companies like OpenAI or Cohere that train large language models. Or open source versions like GPTJ are done on top of Ray. There are many startups and other businesses that fine tune, that, you know, don't want to train the large underlying foundation models, but that do want to fine tune them, do want to adapt them to their purposes, and build products around them and serve them, those are also using Ray and Anyscale for that fine tuning and that serving. And so the reason that Ray and Anyscale are important here is that, you know, building and using foundation models requires a huge scale. It requires a lot of data. It requires a lot of compute, GPUs, TPUs, other resources. And to actually take advantage of that and actually build these scalable applications, there's a lot of infrastructure that needs to happen under the hood. And so you can either use Ray and Anyscale to take care of that and manage the infrastructure and solve those infrastructure problems. Or you can build the infrastructure and manage the infrastructure yourself, which you can do, but it's going to slow your team down. It's going to, you know, many of the businesses we work with simply don't want to be in the business of managing infrastructure and building infrastructure. They want to focus on product development and move faster. >> I know you got a keynote presentation we're going to go to in a second, but I think you hit on something I think is the real tipping point, doing it yourself, hard to do. These are things where opportunities are and the Cloud did that with data centers. Turned a data center and made it an API. The heavy lifting went away and went to the Cloud so people could be more creative and build their product. In this case, build their creativity. Is that kind of what's the big deal? Is that kind of a big deal happening that you guys are taking the learnings and making that available so people don't have to do that? >> That's exactly right. So today, if you want to succeed with AI, if you want to use AI in your business, infrastructure work is on the critical path for doing that. To do AI, you have to build infrastructure. You have to figure out how to scale your applications. That's going to change. We're going to get to the point, and you know, with Ray and Anyscale, we're going to remove the infrastructure from the critical path so that as a developer or as a business, all you need to focus on is your application logic, what you want the the program to do, what you want your application to do, how you want the AI to actually interface with the rest of your product. Now the way that will happen is that Ray and Anyscale will still, the infrastructure work will still happen. It'll just be under the hood and taken care of by Ray in Anyscale. And so I think something like this is really necessary for AI to reach its potential, for AI to have the impact and the reach that we think it will, you have to make it easier to do. >> And just for clarification to point out, if you don't mind explaining the relationship of Ray and Anyscale real quick just before we get into the presentation. >> So Ray is an open source project. We created it. We were at Berkeley doing machine learning. We started Ray so that, in order to provide an easy, a simple open source tool for building and running scalable applications. And Anyscale is the managed version of Ray, basically we will run Ray for you in the Cloud, provide a lot of tools around the developer experience and managing the infrastructure and providing more performance and superior infrastructure. >> Awesome. I know you got a presentation on Ray and Anyscale and you guys are positioning as the infrastructure for foundational models. So I'll let you take it away and then when you're done presenting, we'll come back, I'll probably grill you with a few questions and then we'll close it out so take it away. >> Robert: Sounds great. So I'll say a little bit about how companies are using Ray and Anyscale for foundation models. The first thing I want to mention is just why we're doing this in the first place. And the underlying observation, the underlying trend here, and this is a plot from OpenAI, is that the amount of compute needed to do machine learning has been exploding. It's been growing at something like 35 times every 18 months. This is absolutely enormous. And other people have written papers measuring this trend and you get different numbers. But the point is, no matter how you slice and dice it, it' a astronomical rate. Now if you compare that to something we're all familiar with, like Moore's Law, which says that, you know, the processor performance doubles every roughly 18 months, you can see that there's just a tremendous gap between the needs, the compute needs of machine learning applications, and what you can do with a single chip, right. So even if Moore's Law were continuing strong and you know, doing what it used to be doing, even if that were the case, there would still be a tremendous gap between what you can do with the chip and what you need in order to do machine learning. And so given this graph, what we've seen, and what has been clear to us since we started this company, is that doing AI requires scaling. There's no way around it. It's not a nice to have, it's really a requirement. And so that led us to start Ray, which is the open source project that we started to make it easy to build these scalable Python applications and scalable machine learning applications. And since we started the project, it's been adopted by a tremendous number of companies. Companies like OpenAI, which use Ray to train their large models like ChatGPT, companies like Uber, which run all of their deep learning and classical machine learning on top of Ray, companies like Shopify or Spotify or Instacart or Lyft or Netflix, ByteDance, which use Ray for their machine learning infrastructure. Companies like Ant Group, which makes Alipay, you know, they use Ray across the board for fraud detection, for online learning, for detecting money laundering, you know, for graph processing, stream processing. Companies like Amazon, you know, run Ray at a tremendous scale and just petabytes of data every single day. And so the project has seen just enormous adoption since, over the past few years. And one of the most exciting use cases is really providing the infrastructure for building training, fine tuning, and serving foundation models. So I'll say a little bit about, you know, here are some examples of companies using Ray for foundation models. Cohere trains large language models. OpenAI also trains large language models. You can think about the workloads required there are things like supervised pre-training, also reinforcement learning from human feedback. So this is not only the regular supervised learning, but actually more complex reinforcement learning workloads that take human input about what response to a particular question, you know is better than a certain other response. And incorporating that into the learning. There's open source versions as well, like GPTJ also built on top of Ray as well as projects like Alpa coming out of UC Berkeley. So these are some of the examples of exciting projects in organizations, training and creating these large language models and serving them using Ray. Okay, so what actually is Ray? Well, there are two layers to Ray. At the lowest level, there's the core Ray system. This is essentially low level primitives for building scalable Python applications. Things like taking a Python function or a Python class and executing them in the cluster setting. So Ray core is extremely flexible and you can build arbitrary scalable applications on top of Ray. So on top of Ray, on top of the core system, what really gives Ray a lot of its power is this ecosystem of scalable libraries. So on top of the core system you have libraries, scalable libraries for ingesting and pre-processing data, for training your models, for fine tuning those models, for hyper parameter tuning, for doing batch processing and batch inference, for doing model serving and deployment, right. And a lot of the Ray users, the reason they like Ray is that they want to run multiple workloads. They want to train and serve their models, right. They want to load their data and feed that into training. And Ray provides common infrastructure for all of these different workloads. So this is a little overview of what Ray, the different components of Ray. So why do people choose to go with Ray? I think there are three main reasons. The first is the unified nature. The fact that it is common infrastructure for scaling arbitrary workloads, from data ingest to pre-processing to training to inference and serving, right. This also includes the fact that it's future proof. AI is incredibly fast moving. And so many people, many companies that have built their own machine learning infrastructure and standardized on particular workflows for doing machine learning have found that their workflows are too rigid to enable new capabilities. If they want to do reinforcement learning, if they want to use graph neural networks, they don't have a way of doing that with their standard tooling. And so Ray, being future proof and being flexible and general gives them that ability. Another reason people choose Ray in Anyscale is the scalability. This is really our bread and butter. This is the reason, the whole point of Ray, you know, making it easy to go from your laptop to running on thousands of GPUs, making it easy to scale your development workloads and run them in production, making it easy to scale, you know, training to scale data ingest, pre-processing and so on. So scalability and performance, you know, are critical for doing machine learning and that is something that Ray provides out of the box. And lastly, Ray is an open ecosystem. You can run it anywhere. You can run it on any Cloud provider. Google, you know, Google Cloud, AWS, Asure. You can run it on your Kubernetes cluster. You can run it on your laptop. It's extremely portable. And not only that, it's framework agnostic. You can use Ray to scale arbitrary Python workloads. You can use it to scale and it integrates with libraries like TensorFlow or PyTorch or JAX or XG Boost or Hugging Face or PyTorch Lightning, right, or Scikit-learn or just your own arbitrary Python code. It's open source. And in addition to integrating with the rest of the machine learning ecosystem and these machine learning frameworks, you can use Ray along with all of the other tooling in the machine learning ecosystem. That's things like weights and biases or ML flow, right. Or you know, different data platforms like Databricks, you know, Delta Lake or Snowflake or tools for model monitoring for feature stores, all of these integrate with Ray. And that's, you know, Ray provides that kind of flexibility so that you can integrate it into the rest of your workflow. And then Anyscale is the scalable compute platform that's built on top, you know, that provides Ray. So Anyscale is a managed Ray service that runs in the Cloud. And what Anyscale does is it offers the best way to run Ray. And if you think about what you get with Anyscale, there are fundamentally two things. One is about moving faster, accelerating the time to market. And you get that by having the managed service so that as a developer you don't have to worry about managing infrastructure, you don't have to worry about configuring infrastructure. You also, it provides, you know, optimized developer workflows. Things like easily moving from development to production, things like having the observability tooling, the debug ability to actually easily diagnose what's going wrong in a distributed application. So things like the dashboards and the other other kinds of tooling for collaboration, for monitoring and so on. And then on top of that, so that's the first bucket, developer productivity, moving faster, faster experimentation and iteration. The second reason that people choose Anyscale is superior infrastructure. So this is things like, you know, cost deficiency, being able to easily take advantage of spot instances, being able to get higher GPU utilization, things like faster cluster startup times and auto scaling. Things like just overall better performance and faster scheduling. And so these are the kinds of things that Anyscale provides on top of Ray. It's the managed infrastructure. It's fast, it's like the developer productivity and velocity as well as performance. So this is what I wanted to share about Ray in Anyscale. >> John: Awesome. >> Provide that context. But John, I'm curious what you think. >> I love it. I love the, so first of all, it's a platform because that's the platform architecture right there. So just to clarify, this is an Anyscale platform, not- >> That's right. >> Tools. So you got tools in the platform. Okay, that's key. Love that managed service. Just curious, you mentioned Python multiple times, is that because of PyTorch and TensorFlow or Python's the most friendly with machine learning or it's because it's very common amongst all developers? >> That's a great question. Python is the language that people are using to do machine learning. So it's the natural starting point. Now, of course, Ray is actually designed in a language agnostic way and there are companies out there that use Ray to build scalable Java applications. But for the most part right now we're focused on Python and being the best way to build these scalable Python and machine learning applications. But, of course, down the road there always is that potential. >> So if you're slinging Python code out there and you're watching that, you're watching this video, get on Anyscale bus quickly. Also, I just, while you were giving the presentation, I couldn't help, since you mentioned OpenAI, which by the way, congratulations 'cause they've had great scale, I've noticed in their rapid growth 'cause they were the fastest company to the number of users than anyone in the history of the computer industry, so major successor, OpenAI and ChatGPT, huge fan. I'm not a skeptic at all. I think it's just the beginning, so congratulations. But I actually typed into ChatGPT, what are the top three benefits of Anyscale and came up with scalability, flexibility, and ease of use. Obviously, scalability is what you guys are called. >> That's pretty good. >> So that's what they came up with. So they nailed it. Did you have an inside prompt training, buy it there? Only kidding. (Robert laughs) >> Yeah, we hard coded that one. >> But that's the kind of thing that came up really, really quickly if I asked it to write a sales document, it probably will, but this is the future interface. This is why people are getting excited about the foundational models and the large language models because it's allowing the interface with the user, the consumer, to be more human, more natural. And this is clearly will be in every application in the future. >> Absolutely. This is how people are going to interface with software, how they're going to interface with products in the future. It's not just something, you know, not just a chat bot that you talk to. This is going to be how you get things done, right. How you use your web browser or how you use, you know, how you use Photoshop or how you use other products. Like you're not going to spend hours learning all the APIs and how to use them. You're going to talk to it and tell it what you want it to do. And of course, you know, if it doesn't understand it, it's going to ask clarifying questions. You're going to have a conversation and then it'll figure it out. >> This is going to be one of those things, we're going to look back at this time Robert and saying, "Yeah, from that company, that was the beginning of that wave." And just like AWS and Cloud Computing, the folks who got in early really were in position when say the pandemic came. So getting in early is a good thing and that's what everyone's talking about is getting in early and playing around, maybe replatforming or even picking one or few apps to refactor with some staff and managed services. So people are definitely jumping in. So I have to ask you the ROI cost question. You mentioned some of those, Moore's Law versus what's going on in the industry. When you look at that kind of scale, the first thing that jumps out at people is, "Okay, I love it. Let's go play around." But what's it going to cost me? Am I going to be tied to certain GPUs? What's the landscape look like from an operational standpoint, from the customer? Are they locked in and the benefit was flexibility, are you flexible to handle any Cloud? What is the customers, what are they looking at? Basically, that's my question. What's the customer looking at? >> Cost is super important here and many of the companies, I mean, companies are spending a huge amount on their Cloud computing, on AWS, and on doing AI, right. And I think a lot of the advantage of Anyscale, what we can provide here is not only better performance, but cost efficiency. Because if we can run something faster and more efficiently, it can also use less resources and you can lower your Cloud spending, right. We've seen companies go from, you know, 20% GPU utilization with their current setup and the current tools they're using to running on Anyscale and getting more like 95, you know, 100% GPU utilization. That's something like a five x improvement right there. So depending on the kind of application you're running, you know, it's a significant cost savings. We've seen companies that have, you know, processing petabytes of data every single day with Ray going from, you know, getting order of magnitude cost savings by switching from what they were previously doing to running their application on Ray. And when you have applications that are spending, you know, potentially $100 million a year and getting a 10 X cost savings is just absolutely enormous. So these are some of the kinds of- >> Data infrastructure is super important. Again, if the customer, if you're a prospect to this and thinking about going in here, just like the Cloud, you got infrastructure, you got the platform, you got SaaS, same kind of thing's going to go on in AI. So I want to get into that, you know, ROI discussion and some of the impact with your customers that are leveraging the platform. But first I hear you got a demo. >> Robert: Yeah, so let me show you, let me give you a quick run through here. So what I have open here is the Anyscale UI. I've started a little Anyscale Workspace. So Workspaces are the Anyscale concept for interactive developments, right. So here, imagine I'm just, you want to have a familiar experience like you're developing on your laptop. And here I have a terminal. It's not on my laptop. It's actually in the cloud running on Anyscale. And I'm just going to kick this off. This is going to train a large language model, so OPT. And it's doing this on 32 GPUs. We've got a cluster here with a bunch of CPU cores, bunch of memory. And as that's running, and by the way, if I wanted to run this on instead of 32 GPUs, 64, 128, this is just a one line change when I launch the Workspace. And what I can do is I can pull up VS code, right. Remember this is the interactive development experience. I can look at the actual code. Here it's using Ray train to train the torch model. We've got the training loop and we're saying that each worker gets access to one GPU and four CPU cores. And, of course, as I make the model larger, this is using deep speed, as I make the model larger, I could increase the number of GPUs that each worker gets access to, right. And how that is distributed across the cluster. And if I wanted to run on CPUs instead of GPUs or a different, you know, accelerator type, again, this is just a one line change. And here we're using Ray train to train the models, just taking my vanilla PyTorch model using Hugging Face and then scaling that across a bunch of GPUs. And, of course, if I want to look at the dashboard, I can go to the Ray dashboard. There are a bunch of different visualizations I can look at. I can look at the GPU utilization. I can look at, you know, the CPU utilization here where I think we're currently loading the model and running that actual application to start the training. And some of the things that are really convenient here about Anyscale, both I can get that interactive development experience with VS code. You know, I can look at the dashboards. I can monitor what's going on. It feels, I have a terminal, it feels like my laptop, but it's actually running on a large cluster. And I can, with however many GPUs or other resources that I want. And so it's really trying to combine the best of having the familiar experience of programming on your laptop, but with the benefits, you know, being able to take advantage of all the resources in the Cloud to scale. And it's like when, you know, you're talking about cost efficiency. One of the biggest reasons that people waste money, one of the silly reasons for wasting money is just forgetting to turn off your GPUs. And what you can do here is, of course, things will auto terminate if they're idle. But imagine you go to sleep, I have this big cluster. You can turn it off, shut off the cluster, come back tomorrow, restart the Workspace, and you know, your big cluster is back up and all of your code changes are still there. All of your local file edits. It's like you just closed your laptop and came back and opened it up again. And so this is the kind of experience we want to provide for our users. So that's what I wanted to share with you. >> Well, I think that whole, couple of things, lines of code change, single line of code change, that's game changing. And then the cost thing, I mean human error is a big deal. People pass out at their computer. They've been coding all night or they just forget about it. I mean, and then it's just like leaving the lights on or your water running in your house. It's just, at the scale that it is, the numbers will add up. That's a huge deal. So I think, you know, compute back in the old days, there's no compute. Okay, it's just compute sitting there idle. But you know, data cranking the models is doing, that's a big point. >> Another thing I want to add there about cost efficiency is that we make it really easy to use, if you're running on Anyscale, to use spot instances and these preemptable instances that can just be significantly cheaper than the on-demand instances. And so when we see our customers go from what they're doing before to using Anyscale and they go from not using these spot instances 'cause they don't have the infrastructure around it, the fault tolerance to handle the preemption and things like that, to being able to just check a box and use spot instances and save a bunch of money. >> You know, this was my whole, my feature article at Reinvent last year when I met with Adam Selipsky, this next gen Cloud is here. I mean, it's not auto scale, it's infrastructure scale. It's agility. It's flexibility. I think this is where the world needs to go. Almost what DevOps did for Cloud and what you were showing me that demo had this whole SRE vibe. And remember Google had site reliability engines to manage all those servers. This is kind of like an SRE vibe for data at scale. I mean, a similar kind of order of magnitude. I mean, I might be a little bit off base there, but how would you explain it? >> It's a nice analogy. I mean, what we are trying to do here is get to the point where developers don't think about infrastructure. Where developers only think about their application logic. And where businesses can do AI, can succeed with AI, and build these scalable applications, but they don't have to build, you know, an infrastructure team. They don't have to develop that expertise. They don't have to invest years in building their internal machine learning infrastructure. They can just focus on the Python code, on their application logic, and run the stuff out of the box. >> Awesome. Well, I appreciate the time. Before we wrap up here, give a plug for the company. I know you got a couple websites. Again, go, Ray's got its own website. You got Anyscale. You got an event coming up. Give a plug for the company looking to hire. Put a plug in for the company. >> Yeah, absolutely. Thank you. So first of all, you know, we think AI is really going to transform every industry and the opportunity is there, right. We can be the infrastructure that enables all of that to happen, that makes it easy for companies to succeed with AI, and get value out of AI. Now we have, if you're interested in learning more about Ray, Ray has been emerging as the standard way to build scalable applications. Our adoption has been exploding. I mentioned companies like OpenAI using Ray to train their models. But really across the board companies like Netflix and Cruise and Instacart and Lyft and Uber, you know, just among tech companies. It's across every industry. You know, gaming companies, agriculture, you know, farming, robotics, drug discovery, you know, FinTech, we see it across the board. And all of these companies can get value out of AI, can really use AI to improve their businesses. So if you're interested in learning more about Ray and Anyscale, we have our Ray Summit coming up in September. This is going to highlight a lot of the most impressive use cases and stories across the industry. And if your business, if you want to use LLMs, you want to train these LLMs, these large language models, you want to fine tune them with your data, you want to deploy them, serve them, and build applications and products around them, give us a call, talk to us. You know, we can really take the infrastructure piece, you know, off the critical path and make that easy for you. So that's what I would say. And, you know, like you mentioned, we're hiring across the board, you know, engineering, product, go-to-market, and it's an exciting time. >> Robert Nishihara, co-founder and CEO of Anyscale, congratulations on a great company you've built and continuing to iterate on and you got growth ahead of you, you got a tailwind. I mean, the AI wave is here. I think OpenAI and ChatGPT, a customer of yours, have really opened up the mainstream visibility into this new generation of applications, user interface, roll of data, large scale, how to make that programmable so we're going to need that infrastructure. So thanks for coming on this season three, episode one of the ongoing series of the hot startups. In this case, this episode is the top startups building foundational model infrastructure for AI and ML. I'm John Furrier, your host. Thanks for watching. (upbeat music)
SUMMARY :
episode one of the ongoing and you guys really had and other resources in the Cloud. and particular the large language and what you want to achieve. and the Cloud did that with data centers. the point, and you know, if you don't mind explaining and managing the infrastructure and you guys are positioning is that the amount of compute needed to do But John, I'm curious what you think. because that's the platform So you got tools in the platform. and being the best way to of the computer industry, Did you have an inside prompt and the large language models and tell it what you want it to do. So I have to ask you and you can lower your So I want to get into that, you know, and you know, your big cluster is back up So I think, you know, the on-demand instances. and what you were showing me that demo and run the stuff out of the box. I know you got a couple websites. and the opportunity is there, right. and you got growth ahead
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Robert Nishihara, Anyscale | CUBE Conversation
(upbeat instrumental) >> Hello and welcome to this CUBE conversation. I'm John Furrier, host of theCUBE, here in Palo Alto, California. Got a great conversation with Robert Nishihara who's the co-founder and CEO of Anyscale. Robert, great to have you on this CUBE conversation. It's great to see you. We did your first Ray Summit a couple years ago and congratulations on your venture. Great to have you on. >> Thank you. Thanks for inviting me. >> So you're first time CEO out of Berkeley in Data. You got the Databricks is coming out of there. You got a bunch of activity coming from Berkeley. It's like a, it really is kind of like where a lot of innovations going on data. Anyscale has been one of those startups that has risen out of that scene. Right? You look at the success of what the Data lakes are now. Now you've got the generative AI. This has been a really interesting innovation market. This new wave is coming. Tell us what's going on with Anyscale right now, as you guys are gearing up and getting some growth. What's happening with the company? >> Yeah, well one of the most exciting things that's been happening in computing recently, is the rise of AI and the excitement about AI, and the potential for AI to really transform every industry. Now of course, one of the of the biggest challenges to actually making that happen is that doing AI, that AI is incredibly computationally intensive, right? To actually succeed with AI to actually get value out of AI. You're typically not just running it on your laptop, you're often running it and scaling it across thousands of machines, or hundreds of machines or GPUs, and to, so organizations and companies and businesses that do AI often end up building a large infrastructure team to manage the distributed systems, the computing to actually scale these applications. And that's a, that's a, a huge software engineering lift, right? And so, one of the goals for Anyscale is really to make that easy. To get to the point where, developers and teams and companies can succeed with AI. Can build these scalable AI applications, without really you know, without a huge investment in infrastructure with a lot of, without a lot of expertise in infrastructure, where really all they need to know is how to program on their laptop, how to program in Python. And if you have that, then that's really all you need to succeed with AI. So that's what we've been focused on. We're building Ray, which is an open source project that's been starting to get adopted by tons of companies, to actually train these models, to deploy these models, to do inference with these models, you know, to ingest and pre-process their data. And our goals, you know, here with the company are really to make Ray successful. To grow the Ray community, and then to build a great product around it and simplify the development and deployment, and productionization of machine learning for, for all these businesses. >> It's a great trend. Everyone wants developer productivity seeing that, clearly right now. And plus, developers are voting literally on what standards become. As you look at how the market is open source driven, a lot of that I love the model, love the Ray project love the, love the Anyscale value proposition. How big are you guys now, and how is that value proposition of Ray and Anyscale and foundational models coming together? Because it seems like you guys are in a perfect storm situation where you guys could get a real tailwind and draft off the the mega trend that everyone's getting excited. The new toy is ChatGPT. So you got to look at that and say, hey, I mean, come on, you guys did all the heavy lifting. >> Absolutely. >> You know how many people you are, and what's the what's the proposition for you guys these days? >> You know our company's about a hundred people, that a bit larger than that. Ray's been going really quickly. It's been, you know, companies using, like OpenAI uses Ray to train their models, like ChatGPT. Companies like Uber run all their deep learning you know, and classical machine learning on top of Ray. Companies like Shopify, Spotify, Netflix, Cruise, Lyft, Instacart, you know, Bike Dance. A lot of these companies are investing heavily in Ray for their machine learning infrastructure. And I think it's gotten to the point where, if you're one of these, you know type of businesses, and you're looking to revamp your machine learning infrastructure. If you're looking to enable new capabilities, you know make your teams more productive, increase, speed up the experimentation cycle, you know make it more performance, like build, you know, run applications that are more scalable, run them faster, run them in a more cost efficient way. All of these types of companies are at least evaluating Ray and Ray is an increasingly common choice there. I think if they're not using Ray, if many of these companies that end up not using Ray, they often end up building their own infrastructure. So Ray has been, the growth there has been incredibly exciting over the, you know we had our first in-person Ray Summit just back in August, and planning the next one for, for coming September. And so when you asked about the value proposition, I think there's there's really two main things, when people choose to go with Ray and Anyscale. One reason is about moving faster, right? It's about developer productivity, it's about speeding up the experimentation cycle, easily getting their models in production. You know, we hear many companies say that they, you know they, once they prototype a model, once they develop a model, it's another eight weeks, or 12 weeks to actually get that model in production. And that's a reason they talk to us. We hear companies say that, you know they've been training their models and, and doing inference on a single machine, and they've been sort of scaling vertically, like using bigger and bigger machines. But they, you know, you can only do that for so long, and at some point you need to go beyond a single machine and that's when they start talking to us. Right? So one of the main value propositions is around moving faster. I think probably the phrase I hear the most is, companies saying that they don't want their machine learning people to have to spend all their time configuring infrastructure. All this is about productivity. >> Yeah. >> The other. >> It's the big brains in the company. That are being used to do remedial tasks that should be automated right? I mean that's. >> Yeah, and I mean, it's hard stuff, right? It's also not these people's area of expertise, and or where they're adding the most value. So all of this is around developer productivity, moving faster, getting to market faster. The other big value prop and the reason people choose Ray and choose Anyscale, is around just providing superior infrastructure. This is really, can we scale more? You know, can we run it faster, right? Can we run it in a more cost effective way? We hear people saying that they're not getting good GPU utilization with the existing tools they're using, or they can't scale beyond a certain point, or you know they don't have a way to efficiently use spot instances to save costs, right? Or their clusters, you know can't auto scale up and down fast enough, right? These are all the kinds of things that Ray and Anyscale, where Ray and Anyscale add value and solve these kinds of problems. >> You know, you bring up great points. Auto scaling concept, early days, it was easy getting more compute. Now it's complicated. They're built into more integrated apps in the cloud. And you mentioned those companies that you're working with, that's impressive. Those are like the big hardcore, I call them hardcore. They have a good technical teams. And as the wave starts to move from these companies that were hyper scaling up all the time, the mainstream are just developers, right? So you need an interface in, so I see the dots connecting with you guys and I want to get your reaction. Is that how you see it? That you got the alphas out there kind of kicking butt, building their own stuff, alpha developers and infrastructure. But mainstream just wants programmability. They want that heavy lifting taken care of for them. Is that kind of how you guys see it? I mean, take us through that. Because to get crossover to be democratized, the automation's got to be there. And for developer productivity to be in, it's got to be coding and programmability. >> That's right. Ultimately for AI to really be successful, and really you know, transform every industry in the way we think it has the potential to. It has to be easier to use, right? And that is, and being easier to use, there's many dimensions to that. But an important one is that as a developer to do AI, you shouldn't have to be an expert in distributed systems. You shouldn't have to be an expert in infrastructure. If you do have to be, that's going to really limit the number of people who can do this, right? And I think there are so many, all of the companies we talk to, they don't want to be in the business of building and managing infrastructure. It's not that they can't do it. But it's going to slow them down, right? They want to allocate their time and their energy toward building their product, right? To building a better product, getting their product to market faster. And if we can take the infrastructure work off of the critical path for them, that's going to speed them up, it's going to simplify their lives. And I think that is critical for really enabling all of these companies to succeed with AI. >> Talk about the customers you guys are talking to right now, and how that translates over. Because I think you hit a good thread there. Data infrastructure is critical. Managed services are coming online, open sources continuing to grow. You have these people building their own, and then if they abandon it or don't scale it properly, there's kind of consequences. 'Cause it's a system you mentioned, it's a distributed system architecture. It's not as easy as standing up a monolithic app these days. So when you guys go to the marketplace and talk to customers, put the customers in buckets. So you got the ones that are kind of leaning in, that are pretty peaked, probably working with you now, open source. And then what's the customer profile look like as you go mainstream? Are they looking to manage service, looking for more architectural system, architecture approach? What's the, Anyscale progression? How do you engage with your customers? What are they telling you? >> Yeah, so many of these companies, yes, they're looking for managed infrastructure 'cause they want to move faster, right? Now the kind of these profiles of these different customers, they're three main workloads that companies run on Anyscale, run with Ray. It's training related workloads, and it is serving and deployment related workloads, like actually deploying your models, and it's batch processing, batch inference related workloads. Like imagine you want to do computer vision on tons and tons of, of images or videos, or you want to do natural language processing on millions of documents or audio, or speech or things like that, right? So the, I would say the, there's a pretty large variety of use cases, but the most common you know, we see tons of people working with computer vision data, you know, computer vision problems, natural language processing problems. And it's across many different industries. We work with companies doing drug discovery, companies doing you know, gaming or e-commerce, right? Companies doing robotics or agriculture. So there's a huge variety of the types of industries that can benefit from AI, and can really get a lot of value out of AI. And, but the, but the problems are the same problems that they all want to solve. It's like how do you make your team move faster, you know succeed with AI, be more productive, speed up the experimentation, and also how do you do this in a more performant way, in a faster, cheaper, in a more cost efficient, more scalable way. >> It's almost like the cloud game is coming back to AI and these foundational models, because I was just on a podcast, we recorded our weekly podcast, and I was just riffing with Dave Vellante, my co-host on this, were like, hey, in the early days of Amazon, if you want to build an app, you just, you have to build a data center, and then you go to now you go to the cloud, cloud's easier, pay a little money, penny's on the dollar, you get your app up and running. Cloud computing is born. With foundation models in generative AI. The old model was hard, heavy lifting, expensive, build out, before you get to do anything, as you mentioned time. So I got to think that you're pretty much in a good position with this foundational model trend in generative AI because I just looked at the foundation map, foundation models, map of the ecosystem. You're starting to see layers of, you got the tooling, you got platform, you got cloud. It's filling out really quickly. So why is Anyscale important to this new trend? How do you talk to people when they ask you, you know what does ChatGPT mean for Anyscale? And how does the financial foundational model growth, fit into your plan? >> Well, foundational models are hugely important for the industry broadly. Because you're going to have these really powerful models that are trained that you know, have been trained on tremendous amounts of data. tremendous amounts of computes, and that are useful out of the box, right? That people can start to use, and query, and get value out of, without necessarily training these huge models themselves. Now Ray fits in and Anyscale fit in, in a number of places. First of all, they're useful for creating these foundation models. Companies like OpenAI, you know, use Ray for this purpose. Companies like Cohere use Ray for these purposes. You know, IBM. If you look at, there's of course also open source versions like GPTJ, you know, created using Ray. So a lot of these large language models, large foundation models benefit from training on top of Ray. And, but of course for every company training and creating these huge foundation models, you're going to have many more that are fine tuning these models with their own data. That are deploying and serving these models for their own applications, that are building other application and business logic around these models. And that's where Ray also really shines, because Ray you know, is, can provide common infrastructure for all of these workloads. The training, the fine tuning, the serving, the data ingest and pre-processing, right? The hyper parameter tuning, the and and so on. And so where the reason Ray and Anyscale are important here, is that, again, foundation models are large, foundation models are compute intensive, doing you know, using both creating and using these foundation models requires tremendous amounts of compute. And there there's a big infrastructure lift to make that happen. So either you are using Ray and Anyscale to do this, or you are building the infrastructure and managing the infrastructure yourself. Which you can do, but it's, it's hard. >> Good luck with that. I always say good luck with that. I mean, I think if you really need to do, build that hardened foundation, you got to go all the way. And I think this, this idea of composability is interesting. How is Ray working with OpenAI for instance? Take, take us through that. Because I think you're going to see a lot of people talking about, okay I got trained models, but I'm going to have not one, I'm going to have many. There's big debate that OpenAI is going to be the mother of all LLMs, but now, but really people are also saying that to be many more, either purpose-built or specific. The fusion and these things come together there's like a blending of data, and that seems to be a value proposition. How does Ray help these guys get their models up? Can you take, take us through what Ray's doing for say OpenAI and others, and how do you see the models interacting with each other? >> Yeah, great question. So where, where OpenAI uses Ray right now, is for the training workloads. Training both to create ChatGPT and models like that. There's both a supervised learning component, where you're pre-training this model on doing supervised pre-training with example data. There's also a reinforcement learning component, where you are fine-tuning the model and continuing to train the model, but based on human feedback, based on input from humans saying that, you know this response to this question is better than this other response to this question, right? And so Ray provides the infrastructure for scaling the training across many, many GPUs, many many machines, and really running that in an efficient you know, performance fault tolerant way, right? And so, you know, open, this is not the first version of OpenAI's infrastructure, right? They've gone through iterations where they did start with building the infrastructure themselves. They were using tools like MPI. But at some point, you know, given the complexity, given the scale of what they're trying to do, you hit a wall with MPI and that's going to happen with a lot of other companies in this space. And at that point you don't have many other options other than to use Ray or to build your own infrastructure. >> That's awesome. And then your vision on this data interaction, because the old days monolithic models were very rigid. You couldn't really interface with them. But we're kind of seeing this future of data fusion, data interaction, data blending at large scale. What's your vision? How do you, what's your vision of where this goes? Because if this goes the way people think. You can have this data chemistry kind of thing going on where people are integrating all kinds of data with each other at large scale. So you need infrastructure, intelligence, reasoning, a lot of code. Is this something that you see? What's your vision in all this? Take us through. >> AI is going to be used everywhere right? It's, we see this as a technology that's going to be ubiquitous, and is going to transform every business. I mean, imagine you make a product, maybe you were making a tool like Photoshop or, or whatever the, you know, tool is. The way that people are going to use your tool, is not by investing, you know, hundreds of hours into learning all of the different, you know specific buttons they need to press and workflows they need to go through it. They're going to talk to it, right? They're going to say, ask it to do the thing they want it to do right? And it's going to do it. And if it, if it doesn't know what it's want, what it's, what's being asked of it. It's going to ask clarifying questions, right? And then you're going to clarify, and you're going to have a conversation. And this is going to make many many many kinds of tools and technology and products easier to use, and lower the barrier to entry. And so, and this, you know, many companies fit into this category of trying to build products that, and trying to make them easier to use, this is just one kind of way it can, one kind of way that AI will will be used. But I think it's, it's something that's pretty ubiquitous. >> Yeah. It'll be efficient, it'll be efficiency up and down the stack, and will change the productivity equation completely. You just highlighted one, I don't want to fill out forms, just stand up my environment for me. And then start coding away. Okay well this is great stuff. Final word for the folks out there watching, obviously new kind of skill set for hiring. You guys got engineers, give a plug for the company, for Anyscale. What are you looking for? What are you guys working on? Give a, take the last minute to put a plug in for the company. >> Yeah well if you're interested in AI and if you think AI is really going to be transformative, and really be useful for all these different industries. We are trying to provide the infrastructure to enable that to happen, right? So I think there's the potential here, to really solve an important problem, to get to the point where developers don't need to think about infrastructure, don't need to think about distributed systems. All they think about is their application logic, and what they want their application to do. And I think if we can achieve that, you know we can be the foundation or the platform that enables all of these other companies to succeed with AI. So that's where we're going. I think something like this has to happen if AI is going to achieve its potential, we're looking for, we're hiring across the board, you know, great engineers, on the go-to-market side, product managers, you know people who want to really, you know, make this happen. >> Awesome well congratulations. I know you got some good funding behind you. You're in a good spot. I think this is happening. I think generative AI and foundation models is going to be the next big inflection point, as big as the pc inter-networking, internet and smartphones. This is a whole nother application framework, a whole nother set of things. So this is the ground floor. Robert, you're, you and your team are right there. Well done. >> Thank you so much. >> All right. Thanks for coming on this CUBE conversation. I'm John Furrier with theCUBE. Breaking down a conversation around AI and scaling up in this new next major inflection point. This next wave is foundational models, generative AI. And thanks to ChatGPT, the whole world's now knowing about it. So it really is changing the game and Anyscale is right there, one of the hot startups, that is in good position to ride this next wave. Thanks for watching. (upbeat instrumental)
SUMMARY :
Robert, great to have you Thanks for inviting me. as you guys are gearing up and the potential for AI to a lot of that I love the and at some point you need It's the big brains in the company. and the reason people the automation's got to be there. and really you know, and talk to customers, put but the most common you know, and then you go to now that are trained that you know, and that seems to be a value proposition. And at that point you don't So you need infrastructure, and lower the barrier to entry. What are you guys working on? and if you think AI is really is going to be the next And thanks to ChatGPT,
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Breaking Analysis: ChatGPT Won't Give OpenAI First Mover Advantage
>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> OpenAI The company, and ChatGPT have taken the world by storm. Microsoft reportedly is investing an additional 10 billion dollars into the company. But in our view, while the hype around ChatGPT is justified, we don't believe OpenAI will lock up the market with its first mover advantage. Rather, we believe that success in this market will be directly proportional to the quality and quantity of data that a technology company has at its disposal, and the compute power that it could deploy to run its system. Hello and welcome to this week's Wikibon CUBE insights, powered by ETR. In this Breaking Analysis, we unpack the excitement around ChatGPT, and debate the premise that the company's early entry into the space may not confer winner take all advantage to OpenAI. And to do so, we welcome CUBE collaborator, alum, Sarbjeet Johal, (chuckles) and John Furrier, co-host of the Cube. Great to see you Sarbjeet, John. Really appreciate you guys coming to the program. >> Great to be on. >> Okay, so what is ChatGPT? Well, actually we asked ChatGPT, what is ChatGPT? So here's what it said. ChatGPT is a state-of-the-art language model developed by OpenAI that can generate human-like text. It could be fine tuned for a variety of language tasks, such as conversation, summarization, and language translation. So I asked it, give it to me in 50 words or less. How did it do? Anything to add? >> Yeah, think it did good. It's large language model, like previous models, but it started applying the transformers sort of mechanism to focus on what prompt you have given it to itself. And then also the what answer it gave you in the first, sort of, one sentence or two sentences, and then introspect on itself, like what I have already said to you. And so just work on that. So it it's self sort of focus if you will. It does, the transformers help the large language models to do that. >> So to your point, it's a large language model, and GPT stands for generative pre-trained transformer. >> And if you put the definition back up there again, if you put it back up on the screen, let's see it back up. Okay, it actually missed the large, word large. So one of the problems with ChatGPT, it's not always accurate. It's actually a large language model, and it says state of the art language model. And if you look at Google, Google has dominated AI for many times and they're well known as being the best at this. And apparently Google has their own large language model, LLM, in play and have been holding it back to release because of backlash on the accuracy. Like just in that example you showed is a great point. They got almost right, but they missed the key word. >> You know what's funny about that John, is I had previously asked it in my prompt to give me it in less than a hundred words, and it was too long, I said I was too long for Breaking Analysis, and there it went into the fact that it's a large language model. So it largely, it gave me a really different answer the, for both times. So, but it's still pretty amazing for those of you who haven't played with it yet. And one of the best examples that I saw was Ben Charrington from This Week In ML AI podcast. And I stumbled on this thanks to Brian Gracely, who was listening to one of his Cloudcasts. Basically what Ben did is he took, he prompted ChatGPT to interview ChatGPT, and he simply gave the system the prompts, and then he ran the questions and answers into this avatar builder and sped it up 2X so it didn't sound like a machine. And voila, it was amazing. So John is ChatGPT going to take over as a cube host? >> Well, I was thinking, we get the questions in advance sometimes from PR people. We should actually just plug it in ChatGPT, add it to our notes, and saying, "Is this good enough for you? Let's ask the real question." So I think, you know, I think there's a lot of heavy lifting that gets done. I think the ChatGPT is a phenomenal revolution. I think it highlights the use case. Like that example we showed earlier. It gets most of it right. So it's directionally correct and it feels like it's an answer, but it's not a hundred percent accurate. And I think that's where people are seeing value in it. Writing marketing, copy, brainstorming, guest list, gift list for somebody. Write me some lyrics to a song. Give me a thesis about healthcare policy in the United States. It'll do a bang up job, and then you got to go in and you can massage it. So we're going to do three quarters of the work. That's why plagiarism and schools are kind of freaking out. And that's why Microsoft put 10 billion in, because why wouldn't this be a feature of Word, or the OS to help it do stuff on behalf of the user. So linguistically it's a beautiful thing. You can input a string and get a good answer. It's not a search result. >> And we're going to get your take on on Microsoft and, but it kind of levels the playing- but ChatGPT writes better than I do, Sarbjeet, and I know you have some good examples too. You mentioned the Reed Hastings example. >> Yeah, I was listening to Reed Hastings fireside chat with ChatGPT, and the answers were coming as sort of voice, in the voice format. And it was amazing what, he was having very sort of philosophy kind of talk with the ChatGPT, the longer sentences, like he was going on, like, just like we are talking, he was talking for like almost two minutes and then ChatGPT was answering. It was not one sentence question, and then a lot of answers from ChatGPT and yeah, you're right. I, this is our ability. I've been thinking deep about this since yesterday, we talked about, like, we want to do this segment. The data is fed into the data model. It can be the current data as well, but I think that, like, models like ChatGPT, other companies will have those too. They can, they're democratizing the intelligence, but they're not creating intelligence yet, definitely yet I can say that. They will give you all the finite answers. Like, okay, how do you do this for loop in Java, versus, you know, C sharp, and as a programmer you can do that, in, but they can't tell you that, how to write a new algorithm or write a new search algorithm for you. They cannot create a secretive code for you to- >> Not yet. >> Have competitive advantage. >> Not yet, not yet. >> but you- >> Can Google do that today? >> No one really can. The reasoning side of the data is, we talked about at our Supercloud event, with Zhamak Dehghani who's was CEO of, now of Nextdata. This next wave of data intelligence is going to come from entrepreneurs that are probably cross discipline, computer science and some other discipline. But they're going to be new things, for example, data, metadata, and data. It's hard to do reasoning like a human being, so that needs more data to train itself. So I think the first gen of this training module for the large language model they have is a corpus of text. Lot of that's why blog posts are, but the facts are wrong and sometimes out of context, because that contextual reasoning takes time, it takes intelligence. So machines need to become intelligent, and so therefore they need to be trained. So you're going to start to see, I think, a lot of acceleration on training the data sets. And again, it's only as good as the data you can get. And again, proprietary data sets will be a huge winner. Anyone who's got a large corpus of content, proprietary content like theCUBE or SiliconANGLE as a publisher will benefit from this. Large FinTech companies, anyone with large proprietary data will probably be a big winner on this generative AI wave, because it just, it will eat that up, and turn that back into something better. So I think there's going to be a lot of interesting things to look at here. And certainly productivity's going to be off the charts for vanilla and the internet is going to get swarmed with vanilla content. So if you're in the content business, and you're an original content producer of any kind, you're going to be not vanilla, so you're going to be better. So I think there's so much at play Dave (indistinct). >> I think the playing field has been risen, so we- >> Risen and leveled? >> Yeah, and leveled to certain extent. So it's now like that few people as consumers, as consumers of AI, we will have a advantage and others cannot have that advantage. So it will be democratized. That's, I'm sure about that. But if you take the example of calculator, when the calculator came in, and a lot of people are, "Oh, people can't do math anymore because calculator is there." right? So it's a similar sort of moment, just like a calculator for the next level. But, again- >> I see it more like open source, Sarbjeet, because like if you think about what ChatGPT's doing, you do a query and it comes from somewhere the value of a post from ChatGPT is just a reuse of AI. The original content accent will be come from a human. So if I lay out a paragraph from ChatGPT, did some heavy lifting on some facts, I check the facts, save me about maybe- >> Yeah, it's productive. >> An hour writing, and then I write a killer two, three sentences of, like, sharp original thinking or critical analysis. I then took that body of work, open source content, and then laid something on top of it. >> And Sarbjeet's example is a good one, because like if the calculator kids don't do math as well anymore, the slide rule, remember we had slide rules as kids, remember we first started using Waze, you know, we were this minority and you had an advantage over other drivers. Now Waze is like, you know, social traffic, you know, navigation, everybody had, you know- >> All the back roads are crowded. >> They're car crowded. (group laughs) Exactly. All right, let's, let's move on. What about this notion that futurist Ray Amara put forth and really Amara's Law that we're showing here, it's, the law is we, you know, "We tend to overestimate the effect of technology in the short run and underestimate it in the long run." Is that the case, do you think, with ChatGPT? What do you think Sarbjeet? >> I think that's true actually. There's a lot of, >> We don't debate this. >> There's a lot of awe, like when people see the results from ChatGPT, they say what, what the heck? Like, it can do this? But then if you use it more and more and more, and I ask the set of similar question, not the same question, and it gives you like same answer. It's like reading from the same bucket of text in, the interior read (indistinct) where the ChatGPT, you will see that in some couple of segments. It's very, it sounds so boring that the ChatGPT is coming out the same two sentences every time. So it is kind of good, but it's not as good as people think it is right now. But we will have, go through this, you know, hype sort of cycle and get realistic with it. And then in the long term, I think it's a great thing in the short term, it's not something which will (indistinct) >> What's your counter point? You're saying it's not. >> I, no I think the question was, it's hyped up in the short term and not it's underestimated long term. That's what I think what he said, quote. >> Yes, yeah. That's what he said. >> Okay, I think that's wrong with this, because this is a unique, ChatGPT is a unique kind of impact and it's very generational. People have been comparing it, I have been comparing to the internet, like the web, web browser Mosaic and Netscape, right, Navigator. I mean, I clearly still remember the days seeing Navigator for the first time, wow. And there weren't not many sites you could go to, everyone typed in, you know, cars.com, you know. >> That (indistinct) wasn't that overestimated, the overhyped at the beginning and underestimated. >> No, it was, it was underestimated long run, people thought. >> But that Amara's law. >> That's what is. >> No, they said overestimated? >> Overestimated near term underestimated- overhyped near term, underestimated long term. I got, right I mean? >> Well, I, yeah okay, so I would then agree, okay then- >> We were off the charts about the internet in the early days, and it actually exceeded our expectations. >> Well there were people who were, like, poo-pooing it early on. So when the browser came out, people were like, "Oh, the web's a toy for kids." I mean, in 1995 the web was a joke, right? So '96, you had online populations growing, so you had structural changes going on around the browser, internet population. And then that replaced other things, direct mail, other business activities that were once analog then went to the web, kind of read only as you, as we always talk about. So I think that's a moment where the hype long term, the smart money, and the smart industry experts all get the long term. And in this case, there's more poo-pooing in the short term. "Ah, it's not a big deal, it's just AI." I've heard many people poo-pooing ChatGPT, and a lot of smart people saying, "No this is next gen, this is different and it's only going to get better." So I think people are estimating a big long game on this one. >> So you're saying it's bifurcated. There's those who say- >> Yes. >> Okay, all right, let's get to the heart of the premise, and possibly the debate for today's episode. Will OpenAI's early entry into the market confer sustainable competitive advantage for the company. And if you look at the history of tech, the technology industry, it's kind of littered with first mover failures. Altair, IBM, Tandy, Commodore, they and Apple even, they were really early in the PC game. They took a backseat to Dell who came in the scene years later with a better business model. Netscape, you were just talking about, was all the rage in Silicon Valley, with the first browser, drove up all the housing prices out here. AltaVista was the first search engine to really, you know, index full text. >> Owned by Dell, I mean DEC. >> Owned by Digital. >> Yeah, Digital Equipment >> Compaq bought it. And of course as an aside, Digital, they wanted to showcase their hardware, right? Their super computer stuff. And then so Friendster and MySpace, they came before Facebook. The iPhone certainly wasn't the first mobile device. So lots of failed examples, but there are some recent successes like AWS and cloud. >> You could say smartphone. So I mean. >> Well I know, and you can, we can parse this so we'll debate it. Now Twitter, you could argue, had first mover advantage. You kind of gave me that one John. Bitcoin and crypto clearly had first mover advantage, and sustaining that. Guys, will OpenAI make it to the list on the right with ChatGPT, what do you think? >> I think categorically as a company, it probably won't, but as a category, I think what they're doing will, so OpenAI as a company, they get funding, there's power dynamics involved. Microsoft put a billion dollars in early on, then they just pony it up. Now they're reporting 10 billion more. So, like, if the browsers, Microsoft had competitive advantage over Netscape, and used monopoly power, and convicted by the Department of Justice for killing Netscape with their monopoly, Netscape should have had won that battle, but Microsoft killed it. In this case, Microsoft's not killing it, they're buying into it. So I think the embrace extend Microsoft power here makes OpenAI vulnerable for that one vendor solution. So the AI as a company might not make the list, but the category of what this is, large language model AI, is probably will be on the right hand side. >> Okay, we're going to come back to the government intervention and maybe do some comparisons, but what are your thoughts on this premise here? That, it will basically set- put forth the premise that it, that ChatGPT, its early entry into the market will not confer competitive advantage to >> For OpenAI. >> To Open- Yeah, do you agree with that? >> I agree with that actually. It, because Google has been at it, and they have been holding back, as John said because of the scrutiny from the Fed, right, so- >> And privacy too. >> And the privacy and the accuracy as well. But I think Sam Altman and the company on those guys, right? They have put this in a hasty way out there, you know, because it makes mistakes, and there are a lot of questions around the, sort of, where the content is coming from. You saw that as your example, it just stole the content, and without your permission, you know? >> Yeah. So as quick this aside- >> And it codes on people's behalf and the, those codes are wrong. So there's a lot of, sort of, false information it's putting out there. So it's a very vulnerable thing to do what Sam Altman- >> So even though it'll get better, others will compete. >> So look, just side note, a term which Reid Hoffman used a little bit. Like he said, it's experimental launch, like, you know, it's- >> It's pretty damn good. >> It is clever because according to Sam- >> It's more than clever. It's good. >> It's awesome, if you haven't used it. I mean you write- you read what it writes and you go, "This thing writes so well, it writes so much better than you." >> The human emotion drives that too. I think that's a big thing. But- >> I Want to add one more- >> Make your last point. >> Last one. Okay. So, but he's still holding back. He's conducting quite a few interviews. If you want to get the gist of it, there's an interview with StrictlyVC interview from yesterday with Sam Altman. Listen to that one it's an eye opening what they want- where they want to take it. But my last one I want to make it on this point is that Satya Nadella yesterday did an interview with Wall Street Journal. I think he was doing- >> You were not impressed. >> I was not impressed because he was pushing it too much. So Sam Altman's holding back so there's less backlash. >> Got 10 billion reasons to push. >> I think he's almost- >> Microsoft just laid off 10000 people. Hey ChatGPT, find me a job. You know like. (group laughs) >> He's overselling it to an extent that I think it will backfire on Microsoft. And he's over promising a lot of stuff right now, I think. I don't know why he's very jittery about all these things. And he did the same thing during Ignite as well. So he said, "Oh, this AI will write code for you and this and that." Like you called him out- >> The hyperbole- >> During your- >> from Satya Nadella, he's got a lot of hyperbole. (group talks over each other) >> All right, Let's, go ahead. >> Well, can I weigh in on the whole- >> Yeah, sure. >> Microsoft thing on whether OpenAI, here's the take on this. I think it's more like the browser moment to me, because I could relate to that experience with ChatG, personally, emotionally, when I saw that, and I remember vividly- >> You mean that aha moment (indistinct). >> Like this is obviously the future. Anything else in the old world is dead, website's going to be everywhere. It was just instant dot connection for me. And a lot of other smart people who saw this. Lot of people by the way, didn't see it. Someone said the web's a toy. At the company I was worked for at the time, Hewlett Packard, they like, they could have been in, they had invented HTML, and so like all this stuff was, like, they just passed, the web was just being passed over. But at that time, the browser got better, more websites came on board. So the structural advantage there was online web usage was growing, online user population. So that was growing exponentially with the rise of the Netscape browser. So OpenAI could stay on the right side of your list as durable, if they leverage the category that they're creating, can get the scale. And if they can get the scale, just like Twitter, that failed so many times that they still hung around. So it was a product that was always successful, right? So I mean, it should have- >> You're right, it was terrible, we kept coming back. >> The fail whale, but it still grew. So OpenAI has that moment. They could do it if Microsoft doesn't meddle too much with too much power as a vendor. They could be the Netscape Navigator, without the anti-competitive behavior of somebody else. So to me, they have the pole position. So they have an opportunity. So if not, if they don't execute, then there's opportunity. There's not a lot of barriers to entry, vis-a-vis say the CapEx of say a cloud company like AWS. You can't replicate that, Many have tried, but I think you can replicate OpenAI. >> And we're going to talk about that. Okay, so real quick, I want to bring in some ETR data. This isn't an ETR heavy segment, only because this so new, you know, they haven't coverage yet, but they do cover AI. So basically what we're seeing here is a slide on the vertical axis's net score, which is a measure of spending momentum, and in the horizontal axis's is presence in the dataset. Think of it as, like, market presence. And in the insert right there, you can see how the dots are plotted, the two columns. And so, but the key point here that we want to make, there's a bunch of companies on the left, is he like, you know, DataRobot and C3 AI and some others, but the big whales, Google, AWS, Microsoft, are really dominant in this market. So that's really the key takeaway that, can we- >> I notice IBM is way low. >> Yeah, IBM's low, and actually bring that back up and you, but then you see Oracle who actually is injecting. So I guess that's the other point is, you're not necessarily going to go buy AI, and you know, build your own AI, you're going to, it's going to be there and, it, Salesforce is going to embed it into its platform, the SaaS companies, and you're going to purchase AI. You're not necessarily going to build it. But some companies obviously are. >> I mean to quote IBM's general manager Rob Thomas, "You can't have AI with IA." information architecture and David Flynn- >> You can't Have AI without IA >> without, you can't have AI without IA. You can't have, if you have an Information Architecture, you then can power AI. Yesterday David Flynn, with Hammersmith, was on our Supercloud. He was pointing out that the relationship of storage, where you store things, also impacts the data and stressablity, and Zhamak from Nextdata, she was pointing out that same thing. So the data problem factors into all this too, Dave. >> So you got the big cloud and internet giants, they're all poised to go after this opportunity. Microsoft is investing up to 10 billion. Google's code red, which was, you know, the headline in the New York Times. Of course Apple is there and several alternatives in the market today. Guys like Chinchilla, Bloom, and there's a company Jasper and several others, and then Lena Khan looms large and the government's around the world, EU, US, China, all taking notice before the market really is coalesced around a single player. You know, John, you mentioned Netscape, they kind of really, the US government was way late to that game. It was kind of game over. And Netscape, I remember Barksdale was like, "Eh, we're going to be selling software in the enterprise anyway." and then, pshew, the company just dissipated. So, but it looks like the US government, especially with Lena Khan, they're changing the definition of antitrust and what the cause is to go after people, and they're really much more aggressive. It's only what, two years ago that (indistinct). >> Yeah, the problem I have with the federal oversight is this, they're always like late to the game, and they're slow to catch up. So in other words, they're working on stuff that should have been solved a year and a half, two years ago around some of the social networks hiding behind some of the rules around open web back in the days, and I think- >> But they're like 15 years late to that. >> Yeah, and now they got this new thing on top of it. So like, I just worry about them getting their fingers. >> But there's only two years, you know, OpenAI. >> No, but the thing (indistinct). >> No, they're still fighting other battles. But the problem with government is that they're going to label Big Tech as like a evil thing like Pharma, it's like smoke- >> You know Lena Khan wants to kill Big Tech, there's no question. >> So I think Big Tech is getting a very seriously bad rap. And I think anything that the government does that shades darkness on tech, is politically motivated in most cases. You can almost look at everything, and my 80 20 rule is in play here. 80% of the government activity around tech is bullshit, it's politically motivated, and the 20% is probably relevant, but off the mark and not organized. >> Well market forces have always been the determining factor of success. The governments, you know, have been pretty much failed. I mean you look at IBM's antitrust, that, what did that do? The market ultimately beat them. You look at Microsoft back in the day, right? Windows 95 was peaking, the government came in. But you know, like you said, they missed the web, right, and >> so they were hanging on- >> There's nobody in government >> to Windows. >> that actually knows- >> And so, you, I think you're right. It's market forces that are going to determine this. But Sarbjeet, what do you make of Microsoft's big bet here, you weren't impressed with with Nadella. How do you think, where are they going to apply it? Is this going to be a Hail Mary for Bing, or is it going to be applied elsewhere? What do you think. >> They are saying that they will, sort of, weave this into their products, office products, productivity and also to write code as well, developer productivity as well. That's a big play for them. But coming back to your antitrust sort of comments, right? I believe the, your comment was like, oh, fed was late 10 years or 15 years earlier, but now they're two years. But things are moving very fast now as compared to they used to move. >> So two years is like 10 Years. >> Yeah, two years is like 10 years. Just want to make that point. (Dave laughs) This thing is going like wildfire. Any new tech which comes in that I think they're going against distribution channels. Lina Khan has commented time and again that the marketplace model is that she wants to have some grip on. Cloud marketplaces are a kind of monopolistic kind of way. >> I don't, I don't see this, I don't see a Chat AI. >> You told me it's not Bing, you had an interesting comment. >> No, no. First of all, this is great from Microsoft. If you're Microsoft- >> Why? >> Because Microsoft doesn't have the AI chops that Google has, right? Google is got so much core competency on how they run their search, how they run their backends, their cloud, even though they don't get a lot of cloud market share in the enterprise, they got a kick ass cloud cause they needed one. >> Totally. >> They've invented SRE. I mean Google's development and engineering chops are off the scales, right? Amazon's got some good chops, but Google's got like 10 times more chops than AWS in my opinion. Cloud's a whole different story. Microsoft gets AI, they get a playbook, they get a product they can render into, the not only Bing, productivity software, helping people write papers, PowerPoint, also don't forget the cloud AI can super help. We had this conversation on our Supercloud event, where AI's going to do a lot of the heavy lifting around understanding observability and managing service meshes, to managing microservices, to turning on and off applications, and or maybe writing code in real time. So there's a plethora of use cases for Microsoft to deploy this. combined with their R and D budgets, they can then turbocharge more research, build on it. So I think this gives them a car in the game, Google may have pole position with AI, but this puts Microsoft right in the game, and they already have a lot of stuff going on. But this just, I mean everything gets lifted up. Security, cloud, productivity suite, everything. >> What's under the hood at Google, and why aren't they talking about it? I mean they got to be freaked out about this. No? Or do they have kind of a magic bullet? >> I think they have the, they have the chops definitely. Magic bullet, I don't know where they are, as compared to the ChatGPT 3 or 4 models. Like they, but if you look at the online sort of activity and the videos put out there from Google folks, Google technology folks, that's account you should look at if you are looking there, they have put all these distinctions what ChatGPT 3 has used, they have been talking about for a while as well. So it's not like it's a secret thing that you cannot replicate. As you said earlier, like in the beginning of this segment, that anybody who has more data and the capacity to process that data, which Google has both, I think they will win this. >> Obviously living in Palo Alto where the Google founders are, and Google's headquarters next town over we have- >> We're so close to them. We have inside information on some of the thinking and that hasn't been reported by any outlet yet. And that is, is that, from what I'm hearing from my sources, is Google has it, they don't want to release it for many reasons. One is it might screw up their search monopoly, one, two, they're worried about the accuracy, 'cause Google will get sued. 'Cause a lot of people are jamming on this ChatGPT as, "Oh it does everything for me." when it's clearly not a hundred percent accurate all the time. >> So Lina Kahn is looming, and so Google's like be careful. >> Yeah so Google's just like, this is the third, could be a third rail. >> But the first thing you said is a concern. >> Well no. >> The disruptive (indistinct) >> What they will do is do a Waymo kind of thing, where they spin out a separate company. >> They're doing that. >> The discussions happening, they're going to spin out the separate company and put it over there, and saying, "This is AI, got search over there, don't touch that search, 'cause that's where all the revenue is." (chuckles) >> So, okay, so that's how they deal with the Clay Christensen dilemma. What's the business model here? I mean it's not advertising, right? Is it to charge you for a query? What, how do you make money at this? >> It's a good question, I mean my thinking is, first of all, it's cool to type stuff in and see a paper get written, or write a blog post, or gimme a marketing slogan for this or that or write some code. I think the API side of the business will be critical. And I think Howie Xu, I know you're going to reference some of his comments yesterday on Supercloud, I think this brings a whole 'nother user interface into technology consumption. I think the business model, not yet clear, but it will probably be some sort of either API and developer environment or just a straight up free consumer product, with some sort of freemium backend thing for business. >> And he was saying too, it's natural language is the way in which you're going to interact with these systems. >> I think it's APIs, it's APIs, APIs, APIs, because these people who are cooking up these models, and it takes a lot of compute power to train these and to, for inference as well. Somebody did the analysis on the how many cents a Google search costs to Google, and how many cents the ChatGPT query costs. It's, you know, 100x or something on that. You can take a look at that. >> A 100x on which side? >> You're saying two orders of magnitude more expensive for ChatGPT >> Much more, yeah. >> Than for Google. >> It's very expensive. >> So Google's got the data, they got the infrastructure and they got, you're saying they got the cost (indistinct) >> No actually it's a simple query as well, but they are trying to put together the answers, and they're going through a lot more data versus index data already, you know. >> Let me clarify, you're saying that Google's version of ChatGPT is more efficient? >> No, I'm, I'm saying Google search results. >> Ah, search results. >> What are used to today, but cheaper. >> But that, does that, is that going to confer advantage to Google's large language (indistinct)? >> It will, because there were deep science (indistinct). >> Google, I don't think Google search is doing a large language model on their search, it's keyword search. You know, what's the weather in Santa Cruz? Or how, what's the weather going to be? Or you know, how do I find this? Now they have done a smart job of doing some things with those queries, auto complete, re direct navigation. But it's, it's not entity. It's not like, "Hey, what's Dave Vellante thinking this week in Breaking Analysis?" ChatGPT might get that, because it'll get your Breaking Analysis, it'll synthesize it. There'll be some, maybe some clips. It'll be like, you know, I mean. >> Well I got to tell you, I asked ChatGPT to, like, I said, I'm going to enter a transcript of a discussion I had with Nir Zuk, the CTO of Palo Alto Networks, And I want you to write a 750 word blog. I never input the transcript. It wrote a 750 word blog. It attributed quotes to him, and it just pulled a bunch of stuff that, and said, okay, here it is. It talked about Supercloud, it defined Supercloud. >> It's made, it makes you- >> Wow, But it was a big lie. It was fraudulent, but still, blew me away. >> Again, vanilla content and non accurate content. So we are going to see a surge of misinformation on steroids, but I call it the vanilla content. Wow, that's just so boring, (indistinct). >> There's so many dangers. >> Make your point, cause we got to, almost out of time. >> Okay, so the consumption, like how do you consume this thing. As humans, we are consuming it and we are, like, getting a nicely, like, surprisingly shocked, you know, wow, that's cool. It's going to increase productivity and all that stuff, right? And on the danger side as well, the bad actors can take hold of it and create fake content and we have the fake sort of intelligence, if you go out there. So that's one thing. The second thing is, we are as humans are consuming this as language. Like we read that, we listen to it, whatever format we consume that is, but the ultimate usage of that will be when the machines can take that output from likes of ChatGPT, and do actions based on that. The robots can work, the robot can paint your house, we were talking about, right? Right now we can't do that. >> Data apps. >> So the data has to be ingested by the machines. It has to be digestible by the machines. And the machines cannot digest unorganized data right now, we will get better on the ingestion side as well. So we are getting better. >> Data, reasoning, insights, and action. >> I like that mall, paint my house. >> So, okay- >> By the way, that means drones that'll come in. Spray painting your house. >> Hey, it wasn't too long ago that robots couldn't climb stairs, as I like to point out. Okay, and of course it's no surprise the venture capitalists are lining up to eat at the trough, as I'd like to say. Let's hear, you'd referenced this earlier, John, let's hear what AI expert Howie Xu said at the Supercloud event, about what it takes to clone ChatGPT. Please, play the clip. >> So one of the VCs actually asked me the other day, right? "Hey, how much money do I need to spend, invest to get a, you know, another shot to the openAI sort of the level." You know, I did a (indistinct) >> Line up. >> A hundred million dollar is the order of magnitude that I came up with, right? You know, not a billion, not 10 million, right? So a hundred- >> Guys a hundred million dollars, that's an astoundingly low figure. What do you make of it? >> I was in an interview with, I was interviewing, I think he said hundred million or so, but in the hundreds of millions, not a billion right? >> You were trying to get him up, you were like "Hundreds of millions." >> Well I think, I- >> He's like, eh, not 10, not a billion. >> Well first of all, Howie Xu's an expert machine learning. He's at Zscaler, he's a machine learning AI guy. But he comes from VMware, he's got his technology pedigrees really off the chart. Great friend of theCUBE and kind of like a CUBE analyst for us. And he's smart. He's right. I think the barriers to entry from a dollar standpoint are lower than say the CapEx required to compete with AWS. Clearly, the CapEx spending to build all the tech for the run a cloud. >> And you don't need a huge sales force. >> And in some case apps too, it's the same thing. But I think it's not that hard. >> But am I right about that? You don't need a huge sales force either. It's, what, you know >> If the product's good, it will sell, this is a new era. The better mouse trap will win. This is the new economics in software, right? So- >> Because you look at the amount of money Lacework, and Snyk, Snowflake, Databrooks. Look at the amount of money they've raised. I mean it's like a billion dollars before they get to IPO or more. 'Cause they need promotion, they need go to market. You don't need (indistinct) >> OpenAI's been working on this for multiple five years plus it's, hasn't, wasn't born yesterday. Took a lot of years to get going. And Sam is depositioning all the success, because he's trying to manage expectations, To your point Sarbjeet, earlier. It's like, yeah, he's trying to "Whoa, whoa, settle down everybody, (Dave laughs) it's not that great." because he doesn't want to fall into that, you know, hero and then get taken down, so. >> It may take a 100 million or 150 or 200 million to train the model. But to, for the inference to, yeah to for the inference machine, It will take a lot more, I believe. >> Give it, so imagine, >> Because- >> Go ahead, sorry. >> Go ahead. But because it consumes a lot more compute cycles and it's certain level of storage and everything, right, which they already have. So I think to compute is different. To frame the model is a different cost. But to run the business is different, because I think 100 million can go into just fighting the Fed. >> Well there's a flywheel too. >> Oh that's (indistinct) >> (indistinct) >> We are running the business, right? >> It's an interesting number, but it's also kind of, like, context to it. So here, a hundred million spend it, you get there, but you got to factor in the fact that the ways companies win these days is critical mass scale, hitting a flywheel. If they can keep that flywheel of the value that they got going on and get better, you can almost imagine a marketplace where, hey, we have proprietary data, we're SiliconANGLE in theCUBE. We have proprietary content, CUBE videos, transcripts. Well wouldn't it be great if someone in a marketplace could sell a module for us, right? We buy that, Amazon's thing and things like that. So if they can get a marketplace going where you can apply to data sets that may be proprietary, you can start to see this become bigger. And so I think the key barriers to entry is going to be success. I'll give you an example, Reddit. Reddit is successful and it's hard to copy, not because of the software. >> They built the moat. >> Because you can, buy Reddit open source software and try To compete. >> They built the moat with their community. >> Their community, their scale, their user expectation. Twitter, we referenced earlier, that thing should have gone under the first two years, but there was such a great emotional product. People would tolerate the fail whale. And then, you know, well that was a whole 'nother thing. >> Then a plane landed in (John laughs) the Hudson and it was over. >> I think verticals, a lot of verticals will build applications using these models like for lawyers, for doctors, for scientists, for content creators, for- >> So you'll have many hundreds of millions of dollars investments that are going to be seeping out. If, all right, we got to wrap, if you had to put odds on it that that OpenAI is going to be the leader, maybe not a winner take all leader, but like you look at like Amazon and cloud, they're not winner take all, these aren't necessarily winner take all markets. It's not necessarily a zero sum game, but let's call it winner take most. What odds would you give that open AI 10 years from now will be in that position. >> If I'm 0 to 10 kind of thing? >> Yeah, it's like horse race, 3 to 1, 2 to 1, even money, 10 to 1, 50 to 1. >> Maybe 2 to 1, >> 2 to 1, that's pretty low odds. That's basically saying they're the favorite, they're the front runner. Would you agree with that? >> I'd say 4 to 1. >> Yeah, I was going to say I'm like a 5 to 1, 7 to 1 type of person, 'cause I'm a skeptic with, you know, there's so much competition, but- >> I think they're definitely the leader. I mean you got to say, I mean. >> Oh there's no question. There's no question about it. >> The question is can they execute? >> They're not Friendster, is what you're saying. >> They're not Friendster and they're more like Twitter and Reddit where they have momentum. If they can execute on the product side, and if they don't stumble on that, they will continue to have the lead. >> If they say stay neutral, as Sam is, has been saying, that, hey, Microsoft is one of our partners, if you look at their company model, how they have structured the company, then they're going to pay back to the investors, like Microsoft is the biggest one, up to certain, like by certain number of years, they're going to pay back from all the money they make, and after that, they're going to give the money back to the public, to the, I don't know who they give it to, like non-profit or something. (indistinct) >> Okay, the odds are dropping. (group talks over each other) That's a good point though >> Actually they might have done that to fend off the criticism of this. But it's really interesting to see the model they have adopted. >> The wildcard in all this, My last word on this is that, if there's a developer shift in how developers and data can come together again, we have conferences around the future of data, Supercloud and meshs versus, you know, how the data world, coding with data, how that evolves will also dictate, 'cause a wild card could be a shift in the landscape around how developers are using either machine learning or AI like techniques to code into their apps, so. >> That's fantastic insight. I can't thank you enough for your time, on the heels of Supercloud 2, really appreciate it. All right, thanks to John and Sarbjeet for the outstanding conversation today. Special thanks to the Palo Alto studio team. My goodness, Anderson, this great backdrop. You guys got it all out here, I'm jealous. And Noah, really appreciate it, Chuck, Andrew Frick and Cameron, Andrew Frick switching, Cameron on the video lake, great job. And Alex Myerson, he's on production, manages the podcast for us, Ken Schiffman as well. Kristen Martin and Cheryl Knight help get the word out on social media and our newsletters. Rob Hof is our editor-in-chief over at SiliconANGLE, does some great editing, thanks to all. Remember, all these episodes are available as podcasts. All you got to do is search Breaking Analysis podcast, wherever you listen. Publish each week on wikibon.com and siliconangle.com. Want to get in touch, email me directly, david.vellante@siliconangle.com or DM me at dvellante, or comment on our LinkedIn post. And by all means, check out etr.ai. They got really great survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, We'll see you next time on Breaking Analysis. (electronic music)
SUMMARY :
bringing you data-driven and ChatGPT have taken the world by storm. So I asked it, give it to the large language models to do that. So to your point, it's So one of the problems with ChatGPT, and he simply gave the system the prompts, or the OS to help it do but it kind of levels the playing- and the answers were coming as the data you can get. Yeah, and leveled to certain extent. I check the facts, save me about maybe- and then I write a killer because like if the it's, the law is we, you know, I think that's true and I ask the set of similar question, What's your counter point? and not it's underestimated long term. That's what he said. for the first time, wow. the overhyped at the No, it was, it was I got, right I mean? the internet in the early days, and it's only going to get better." So you're saying it's bifurcated. and possibly the debate the first mobile device. So I mean. on the right with ChatGPT, and convicted by the Department of Justice the scrutiny from the Fed, right, so- And the privacy and thing to do what Sam Altman- So even though it'll get like, you know, it's- It's more than clever. I mean you write- I think that's a big thing. I think he was doing- I was not impressed because You know like. And he did the same thing he's got a lot of hyperbole. the browser moment to me, So OpenAI could stay on the right side You're right, it was terrible, They could be the Netscape Navigator, and in the horizontal axis's So I guess that's the other point is, I mean to quote IBM's So the data problem factors and the government's around the world, and they're slow to catch up. Yeah, and now they got years, you know, OpenAI. But the problem with government to kill Big Tech, and the 20% is probably relevant, back in the day, right? are they going to apply it? and also to write code as well, that the marketplace I don't, I don't see you had an interesting comment. No, no. First of all, the AI chops that Google has, right? are off the scales, right? I mean they got to be and the capacity to process that data, on some of the thinking So Lina Kahn is looming, and this is the third, could be a third rail. But the first thing What they will do out the separate company Is it to charge you for a query? it's cool to type stuff in natural language is the way and how many cents the and they're going through Google search results. It will, because there were It'll be like, you know, I mean. I never input the transcript. Wow, But it was a big lie. but I call it the vanilla content. Make your point, cause we And on the danger side as well, So the data By the way, that means at the Supercloud event, So one of the VCs actually What do you make of it? you were like "Hundreds of millions." not 10, not a billion. Clearly, the CapEx spending to build all But I think it's not that hard. It's, what, you know This is the new economics Look at the amount of And Sam is depositioning all the success, or 150 or 200 million to train the model. So I think to compute is different. not because of the software. Because you can, buy They built the moat And then, you know, well that the Hudson and it was over. that are going to be seeping out. Yeah, it's like horse race, 3 to 1, 2 to 1, that's pretty low odds. I mean you got to say, I mean. Oh there's no question. is what you're saying. and if they don't stumble on that, the money back to the public, to the, Okay, the odds are dropping. the model they have adopted. Supercloud and meshs versus, you know, on the heels of Supercloud
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Lena Smart, MongoDB | AWS re:Invent 2022
(bright music) >> Hello everyone and welcome back to AWS re:Invent, here in wonderful Las Vegas, Nevada. We're theCUBE. I am Savannah Peterson. Joined with my co-host, Dave Vellante. Day four, you look great. Your voice has come back somehow. >> Yeah, a little bit. I don't know how. I took last night off. You guys, I know, were out partying all night, but - >> I don't know what you're talking about. (Dave laughing) >> Well, you were celebrating John's birthday. John Furrier's birthday today. >> Yes, happy birthday John! >> He's on his way to England. >> Yeah. >> To attend his nephew's wedding. Awesome family. And so good luck, John. I hope you feel better, he's got a little cold. >> I know, good luck to the newlyweds. I love this. I know we're both really excited for our next guest, so I'm going to bring out, Lena Smart from MongoDB. Thank you so much for being here. >> Thank you for having me. >> How's the show going for you? >> Good. It's been a long week. And I just, not much voice left, so. >> We'll be gentle on you. >> I'll give you what's left of it. >> All right, we'll take that. >> Okay. >> You had a fireside chat, at the show? >> Lena: I did. >> Can you tell us a little bit about that? >> So we were talking about the Rise, The developer is a platform. In this massive theater. I thought it would be like an intimate, you know, fireside chat. I keep believing them when they say to me come and do these talks, it'll be intimate. And you turn up and there's a stage and a theater and it's like, oh my god. But it was really interesting. It was well attended. Got some really good questions at the end as well. Lots of follow up, which was interesting. And it was really just about, you know, how we've brought together this developer platform that's got our integrated services. It's just what developers want, it gives them time to innovate and disrupt, rather than worry about the minutia of management. >> Savannah: Do the cool stuff. >> Exactly. >> Yeah, so you know Lena, it's funny that you're saying that oh wow, the lights came on and it was this big thing. When when we were at re:Inforced, Lena was on stage and it was so funny, Lena, you were self deprecating like making jokes about the audience. >> Savannah: (indistinct) >> It was hilarious. And so, but it was really endearing to the audience and so we were like - >> Lena: It was terrifying. >> You got huge props for that, I'll tell you. >> Absolutely terrifying. Because they told me I wouldn't see anyone. Because we did the rehearsal the day before, and they were like, it's just going to be like - >> Sometimes it just looks like blackness out there. >> Yeah, yeah. It wasn't, they lied. I could see eyeballs. It was terrifying. >> Would you rather know that going in though? Or is it better to be, is ignorance bliss in that moment? >> Ignorance is bliss. >> Yeah, yeah yeah. >> Good call Savannah, right? Yeah, just go. >> The older I get, the more I'm just, I'm on the ignorance is bliss train. I just, I don't need to know anything that's going to hurt my soul. >> Exactly. >> One of the things that you mentioned, and this has actually been a really frequent theme here on the show this week, is you said that this has been a transformative year for developers. >> Lena: Yeah. >> What did you mean by that? >> So I think developers are starting to come to the fore, if you like, the fore. And I'm not in any way being deprecating about developers 'cause I love them. >> Savannah: I think everyone here does. >> I was married to one, I live with one now. It's like, they follow me everywhere. They don't. But, I think they, this is my opinion obviously but I think that we're seeing more and more the value that developers bring to the table. They're not just code geeks anymore. They're not just code monkeys, you know, churning out lines and lines of code. Some of the most interesting discussions I've had this week have been with developers. And that's why I'm so pleased that our developer data platform is going to give these folks back time, so that they can go and innovate. And do super interesting things and do the next big thing. It was interesting, I was talking to Mary, our comms person earlier and she had said that Dave I guess, my boss, was on your show - >> Dave: Yeah, he was over here last night. >> Yeah. And he was saying that two thirds of the companies that had been mentioned so far, within the whole gamut of this conference use MongoDB. And so take that, extrapolate that, of all the developers >> Wow. >> who are there. I know, isn't that awesome? >> That's awesome. Congrats on that, that's like - >> Did I hear that right now? >> I know, I just had that moment. >> I know she just told me, I'm like, really? That's - >> That's so cool. >> 'Cause the first thing I thought of was then, oh my god, how many developers are we reaching then? 'Cause they're the ones. I mean, it's kind of interesting. So my job has kind of grown from, over the years, being the security geek in the back room that nobody talks to, to avoiding me in the lift, to I've got a seat at the table now. We meet with the board. And I think that I can see that that's where the developer mindset is moving towards. It's like, give us the right tools and we'll change your world. >> And let the human capital go back to doing the fun stuff and not just the maintenance stuff. >> And, but then you say that, you can't have everything automated. I get that automation is also the buzzword of the week. And I get that, trust me. Someone has to write the code to do the automation. >> Savannah: Right. >> So, so yeah, definitely give these people back time, so that they can work on ML, AI, choose your buzzword. You know, by giving people things like queriable encryption for example, you're going to free up a whole bunch of head space. They don't have to worry about their data being, you know harvested from memory or harvested while at rest or in motion. And it's like, okay, I don't have to worry about that now, let me go do something fun. >> How about the role of the developer as it relates to SecOps, right? They're being asked to do a lot. You and I talked about this at re:Inforce. You seem to have a pretty good handle on it. Like a lot of companies I think are struggling with it. I mean, the other thing you said said to me is you don't have a lack of talent at Mongo, right? 'Cause you're Mongo. But a lot of companies do. But a lot of the developers, you know we were just talking about this earlier with Capgemini, the developer metrics or the application development team's metrics might not be aligned with the CSO's metrics. How, what are you seeing there? What, how do you deal with it within Mongo? What do you advise your customers? >> So in terms of internal, I work very closely with our development group. So I work with Tara Hernandez, who's our new VP of developer productivity. And she and her team are very much interested in making developers more productive. That's her job. And so we get together because sometimes security can definitely be seen as a blocker. You know, funnily enough, I actually had a Slack that I had to respond to three seconds before I come on here. And it was like, help, we need some help getting this application through procurement, because blah, blah, blah. And it's weird the kind of change, the shift in mindset. Whereas before they might have gone to procurement or HR or someone to ask for this. Now they're coming to the CSO. 'Cause they know if I say yes, it'll go through. >> Talk about social engineering. >> Exactly. >> You were talking about - >> But turn it around though. If I say no, you know, I don't like to say no. I prefer to be the CSO that says yes, but. And so that's what we've done. We've definitely got that culture of ask, we'll tell you the risks, and then you can go away and be innovative and do what you need to do. And we basically do the same with our customers. Here's what you can do. Our application is secure out of the box. Here's how we can help you make it even more, you know, streamlined or bespoke to what you need. >> So mobile was a big inflection point, you know, I dunno, it seems like forever ago. >> 2007. >> 2007. Yeah, iPhone came out in 2007. >> You remember your first iPhone? >> Dave: Yeah. >> Yeah? Same. >> Yeah. It was pretty awesome, actually. >> Yeah, I do too. >> Yeah, I was on the train to Boston going up to see some friends at MIT on the consortium that I worked with. And I had, it was the wee one, 'member? But you thought it was massive. >> Oh, it felt - >> It felt big. And I remember I was sitting on the train to Boston it was like the Estella and there was these people, these two women sitting beside me. And they were all like glam, like you and unlike me. >> Dave: That's awesome. >> And they, you could see them like nudging each other. And I'm being like, I'm just sitting like this. >> You're chilling. >> Like please look at my phone, come on just look at it. Ask me about it. And eventually I'm like - >> You're baiting them. >> nonchalantly laid it on the table. And you know, I'm like, and they're like, is that an iPhone? And I'm like, yeah, you want to see it? >> I thought you'd never ask. >> I know. And I really played with it. And I showed them all the cool stuff, and they're like, oh we're going to buy iPhones. And so I should have probably worked for Apple, but I didn't. >> I was going to say, where was your referral kickback on that? Especially - >> It was a little like Tesla, right? When you first, we first saw Tesla, it was Ray Wong, you know, Ray? From Pasadena? >> It really was a moment and going from the Blackberry keyboard to that - >> He's like want to see my car? And I'm like oh yeah sure, what's the big deal? >> Yeah, then you see it and you're like, ooh. >> Yeah, that really was such a pivotal moment. >> Anyway, so we lost a track, 2007. >> Yeah, what were we talking about? 2007 mobile. >> Mobile. >> Key inflection point, is where you got us here. Thank you. >> I gotchu Dave, I gotchu. >> Bring us back here. My mind needs help right now. Day four. Okay, so - >> We're all getting here on day four, we're - >> I'm socially engineering you to end this, so I can go to bed and die quietly. That's what me and Mary are, we're counting down the minutes. >> Holy. >> That's so sick. >> You're breaking my heart right now. I love it. I'm with you, sis, I'm with you. >> So I dunno where I was, really where I was going with this, but, okay, there's - >> 2007. Three things happened. >> Another inflection point. Okay yeah, tell us what happened. But no, tell us that, but then - >> AWS, clones, 2006. >> Well 2006, 2007. Right, okay. >> 2007, the iPhone, the world blew up. So you've already got this platform ready to take all this data. >> Dave: Right. >> You've got this little slab of gorgeousness called the iPhone, ready to give you all that data. And then MongoDB pops up, it's like, woo-hoo. But what we could offer was, I mean back then was awesome, but it was, we knew that we would have to iterate and grow and grow and grow. So that was kind of the three things that came together in 2007. >> Yeah, and then Cloud came in big time, and now you've got this platform. So what's the next inflection point do you think? >> Oh... >> Good question, Dave. >> Don't even ask me that. >> I mean, is it Edge? Is it IOT? Is there another disruptor out there? >> I think it's going to be artificial intelligence. >> Dave: Is it AI? >> I mean I don't know enough about it to talk about it, to any level, so don't ask me any questions about it. >> This is like one of those ignorance is bliss moments. It feels right. >> Yeah. >> Well, does it scare you, from a security perspective? Or? >> Great question, Dave. >> Yeah, it scares me more from a humanity standpoint. Like - >> More than social scared you? 'Cause social was so benign when it started. >> Oh it was - >> You're like, oh - I remember, >> It was like a yearbook. I was on the Estella and we were - >> Shout out to Amtrak there. >> I was with, we were starting basically a wikibond, it was an open source. >> Yeah, yeah. >> Kind of, you know, technology community. And we saw these and we were like enamored of Facebook. And there were these two young kids on the train, and we were at 'em, we were picking the brain. Do you like Facebook? "I love Facebook." They're like "oh, Facebook's unbelievable." Now, kids today, "I hate Facebook," right? So, but social at the beginning it was kind of, like I say, benign and now everybody's like - >> Savannah: We didn't know what we were getting into. >> Right. >> I know. >> Exactly. >> Can you imagine if you could have seen into the future 20 years ago? Well first of all, we'd have all bought Facebook and Apple stock. >> Savannah: Right. >> And Tesla stock. But apart from, but yeah apart from that. >> Okay, so what about Quantum? Does that scare you at all? >> I think the only thing that scares me about Quantum is we have all this security in place today. And I'm not an expert in Quantum, but we have all this security in place that's securing what we have today. And my worry is, in 10 years, is it still going to be secure? 'Cause we're still going to be using that data in some way, shape, or form. And my question is to the quantum geniuses out there, what do we do in 10 years like to retrofit the stuff? >> Dave: Like a Y2K moment? >> Kind of. Although I think Y2K is coming in 2038, isn't it? When the Linux date flips. I'll be off the grid by then, I'll be living in Scotland. >> Somebody else's problem. >> Somebody else's problem. I'll be with the sheep in Glasgow, in Scotland. >> Y2K was a boondoggle for tech, right? >> What a farce. I mean, that whole - >> I worked in the power industry in Y2K. That was a nightmare. >> Dave: Oh I bet. >> Savannah: Oh my God. >> Yeah, 'cause we just assumed that the world was going to stop and there been no power, and we had nuclear power plants. And it's like holy moly. Yeah. >> More than moly. >> I was going to say, you did a good job holding that other word in. >> I think I was going to, in case my mom hears this. >> I grew up near Diablo Canyon in, in California. So you were, I mean we were legitimately worried that that exactly was going to happen. And what about the waste? And yeah it was chaos. We've covered a lot. >> Well, what does worry you? Like, it is culture? Is it - >> Why are you trying to freak her out? >> No, no, because it's a CSO, trying to get inside the CSO's head. >> You don't think I have enough to worry about? You want to keep piling on? >> Well if it's not Quantum, you know? Maybe it's spiders or like - >> Oh but I like spiders, well spiders are okay. I don't like bridges, that's my biggest fear. Bridges. >> Seriously? >> And I had to drive over the Tappan Zee bridge, which is one of the longest, for 17 years, every day, twice. The last time I drove over it, I was crying my heart out, and happy as anything. >> Stay out of Oakland. >> I've never driven over it since. Stay out of where? >> Stay out of Oakland. >> I'm staying out of anywhere that's got lots of water. 'Cause it'll have bridges. >> Savannah: Well it's good we're here in the desert. >> Exactly. So what scares me? Bridges, there you go. >> Yeah, right. What? >> Well wait a minute. So if I'm bridging technology, is that the scary stuff? >> Oh God, that was not - >> Was it really bad? >> It was really bad. >> Wow. Wow, the puns. >> There's a lot of seems in those bridges. >> It is lit on theCUBE A floor, we are all struggling. I'm curious because I've seen, your team is all over the place here on the show, of course. Your booth has been packed the whole time. >> Lena: Yes. >> The fingerprint. Talk to me about your shirt. >> So, this was designed by my team in house. It is the most wanted swag in the company, because only my security people wear it. So, we make it like, yeah, you could maybe have one, if this turns out well. >> I feel like we're on the right track. >> Dave: If it turns out well. >> Yeah, I just love it. It's so, it's just brilliant. I mean, it's the leaf, it's a fingerprint. It's just brilliant. >> That's why I wanted to call it out. You know, you see a lot of shirts, a lot of swag shirts. Some are really unfortunately sad, or not funny, >> They are. >> or they're just trying too hard. Now there's like, with this one, I thought oh I bet that's clever. >> Lena: It is very cool. Yes, I love it. >> I saw a good one yesterday. >> Yeah? >> We fix shit, 'member? >> Oh yeah, yeah. >> That was pretty good. >> I like when they're >> That's a pretty good one. >> just straightforward, like that, yeah yeah. >> But the only thing with this is when you're say in front of a green screen, you look as though you've got no tummy. >> A portal through your body. >> And so, when we did our first - >> That's a really good point, actually. >> Yeah, it's like the black hole to nothingless. And I'm like wow, that's my soul. >> I was just going to say, I don't want to see my soul like that. I don't want to know. >> But we had to do like, it was just when the pandemic first started, so we had to do our big presentation live announcement from home. And so they shipped us all this camera equipment for home and thank God my partner knows how that works, so he set it all up. And then he had me test with a green screen, and he's like, you have no tummy. I'm like, what the hell are you talking about? He's like, come and see. It's like this, I dunno what it was. So I had to actually go upstairs and felt tip with a magic marker and make it black. >> Wow. >> So that was why I did for two hours on a Friday, yeah. >> Couldn't think of another alternative, huh? >> Well no, 'cause I'm myopic when it comes to marketing and I knew I had to keep the tshirt on, and I just did that. >> Yeah. >> In hindsight, yes I could have worn an "I Fix Shit" tshirt, but I don't think my husband would've been very happy. I secure shit? >> There you go, yeah. >> There you go. >> Over to you, Savannah. >> I was going to say, I got acquainted, I don't know if I can say this, but I'm going to say it 'cause we're here right now. I got acquainted with theCUBE, wearing a shirt that said "Unfuck Kubernetes," 'cause it was a marketing campaign that I was running for one of my clients at Kim Con last year. >> That's so good. >> Yeah, so - >> Oh my God. I'll give you one of these if you get me one of those. >> I can, we can do a swapskee. We can absolutely. >> We need a few edits on this film, on the file. >> Lena: Okay, this is nothing - >> We're fallin' off the wheel. Okay, on that note, I'm going to bring us to our challenge that we discussed, before we got started on this really diverse discussion that we have had in the last 15 minutes. We've covered everything from felt tip markers to nuclear power plants. >> To the darkness of my soul. >> To the darkness of all of our souls. >> All of our souls, yes. >> Which is perhaps a little too accurate, especially at this stage in the conference. You've obviously seen a lot Lena, and you've been rockin' it, I know John was in your suite up here, at at at the Venetian. What's your 30 second hot take? Most important story, coming out of the show or for you all at Mongo this year? >> Genuinely, it was when I learned that two-thirds of the customers that had been mentioned, here, are MongoDB customers. And that just exploded in my head. 'Cause now I'm thinking of all the numbers and the metrics and how we can use that. And I just think it's amazing, so. >> Yeah, congratulations on that. That's awesome. >> Yeah, I thought it was amazing. >> And it makes sense actually, 'cause Mongo so easy to use. We were talking about Tengen. >> We knew you when, I feel that's our like, we - >> Yeah, but it's true. And so, Mongo was just really easy to use. And people are like, ah, it doesn't scale. It's like, turns out it actually does scale. >> Lena: Turns out, it scales pretty well. >> Well Lena, without question, this is my favorite conversation of the show so far. >> Thank you. >> Thank you so much for joining us. >> Thank you very much for having me. >> Dave: Great to see you. >> It's always a pleasure. >> Dave: Thanks Lena. >> Thank you. >> And thank you all, tuning in live, for tolerating wherever we take these conversations. >> Dave: Whatever that was. >> I bet you weren't ready for this one, folks. We're at AWS re:Invent in Las Vegas, Nevada. With Dave Vellante, I'm Savannah Peterson. You're washing theCUBE, the leader for high tech coverage.
SUMMARY :
I am Savannah Peterson. I don't know how. I don't know Well, you were I hope you feel better, I know, good luck to the newlyweds. And I just, not much voice left, so. And it was really just about, you know, Yeah, so you know Lena, it's funny And so, but it was really endearing for that, I'll tell you. I wouldn't see anyone. Sometimes it just looks I could see eyeballs. Yeah, just go. I just, I don't need to know anything One of the things that you mentioned, to the fore, if you like, the fore. I was married to one, Dave: Yeah, he was And he was saying that two I know, isn't that Congrats on that, that's like - And I think that I can And let the human capital go back And I get that, trust me. being, you know harvested from memory But a lot of the developers, you know And it was like, help, we need some help I don't like to say no. I dunno, it seems like forever ago. Yeah? actually. And I had, it was the wee one, 'member? And I remember I was sitting And they, you could see And eventually I'm like - And I'm like, yeah, you want to see it? And I really played with it. Yeah, then you see Yeah, that really was Yeah, what were we talking about? is where you got us here. I gotchu Dave, Okay, so - you to end this, so I can I love it. Three things happened. But no, tell us that, but then - Well 2006, 2007. 2007, the iPhone, the world blew up. I mean back then was awesome, point do you think? I think it's going to I mean I don't know enough about it This is like one of Yeah, it scares me more 'Cause social was so I was on the Estella and we were - I was with, we were starting basically And we saw these and we were what we were getting into. Can you imagine if you could And Tesla stock. And my question is to the Although I think Y2K is I'll be with the sheep in Glasgow, I mean, that whole - I worked in the power industry in Y2K. assumed that the world I was going to say, you I think I was going to, that that exactly was going to happen. No, no, because it's a CSO, I don't like bridges, And I had to drive over Stay out of where? I'm staying out of anywhere Savannah: Well it's good Bridges, there you go. Yeah, right. the scary stuff? Wow, the puns. There's a lot of seems is all over the place here Talk to me about your shirt. So, we make it like, yeah, you could I mean, it's the leaf, it's a fingerprint. You know, you see a lot of I thought oh I bet that's clever. Lena: It is very cool. That's a pretty like that, yeah yeah. But the only thing with this is That's a really good point, the black hole to nothingless. I was just going to say, I don't and he's like, you have no tummy. So that was why I did for and I knew I had to keep the I secure shit? I was going to say, I got acquainted, I'll give you one of these I can, we can do a swapskee. on this film, on the file. Okay, on that note, I'm going to bring us I know John was in your suite And I just think it's amazing, so. Yeah, congratulations on that. it was amazing. And it makes sense actually, And so, Mongo was just really easy to use. of the show so far. And thank you all, tuning in live, I bet you weren't
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Robert Nishihara, Anyscale | AWS re:Invent 2022 - Global Startup Program
>>Well, hello everybody. John Walls here and continuing our coverage here at AWS Reinvent 22 on the queue. We continue our segments here in the Global Startup program, which of course is sponsored by AWS Startup Showcase, and with us to talk about any scale as the co-founder and CEO of the company, Robert and n, you are Robert. Good to see you. Thanks for joining us. >>Yeah, great. And thank you. >>You bet. Yeah. Glad to have you aboard here. So let's talk about Annie Scale, first off, for those at home and might not be familiar with what you do. Yeah. Because you've only been around for a short period of time, you're telling me >>Company's about >>Three years now. Three >>Years old, >>Yeah. Yeah. So tell us all about it. Yeah, >>Absolutely. So one of the biggest things happening in computing right now is the proliferation of ai. AI is just spreading throughout every industry has the potential to transform every industry. But the thing about doing AI is that it's incredibly computationally intensive. So if you wanna do do ai, you're not, you're probably not just doing it on your laptop, you're doing it across many machines, many gpu, many compute resources, and that's incredibly hard to do. It requires a lot of software engineering expertise, a lot of infrastructure expertise, a lot of cloud computing expertise to build the software infrastructure and distributed systems to really scale AI across all of the, across the cloud. And to do it in a way where you're really getting value out of ai. And so that is the, the problem statement that AI has tremendous potential. It's incredibly hard to do because of the, the scale required. >>And what we are building at any scale is really trying to make that easy. So trying to get to the point where, as a developer, if you know how to program on your laptop, then if you know how to program saying Python on your laptop, then that's enough, right? Then you can do ai, you can get value out of it, you can scale it, you can build the kinds of, you know, incredibly powerful applica AI applications that companies like Google and, and Facebook and others can build. But you don't have to learn about all of the distributed systems and infrastructure. It just, you know, we'll handle that for you. So that's, if we're successful, you know, that's what we're trying to achieve here. >>Yeah. What, what makes AI so hard to work with? I mean, you talk about the complexity. Yeah. A lot of moving parts. I mean, literally moving parts, but, but what is it in, in your mind that, that gets people's eyes spinning a little bit when they, they look at great potential. Yeah. But also they look at the downside of maybe having to work your way through Pike mere of sorts. >>So, so the potential is definitely there, but it's important to remember that a lot of AI initiatives fail. Like a lot of initiative AI initiatives, something like 80 or 90% don't make it out of, you know, the research or prototyping phase and inter production. Hmm. So, some of the things that are hard about AI and the reasons that AI initiatives can fail, one is the scale required, you know, moving. It's one thing to develop something on your laptop, it's another thing to run it across thousands of machines. So that's scale, right? Another is the transition from development and prototyping to production. Those are very different, have very different requirements. Absolutely. A lot of times it's different teams within a company. They have different tech stacks, different software they're using. You know, we hear companies say that when they move from develop, you know, once they prototype and develop a model, it could take six to 12 weeks to get that model in production. >>And that often involves rewriting a lot of code and handing it off to another team. So the transition from development to production is, is a big challenge. So the scale, the development to production handoff. And then lastly, a big challenge is around flexibility. So AI's a fast moving field, you see new developments, new algorithms, new models coming out all the time. And a lot of teams we work with, you know, they've, they've built infrastructure. They're using products out there to do ai, but they've found that it's sort of locking them into rigid workflows or specific tools, and they don't have the flexibility to adopt new algorithms or new strategies or approaches as they're being developed as they come out. And so they, but their developers want the flexibility to use the latest tools, the latest strategies. And so those are some of the main problems we see. It's really like, how do you scale scalability? How do you move easily from development and production and back? And how do you remain flexible? How do you adapt and, and use the best tools that are coming out? And so those are, yeah, just those are and often reasons that people start to use Ray, which is our open source project in any scale, which is our, our product. So tell >>Me about Ray, right? Yeah. Opensource project. I think you said you worked on it >>At Berkeley. That's right. Yeah. So before this company, I did a PhD in machine learning at Berkeley. And one of the challenges that we were running into ourselves, we were trying to do machine learning. We actually weren't infrastructure or distributed systems people, but we found ourselves in order to do machine learning, we found ourselves building all sorts of tools, ad hoc tools and systems to scale the machine learning, to be able to run it in a reasonable amount of time and to be able to leverage the compute that we needed. And it wasn't just us people all across, you know, machine learning researchers, machine learning practitioners were building their own tooling and infrastructure. And that was one of the things that we felt was really holding back progress. And so that's how we slowly and kind of gradually got into saying, Hey, we could build better tools here. >>We could build, we could try to make this easier to do so that all of these people don't have to build their own infrastructure. They can focus on the actual machine learning applications that they're trying to build. And so we started, Ray started this open source project for basically scaling Python applications and scaling machine learning applications. And, well, initially we were running around Berkeley trying to get all of our friends to try it out and, and adopt it and, you know, and give us feedback. And if it didn't work, we would debug it right away. And that slow, you know, that gradually turned into more companies starting to adopt it, bigger teams starting to adopt it, external contributors starting to, to contribute back to the open source project and make it better. And, you know, before you know it, we were hosting meetups, giving to talks, running tutorials, and the project was just taking off. And so that's a big part of what we continue to develop today at any scale, is like really fostering this open source community, growing the open source user base, making sure Ray is just the best way to scale Python applications and, and machine learning applications. >>So, so this was a graduate school project That's right. You say on, on your way to getting your doctorate and now you commercializing now, right? Yeah. I mean, so you're being able to offer it, first off, what a journey that was, right? I mean, who would've thought Absolutely. I guess you probably did think that at some point, but >>No, you know, when we started, when we were working on Ray, we actually didn't anticipate becoming a company, or we at least just weren't looking that far ahead. We were really excited about solving this problem of making distributed computing easy, you know, getting to the point where developers just don't have to learn about infrastructure and distributed systems, but get all the benefits. And of course, it wasn't until, you know, later on as we were graduating from Berkeley and we wanted to continue really taking this project further and, and really solving this problem that it, we realized it made sense to start a company. >>So help me out, like, like what, what, and I might have missed this, so I apologize if I did, but in terms of, of Ray's that building block and essential for your, your ML or AI work down the road, you know, what, what is it doing for me or what, what will it allow me to do in either one of those realms that I, I can't do now? >>Yeah. And so, so like why use Ray versus not using Ray? Yeah, I think the, the answer is that you, you know, if you're doing ai, you need to scale. It's becoming, if you don't find that to be the case today, you probably will tomorrow, you know, or the day after that. And so it's really increasingly, it's a requirement. It's not an option. And so if you're scaling, if you're trying to build these scalable applications you are building, you're either going to use Ray or, or something like Ray or you're going to build the infrastructure yourself and building the infrastructure yourself, that's a long journey. >>So why take that on, right? >>And many of the companies we work with don't want to be in the business of building and managing infrastructure. No. Because, you know, if they, they want their their best engineers to build their product, right? To, to get their product to market faster. >>I want, I want you to do that for me. >>Right? Exactly. And so, you know, we can really accelerate what these teams can do and, you know, and if we can make the infrastructure something they just don't have to think about, that's, that's why you would choose to use Ray. >>Okay. You know, between a and I and ml are, are they different animals in terms of what you're trying to get done or what Ray can do? >>Yeah, and actually I should say like, it's not just, you know, teams that are new teams that are starting out, that are using Ray, many companies that have built, already built their own infrastructure will then switch to using Ray. And to give you a few examples, like Uber runs all their deep learning on Ray, okay. And, you know, open ai, which is really at the frontier of training large models and, and you know, pushing the boundaries of, of ai, they train their largest models using Ray. You know, companies like Shopify rebuilt their entire machine learning platform using Ray, >>But they started somewhere else. >>They had, this is all, you know, like, it's not like the v1, you know, of their, of their machine learning infrastructure. This is like, they did it a different way before, this is like the second version or the third iteration of of, of how they're doing it. And they realize often it's because, you know, I mean in the case of, of Uber, just to give you one example, they built a system called hova for scaling deep learning on a bunch of GPUs. Right Now, as you scale deep learning on GPUs for them, the bottleneck shifted away from, you know, as you scale GPU's training, the bottleneck shifted away from training and to the data ingest and pre-processing. And they wanted to scale data ingest and pre-processing on CPUs. So now Hova, it's a deep learning framework. It doesn't do the data ingest and pre-processing on CPUs, but you can, if you run Hova on top of Ray, you can scale training on GPUs. >>And then Ray has another library called Ray Data you can, that lets you scale the ingest and pre-processing on CPUs. You can pipeline them together. And that allowed them to train larger models on more data before, just to take one example, ETA prediction, if you get in an Uber, it tells you what time you're supposed to arrive. Sure. That uses a deep learning model called d eta. And before they were able to train on about two weeks worth of data. Now, you know, using Ray and for scaling the data, ingestive pre-processing and training, they can train on much more data. You know, you can get more accurate ETA predictions. So that's just one example of the kind of benefit they were able to get. Right. Also, because it's running on top of, of Ray and Ray has this ecosystem of libraries, you know, they can also use Ray's hyper parameter tuning library to do hyper parameter tuning for their deep learning models. >>They can also use it for inference and you know, because these are all built on top of Ray, they inherit the like, elasticity and fault tolerance of running on top of Ray. So really it simplifies things on the infrastructure side cuz there's just, if you have Ray as common infrastructure for your machine learning workloads, there's just one system to, to kind of manage and operate. And if you are, it simplifies things for the end users like the developers because from their perspective, they're just writing a Python application. They don't have to learn how to use three different distributed systems and stitch them together and all of this. >>So aws, before I let you go, how do they come into play here for you? I mean, are you part of the showcase, a startup showcase? So obviously a major partner and major figure in the offering that you're presenting >>People? Yeah, well you can run. So any scale is a managed ray service. Like any scale is just the best way to run Ray and deploy Ray. And we run on top of aws. So many of our customers are, you know, using Ray through any scale on aws. And so we work very closely together and, and you know, we have, we have joint customers and basically, and you know, a lot of the value that any scale is adding on top of Ray is around the production story. So basically, you know, things like high availability, things like failure handling, retry alerting, persistence, reproducibility, these are a lot of the value, the values of, you know, the value that our platform adds on top of the open source project. A lot of stuff as well around collaboration, you know, imagine you are, you, something goes wrong with your application, your production job, you want to debug it, you can just share the URL with your, your coworker. They can click a button, reproduce the exact same thing, look at the same logs, you know, and, and, and figure out what's going on. And also a lot around, one thing that's, that's important for a lot of our customers is efficiency around cost. And so we >>Support every customer. >>Exactly. A lot of people are spending a lot of money on, on aws. Yeah. Right? And so any scale supports running out of the box on cheaper like spot instances, these preempt instances, which, you know, just reduce costs by quite a bit. And so things like that. >>Well, the company is any scale and you're on the show floor, right? So if you're having a chance to watch this during reinvent, go down and check 'em out. Robert Ashihara joining us here, the co-founder and ceo and Robert, thanks for being with us. Yeah. Here on the cube. Really enjoyed it. Me too. Thanks so much. Boy, three years graduate program and boom, here you are, you know, with off to the enterprise you go. Very nicely done. All right, we're gonna continue our coverage here on the Cube with more here from Las Vegas. We're the Venetian, we're AWS Reinvent 22 and you're watching the Cube, the leader in high tech coverage.
SUMMARY :
scale as the co-founder and CEO of the company, Robert and n, you are Robert. And thank you. for those at home and might not be familiar with what you do. Three years now. Yeah, So if you wanna do do ai, you're not, you're probably not just doing it on your laptop, It just, you know, we'll handle that for you. I mean, you talk about the complexity. can fail, one is the scale required, you know, moving. And how do you remain flexible? I think you said you worked on it you know, machine learning researchers, machine learning practitioners were building their own tooling And, you know, before you know it, we were hosting meetups, I guess you probably did think that at some point, distributed computing easy, you know, getting to the point where developers just don't have to learn It's becoming, if you don't find that to be the case today, No. Because, you know, if they, they want their their best engineers to build their product, And so, you know, we can really accelerate what these teams can do to get done or what Ray can do? And to give you a few examples, like Uber runs all their deep learning on Ray, They had, this is all, you know, like, it's not like the v1, And then Ray has another library called Ray Data you can, that lets you scale the ingest and pre-processing on CPUs. And if you are, it simplifies things for the end users reproduce the exact same thing, look at the same logs, you know, and, and, and figure out what's going on. these preempt instances, which, you know, just reduce costs by quite a bit. Boy, three years graduate program and boom, here you are, you know, with off to the enterprise you
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Rob Enslin, UiPath & Daniel Dines, UiPath | UiPath Forward 5
>> Male: TheCUBE presents, UIPATH, Forward 5 brought to you by, UIPATH. >> Okay the party has started here at forward 5 UIPATH big customer event if you're watching the cube. We're wrapping up day one with the co-CE0 segment. Daniel Dines is here. He's the founder and Co-CEO of UIPATH and Rob Enslin, is co-CEO. Gents, great to see you. Thanks for spending some time with us. I know you're super busy. >> Thanks Dave. >> So I've been looking forward to this. Daniel you know I've followed the company for a long time. The really interesting path you took, to get to where you are today. How did you guys meet? And why did you decide to hire Rob? >> Male: (laughs) >> Rob: Well let me start. I uh, I was looking for a partner. Actually, in our work to your stand here, we are talking about how, how you feel in this job. You feel so alone. Because you are the center of all pressure points. And having a partner, having someone that has your back, it's kind of awesome. So I was looking for a partner. And our current friend, Carl Escenbach, he introduced us to each other, and we instantly clicked. And this is the type of job where it's uh either work well or it doesn't. It cannot be anything in the middle. >> Right, okay with Carl, we know Carl well. Awesome operator. Knows the business super well. So Rob, what attracted you to UIPATH? You had a great situation at google. You guys were growing like crazy. Why did you decide to come here? What did you see that attracted you? >> Yeah you know when I, when I went to google, I went to google because I really believed that data and AI was necessary for companies. And business is to be competitive in the future. And we did some great stuff at google cloud in the 3 years. But I knew UIPATH from a couple of years ago when they were mainly a RPA space. And I just felt that there was a place in time when automation was going expand. And as I sat down with Carl a couple of times, spoke to carl. And then I sat down with Daniel, I knew that there was something special with UIPATH, that could be a generational opportunity. Not any for myself but for the company in the future. And then I, you know I got to know Daniel. And at this stage of my career I was like, I'm pretty fussy about what I want to do and what I want and where I want to go. First of all, I want to go to a company that had great product, had a great culture, and I wanted to work with somebody that we could shake the future together and you know, Daniel and I just hit it off from the very first time we met. He got to meet my family, my dogs and we did the whole, we did the whole courting thing before we actually decided this was going to be a good thing for both of us. >> Dave: That's good. >> Rob: Yeah. >> Dave: You got to meet the family. That's very good. >> We just had, John Furrier and I just had, Mohit Aron and Sanjay Poonen into out studio. Cause Mohit, you know, formal google. Long time. And they decided to kind of split duties. Mohit's going into product, he didn't keep his CEO title. He walked. How are you guys splitting you time? What are each of you going to, responsible for? >> Daniel: Well its, its kind of similar. On a day by day operation I, I rely heavily on Rob. We do it together. Strategic decisions about the company's destiny. I'm doing mostly the product these days. Which is a big relief for me. And I think we also split a bit of customers visit. Which is great. I still enjoy meeting customers. I need, customers are food for my cause. >> Dave: (laughs) yeah and your awesome product visionary. You've been there since day one. Now Rob, you said in the key note today that you've seen around about a hundred customers. You've transverse the world. What did you learn from them that informed you? That gave you confidence that the the move to the internet platform, even though you had already started that. >> Male: Yeah. >> But you're really doubling down on that >> Rob: You know when I... >> from a stand point. >> Rob: You know Dave, when you think about it, like I was, I was so impressed that Daniel had the vision to create a platform 3 years ago. >> Dave: Yeah. >> All right. And as we went around the world. As I went around the world, and it was one of the very first things I've seen. I've got to understand how customers see UIPATH, from their advantage point. What are they looking for from us? Why is this company, why doe customers like this company so much? And as I went around the world. I went to Asia a couple, I went to Asia, Australia, Singapore, Japan. I was in Europe twice. We did the trip together. We went to visit customers. And it was very much the same thing. Helps us expand automation faster. And we are so surprise, at the break of your platform. We never knew that. And so it kind of just had, for me, it was conviction. It's like, this walls is the right decision you've made. There's so much opportunity there. And that's, you know that's kind of what I've learned through the last four five months. >> Dave: Now as you know Daniel, I've written a lot about your company. One of the things I've said is that, that start ups, if I can call you that back pre-IPO, typically don't have as much international exposure as UIPATH had. I mean you sort of, you sort of started as an international company and became more US centric. You said, in the, in the key note today, you're talking to Ray Wong about people may don't understand that challenges of FX. Point being, when you convert international dollars into US dollars there are less of them cause the dollars stronger. But still, I've always felt like that international footprint is an advantage. Rob you came from SAP, you know, again European based company. I don't, (stutters), do you regret that? Now? I mean I know it's technical, I'm sure you don't, but talk about that sort of international exposure? Why that's a long term benefit. >> Well, you, first of all, you expand faster. I think we expanded faster than our competition because our global footprint was larger. And we had the courage. Go in Japan, for instance. Everybody told me, it's impossible to make for such a small starter. It's impossible to make a business in Japan. But we didn't believe it. We're just crazy and we went there, and be built a very sizable business in Japan. Fifty-five percent of our revenue, even today, it's outside U.S. Now of course that has a down side. When uh, When the local currencies, you know, are losing the value compared to the dollars, we're impacted. As we go to... to investors, until now, so we are seeing like a (indistinct) in terms of ARI. It's huge. Only because (indistinct) and losing the business in Russia. But it still, it's the strength of our company. Things will come back. And then, you know, the growth engine will re-accelerate again. >> Dave: Yeah but when the dollars weakens that'll be in your favor. Rob I want to pick up on something you said today in your keynote. You went back and started, you know the cycles of ERP and you know, internet, et cetera. I kind of have a love hate with ERP. I have to be honest. >> Male: (laughing) >> But it, but but (chuckles) but if I go back to that. Late eighties nineties, you wouldn't have be able to pick SAP as the winner. And then SAP emerged. You know, very clearly. But the more interesting thing, is that the customers who are implementing ERP well. The practitioners did better than their peers, and dominated their industries. And their stocks went up. Their evaluations went up. Different worlds obviously but, do you see the same thing happening with RPA and automation? What gives you confidence that that's the case? >> I absolutely do see the same thing happening with automation and RPA being a part of, in being a part of that. The reason, the reason I believe that is speed is so critical. (stutters) And if you think about how hard it is for a CIO or a c level executive to consume the technology coming at them, plus all the changes in the world being thrown at them. It's compiling and compiling and compiling. We have an incredible solution, that can help companies. And there comes certain times, the love outcomes to the business. Like no one else gets. And when I see that, I view that as just like the beginning of what's going to happen in the future so, in many ways, and I've said this to many of my friends, it feels like 1992, 1993 to me. And it's interesting because no one really understood then why SAP would be great in 1992 and 93. And they got a couple of things right. They got the eco system right. Their new partners were important. And the knew they needed to drive business outcome for companies, in which they did. And so I feel like we are in a very similar place. Very different technology obviously. And the speed of change now is so dramatic, compared to what it was. And there's very few technology that can provide that level of speed and accomodation to their customers. >> All right, let's talk about priorities. You guys got a lot of work to do and you've, you've laid it out to the financial community. You've got to have profitable growth, because of FX, it part, you've lowered your forecast. But I think there's some conservative in their as well. Um, but you got to do that balance. You've given some guidance on gross margins. Cloud maybe brings that down a little bit. RnD I saw wide range. Thirteen to seventeen percent. I hope you keep spending on RnD. Big fan of that. You know stock buybacks and, RnD if in your position are going to be better. And the product priorities, continue to build that out. But question, let's start with the product. So you've got an on-prem stack and you've got a cloud stack that's emerging, how do you balance those out? How do you do the integration? You've done a great job with the integration. Does it, are you concerned about your ability to continue to work at that speed with two code bases? I wonder if you could address that? >> Daniel: We've become a cloud first company. We deliver all of our products first in the cloud. We've deliver on the two week (indistinct) in the cloud. So that helps us integrate quite fast. I think we made a very good business decision to build our cloud team in Seattle. In Bellevue to be specific. And we have access to great talent that knows how to build serious cloud service. Which is hard to find dollar. And uh, so, and also we, we have, we benef- one of our only benefits was, we have the really good architecture. We have an architecture that work easily on-prem and on the cloud. And even today, our work flow foundation, our local designers, were easy to modernize. So right now we are launching studio weapon. But behind the scene, it's the same workflow engine. Our customers don't have to rewrite anything. It just works. And it does the same to take our own brand product and brand it in the multicloud. So, it's, there is no friction at all. Actually cloud is just helping us accelerate. But we benefit then again of a really solid architectural foundation. >> Daniel: Architecture matters. We've seen that in this industry. We got the B52s rocking out in the background, I love it, but I've got so many questions for you guys. I want to talk about the go to market. Because Rob, it's obviously a strength of yours. You've come in. You've communicated to the street, that you're reshaping the sales floors. Are they lowering the ratios of sales? People, the customers at the high end, mid range as well, using digital. I mean the numbers are one to ten now. At the top. One to maybe fifty at the mid range. Where are you in terms of that journey? You've got to find people, you got to train them, how do you get the productivity out of those guys? Take us through your thinking there? >> Rob: Yeah firstly, I think we have enough resources. Having resources is not an issue. Um, we have an incredible vehicle to acquire customers inside the company. Our digital sales motion, it's probably the best I've seen. And so we have the ability to acquire customers really fast. And we get the first workload in really fast. The challenge is we need to, we need to be able to drive a (indistinct) model and we graduate customs when we acquire them into the direct sales floors. And then direct sales floors, we're not going to go one to thirty, we're talking one to ten for the direct sales floor. And even the high up in the pyramid, we want to have an even denser model than that. And the whole purpose is to drive the time to consumption much quicker, much faster. So we know exactly if we acquire a customer, will they spend? Do they have a (indistinct) spend? On what level do they have a (indistinct) spend? And therefore when we capture them, we can immediately surround them, and put the right resources so we can grow faster. We think this will have a significant impact on the organization. We'll start to implement certain pieces in the next quarter. Um, things like packaging solutions. Putting them in, enabling the sales organization. And buy the beginning of next year, we'll be ready to actually go full board, globally. We already put some pieces in place when I joined. Chris Weber, my chief business officer, did a great job doing some of those pieces. So we're on the journey already. >> Dave: Yeah and even before you guys were public and you weren't publishing your NRR numbers. Our ETR survey partner, we, we always thought you had very low churn. And I think you broke out just yesterday. The, the NRR for overseas vs U.S, U.S I think was 140 plus percent. >> Male: Yeah >> Very very strong. A little, a little less overseas but the churn is still very low. >> Male: Yep. >> Okay so that's super positive. Customer affinity, I was wanted to code these events. I listen to the key notes very carefully, and then interview customers on the cube, and I try to identify, is there alignment there? And I see very strong alignment, I have to say, and strong customer affinity. So that's in your favor. I have, Daniel, I got another question for you on product. What is Symantec automation? What the heck is that? Can you explain that? I don't understand >> Dave, have you seen the demo in my (indistinct)? >> Dave: You know, I had to leave and do interviews, so I, uh, I missed it. >> I think, I think that demo answer complete your question. So in the s-, you know there saying that great, you can not distinguish great technology by magic. I think technology should be simple. And we, we show today, one of the simplest demo that you can imagine. But it's so, such a complex technology behind the scene, that you also can not imagine. So what was demo? We show how one business user, without any technical skills, can build any type of document. Can be a passport, can be an invoice, can be a legal (indistinct), and just go, "I want to copy data from here, and I want to paste data there". Can be a spreadsheet, can be another obligation, and like a human user, without understanding, without having prior knowledge about data, document layout, about screens, screens layouts, nothing, we analyze real time. Document. We discover, we discover the meaning of the information. We analyze the screen. We understand the screen but we understand the meaning of the screen. And we understand how the information in one side relate to the other side. And we just connects the dots and we copy the information and we paste it. A job that you'll do as a human user, maybe three minutes, is done in ten seconds. This is powerful. >> Yeah that is powerful. Thank you for that. I mean, and you take the date, whether it's transaction data or unstructured data and and and bring meaning out of it. That's powerful. Last question and I'll let you guys go. Rob, you got traders, and you've got long term investors. All right traders going to be defensive, today. I get that. Make the case for UIPATH, for long term investors. >> Rob: I think we're going to be a multi-gern- multi-billion company and we're going to be a generational company of our time. And we will define enterprise automation. And it's going to be a long term game and we feel like really strong that we'll be the lead in that game. >> Dave: Guys, thanks so much for coming to the cube. Great show. Always fun at UiPath Forward. Really appreciate your time. Thank you. >> Thanks dave. >> Appreciate it as well. >> Okay wrap it up, day one, we're here tomorrow, first thing, Dave Vellante and Dave Nicholson. Thanks for watching, forward 5, Uipath big customer event, we'll see you tomorrow. (music)
SUMMARY :
brought to you by, UIPATH. Okay the party has started to get to where you are today. It cannot be anything in the middle. So Rob, what attracted you to UIPATH? And then I, you know I got to know Daniel. Dave: You got to meet the And they decided to kind of split duties. And I think we also split the move to the internet platform, that Daniel had the vision And that's, you know that's I mean you sort of, you sort of started When the local currencies, you know, I have to be honest. is that the customers who the love outcomes to the business. And the product priorities, And it does the same to I mean the numbers are one And so we have the ability to And I think you broke out just yesterday. but the churn is still very low. I listen to the key notes very carefully, to leave and do interviews, And we just connects the dots I mean, and you take the date, And it's going to be a long term game much for coming to the cube. we'll see you tomorrow.
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Architecting SaaS Superclouds | Supercloud22
>>Welcome back to super cloud 22, our inaugural event. It's a pilot event here in the cube studios we're live and streaming virtually until we do it in person. Maybe next year. I'm John fury, host of the cube with Dave Lon two great guests, distinguished engineers managers, CTOs investors. Mariana Tessel is a CTO of Intuit ins Ray founder of vertex ventures. Both have a lot of DNA. Founder allow cloud here with mark Andre and Ben Horowitz, a variety of other great ventures you've done. And now you're an investor. Yep. Maria, you've been a seasoned CTO, VP of engineering, VMware Docker Intuit. Now thanks for joining us. >>Absolutely. >>So super cloud is a, is a thing. And apparently it's got a lot of momentum and you guys got stats over there at, at Intuit in, so you're investing and we were challenged on super cloud. Our initial thesis was you build on the clouds, get all that leverage like snowflake, you get a good differentiation and then you compete and then move to other clouds. Now it's becoming a thing where I can do this. Every enterprise could possibly do it. So I want to get your guys thoughts on what you think of super cloud concept and where are the holes in it, what needs to be defined. And so we'll start with you. You've done a lot of cloud things in your day. What >>Do you think? Yeah, it's the whole cloud journey started with a desire to consolidate and desire to actually provide uniformity and, and standards driven ways of doing things. And I think Amazon was a leader there. They helped kind of teach everybody else. You know, when I was in loud cloud, we were trying to do it with proprietary stacks just wouldn't work. But once everyone standardized upon Unix and you know, the chip sets no longer became as relevant. They did a lot of good things there, but what's happened since then is now you've got competing standards at the API layer at the interface layer no longer at the chip set layer, no longer at the operating system layer. Right? So the evolution of the, the, the battles are still there. When you talk about multicloud and super cloud, though, like one of the big things you have to keep in mind is latency is not free. Latency is very expensive and it's getting even more expensive now with, with multi-cloud. So you have to really understand where the separations of boundaries are between your data, your compute, and, and the network is just there as a facilitator to help binding compute and data. Right? And I think there's a lot of bets being made across different vendors like CloudFlare Akamai, as well as Amazon Google Microsoft in terms of how they think we should take computing either to the edge, from the core or back and forth. >>These, this is structural change. I mean, this is structural, >>It's desired by incumbents, but it's not something that I'm seeing from the consumption. I'd love to hear, hear from our end's per perspective, from a consumption point of view, like how much edge computing really matters. Right. >>Mario. >>So I think there's like, there's kind of a, a story of like two, like it's kind of, you can cut it for both edges. No, no pun intended on one end. It is really simplifying to actually go into like a single cloud and standardize on it and just have everything there. But I think what over time companies find is that they end up in multiple clouds, whether like, you know, through acquisitions or through like needing to use a service in another cloud. So you do find yourself in a situation where you have multi multi-cloud and you have to kind of work through it and understand how to make it all like work and latency is an issue, but also for many, many workloads, you can work around it and you can make it work where you have workloads that actually span multiple vendors and clouds. You know, again, having said that, I would say the world is such, that is still a simplifying assumption. When if you go to a single cloud, it's much easier to just go and, and bet on that >>Easier in terms of everything's integrated, IAS works with SAS, they solve a lot of problems. >>Correct. And you can do like for your developers, you can actually provide an environment that's super homogenous, simple. You can use services easily up and down the stack. And, you know, we, we actually made that deliberate decision. When we started migrating to the cloud at the beginning, it was like, oh, let's do like hybrid we'll, you know, make it, so it work anywhere. It was so complicated. It was not worth it. >>When was the, when did you give up, what was the moment? Was there a flash point where you said, oh, this is terrible. This is >>Dead. Yeah. When, when we started to try to make it interoperable and you just see what it requires to do that and the complexity of the architecture that it just became not worth it for the gains you have. >>So speaking obviously as a SAS provider, right. So it just doesn't, it didn't make business case sense for you guys to do that. So it was super cloud. Then an infrastructure thing we just heard from Ben wa deja VI that they're not, they're going beyond instantiating their, their data cloud. They're actually running, you know, their own little snow grid. They called it. And, and then when I asked him, well, what about latency? He said, well, we copied data over, you know, so, okay. That's you have to do, but that's a singular experience with the same governance or the same security. Just wasn't worth it for you guys is what I'm hearing. >>Correct. But again, like for some workload or for some services that we want to use, we are gonna go there and we are gonna then figure out what is the work around the latency issue, whether it's like copy or, you know, redundancy. >>Well, the question I have Dave on snowflake is maybe the question for you and in the panel is snowflake a tan expansion opportunity, or is there a technical reason to go to other clouds? >>I think they wanted to leverage the hyperscale infrastructure globally. And they said that they're out there, it's a free gift. We're gonna go take it. I, I think it started with we're on AWS. Do you think? And then we're on Azure and then we're on Google. And then they said, why don't we just connect all these and make it a singular experience? And yeah, I guess it's a TA expansion as a differentiator and it's, it adds value. Right. If I can share data across that global network, >>We have customers on Azure now, >>Right? Yeah. Yeah. Of course. >>You guys don't need to go CP. What do you think about that? >>Well, I think Snowflake's in a good position cuz they work mostly with analytical workloads and you have capacity. That's always gonna increase like no one subtracts, their analytical workload like ever, right. So there was just compounded growth is like 50% or 80% for, you know, many enterprises despite their best intentions, not to collect more data, they just can't stop doing it. So it's different than if you're like an Oracle or a transactional database where you don't have those, you know, like kind of infinite growth paths. So Snowflake's gonna continue to expand footprint their customers. They don't mind as long as you, they can figure out the, the lowest cost on denominator for, for that. >>Yeah. So it makes sense to be in all the clouds >>For them, for, for them, for sure. Yeah. >>But, but, but Oracle just announced with Microsoft what I would call super cloud, a, a cross cloud database service running on OCI and Azure with very low latency and a database that looks like a, the singular experience. Yeah. With, with a PAs layers >>That lost me after OCI that's >>Okay. You know, but that's the, that's the, the BS answer for all U VCs. The do nobody develops on Oracle? Well, it's a 240 billion market cap company. Show me who you all want be. >>We're gonna talk about SRDF and em C next, you >>All want Oracle. So there we go. You throw that into, you all want Oracle to buy your companies, your funding, you know, cause, cause we all wanna be like Oracle with that kinda cash flow. But, but anyway, >>Here's, here's one thing that I'm noticing that is gonna be really practical. I think for companies that do run SA is because like, you know, you have all these solutions, whether it's like analytics or like monitoring or logging or whatever. And each one of them is very data hungry and all of them have like SAS solutions that end up copy the data, moving data to their cloud, and then they might charge you by the size of your data. It does become kind of overwhelming for companies to use that many tools and basically maybe have that data kind of charge for it, multiple places because you use it for different purposes or just in general, if you have a lot of data, you know, that that is becoming an issue. So that's something that I've noticed in our, in our own kind of, you know, a world, but it's just something that I think companies need to think about how they solve because eventually a lot of companies will say, I cannot have all these solutions, so there's no way I'm gonna be willing to have so many copies of the data and actually pay for that. >>So many times, just something to think about. >>But one of the criticisms of the super cloud concept is that it's just SAS. If I'm running workload on prem and I, and I've got, you know, a connection to the cloud, which you probably do, that's, that's SAS, what's, what's the big deal and that's not anything new or different. So I'd love to get your thoughts on that. But Goldman Sachs, for instance, just announced the service last reinvent with AWS, connecting their tools, their data, and their software from on-prem to AWS, they're offering it as a service. I'm like, Hmm. Kind of looking like Supercloud, but maybe it's just SAS. >>It could be. And like, what I'm talking about is not so much like, you know, like what you wanna connect your data. But the idea is like a lot of the providers of different services, like in the past and, and like higher layer, they're actually COPI the data. They need the data in their cloud or their solution. And it just becomes complicated and expensive is, is kind of like my point. So yes, connecting it like for you to have the data in one place and then be able to connect to it. I think that is a valid, if, if that's kinda what you think about as a super cloud, that is a valid need, I think that companies will >>Have where developers actually want access to tools that might exist. >>Also the key is developers, right? Yeah. Developers decide all decisions, not database on administrators, not, you know, a hundred percent security engineers, not admins. So what's really interesting is where are the developers going next? If you look at the current winners in the current ecosystem, companies like MongoDB, I mean, they capture the minds of yeah. The JavaScript, you know, no JS developers absolutely very early on. And I started catch base and I could tell you like the difference was that capture motion was so important. So developers are basically used to this game-like experience now where they want to see tools that are free, whether it's open source or not, they actually don't care. They just want, and they want it SAS. They want it SAS delivered on demand. Right. And pay as you go. And so there's a lot of these different frameworks coming out next generation, no code, low code, whether it's Java, JavaScript, rust, you know, whatever, you know, go Lang. And there's a lot of people fighting religious wars about how to develop the next kind of modern pattern design pattern. Okay. And that's where a lot of excitement is how we look at like investment opportunities. Like where are those big bets who are, you know, frustrated developers, who are they frustrated, what's wrong with their current environment? You know, do they really enjoy using Kubernetes or trying to use Kubernetes? Yeah. Right. Like developers have a very different view than operator, >>But you mentioned couch base. I mean, I look at couch base what they're doing with Capellas as a form of Supercloud. I mean, I think that's an excellent, they're bringing that out to the edge. We're gonna hear later on from someone from couch base. That's gonna talk about that now. It's kind of a lightweight, you know, sort of, it's gonna be a, a synchronization, but it's the beginning >>A cool new venture deal that I'm not in, but was like duck DB. I'm like, what's duck DB like, well, it's an Emory database that has like this like remote store thing. I'm like, okay, that sounds interesting. Like let's call Mike Olson cuz that sounds like sleepy cat redone red distributed world. But like it's, it's like there's a lot of people refactoring design patterns that we're all grew up with since the popup days of, you know, typical round. Right? >>Yeah. That's the refactory I think that's the big pattern. So I have to ask you guys, what are you guys investing in? We've got a couple minutes left to chat about that. What are you investing at into it from a, from a, a CTO engineering perspective and what are you investing in that feels super cloud like to you? >>Well, the, the thing that like I'm focused on is to make sure that we have absolutely best in the world development environment for our engineers, where it's modern, it's easy to use and it incorporates as many things as we can into that environment. So the engineers don't have to think about it. Like one big example would be security and how we incorporated that into development environment. So again, the engineers don't have to bother with trying to think through how they secure their workloads and every step of the way their other things that we incorporated, whether it's like rollbacks or monitoring or, you know, like baly enough other things. But I think that's really an investment that has panned off for us. We actually started investing in development environment several years ago. We started measure our development velocity and we, it actually went up by six X justly investing. So >>User experience, developer experience and productivity pretty much right. >>Yeah. AB absolutely. Yeah. That's like a big investment area for us that, you know, cloud cloud >>Sounds like super cloudlike factor and I'm assuming it's you're on AWS. >>We are mostly on AWS. Yes. >>And so what are you investing in that from a VC money doling out standpoint? That feels super cloudlike >>So very similar to what we just touched on a lot of developer tool experiences. We have a company that we've invested in called ops level that the service catalogs it's, it's helping, you know, understand your, where your services live and how they could be accessed and, and you know, enterprise kind of that come with that. And then we have a company called Lugo that helps you do serverless debugging container debugging, cuz it turns out debugging distributed, you know, applications is a real problem right now just you can only do so much by log tracing, right? We have a company haven't announced yet that's in the web assembly space. So we're looking at modernizing the next generation past stack and throwing everything out the window, including Java and all of the, you know, current prebuilt components because turns out 90% of enterprise workloads are actually not used. They're they're just policy code. You compiled with they're sitting there as vulnerabilities that no one's actually accessing, but you still have to compile with all of it. So we have a lot of bloatware happening in the enterprise. So we're thinking about how do you skinny that up with the next generation paths that's enterprise capable with security context and frameworks >>Super pass. >>Well, yeah, super pass. That's a kind of good way to, well, is >>It, is it a consistent developer experience across clouds? >>It is. And, and, and, and web assembly is a very raw standard if you can call it that. I mean it's, but it's supported by every modern browser, every major platform, vendor cloud, and Adobe and others, and are using it for their uses. And it's not just about your edge browser compute. It's really, you can take the same framework and compile it down to server side as well as client site, just like JavaScript was a client side tool before it became node. Right. Right. So we're looking at that as a very interesting opportunity. It's very nascent. Yeah. >>Great patterns. Yeah. Well, thanks so much for spending the time outta your busy day. Ariana. Thanks for your commentary. Appreciate your coming on the cubes first in IGUR super cloud event, pilot. Thanks for, for sharing. Thanks for having, thanks for having us. Okay. More coverage here. Super cloud 2022. I'm Jeff David Alane stay with us. We got our cloud ARA panel coming up next.
SUMMARY :
I'm John fury, host of the cube with Dave Lon two great guests, distinguished engineers managers, lot of momentum and you guys got stats over there at, at Intuit in, So you have to really understand where the separations of boundaries are between your data, I mean, this is structural, It's desired by incumbents, but it's not something that I'm seeing from the consumption. whether like, you know, through acquisitions or through like needing to use a service And you can do like for your developers, you can actually provide an environment When was the, when did you give up, what was the moment? just became not worth it for the gains you have. They're actually running, you know, their own little snow grid. issue, whether it's like copy or, you know, redundancy. Do you think? Right? What do you think about that? So there was just compounded growth is like 50% or 80% for, you know, many enterprises despite Yeah. that looks like a, the singular experience. Show me who you all want be. You throw that into, you all want Oracle to buy your companies, moving data to their cloud, and then they might charge you by the size of your data. and I, and I've got, you know, a connection to the cloud, which you probably do, that's, And like, what I'm talking about is not so much like, you know, like what you wanna connect your data. And I started catch base and I could tell you like the difference was It's kind of a lightweight, you know, sort of, patterns that we're all grew up with since the popup days of, you know, typical round. So I have to ask you guys, what are you guys investing in? So again, the engineers don't have to bother with trying to think through how you know, cloud cloud We are mostly on AWS. And then we have a company called Lugo that helps you do serverless debugging container debugging, That's a kind of good way to, well, is It's really, you can take the same framework and compile it down to server side as well as client Thanks for your commentary.
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Sanjay Poonen, CEO & President, Cohesity | VMware Explore 2022
>>Good afternoon, everyone. And welcome back to the VMware Explorer. 2022 live from San Francisco. Lisa Martin, here with Dave. Valante good to be sitting next to you, sir. >>Yeah. Yeah. The big set >>And we're very excited to be welcoming buck. One of our esteemed alumni Sanja poin joins us, the CEO and president of cohesive. Nice to see >>You. Thank you, Lisa. Thank you, Dave. It's great to meet with you all the time and the new sort of setting here, but first >>Time, first time we've been in west, is that right? We've been in north. We've been in south. We've been in Las Vegas, right. But west, >>I mean, it's also good to be back with live shows with absolutely, you know, after sort of the two or three or hiatus. And it was a hard time for the whole world, but I'm kind of driving a little bit of adrenaline just being here with people. So >>You've also got some adrenaline, sorry, Dave. Yeah, you're good because you are new in the role at cohesive. You wrote a great blog that you are identified. The four reasons I came to cohesive. Tell the audience, just give 'em a little bit of a teaser about that. >>Yeah, I think you should all read it. You can Google and, and Google find that article. I talked about the people Mohi is a fantastic founder. You know, he was the, you know, the architect of the Google file system. And you know, one of the senior Google executives was on my board. Bill Corrin said one of the smartest engineers. He was the true father of hyperconverge infrastructure. A lot of the code of Nutanix. He wrote, I consider him really the father of that technology, which brought computer storage. And when he took that same idea of bringing compute to secondary storage, which is really what made the scale out architect unique. And we were at your super cloud event talking about that, Dave. Yeah. Right. So it's a people I really got to respect his smarts, his integrity and the genius, what he is done. I think the customer base, I called a couple of customers. One of them, a fortune 100 customer. I, I can't tell you who it was, but a very important customer. I've known him. He said, I haven't seen tech like this since VMware, 20 years ago, Amazon 10 years ago and now Ko. So that's special league. We're winning very much in the enterprise and that type of segment, the partners, you know, we have HPE, Cisco as investors. Amazon's an investors. So, you know, and then finally the opportunity, I think this whole area of data management and data security now with threats, like ransomware big opportunity. >>Okay. So when you were number two at VMware, you would come on and say, we'd love all our partners and of course, okay. So you know, a little bit about how to work with, with VMware. So, so when you now think about the partnership between cohesive and VMware, what are the things that you're gonna stress to your constituents on the VMware side to convince them that Hey, partnering with cohesive is gonna gonna drive more value for customers, you know, put your thumb on the scale a little bit. You know, you gotta, you gotta unfair advantage somewhat, but you should use it. So what's the narrative gonna be like? >>Yeah, I think listen with VMware and Amazon, that probably their top two partners, Dave, you know, like one of the first calls I made was to Raghu and he knew about this decision before. That's the level of trust I have in him. I even called Michael Dell, you know, before I made the decision, there's a little bit of overlap with Dell, but it's really small compared to the overlap, the potential with Dell hardware that we could compliment. And then I called four CEOs. I was, as I was making this decision, Andy Jassey at Amazon, he was formerly AWS CEO sat Nadela at Microsoft Thomas cor at Google and Arvin Christian, IBM to say, I'm thinking about this making decision. They are many of the mentors and friends to me. So I believe in an ecosystem. And you know, even Chuck Robbins, who the CEO of Cisco is an investor, I texted him and said, Hey, finally, we can be friends. >>It was harder to us to be friends with Cisco, given the overlap of NSX. So I have a big tent towards everybody in our ecosystem with VMware. I think the simple answer is there's no overlap okay. With, with the kind of the primary storage capabilities with VSAN. And by the same thing with Nutanix, we will be friends and, and extend that to be the best data protection solution. But given also what we could do with security, I think this is gonna go a lot further. And then it's all about meet the field. We have common partners. I think, you know, sort of the narrative I talked about in that blog is just like snowflake was replacing Terada and ServiceNow replace remedy and CrowdStrike, replacing Symantec, we're replacing legacy vendors. We are viewed as the modern solution cloud optimized for private and public cloud. We can help you and make VMware and vs a and VCF very relevant to that part of the data management and data security continuum, which I think could end VMware. And by the way, the same thing into the public cloud. So most of the places where we're being successful is clearly withs, but increasingly there's this discussion also about playing into the cloud. So I think both with VMware and Amazon, and of course the other partners in the hyperscaler service, storage, networking place and security, we have some big plans. >>How, how much do you see this? How do you see this multi-cloud narrative that we're hearing here from, from VMware evolving? How much of an opportunity is it? How are customers, you know, we heard about cloud chaos yesterday at the keynote, are customers, do they, do they admit that there's cloud chaos? Some probably do some probably don't how much of an opportunity is that for cohesive, >>It's tremendous opportunity. And I think that's why you need a Switzerland type player in this space to be successful. And you know, and you can't explicitly rule out the fact that the big guys get into this space, but I think it's, if you're gonna back up office 365 or what they call now, Microsoft 365 into AWS or Google workspace into Azure or Salesforce into one of those clouds, you need a Switzerland player. It's gonna be hard. And in many cases, if you're gonna back up data or you protect that data into AWS banks need a second copy of that either on premise or Azure. So it's very hard, even if they have their own native data protection for them to be dual cloud. So I think a multi-cloud story and the fact that there's at least three big vendors of cloud in, in the us, you know, one in China, if include Alibaba creates a Switzerland opportunity for us, that could be fairly big. >>And I think, you know, what we have to do is make sure while we'll be optimized, our preferred cloud is AWS. Our control plane runs there. We can't take an all in AWS stack with the control plane and the data planes at AWS to Walmart. So what I've explained to both Microsoft and AWS is that data plane will need to be multi-cloud. So I can go to an, a Walmart and say, I can back up your data into Azure if you choose to, but the control plane's still gonna be an AWS, same thing with Google. Maybe they have another account. That's very Google centric. So that's how we're gonna believe the, the control plane will be in AWS. We'll optimize it there, but the data plane will be multicloud. >>Yeah. And that's what Mo had explained at Supercloud. You know, and I talked to him, he really helped me hone in on the deployment models. Yes. Where, where, where the cohesive deployment model is instantiating that technology stack into each cloud region and each cloud, which gives you latency advantages and other advantages >>And single code based same platform. >>And then bringing it, tying it together with a unified, you know, interface. That was he, he was, he was key. In fact, I, I wrote about it recently and, and gave him and the other 29 >>Quite a bit in that session, he went deep with you. I >>Mean, with Mohi, when you get a guy who developed a Google file system, you know, who can technically say, okay, this is technically correct or no, Dave, your way off be. So I that's why I had to >>Go. I, I thought you did a great job in that interview because you probed him pretty deep. And I'm glad we could do that together with him next time. Well, maybe do that together here too, but it was really helpful. He's the, he's the, he's the key reason I'm here. >>So you say data management is ripe for disrupt disruption. Talk about that. You talked about this Switzerland effect. That sounds to me like a massive differentiator for cohesive. Why is data management right for disruption and why is cohesive the right partner to do it? >>Yeah, I think, listen, everyone in this sort of data protection backup from years ago have been saying the S Switzerland argument 18 years ago, I was a at Veras an executive there. We used the Switzerland argument, but what's changed is the cloud. And what's changed as a threat vector in security. That's, what's changed. And in that the proposition of a, a Switzerland player has just become more magnified because you didn't have a sales force or Workday service now then, but now you do, you didn't have multi-cloud. You had hardware vendors, you know, Dell, HPE sun at the time. IBM, it's now Lenovo. So that heterogeneity of, of on-premise service, storage, networking, HyperCloud, and, and the apps world has gotten more and more diverse. And I think you really need scale out architectures. Every one of the legacy players were not built with scale out architectures. >>If you take that fundamental notion of bringing compute to storage, you could almost paralyze. Imagine you could paralyze backup recovery and bring so much scale and speed that, and that's what Mo invented. So he took that idea of how he had invented and built Nutanix and applied that to secondary storage. So now everything gets faster and cheaper at scale. And that's a disruptive technology ally. What snowflake did to ator? I mean, the advantage of snowflake is when you took that same concept data, warehousing is not a new concept it's existed from since Ralph Kimball and bill Inman and the people who are fathers of data warehousing, they took that to Webscale. And in that came a disruptive force toter data, right on snowflake. And then of course now data bricks and big query, similar things. So we're doing the same thing. We just have to showcase the customers, which we do. And when large customers see that they're replacing the legacy solutions, I have a lot of respect for legacy solutions, but at some point in time of a solution was invented in 1995 or 2000, 2005. It's right. For change. >>So you use snowflake as an example, Frank SL doesn't like when I say playbook, cuz I says, Dave, I'm a situational CEO, no playbook, but there are patterns here. And one of the things he did is to your point go after, you know, Terra data with a better data warehouse, simplify scale, et cetera. And now he's, he's a constructing a Tam expansion strategy, same way he did at ServiceNow. And I see you guys following a similar pattern. Okay. You get your foot in the door. Let's face it. I mean, a lot of this started with, you know, just straight back. Okay, great. Now it's extending into data management now extending to multi-cloud that's like concentric circles in a Tam expansion strategy. How, how do you, as, as a CEO, that's part of your job is Tam expansion. >>So yeah, I think the way to think about the Tam is, I mean, people say it's 20, 30 billion, but let me tell you how you can piece it apart in size, Dave and Lisa number one, I estimate there's probably about 10 to 20 exabytes of data managed by these legacy players of on-prem stores that they back up to. Okay. So you add them all up in the market shares that they respectively are. And by the way, at the peak, the biggest of these companies got to 2 billion and then shrunk. That was Verto when I was there in 2004, 2 billion, every one of them is small and they stopped growing. You look at the IDC charts. Many of them are shrinking. We are the fastest growing in the last two years, but I estimate there's about 20 exabytes of data that collectively among the legacy players, that's either gonna stay on prem or move to the cloud. Okay. So the opportunity as they replace one of those legacy tools with us is first off to manage that 20 X by cheaper, faster with the Webscale glass offer the cloud guys, we could tip that into the cloud. Okay. >>But you can't stop there. >>Okay. No, we are not doing just backup recovery. We have a platform that can do files. We can do test dev analytics and now security. Okay. That data is potentially at a risk, not so much in the past, but for ransomware, right? How do we classify that? How do we govern that data? How do we run potential? You know, the same way you did antivirus some kind of XDR algorithms on the data to potentially not just catch the recovery process, which is after fact, but maybe the predictive act of before to know, Hey, there's somebody loitering around this data. So if I'm basically managing in the exabytes of data and I can proactively tell you what, this is, one CIO described this very simply to me a few weeks ago that I, and she said, I have 3000 applications, okay. I wanna be prepared for a black Swan event, except it's not a nine 11 planes getting the, the buildings. >>It is an extortion event. And I want to know when that happens, which of my 3000 apps I recover within one hour within one day within one week, no later than one month. Okay. And I don't wanna pay the bad guys at penny. That's what we do. So that's security discussions. We didn't have that discussion in 2004 when I was at another company, because we were talking about flood floods and earthquakes as a disaster recovery. Now you have a lot more security opportunity to be able to describe that. And that's a boardroom discussion. She needs to have that >>Digital risk. O O okay, go ahead please. I >>Was just gonna say, ransomware attack happens every what? One, every 11, 9, 11 seconds. >>And the dollar amount are going up, you know, dollar are going up. Yep. >>And, and when you pay the ransom, you don't always get your data back. So you that's not. >>And listen, there's always an ethical component. Should you do it or not do it? If you, if you don't do it and you're threatened, they may have left an Easter egg there. Listen, I, I feel very fortunate that I've been doing a lot in security, right? I mean, I built the business at, at, at VMware. We got it to over a billion I'm on the board of sneak. I've been doing security and then at SAP ran. So I know a lot about security. So what we do in security and the ecosystem that supports us in security, we will have a very carefully crafted stay tuned. Next three weeks months, you'll see us really rolling out a very kind of disciplined aspect, but we're not gonna pivot this company and become a cyber security company. Some others in our space have done that. I think that's not who we are. We are a data management and a data security company. We're not just a pure security company. We're doing both. And we do it well, intelligently, thoughtfully security is gonna be built into our platform, not voted on. Okay. And there'll be certain security things that we do organically. There's gonna be a lot that we do through partnerships, this >>Security market that's coming to you. You don't have to go claim that you're now a security vendor, right? The market very naturally saying, wow, a comprehensive security strategy has to incorporate a data protection strategy and a recovery, you know, and the things that we've talking about Mount ransomware, I want to ask you, you I've been around a long time, longer than you actually Sanjay. So, but you you've, you've seen a lot. You look, >>Thank you. That's all good. Oh, >>Shucks. So the market, I've never seen a market like this, right? I okay. After the.com crash, we said, and I know you can't talk about IPO. That's not what I'm talking about, but everything was bad after that. Right. 2008, 2000, everything was bad. I've never seen a market. That's half full, half empty, you know, snowflake beats and raises the stock, goes through the roof. Dev if it, if the area announced today, Mongo, DB, beat and Ray, that things getting crushed and, and after market never seen anything like this. It's so fed, driven and, and hard to protect. And, and of course, I know it's a marathon, you know, it's not a sprint, but have you ever seen anything like this? >>Listen, I walk worked through 18 quarters as COO of VMware. You've seen where I've seen public quarters there and you know, was very fortunate. Thanks to the team. I don't think I missed my numbers in 18 quarters except maybe once close. But we, it was, it's tough. Being a public company of the company is tough. I did that also at SAP. So the journey from 10 to 20 billion at SAP, the journey from six to 12 at VMware, that I was able to be fortunate. It's humbling because you, you really, you know, we used to have this, we do the earnings call and then we kind of ask ourselves, what, what do you think the stock price was gonna be a day and a half later? And we'd all take bets as to where this, I think you just basically, as a, as a sea level executive, you try to build a culture of beaten, raise, beaten, raise, beaten, raise, and you wanna set expectations in a way that you're not setting them up for failure. >>And you know, it's you, there's, Dave's a wonderful CEO as is Frank Salman. So it's hard for me to dissect. And sometimes the market are fickle on some small piece of it. But I think also the, when I, I encourage people say, take the long term view. When you take the long term view, you're not bothered about the ups and downs. If you're building a great company over the length of time, now it will be very clear over the arc of many, many quarters that you're business is trouble. If you're starting to see a decay in growth. And like, for example, when you start to see a growth, start to decay significantly by five, 10 percentage points, okay, there's something macro going on at this company. And that's what you won't avoid. But these, you know, ups and downs, my view is like, if you've got both Mongo D and snowflake are fantastic companies, they're CEOs of people I respect. They've actually kind of an, a, you know, advisor to us as a company, you knows moat very well. So we respect him, respect Frank, and you, there have been other quarters where Frank's, you know, the Snowflake's had a down result after that. So you build a long term and they are on the right side of history, snowflake, and both of them in terms of being a modern cloud relevant in the case of MongoDB, open source, two data technology, that's, you know, winning, I, I, we would like to be like them one day >>As, as the new CEO of cohesive, what are you most ask? What are you most anxious about and what are you most excited about? >>I think, listen, you know, you know, everything starts with the employee. You, I always believe I wrote my first memo to all employees. There was an article in Harvard business review called service profit chains that had a seminal impact on my leadership, which is when they studied companies who had been consistently profitable over a long period of time. They found that not just did those companies serve their customers well, but behind happy engaged customers were happy, engaged employees. So I always believe you start with the employee and you ensure that they're engaged, not just recruiting new employees. You know, I put on a tweet today, we're hiring reps and engineers. That's okay. But retaining. So I wanna start with ensuring that everybody, sometimes we have to make some unfortunate decisions with employees. We've, we've got a part company with, but if we can keep the best and brightest retained first, then of course, you know, recruiting machine, I'm trying to recruit the best and brightest to this company, people all over the place. >>I want to get them here. It's been, so I mean, heartwarming to come Tom world and just see people from all walks, kind of giving me hugs. I feel incredibly blessed. And then, you know, after employees, it's customers and partners, I feel like the tech is in really good hands. I don't have to worry about that. Cuz Mo it's in charge. He's got this thing. I can go to bed knowing that he's gonna keep innovating the future. Maybe in some of the companies I've worried about the tech innovation piece, but most doing a great job there. I can kind of leave that in his cap of hands, but employees, customers, partners, that's kind of what I'm focused on. None of them are for me, like a keep up at night, but there are are opportunities, right? And sometimes there's somebody you're trying to salvage to make sure or somebody you're trying to convince to join. >>But you know, customers, I love pursuing customers. I love the win. I hate to lose. So fortune 1000 global, 2000 companies, small companies, big companies, I wanna win every one of them. And it's not, it's not like, I mean, I know all these CEOs in my competitors. I texted him the day I joined and said, listen, I'll compete, honorably, whatever have you, but it's like Kobe and LeBron Kobe's passed away now. So maybe it's Steph Curry. LeBron, whoever your favorite athlete is you put your best on the court and you win. And that's how I am. That's nothing I've known no other gear than to put my best on the court and win, but do it honorably. It should not be the one that you're doing it. Unethically. You're doing it personally. You're not calling people's names. You're competing honorably. And when you win the team celebrates, it's not a victory for me. It's a victory for the team. >>I always think I'm glad that you brought up the employee experience and we're almost out of time, but I always think the employee experience and the customer experience are inextricably linked. This employees have to be empowered. They have to have the data that they need to do their job so that they can deliver to the customer. You can't do one without the other. >>That's so true. I mean, I, it's my belief. And I've talked also on this show and others about servant leadership. You know, one of my favorite poems is Brenda Naor. I went to bed in life. I dreamt that life was joy. I woke up and realized life was service. I acted in service was joy. So when you have a leadership model, which is it's about, I mean, there's lots of layers between me and the individual contributor, but I really care about that sales rep and the engineer. That's the leaf level of the organization. What can I get obstacle outta their way? I love skipping levels of going right. That sales rep let's go and crack this deal. You know? So you have that mindset. Yeah. I mean, you, you empower, you invert the pyramid and you realize the power is at the leaf level of an organization. >>So that's what I'm trying to do. It's a little easier to do it with 2000 people than I dunno, either 20, 20, 2000 people or 35,000 reported me at VMware. And I mean a similar number at SAP, which was even bigger, but you can shape this. Now we are, we're not a startup anymore. We're a midsize company. We'll see. Maybe along the way, there's an IP on the path. We'll wait for that. When it comes, it's a milestone. It's not the destination. So we do that and we are, we, I told people we are gonna build this green company. Cohesive is gonna be a great company like VMware one day, like Amazon. And there's always a day of early beginnings, but we have to work harder. This is kind of like the, you know, eight year old version of your kid, as opposed to the 18 year old version of the kid. And you gotta work a little harder. So I love it. Yeah. >>Good luck. Awesome. Thank you. Best of luck. Congratulations. On the role, it sounds like there's a tremendous amount of adrenaline, a momentum carrying you forward Sanjay. We always appreciate having you. Thank >>You for having in your show. >>Thank you. Our pleasure, Lisa. Thank you for Sanja poin and Dave ante. I'm Lisa Martin. You're watching the cube live from VMware Explorer, 2022, stick around our next guest. Join us momentarily.
SUMMARY :
Valante good to be sitting next to you, sir. And we're very excited to be welcoming buck. It's great to meet with you all the time and the new sort of setting here, We've been in north. I mean, it's also good to be back with live shows with absolutely, you know, after sort of the two or three or hiatus. You wrote a great blog that you are identified. And you know, one of the senior Google executives was on my board. So you know, a little bit about how to work with, with VMware. And you know, even Chuck Robbins, who the CEO of I think, you know, sort of the narrative I talked about in that blog is And I think that's why you need a Switzerland type player in this space to And I think, you know, what we have to do is make sure while we'll be optimized, our preferred cloud is AWS. stack into each cloud region and each cloud, which gives you latency advantages and other advantages And then bringing it, tying it together with a unified, you know, interface. Quite a bit in that session, he went deep with you. Mean, with Mohi, when you get a guy who developed a Google file system, you know, who can technically Go. I, I thought you did a great job in that interview because you probed him pretty deep. So you say data management is ripe for disrupt disruption. And I think you really need scale out architectures. the advantage of snowflake is when you took that same concept data, warehousing is not a new concept it's existed from since And I see you guys following a similar pattern. So yeah, I think the way to think about the Tam is, I mean, people say it's 20, 30 billion, but let me tell you how you can piece it apart You know, the same way you did antivirus some kind of XDR And I want to know when that happens, which of my 3000 apps I I Was just gonna say, ransomware attack happens every what? And the dollar amount are going up, you know, dollar are going up. And, and when you pay the ransom, you don't always get your data back. I mean, I built the business at, at, at VMware. protection strategy and a recovery, you know, and the things that we've talking about Mount ransomware, Thank you. And, and of course, I know it's a marathon, you know, it's not a sprint, I think you just basically, as a, as a sea level executive, you try to build a culture of And you know, it's you, there's, Dave's a wonderful CEO as is Frank Salman. I think, listen, you know, you know, everything starts with the employee. And then, you know, And when you win the team celebrates, I always think I'm glad that you brought up the employee experience and we're almost out of time, but I always think the employee experience and the customer So when you have a leadership model, which is it's about, I mean, This is kind of like the, you know, eight year old version of your kid, as opposed to the 18 year old version of a momentum carrying you forward Sanjay. Thank you.
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Sanjay Poonen | VMware Explore 2022
>>Good afternoon, everyone. And welcome back to the Cube's day two coverage of VMware Explorer, 2022 live from San Francisco. Lisa Martin, here with Dave. Valante good to be sitting next to you, sir. >>Yeah, the big >>Set and we're very excited to be welcoming back. One of our esteemed alumni Sanja poin joins us, the CEO and president of cohesive. Nice to see >>You. Thank you, Lisa. Thank you, Dave. It's great to meet with you all the time and the new sort of setting here, but >>First time we've been in west, is that right? We've been in north. We've been in south. We've been in Las Vegas, right. But west >>Nice. Well, I mean, it's also good to be back with live shows with absolutely, you know, after sort of the two or three or high. And it was a hard time for the whole world, but I'm kind of driving a little bit of adrenaline just being here with people. So >>You've also got some adrenaline, sorry, Dave. Yeah, you're good because you are new in the role at cohesive. You wrote a great blog that you are identified. The four reasons I came to cohesive. Tell the audience, just give 'em a little bit of a teaser about that. >>Yeah, I think you should all read it. You can Google and, and Google find that article. I talked about the people Mohi is a fantastic founder. You know, he was the, you know, the architect of the Google file system. And you know, one of the senior Google executives who was on my board, bill Corrin said one of the smartest engineers. He was the true father of hyperconverge infrastructure. A lot of the code of Nutanix. He wrote, I consider him really the father of that technology, which brought computer storage. And when he took that same idea of bringing compute to secondary storage, which is really what made the scale out architect unique. And we were at your super cloud event talking about that, Dave. Yeah. Right. So it's a people I really got to respect his smarts, his integrity and the genius, what he is done. >>I think the customer base, I called a couple of customers. One of them, a fortune 100 customer. I, I can't tell you who it was, but a very important customer. I've known him. He said, I haven't seen tech like this since VMware, 20 years ago, Amazon 10 years ago. And now COER so that's special league. We're winning very much in the enterprise and that type of segment, the partners, you know, we have HPE, Cisco as investors, Amazon's an investors. So, you know, and then finally the opportunity, I think this whole area of data management and data security now with threats, like ransomware big opportunity. >>Sure. Okay. So when you were number two at VMware, you would come on and say, we'd love all our partners and of course, okay. So you know, a little bit about how to work with, with VMware. So, so when you now think about the partnership between cohesive and VMware, what are the things that you're gonna stress to your constituents on the VMware side to convince them that Hey, partnering with cohesive is gonna gonna drive more value for customers, you know, put your thumb on the scale a little bit. You know, you gotta, you gotta unfair advantage somewhat, but you should use it. So what's the narrative gonna be like? >>Yeah. I think listen with VMware and Amazon, that probably their top two partners, Dave, you know, like one of the first calls I made was to Raghu and he knew about this decision before. That's the level of trust I have in him. I even called Michael Dell, you know, before I made the decision, there's a little bit of an overlap with Dell, but it's really small compared to the overlap, the potential with Dell hardware that we could compliment. And then I called four CEOs. I was, as I was making this decision, Andy Jassy at Amazon, he was formerly AWS CEO sat Nadela at Microsoft Thomas cor at Google and Arvin Christian at IBM to say, I'm thinking about this making decision. They are many of the mentors and friends to me. So I believe in an ecosystem. And you know, even Chuck Robbins, who the CEO of Cisco is an investor, I texted him and said, Hey, finally, we can be friends. >>It was harder to us to be friends with Cisco, given the overlap of NEX. So I have a big tent towards everybody in our ecosystem with VMware. I think the simple answer is there's no overlap okay. With, with the kind of the primary storage capabilities with VSAN. And by the same thing with Nutanix, we will be friends and, and extend that to be the best data protection solution. But given also what we could do with security, I think this is gonna go a lot further. And then it's all about meet in the field. We have common partners. I think, you know, sort of the narrative I talked about in that blog is just like snowflake was replacing Terada and ServiceNow replace remedy and CrowdStrike, replacing Symantec, we're replacing legacy vendors. We are viewed as the modern solution cloud optimized for private and public cloud. We can help you and make VMware and VSAN and VCF very relevant to that part of the data management and data security continuum, which I think could enhance VMware. And by the way, the same thing into the public cloud. So most of the places where we're being successful is clearly withs, but increasingly there's this discussion also about playing into the cloud. So I think both with VMware and Amazon, and of course the other partners in the hyperscaler service, storage, networking place and security, we have some big plans. >>How, how much do you see this? How do you see this multi-cloud narrative that we're hearing here from, from VMware evolving? How much of an opportunity is it? How are customers, you know, we heard about cloud chaos yesterday at the keynote, are customers, do they, do they admit that there's cloud chaos? Some probably do some probably don't how much of an opportunity is that for cohesive, >>It's tremendous opportunity. And I think that's why you need a Switzerland type player in this space to be successful. And you know, and you can't explicitly rule out the fact that the big guys get into this space, but I think it's, if you're gonna back up office 365 or what they call now, Microsoft 365 into AWS or Google workspace into Azure or Salesforce into one of those clouds, you need a Switzerland player it's gonna be out. And in many cases, if you're gonna back up data or you protect that data into AWS banks need a second copy of that either on premise or Azure. So it's very hard, even if they have their own native data protection for them to be dual cloud. So I think a multi-cloud story and the fact that there's at least three big vendors of cloud in, in the us, you know, one in China, if include Alibaba creates a Switzerland opportunity for us, that could be fairly big. >>And I think, you know, what we have to do is make sure while we'll be optimized, our preferred cloud is AWS. Our control plane runs there. We can't take an all in AWS stack with the control plane and the data planes at AWS to Walmart. So what I've explained to both Microsoft and AWS is that data plane will need to be multicloud. So I can go to an a Walmart and say, I can back up your data into Azure if you choose to, but the control, plane's still gonna be an AWS, same thing with Google. Maybe they have another account. That's very Google centric. So that's how we're gonna play the, the control plane will be in AWS. We'll optimize it there, but the data plane will be multi-cloud. >>Yeah. And that's what Mo had explained at Supercloud. You know, and I talked to, he really helped me hone in on the deployment models. Yes. Where, where, where the cohesive deployment model is instantiating that technology stack into each cloud region and each cloud, which gives you latency advantages and other advantages >>And single code based same platform, >>And then bringing it, tying it together with a unified, you know, interface. That was he, he was, he was key. In fact, I, I wrote about it recently and, and gave him and the other 20, >>Quite a bit in that session. Yeah. So he went deep with you. I >>Mean, with Mohi, when you get a guy who developed a Google file system, you know, who can technically say, okay, this is technically correct or no, Dave, your way off be so I that's why I had to >>Go. I, I thought you did a great job in that interview because you probed him pretty deep and I'm glad we could do that together with him next time. Well, maybe do that together here too, but it was really helpful. He's the, he's the, he's the key reason I'm here. >>So you say data management is ripe for disrupt disruption. Talk about that. You talked about this Switzerland effect. That sounds to me like a massive differentiator for cohesive. Why is data management right. For disruption and why is cohesive the right partner to do it? >>Yeah, I think, listen, everyone in this sort of data protection backup from years ago have been saying the S Switzerland argument 18 years ago, I was a at Veras an executive there. We used the Switzerland argument, but what's changed is the cloud. And what's changed as a threat vector in security. That's, what's changed. And in that the proposition of a, a Switzerland player has just become more magnified because you didn't have a sales force or Workday service now then, but now you do, you didn't have multi-cloud. You had hardware vendors, you know, Dell, HPE sun at the time. IBM, it's now Lenovo. So that heterogeneity of, of on-premise service, storage, networking, HyperCloud, and, and the apps world has gotten more and more diverse. And I think you really need scale out architectures. Every one of the legacy players were not built with scale out architectures. >>If you take that fundamental notion of bringing compute to storage, you could almost paralyze. Imagine you could paralyze backup recovery and bring so much scale and speed that, and that's what Mo invented. So he took that idea of how he had invented and built Nutanix and applied that to secondary storage. So now everything gets faster and cheaper at scale. And that's a disruptive technology ally. What snowflake did to ator? I mean, the advantage of snowflake is when you took that same concept data, warehousing is not a new concept it's existed from since Ralph Kimble and bill Inman and the people who are fathers of data warehousing, they took that to Webscale. And in that came a disruptive force toter data, right? And snowflake. And then of course now data bricks and big query, similar things. So we're doing the same thing. We just have to showcase the customers, which we do. And when large customers see that they're replacing the legacy solutions, I have a lot of respect for legacy solutions, but at some point in time of a solution was invented in 1995 or 2000, 2005. It's right. For change. >>So you use snowflake as an example, Frank sluman doesn't like when I say playbook, cuz I says, Dave, I'm a situational. See you no playbook, but there are patterns here. And one of the things he did is to your point go after, you know, Terra data with a better data warehouse, simplify scale, et cetera. And now he's, he's a constructing a Tam expansion strategy, same way he did at ServiceNow. And I, you guys following a similar pattern. Okay. You get your foot in the door. Let's face it. I mean, a lot of this started with, you know, just straight back. Okay, great. Now it's extending into data management now extending to multi-cloud that's like concentric circles in a Tam expansion strategy. How, how do as, as a CEO, that's part of your job is Tam expansion. >>So yeah, I think the way to think about the Tam is, I mean, people say it's 20, 30 billion, but let me tell you how you can piece it apart in size, Dave and Lisa number one, I estimate there's probably about 10 to 20 exabytes of data managed by these legacy players of on-prem stores that they back up to. Okay. So you add them all up in the market shares that they respectively are. And by the way, at the peak, the biggest of these companies got to 2 billion and then shrunk. That was Verto when I was there in 2004, 2 billion, every one of them is small and they stopped growing. You look at the IDC charts. Many of them are shrinking. We are the fastest growing in the last two years, but I estimate there's about 20 exabytes of data that collectively among the legacy players, that's either gonna stay on prem or move to the cloud. Okay. So the opportunity as they replace one of those legacy tools with us is first off to manage that 20 X bike cheaper, faster with the Webscale, a glass or for the cloud guys, we could tip that into the cloud. Okay. >>But you can't stop there. >>Okay. No, we are not doing just back recovery. Right. We have a platform that can do files. We can do test dev analytics and now security. Okay. That data is potentially at a risk, not so much in the past, but for ransomware, right? How do we classify that? How do we govern that data? How do we run potential? You know, the same way you did antivirus some kind of XDR algorithms on the data to potentially not just catch the recovery process, which is after fact, but maybe the predictive act of before to know, Hey, there's somebody loitering around this data. So if I'm basically managing in the exabytes of data and I can proactively tell you what, this is, one CIO described this very simply to me a few weeks ago that I, and she said, I have 3000 applications, okay. I wanna be prepared for a black Swan event, except it's not a nine 11 planes hitting the, the buildings. >>It is an extortion event. And I want to know when that happens, which of my 3000 apps I recover within one hour within one day within one week, no lay than one month. Okay. And I don't wanna pay the bad guys of penny. That's what we do. So that's security discussions. We didn't have that discussion in 2004 when I was at another company, because we were talking about flood floods and earthquakes as a disaster recovery. Now you have a lot more security opportunity to be able to describe that. And that's a boardroom discussion. She needs to have that >>Digital risk. O O okay, go ahead please. I >>Was just gonna say, ransomware attack happens every what? One, every 11, 9, 11 seconds. >>And the dollar amount are going up, you know, dollar of what? >>Yep. And, and when you pay the ransom, you don't always get your data back. So you that's >>Not. And listen, there's always an ethical component. Should you do it or not do it? If you, if you don't do it and you're threatened, they may have left an Easter egg there. Listen, I, I feel very fortunate that I've been doing a lot in security, right? I mean, I built the business at, at, at VMware. We got it to over a billion I'm on the board of sneak. I've been doing security and then at SAP ran. So I know a lot about security. So what we do in security and the ecosystem that supports us in security, we will have a very carefully crafted stay tuned. Next three weeks months, you'll see us really rolling out a very kind of disciplined aspect, but we're not gonna pivot this company and become a cyber security company. Some others in our space have done that. I think that's not who we are. We are a data management and a data security company. We're not just a pure security company. We're doing both. And we do it well, intelligently, thoughtfully security is gonna be built into our platform, not bolted on, okay. And there'll be certain security things that we do organically. There's gonna be a lot that we do through partnerships, >>This security market that's coming to you. You don't have to go claim that you're now a security vendor, right? The market very naturally saying, wow, a comprehensive security strategy has to incorporate a data protection strategy and a recovery, you know, and the things we've talking about, Mount ransomware, I want to ask you, you know, I've been around a long time, longer than you actually Sanjay. So, but you you've, you've seen a lot. You look incredibly, >>Thank you. That's all good. Oh, >>Shocks. So the market, I've never seen a market like this, right? I okay. After the.com crash, we said, and I know you can't talk about IPO. That's not what I'm talking about, but everything was bad after that. Right. 2008, 2000, everything was bad. I've never seen a market. That's half full, half empty, you know, snowflake beats and raises the stock, goes through the roof. Dev if it, the area announced today, Mongo, DB, beat and Ray, that things getting crushed. And, and after market never seen anything like this. It's so fed, driven and, and hard to protect. And, and of course, I know it's a marathon, you know, it's not a sprint, but have you ever seen anything like this? >>Listen, I walk worked through 18 quarters as COO of VMware. You seen, I've seen public quarters there and you know, was very fortunate. Thanks to the team. I don't think I missed my numbers in 18 quarters except maybe once close. But we, it was, it's tough. Being a public company. Officer of the company is tough. I did that also at SAP. So the journey from 10 to 20 billion at SAP, the journey from six to 12 at VMware, that I was able to be fortunate. It's humbling because you, you really, you know, we used to have this, we do the earnings call and then we kind of ask ourselves, what, what do you think the stock price was gonna be a day and a half later? And we'd all take bets as to wear this. I think you just basically, as a, as a sea level executive, you try to build a culture of beaten, raise, beaten, raise, beaten, raise, and you wanna set expectations in a way that you're not setting them up for failure. >>And you know, it's you, there's, Dave's a wonderful CEO as is Frank movement. So it's hard for me to dissect. And sometimes the market are fickle on some small piece of it. But I think also the, when I, I encourage people say, take the long term view. When you take the long term view, you're not bothered about the ups and downs. If you're building a great company over the length of time, now it will be very clear over the arc of many, many quarters that you're business is trouble. If you're starting to see a decay in growth. And like, for example, when you start to see a growth, start to decay significantly by five, 10 percentage points, okay, there's something macro going on at this company. And that's what you won't avoid. But these, you know, ups and downs, my view is like, if you've got both Mongo, DIA and snowflake are fantastic companies, they're CEOs of people I respect. They've actually a kind of an, a, you know, advisor to us as a company, you knows mot very well. So we respect him, respect Frank, and you, there have been other quarters where Frank's, you know, the snowflakes had a down result after that. So you build a long term and they are on the right side of history, snowflake, and both of them in terms of being a modern cloud relevant in the case of MongoDB open source to data technology, that's, you know, winning, I, we would like to be like them one day >>As, as the new CEO of cohesive, what are you most, what are you most anxious about? And what are you most excited about? >>I think, listen, you know, you know, everything starts with the employee. You, I always believe I wrote my first memo to all employees. There was an article in Harvard business review called service profit chains that had a seminal impact on my leadership, which is when they studied companies who had been consistently profitable over a long period of time. They found that not just did those companies serve their customers well, but behind happy engaged customers were happy, engaged employees. So I always believe you start with the employee and you ensure that they're engaged, not just recruiting new employees. You know, I put on a tweet today, we're hiring reps and engineers. That's okay. But retaining. So I wanna start with ensuring that everybody, sometimes we have to make some unfortunate decisions with employees. We've, we've got a part company with, but if we can keep the best and brightest retained first, then of course, you know, recruiting machine, I'm trying to recruit the best and brightest to this company, people all over the place. >>I want to get them here. It's been, so I mean, heartwarming to come to world and just see people from all walks, kind of giving me hugs. I feel incredibly blessed. And then, you know, after employees, it's customers and partners, I feel like the tech is in really good hands. I don't have to worry about that. Cuz Mo it's in charge. He's got this thing. I can go to bed knowing that he's gonna keep innovating the future. Maybe in some of the companies, I would worried about the tech innovation piece, but most doing a great job there. I can kind of leave that in his cap of hands, but employees, customers, partners, that's kind of what I'm focused on. None of them are for me, like a keep up at night, but they're are opportunities, right? And sometimes there's somebody you're trying to salvage to make sure or somebody you're trying to convince to join. >>But you know, customers, I love pursuing customers. I love the win. I hate to lose. So fortune 1000 global, 2000 companies, small companies, big companies, I wanna win every one of 'em and it's not, it's not like, I mean, I know all these CEOs in my competitors. I texted him the day I joined and said, listen, I'll compete, honorably, whatever have you, but it's like Kobe and LeBron Kobe's passed away now. So maybe it's step Curry. LeBron, whoever your favorite athlete is you put your best on the court and you win. And that's how I am. That's nothing I've known no other gear than to put my best on the court and win, but do it honorably. It should not be the one that you're doing it. Unethically. You're doing it personally. You're not calling people's names. You're competing honorably. And when you win the team celebrates, it's not a victory for me, it's a victory for the team. >>I always think I'm glad that you brought out the employee experience and we're almost out of time, but I always think the employee experience and the customer experience are inextricably linked. This employees have to be empowered. They have to have the data that they need to do their job so that they can deliver to the customer. You can't do one without the other. >>That's so true. I mean, I, it's my belief. And I've talked also on this show and others about servant leadership. You know, one of my favorite poems is Brenda NA Tago. I went to bed in life. I dreamt that life was joy. I woke up and realized life was service. I acted in service was joy. So when you have a leadership model, which is it's about, I mean, there's lots of layers between me and the individual contributor, but I really care about that sales rep and the engineer. That's the leaf level of the organization. What can I get obstacle outta their way? I love skipping levels and going write that sales rep let's go and crack this deal. You know? So you have that mindset. Yeah. I mean, you, you empower, you invert the pyramid and you realize the power is at the leaf level of an organization. >>So that's what I'm trying to do. It's a little easier to do it with 2000 people than I dunno, either 20, 20, 2000 people or 35,000 reported me at VMware. And I mean a similar number at SAP, which was even bigger, but you can shape this. Now we are, we're not a startup anymore. We're a mid-size company. We'll see. Maybe along the way, there's an IP on the path. We'll wait for that. When it comes, it's a milestone. It's not the destination. So we do that and we are, we, I told people we are gonna build this green company. Cohesive is gonna be a great company like VMware one day, like Amazon. And there's always a day of early beginnings, but we have to work harder. This is kind of like the, you know, eight year old version of your kid, as opposed to the 18 year old version of the kid. And you gotta work a little harder. So I love it. Yeah. >>Good luck. Awesome. Thank you too. Best of luck. Congratulations on the role, it sounds like there's a tremendous amount of adrenaline, a momentum carrying you forward Sanja. We always appreciate having thank >>You for having in your show. >>Thank you. Our pleasure, Lisa. Thank you for Sanjay poin and Dave ante. I'm Lisa Martin. You're watching the cube live from VMware Explorer, 2022, stick around our next guest. Join us momentarily.
SUMMARY :
Valante good to be sitting next to you, sir. the CEO and president of cohesive. It's great to meet with you all the time and the new sort of setting here, We've been in north. And it was a hard time for the whole world, but I'm kind of driving a little bit of adrenaline just being You wrote a great blog that you are identified. And you know, one of the senior Google executives who was on my board, We're winning very much in the enterprise and that type of segment, the partners, you know, we have HPE, So you know, a little bit about how to work with, with VMware. And you know, even Chuck Robbins, who the CEO of I think, you know, sort of the narrative I talked about in that blog is and the fact that there's at least three big vendors of cloud in, in the us, you know, And I think, you know, what we have to do is make sure while we'll be optimized, our preferred cloud is AWS. stack into each cloud region and each cloud, which gives you latency advantages and other advantages And then bringing it, tying it together with a unified, you know, interface. So he went deep with you. Go. I, I thought you did a great job in that interview because you probed him pretty deep and I'm glad we could do that together with him So you say data management is ripe for disrupt disruption. And I think you really need scale out architectures. the advantage of snowflake is when you took that same concept data, warehousing is not a new concept it's existed from since I mean, a lot of this started with, you know, So yeah, I think the way to think about the Tam is, I mean, people say it's 20, 30 billion, but let me tell you how you can piece it apart You know, the same way you did antivirus some kind of XDR And I want to know when that happens, which of my 3000 apps I I Was just gonna say, ransomware attack happens every what? So you that's I mean, I built the business at, at, at VMware. a data protection strategy and a recovery, you know, and the things we've talking about, Mount ransomware, That's all good. And, and of course, I know it's a marathon, you know, it's not a sprint, I think you just basically, as a, as a sea level executive, you try to build a culture of And you know, it's you, there's, Dave's a wonderful CEO as is Frank movement. I think, listen, you know, you know, everything starts with the employee. And then, you know, And when you win the team celebrates, I always think I'm glad that you brought out the employee experience and we're almost out of time, but I always think the employee experience and the customer So when you have a leadership model, which is it's about, I mean, This is kind of like the, you know, eight year old version of your kid, as opposed to the 18 year old version of a momentum carrying you forward Sanja. Thank you.
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