>>Hi everybody. This is Dave Volante. Welcome to this cube conversation where we're going to go back in time a little bit and explore the early days of Kubernetes. Talk about how it formed the improbable events, perhaps that led to it. And maybe how customers are taking advantage of containers and container orchestration today, and maybe where the industry is going. Matt Provo is here. He's the founder and CEO of storm forge and Chandler Huntington hoes. Hoisington is the general manager of EKS edge and hybrid AWS guys. Thanks for coming on. Good to see you. Thanks for having me. Thanks. So, Jenny, you were the vice president of engineering at miso sphere. Is that, is that correct? >>Well, uh, vice-president engineering basis, fear and then I ran product and engineering for DTQ masons. >>Yeah. Okay. Okay. So you were there in the early days of, of container orchestration and Matt, you, you were working at a S a S a Docker swarm shop, right? Yep. Okay. So I mean, a lot of people were, you know, using your platform was pretty novel at the time. Uh, it was, it was more sophisticated than what was happening with, with Kubernetes. Take us back. What was it like then? Did you guys, I mean, everybody was coming out. I remember there was, I think there was one Docker con and everybody was coming, the Kubernetes was announced, and then you guys were there, doc Docker swarm was, was announced and there were probably three or four other startups doing kind of container orchestration. And what, what were those days like? Yeah. >>Yeah. I wasn't actually atmosphere for those days, but I know them well, I know the story as well. Um, uh, I came right as we started to pivot towards Kubernetes there, but, um, it's a really interesting story. I mean, obviously they did a documentary on it and, uh, you know, people can watch that. It's pretty good. But, um, I think that, from my perspective, it was, it was really interesting how this happened. You had basically, uh, con you had this advent of containers coming out, right? So, so there's new novel technology and Solomon, and these folks started saying, Hey, you know, wait a second, wait if I put a UX around these couple of Linux features that got launched a couple of years ago, what does that look like? Oh, this is pretty cool. Um, so you have containers starting to crop up. And at the same time you had folks like ThoughtWorks and other kind of thought leaders in the space, uh, starting to talk about microservices and saying, Hey, monoliths are bad and you should break up these monoliths into smaller pieces. >>And any Greenfield application should be broken up into individuals, scalable units that a team can can own by themselves, and they can scale independent of each other. And you can write tests against them independently of other components. And you should break up these big, big mandalas. And now we are kind of going back to model this, but that's for another day. Um, so, so you had microservices coming out and then you also had containers coming out, same time. So there was like, oh, we need to put these microservices in something perfect. We'll put them in containers. And so at that point, you don't really, before that moment, you didn't really need container orchestration. You could just run a workload in a container and be done with it, right? You didn't need, you don't need Kubernetes to run Docker. Um, but all of a sudden you had tons and tons of containers and you had to manage these in some way. >>And so that's where container orchestration came, came from. And, and Ben Heineman, the founder of Mesa was actually helping schedule spark at the time at Berkeley. Um, and that was one of the first workloads with spark for Macy's. And then his friends at Twitter said, Hey, come over, can you help us do this with containers at Twitter? He said, okay. So when it helped them do it with containers at Twitter, and that's kinda how that branch of the container wars was started. And, um, you know, it was really, really great technology and it actually is still in production in a lot of shops today. Um, uh, more and more people are moving towards Kubernetes and Mesa sphere saw that trend. And at the end of the day, Mesa sphere was less concerned about, even though they named the company Mesa sphere, they were less concerned about helping customers with Mesa specifically. They really want to help customers with these distributed problems. And so it didn't make sense to, to just do Mesa. So they would took on Kubernetes as well. And I hope >>I don't do that. I remember, uh, my, my co-founder John furrier introduced me to Jerry Chen way back when Jerry is his first, uh, uh, VC investment with Greylock was Docker. And we were talking in these very, obviously very excited about it. And, and his Chandler was just saying, it said Solomon and the team simplified, you know, containers, you know, simple and brilliant. All right. So you guys saw the opportunity where you were Docker swarm shop. Why? Because you needed, you know, more sophisticated capabilities. Yeah. But then you, you switched why the switch, what was happening? What was the mindset back then? We ran >>And into some scale challenges in kind of operationalize or, or productizing our kind of our core machine learning. And, you know, we, we, we saw kind of the, the challenges, luckily a bit ahead of our time. And, um, we happen to have someone on the team that was also kind of moonlighting, uh, as one of the, the original core contributors to Kubernetes. And so as this sort of shift was taking place, um, we, we S we saw the flexibility, uh, of what was becoming Kubernetes. Um, and, uh, I'll never forget. I left on a Friday and came back on a Monday and we had lifted and shifted, uh, to Kubernetes. Uh, the challenge was, um, you know, you, at that time, you, you didn't have what you have today through EKS. And, uh, those kinds of services were, um, just getting that first cluster up and running was, was super, super difficult, even in a small environment. >>And so I remember we, you know, we, we finally got it up and running and it was like, nobody touch it, don't do anything. Uh, but obviously that doesn't, that doesn't scale either. And so that's really, you know, being kind of a data science focused shop at storm forge from the very beginning. And that's where our core IP is. Uh, our, our team looked at that problem. And then we looked at, okay, there are a bunch of parameters and ways that I can tune this application. And, uh, why are the configurations set the way that they are? And, you know, uh, is there room to explore? And that's really where, unfortunately, >>Because Mesa said much greater enterprise capabilities as the Docker swarm, at least they were heading in that direction, but you still saw that Kubernetes was, was attractive because even though it didn't have all the security features and enterprise features, because it was just so simple. I remember Jen Goldberg who was at Google at the time saying, no, we were focused on keeping it simple and we're going from mass adoption, but does that kind of what you said? >>Yeah. And we made a bet, honestly. Uh, we saw that the, uh, you know, the growing community was really starting to, you know, we had a little bit of an inside view because we had, we had someone that was very much in the, in the original part, but you also saw the, the tool chain itself start to, uh, start to come into place right. A little bit. And it's still hardening now, but, um, yeah, we, as any, uh, as any startup does, we, we made a pivot and we made a bet and, uh, this, this one paid off >>Well, it's interesting because, you know, we said at the time, I mean, you had, obviously Amazon invented the modern cloud. You know, Microsoft has the advantage of has got this huge software stays, Hey, just now run it into the cloud. Okay, great. So they had their entry point. Google didn't have an entry point. This is kind of a hail Mary against Amazon. And, and I, I wrote a piece, you know, the improbable, Verizon, who Kubernetes to become the O S you know, the cloud, but, but I asked, did it make sense for Google to do that? And it never made any money off of it, but I would argue they, they were kind of, they'd be irrelevant if they didn't have, they hadn't done that yet, but it didn't really hurt. It certainly didn't hurt Amazon EKS. And you do containers and your customers you've embraced it. Right. I mean, I, I don't know what it was like early days. I remember I've have talked to Amazon people about this. It's like, okay, we saw it and then talk to customers, what are they doing? Right. That's kind of what the mindset is, right? Yeah. >>That's, I, I, you know, I've, I've been at Amazon a couple of years now, and you hear the stories of all we're customer obsessed. We listened to our customers like, okay, okay. We have our company values, too. You get told them. And when you're, uh, when you get first hired in the first day, and you never really think about them again, but Amazon, that really is preached every day. It really is. Um, uh, and that we really do listen to our customers. So when customers start asking for communities, we said, okay, when we built it for them. So, I mean, it's, it's really that simple. Um, and, and we also, it's not as simple as just building them a Kubernetes service. Amazon has a big commitment now to start, you know, getting involved more in the community and working with folks like storm forage and, and really listening to customers and what they want. And they want us working with folks like storm florigen and that, and that's why we're doing things like this. So, well, >>It's interesting, because of course, everybody looks at the ecosystem, says, oh, Amazon's going to kill the ecosystem. And then we saw an article the other day in, um, I think it was CRN, did an article, great job by Amazon PR, but talk about snowflake and Amazon's relationship. And I've said many times snowflake probably drives more than any other ISV out there. And so, yeah, maybe the Redshift guys might not love snowflake, but Amazon in general, you know, they're doing great three things. And I remember Andy Jassy said to me, one time, look, we love the ecosystem. We need the ecosystem. They have to innovate too. If they don't, you know, keep pace, you know, they're going to be in trouble. So that's actually a healthy kind of a dynamic, I mean, as an ecosystem partner, how do you, >>Well, I'll go back to one thing without the work that Google did to open source Kubernetes, a storm forge wouldn't exist, but without the effort that AWS and, and EKS in particular, um, provides and opens up for, for developers to, to innovate and to continue, continue kind of operationalizing the shift to Kubernetes, um, you know, we wouldn't have nearly the opportunity that we do to actually listen to them as well, listen to the users and be able to say, w w w what do you want, right. Our entire reason for existence comes from asking users, like, how painful is this process? Uh, like how much confidence do you have in the, you know, out of the box, defaults that ship with your, you know, with your database or whatever it is. And, uh, and, and how much do you love, uh, manually tuning your application? >>And, and, uh, obviously nobody's said, I love that. And so I think as that ecosystem comes together and continues expanding, um, it's just, it opens up a huge opportunity, uh, not only for existing, you know, EKS and, uh, AWS users to continue innovating, but for companies like storm forge, to be able to provide that opportunity for them as well. And, and that's pretty powerful. So I think without a lot of the moves they've made, um, you know, th the door wouldn't be nearly as open for companies like, who are, you know, growing quickly, but are smaller to be able to, you know, to exist. >>Well, and I was saying earlier that, that you've, you're in, I wrote about this, you're going to get better capabilities. You're clearly seeing that cluster management we've talked about better, better automation, security, the whole shift left movement. Um, so obviously there's a lot of momentum right now for Kubernetes. When you think about bare metal servers and storage, and then you had VM virtualization, VMware really, and then containers, and then Kubernetes as another abstraction, I would expect we're not at the end of the road here. Uh, what's next? Is there another abstraction layer that you would think is coming? Yeah, >>I mean, w for awhile, it looked like, and I remember even with our like board members and some of our investors said, well, you know, well, what about serverless? And, you know, what's the next Kubernetes and nothing, we, as much as I love Kubernetes, um, which I do, and we do, um, nothing about what we particularly do. We are purpose built for Kubernetes, but from a core kind of machine learning and problem solving standpoint, um, we could apply this elsewhere, uh, if we went that direction and so time will tell what will be next, then there will be something, uh, you know, that will end up, you know, expanding beyond Kubernetes at some point. Um, but, you know, I think, um, without knowing what that is, you know, our job is to, to, to serve our, you know, to serve our customers and serve our users in the way that they are asking for that. >>Well, serverless obviously is exploding when you look again, and we tucked the ETR survey data, when you look at, at the services within Amazon and other cloud providers, you know, the functions off, off the charts. Uh, so that's kind of an interesting and notable now, of course, you've got Chandler, you've got edge in your title. You've got hybrid in, in your title. So, you know, this notion of the cloud expanding, it's not just a set of remote services, just only in the public cloud. Now it's, it's coming to on premises. You actually got Andy, Jesse, my head space. He said, one time we just look at it. The data centers is another edge location. Right. Okay. That's a way to look at it and then you've got edge. Um, so that cloud is expanding, isn't it? The definition of cloud is, is, is evolving. >>Yeah, that's right. I mean, customers one-on-one run workloads in lots of places. Um, and that's why we have things like, you know, local zones and wavelengths and outposts and EKS anywhere, um, EKS, distro, and obviously probably lots more things to come. And there's, I always think of like, Amazon's Kubernetes strategy on a manageability scale. We're on one far end of the spectrum, you have EKS distro, which is just a collection of the core Kubernetes packages. And you could, you could take those and stand them up yourself in a broom closet, in a, in a retail shop. And then on the other far in the spectrum, you have EKS far gate where you can just give us your container and we'll handle everything for you. Um, and then we kind of tried to solve everything in between for your data center and for the cloud. And so you can, you can really ask Amazon, I want you to manage my control plane. I want you to manage this much of my worker nodes, et cetera. And oh, I actually want help on prem. And so we're just trying to listen to customers and solve their problems where they're asking us to solve them. Cut, >>Go ahead. No, I would just add that in a more vertically focused, uh, kind of orientation for us. Like we, we believe that op you know, optimization capabilities should transcend the location itself. And, and, and so whether that's part public part, private cloud, you know, that's what I love part of what I love about EKS anywhere. Uh, it, you know, you shouldn't, you should still be able to achieve optimal results that connect to your business objectives, uh, wherever those workloads, uh, are, are living >>Well, don't wince. So John and I coined this term called Supercloud and people laugh about it, but it's different. It's, it's, you know, people talk about multi-cloud, but that was just really kind of vendor diversity. Right? I got to running here, I'm running their money anywhere. Uh, but, but individually, and so Supercloud is this concept of this abstraction layer that floats wherever you are, whether it's on prem, across clouds, and you're taking advantage of those native primitives, um, and then hiding that underlying complexity. And that's what, w re-invent the ecosystem was so excited and they didn't call it super cloud. We, we, we called it that, but they're clearly thinking differently about the value that they can add on top of Goldman Sachs. Right. That to me is an example of a Supercloud they're taking their on-prem data and their, their, their software tooling connecting it to AWS. They're running it on AWS, but they're, they're abstracting that complexity. And I think you're going to see a lot, a lot more of that. >>Yeah. So Kubernetes itself, in many cases is being abstracted away. Yeah. There's a disability of a disappearing act for Kubernetes. And I don't mean that in a, you know, in an, a, from an adoption standpoint, but, uh, you know, Kubernetes itself is increasingly being abstracted away, which I think is, is actually super interesting. Yeah. >>Um, communities doesn't really do anything for a company. Like we run Kubernetes, like, how does that help your bottom line? That at the end of the day, like companies don't care that they're running Kubernetes, they're trying to solve a problem, which is the, I need to be able to deploy my applications. I need to be able to scale them easily. I need to be able to update them easily. And those are the things they're trying to solve. So if you can give them some other way to do that, I'm sure you know, that that's what they want. It's not like, uh, you know, uh, a big bank is making more money because they're running Kubernetes. That's not, that's not the current, >>It gets subsumed. It's just become invisible. Right. Exactly. You guys back to the office yet. What's, uh, what's the situation, >>You know, I, I work for my house and I, you know, we go into the office a couple of times a week, so it's, it's, uh, yeah, it's, it's, it's a crazy time. It's a crazy time to be managing and hiring. And, um, you know, it's, it's, it's, it's definitely a challenge, but there's a lot of benefits of working home. I got two young kids, so I get to see them, uh, grow up a little bit more working, working out of my house. So it's >>Nice also. >>So we're in, even as a smaller startup, we're in 26, 27 states, uh, Canada, Germany, we've got a little bit of presence in Japan, so we're very much distributed. Um, we, uh, have not gone back and I'm not sure we will >>Permanently remote potentially. >>Yeah. I mean, w we made a, uh, pretty like for us, the timing of our series B funding, which was where we started hiring a lot, uh, was just before COVID started really picking up. So we, you know, thankfully made a, a pretty good strategic decision to say, we're going to go where the talent is. And yeah, it was harder to find for sure, especially in w we're competing, it's incredibly competitive. Uh, but yeah, we've, it was a good decision for us. Um, we are very about, you know, getting the teams together in person, you know, as often as possible and in the safest way possible, obviously. Um, but you know, it's been a, it's been a pretty interesting, uh, journey for us and something that I'm, I'm not sure I would, I would change to be honest with you. Yeah. >>Well, Frank Slootman, snowflakes HQ to Montana, and then can folks like Michael Dell saying, Hey, same thing as you, wherever they want to work, bring yourself and wherever you are as cool. And do you think that the hybrid mode for your team is kind of the, the, the operating mode for the, for the foreseeable future is a couple of, >>No, I think, I think there's a lot of benefits in both working from the office. I don't think you can deny like the face-to-face interactions. It feels good just doing this interview face to face. Right. And I can see your mouth move. So it's like, there's a lot of benefits to that, um, over a chime call or a zoom call or whatever, you know, that, that also has advantages, right. I mean, you can be more focused at home. And I think some version of hybrid is probably in the industry's future. I don't know what Amazon's exact plans are. That's above my pay grade, but, um, I know that like in general, the industry is definitely moving to some kind of hybrid model. And like Matt said, getting people I'm a big fan at Mesa sphere, we ran a very diverse, like remote workforce. We had a big office in Germany, but we'd get everybody together a couple of times a year for engineering week or, or something like this. And you'd get a hundred people, you know, just dedicated to spending time together at a hotel and, you know, Vegas or Hamburg or wherever. And it's a really good time. And I think that's a good model. >>Yeah. And I think just more ETR data, the current thinking now is that, uh, the hybrid is the number one sort of model, uh, 36% that the CIO is believe 36% of the workforce are going to be hybrid permanently is kind of their, their call a couple of days in a couple of days out. Um, and the, the percentage that is remote is significantly higher. It probably, you know, high twenties, whereas historically it's probably 15%. Yeah. So permanent changes. And that, that changes the infrastructure. You need to support it, the security models and everything, you know, how you communicate. So >>When COVID, you know, really started hitting and in 2020, um, the big banks for example, had to, I mean, you would want to talk about innovation and ability to, to shift quickly. Two of the bigger banks that have in, uh, in fact, adopted Kubernetes, uh, were able to shift pretty quickly, you know, systems and things that were, you know, historically, you know, it was in the office all the time. And some of that's obviously shifted back to a certain degree, but that ability, it was pretty remarkable actually to see that, uh, take place for some of the larger banks and others that are operating in super regulated environments. I mean, we saw that in government agencies and stuff as well. >>Well, without the cloud, no, this never would've happened. Yeah. >>And I think it's funny. I remember some of the more old school manager thing people are, aren't gonna work less when they're working from home, they're gonna be distracted. I think you're seeing the opposite where people are too much, they get burned out because you're just running your computer all day. And so I think that we're learning, I think everyone, the whole industry is learning. Like, what does it mean to work from home really? And, uh, it's, it's a fascinating thing is as a case study, we're all a part of right now. >>I was talking to my wife last night about this, and she's very thoughtful. And she w when she was in the workforce, she was at a PR firm and a guy came in a guest speaker and it might even be in the CEO of the company asking, you know, what, on average, what time who stays at the office until, you know, who leaves by five o'clock, you know, a few hands up, or who stays until like eight o'clock, you know, and enhancement. And then, so he, and he asked those people, like, why, why can't you get your work done in a, in an eight hour Workday? I go home. Why don't you go in? And I sit there. Well, that's interesting, you know, cause he's always looking at me like, why can't you do, you know, get it done? And I'm saying the world has changed. Yeah. It really has where people are just on all the time. I'm not sure it's sustainable, quite frankly. I mean, I think that we have to, you know, as organizations think about, and I see companies doing it, you guys probably do as well, you know, take a four day, you know, a week weekend, um, just for your head. Um, but it's, there's no playbook. >>Yeah. Like I said, we're a part of a case study. It's also hard because people are distributed now. So you have your meetings on the east coast, you can wake up at seven four, and then you have meetings on the west coast. You stay until seven o'clock therefore, so your day just stretches out. So you've got to manage this. And I think we're, I think we'll figure it out. I mean, we're good at figuring this stuff. >>There's a rise in asynchronous communication. So with things like slack and other tools, as, as helpful as they are in many cases, it's a, it, isn't always on mentality. And like, people look for that little green dot and you know, if you're on the you're online. So my kids, uh, you know, we have a term now for me, cause my office at home is upstairs and I'll come down. And if it's, if it's during the day, they'll say, oh dad, you're going for a walk and talk, you know, which is like, it was my way of getting away from the desk, getting away from zoom. And like, you know, even in Boston, uh, you know, getting outside, trying to at least, you know, get a little exercise or walk and get, you know, get my head away from the computer screen. Um, but even then it's often like, oh, I'll get a slack notification on my phone or someone will call me even if it's not a scheduled walk and talk. Um, uh, and so it is an interesting, >>A lot of ways to get in touch or productivity is presumably going to go through the roof. But now, all right, guys, I'll let you go. Thanks so much for coming to the cube. Really appreciate it. And thank you for watching this cube conversation. This is Dave Alante and we'll see you next time.

>> From theCUBE studios in Palo Alto, in Boston, bringing you data driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vollante. >> The rise of Kubernetes came about through a combination of forces that were, in hindsight, quite a long shot. Amazon's dominance created momentum for Cloud native application development, and the need for newer and simpler experiences, beyond just easily spinning up computer as a service. This wave crashed into innovations from a startup named Docker, and a reluctant competitor in Google, that needed a way to change the game on Amazon and the Cloud. Now, add in the effort of Red Hat, which needed a new path beyond Enterprise Linux, and oh, by the way, it was just about to commit to a path of a Kubernetes alternative for OpenShift and figure out a governance structure to hurt all the cats and the ecosystem and you get the remarkable ascendancy of Kubernetes. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this breaking analysis, we tapped the back stories of a new documentary that explains the improbable events that led to the creation of Kubernetes. We'll share some new survey data from ETR and commentary from the many early the innovators who came on theCUBE during the exciting period since the founding of Docker in 2013, which marked a new era in computing, because we're talking about Kubernetes and developers today, the hoodie is on. And there's a new two part documentary that I just referenced, it's out and it was produced by Honeypot on Kubernetes, part one and part two, tells a story of how Kubernetes came to prominence and many of the players that made it happen. Now, a lot of these players, including Tim Hawkin Kelsey Hightower, Craig McLuckie, Joe Beda, Brian Grant Solomon Hykes, Jerry Chen and others came on theCUBE during formative years of containers going mainstream and the rise of Kubernetes. John Furrier and Stu Miniman were at the many shows we covered back then and they unpacked what was happening at the time. We'll share the commentary from the guests that they interviewed and try to add some context. Now let's start with the concept of developer defined structure, DDI. Jerry Chen was at VMware and he could see the trends that were evolving. He left VMware to become a venture capitalist at Greylock. Docker was his first investment. And he saw the future this way. >> What happens is when you define infrastructure software you can program it. You make it portable. And that the beauty of this cloud wave what I call DDI's. Now, to your point is every piece of infrastructure from storage, networking, to compute has an API, right? And, and AWS there was an early trend where S3, EBS, EC2 had API. >> As building blocks too. >> As building blocks, exactly. >> Not monolithic. >> Monolithic building blocks every little building bone block has it own API and just like Docker really is the API for this unit of the cloud enables developers to define how they want to build their applications, how to network them know as Wills talked about, and how you want to secure them and how you want to store them. And so the beauty of this generation is now developers are determining how apps are built, not just at the, you know, end user, you know, iPhone app layer the data layer, the storage layer, the networking layer. So every single level is being disrupted by this concept of a DDI and where, how you build use and actually purchase IT has changed. And you're seeing the incumbent vendors like Oracle, VMware Microsoft try to react but you're seeing a whole new generation startup. >> Now what Jerry was explaining is that this new abstraction layer that was being built here's some ETR data that quantifies that and shows where we are today. The chart shows net score or spending momentum on the vertical axis and market share which represents the pervasiveness in the survey set. So as Jerry and the innovators who created Docker saw the cloud was becoming prominent and you can see it still has spending velocity that's elevated above that 40% red line which is kind of a magic mark of momentum. And of course, it's very prominent on the X axis as well. And you see the low level infrastructure virtualization and that even floats above servers and storage and networking right. Back in 2013 the conversation with VMware. And by the way, I remember having this conversation deeply at the time with Chad Sakac was we're going to make this low level infrastructure invisible, and we intend to make virtualization invisible, IE simplified. And so, you see above the two arrows there related to containers, container orchestration and container platforms, which are abstraction layers and services above the underlying VMs and hardware. And you can see the momentum that they have right there with the cloud and AI and RPA. So you had these forces that Jerry described that were taking shape, and this picture kind of summarizes how they came together to form Kubernetes. And the upper left, Of course you see AWS and we inserted a picture from a post we did, right after the first reinvent in 2012, it was obvious to us at the time that the cloud gorilla was AWS and had all this momentum. Now, Solomon Hykes, the founder of Docker, you see there in the upper right. He saw the need to simplify the packaging of applications for cloud developers. Here's how he described it. Back in 2014 in theCUBE with John Furrier >> Container is a unit of deployment, right? It's the format in which you package your application all the files, all the executables libraries all the dependencies in one thing that you can move to any server and deploy in a repeatable way. So it's similar to how you would run an iOS app on an iPhone, for example. >> A Docker at the time was a 30% company and it just changed its name from .cloud. And back to the diagram you have Google with a red question mark. So why would you need more than what Docker had created. Craig McLuckie, who was a product manager at Google back then explains the need for yet another abstraction. >> We created the strong separation between infrastructure operations and application operations. And so, Docker has created a portable framework to take it, basically a binary and run it anywhere which is an amazing capability, but that's not enough. You also need to be able to manage that with a framework that can run anywhere. And so, the union of Docker and Kubernetes provides this framework where you're completely abstracted from the underlying infrastructure. You could use VMware, you could use Red Hat open stack deployment. You could run on another major cloud provider like rec. >> Now Google had this huge cloud infrastructure but no commercial cloud business compete with AWS. At least not one that was taken seriously at the time. So it needed a way to change the game. And it had this thing called Google Borg, which is a container management system and scheduler and Google looked at what was happening with virtualization and said, you know, we obviously could do better Joe Beda, who was with Google at the time explains their mindset going back to the beginning. >> Craig and I started up Google compute engine VM as a service. And the odd thing to recognize is that, nobody who had been in Google for a long time thought that there was anything to this VM stuff, right? Cause Google had been on containers for so long. That was their mindset board was the way that stuff was actually deployed. So, you know, my boss at the time, who's now at Cloudera booted up a VM for the first time, and anybody in the outside world be like, Hey, that's really cool. And his response was like, well now what? Right. You're sitting at a prompt. Like that's not super interesting. How do I run my app? Right. Which is, that's what everybody's been struggling with, with cloud is not how do I get a VM up? How do I actually run my code? >> Okay. So Google never really did virtualization. They were looking at the market and said, okay what can we do to make Google relevant in cloud. Here's Eric Brewer from Google. Talking on theCUBE about Google's thought process at the time. >> One interest things about Google is it essentially makes no use of virtual machines internally. And that's because Google started in 1998 which is the same year that VMware started was kind of brought the modern virtual machine to bear. And so Google infrastructure tends to be built really on kind of classic Unix processes and communication. And so scaling that up, you get a system that works a lot with just processes and containers. So kind of when I saw containers come along with Docker, we said, well, that's a good model for us. And we can take what we know internally which was called Borg a big scheduler. And we can turn that into Kubernetes and we'll open source it. And suddenly we have kind of a cloud version of Google that works the way we would like it to work. >> Now, Eric Brewer gave us the bumper sticker version of the story there. What he reveals in the documentary that I referenced earlier is that initially Google was like, why would we open source our secret sauce to help competitors? So folks like Tim Hockin and Brian Grant who were on the original Kubernetes team, went to management and pressed hard to convince them to bless open sourcing Kubernetes. Here's Hockin's explanation. >> When Docker landed, we saw the community building and building and building. I mean, that was a snowball of its own, right? And as it caught on we realized we know what this is going to we know once you embrace the Docker mindset that you very quickly need something to manage all of your Docker nodes, once you get beyond two or three of them, and we know how to build that, right? We got a ton of experience here. Like we went to our leadership and said, you know, please this is going to happen with us or without us. And I think it, the world would be better if we helped. >> So the open source strategy became more compelling as they studied the problem because it gave Google a way to neutralize AWS's advantage because with containers you could develop on AWS for example, and then run the application anywhere like Google's cloud. So it not only gave developers a path off of AWS. If Google could develop a strong service on GCP they could monetize that play. Now, focus your attention back to the diagram which shows this smiling, Alex Polvi from Core OS which was acquired by Red Hat in 2018. And he saw the need to bring Linux into the cloud. I mean, after all Linux was powering the internet it was the OS for enterprise apps. And he saw the need to extend its path into the cloud. Now here's how he described it at an OpenStack event in 2015. >> Similar to what happened with Linux. Like yes, there is still need for Linux and Windows and other OSs out there. But by and large on production, web infrastructure it's all Linux now. And you were able to get onto one stack. And how were you able to do that? It was, it was by having a truly open consistent API and a commitment into not breaking APIs and, so on. That allowed Linux to really become ubiquitous in the data center. Yes, there are other OSs, but Linux buy in large for production infrastructure, what is being used. And I think you'll see a similar phenomenon happen for this next level up cause we're treating the whole data center as a computer instead of trading one in visual instance is just the computer. And that's the stuff that Kubernetes to me and someone is doing. And I think there will be one that shakes out over time and we believe that'll be Kubernetes. >> So Alex saw the need for a dominant container orchestration platform. And you heard him, they made the right bet. It would be Kubernetes. Now Red Hat, Red Hat is been around since 1993. So it has a lot of on-prem. So it needed a future path to the cloud. So they rang up Google and said, hey. What do you guys have going on in this space? So Google, was kind of non-committal, but it did expose that they were thinking about doing something that was you know, pre Kubernetes. It was before it was called Kubernetes. But hey, we have this thing and we're thinking about open sourcing it, but Google's internal debates, and you know, some of the arm twisting from the engine engineers, it was taking too long. So Red Hat said, well, screw it. We got to move forward with OpenShift. So we'll do what Apple and Airbnb and Heroku are doing and we'll build on an alternative. And so they were ready to go with Mesos which was very much more sophisticated than Kubernetes at the time and much more mature, but then Google the last minute said, hey, let's do this. So Clayton Coleman with Red Hat, he was an architect. And he leaned in right away. He was one of the first outside committers outside of Google. But you still led these competing forces in the market. And internally there were debates. Do we go with simplicity or do we go with system scale? And Hen Goldberg from Google explains why they focus first on simplicity in getting that right. >> We had to defend of why we are only supporting 100 nodes in the first release of Kubernetes. And they explained that they know how to build for scale. They've done that. They know how to do it, but realistically most of users don't need large clusters. So why create this complexity? >> So Goldberg explains that rather than competing right away with say Mesos or Docker swarm, which were far more baked they made the bet to keep it simple and go for adoption and ubiquity, which obviously turned out to be the right choice. But the last piece of the puzzle was governance. Now Google promised to open source Kubernetes but when it started to open up to contributors outside of Google, the code was still controlled by Google and developers had to sign Google paper that said Google could still do whatever it wanted. It could sub license, et cetera. So Google had to pass the Baton to an independent entity and that's how CNCF was started. Kubernetes was its first project. And let's listen to Chris Aniszczyk of the CNCF explain >> CNCF is all about providing a neutral home for cloud native technology. And, you know, it's been about almost two years since our first board meeting. And the idea was, you know there's a certain set of technology out there, you know that are essentially microservice based that like live in containers that are essentially orchestrated by some process, right? That's essentially what we mean when we say cloud native right. And CNCF was seated with Kubernetes as its first project. And you know, as, as we've seen over the last couple years Kubernetes has grown, you know, quite well they have a large community a diverse con you know, contributor base and have done, you know, kind of extremely well. They're one of actually the fastest, you know highest velocity, open source projects out there, maybe. >> Okay. So this is how we got to where we are today. This ETR data shows container orchestration offerings. It's the same X Y graph that we showed earlier. And you can see where Kubernetes lands not we're standing that Kubernetes not a company but respondents, you know, they doing Kubernetes. They maybe don't know, you know, whose platform and it's hard with the ETR taxon economy as a fuzzy and survey data because Kubernetes is increasingly becoming embedded into cloud platforms. And IT pros, they may not even know which one specifically. And so the reason we've linked these two platforms Kubernetes and Red Hat OpenShift is because OpenShift right now is a dominant revenue player in the space and is increasingly popular PaaS layer. Yeah. You could download Kubernetes and do what you want with it. But if you're really building enterprise apps you're going to need support. And that's where OpenShift comes in. And there's not much data on this but we did find this chart from AMDA which show was the container software market, whatever that really is. And Red Hat has got 50% of it. This is revenue. And, you know, we know the muscle of IBM is behind OpenShift. So there's really not hard to believe. Now we've got some other data points that show how Kubernetes is becoming less visible and more embedded under of the hood. If you will, as this chart shows this is data from CNCF's annual survey they had 1800 respondents here, and the data showed that 79% of respondents use certified Kubernetes hosted platforms. Amazon elastic container service for Kubernetes was the most prominent 39% followed by Azure Kubernetes service at 23% in Azure AKS engine at 17%. With Google's GKE, Google Kubernetes engine behind those three. Now. You have to ask, okay, Google. Google's management Initially they had concerns. You know, why are we open sourcing such a key technology? And the premise was, it would level the playing field. And for sure it has, but you have to ask has it driven the monetization Google was after? And I would've to say no, it probably didn't. But think about where Google would've been. If it hadn't open source Kubernetes how relevant would it be in the cloud discussion. Despite its distant third position behind AWS and Microsoft or even fourth, if you include Alibaba without Kubernetes Google probably would be much less prominent or possibly even irrelevant in cloud, enterprise cloud. Okay. Let's wrap up with some comments on the state of Kubernetes and maybe a thought or two about, you know, where we're headed. So look, no shocker Kubernetes for all its improbable beginning has gone mainstream in the past year or so. We're seeing much more maturity and support for state full workloads and big ecosystem support with respect to better security and continued simplification. But you know, it's still pretty complex. It's getting better, but it's not VMware level of maturity. For example, of course. Now adoption has always been strong for Kubernetes, for cloud native companies who start with containers on day one, but we're seeing many more. IT organizations adopting Kubernetes as it matures. It's interesting, you know, Docker set out to be the system of the cloud and Kubernetes has really kind of become that. Docker desktop is where Docker's action really is. That's where Docker is thriving. It sold off Docker swarm to Mirantis has made some tweaks. Docker has made some tweaks to its licensing model to be able to continue to evolve its its business. To hear more about that at DockerCon. And as we said, years ago we expected Kubernetes to become less visible Stu Miniman and I talked about this in one of our predictions post and really become more embedded into other platforms. And that's exactly what's happening here but it's still complicated. Remember, remember the... Go back to the early and mid cycle of VMware understanding things like application performance you needed folks in lab coats to really remediate problems and dig in and peel the onion and scale the system you know, and in some ways you're seeing that dynamic repeated with Kubernetes, security performance scale recovery, when something goes wrong all are made more difficult by the rapid pace at which the ecosystem is evolving Kubernetes. But it's definitely headed in the right direction. So what's next for Kubernetes we would expect further simplification and you're going to see more abstractions. We live in this world of almost perpetual abstractions. Now, as Kubernetes improves support from multi cluster it will be begin to treat those clusters as a unified group. So kind of abstracting multiple clusters and treating them as, as one to be managed together. And this is going to create a lot of ecosystem focus on scaling globally. Okay, once you do that, you're going to have to worry about latency and then you're going to have to keep pace with security as you expand the, the threat area. And then of course recovery what happens when something goes wrong, more complexity, the harder it is to recover and that's going to require new services to share resources across clusters. So look for that. You also should expect more automation. It's going to be driven by the host cloud providers as Kubernetes supports more state full applications and begins to extend its cluster management. Cloud providers will inject as much automation as possible into the system. Now and finally, as these capabilities mature we would expect to see better support for data intensive workloads like, AI and Machine learning and inference. Schedule with these workloads becomes harder because they're so resource intensive and performance management becomes more complex. So that's going to have to evolve. I mean, frankly, many of the things that Kubernetes team way back when, you know they back burn it early on, for example, you saw in Docker swarm or Mesos they're going to start to enter the scene now with Kubernetes as they start to sort of prioritize some of those more complex functions. Now, the last thing I'll ask you to think about is what's next beyond Kubernetes, you know this isn't it right with serverless and IOT in the edge and new data, heavy workloads there's something that's going to disrupt Kubernetes. So in that, by the way, in that CNCF survey nearly 40% of respondents were using serverless and that's going to keep growing. So how is that going to change the development model? You know, Andy Jassy once famously said that if they had to start over with Amazon retail, they'd start with serverless. So let's keep an eye on the horizon to see what's coming next. All right, that's it for now. I want to thank my colleagues, Stephanie Chan who helped research this week's topics and Alex Myerson on the production team, who also manages the breaking analysis podcast, Kristin Martin and Cheryl Knight help get the word out on socials, so thanks to all of you. Remember these episodes, they're all available as podcasts wherever you listen, just search breaking analysis podcast. Don't forget to check out ETR website @etr.ai. We'll also publish. We publish a full report every week on wikibon.com and Silicon angle.com. You can get in touch with me, email me directly david.villane@Siliconangle.com or DM me at D Vollante. You can comment on our LinkedIn post. This is Dave Vollante for theCUBE insights powered by ETR. Have a great week, everybody. Thanks for watching. Stay safe, be well. And we'll see you next time. (upbeat music)

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante last week's aws re invent brought into focus the degree to which cloud computing generally and aws specifically have impacted the technology landscape from making infrastructure orders of magnitude simpler to deploy to accelerating the pace of innovation to the formation of the world's most active and vibrant infrastructure ecosystem it's clear that aws has been the number one force for change in the technology industry in the last decade now going forward we see three high-level contributors from aws that will drive the next 10 years of innovation including one the degree to which data will play a defining role in determining winners and losers two the knowledge assimilation effect of aws's cultural processes such as two pizza teams customer obsession and working backwards and three the rise of super clouds that is clouds that run on top of hyperscale infrastructure that focus not only on i.t transformation but deeper business integration and digital transformation of entire industries hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll review some of the takeaways from the 10th annual aws re invent conference and focus on how we see the rise of super clouds impacting the future of virtually all industries one of the most poignant moments for me was a conversation with steve mullaney at aw aws re invent he's the ceo of networking company aviatrix now just before we went on the cube nick sterile one of aviatrix's vcs looked up at steve and said it's happening now before i explain what that means this was the most important hybrid event of the year you know no one really knew what the crowd would be like but well over twenty 000 people came to reinvent and i'd say at least 25 to 26 000 people attended the expo and probably another 10 000 or more came without badges to have meetings and side meetings and do networking off the expo floor so let's call it somewhere between thirty to forty thousand people physically attended the reinvent and another two hundred thousand or more online so huge event now what nick sterile meant by its happening was the next era of cloud innovation is upon us and it's happening in earnest the cloud is expanding out to the edge aws is bringing its operating model its apis its primitives and services to more and more locations yes data and machine learning are critical we talk about that all the time but the ecosystem flywheel was so evident at this year's re invent more so than any other re invent partners were charged up you know there wasn't nearly as much chatter about aws competing with them rather there was much more excitement around the value that partners are creating on top of aws's massive platform now despite aggressive marketing from competitive hyperscalers other cloud providers and as a service or on-prem slash hybrid offerings aws lead appears to be accelerating a notable example is aws's efforts around custom silicon far more companies especially isvs are tapping into aws's silicon advancements we saw the announcement of graviton 3 and new chips for training and inference and as we've reported extensively aws is now on a curve a silicon curve that will outpace x86 vis-a-vis performance price performance cost power consumption and speed of innovation and its nitro platform is giving aws and its partners the greatest degree of optionality in the industry from cpus gpus intel amd and nvidia and very importantly arm-based custom silicon springing from aws's acquisition of annapurna aws started its custom silicon journey in 2008 and is and it has invested massive resources into this effort other hyperscalers notably microsoft google and alibaba which have the scale economics to justify such custom silicon efforts are just recently announcing initiatives in this regard others who don't have the scale will be relying on third-party silicon providers a perfectly reasonable strategy but because aws has control of the entire stack we believe it has a strategic advantage in this respect silicon especially is a domain where to quote andy jassy there is no compression algorithm for experience b on the curve matters a lot and the biggest story in my view this past week was the rise of the super clouds in his 2020 book with steve hamm frank slootman laid out the case for the rise of data cloud a title which i've conveniently stolen for this breaking analysis rise of the super cloud thank you frank in his book slootman made a case for companies to put data at the center of their organizations rather than organizing just around people for example the idea is to create data networks while people of course are critical organizing around data and enabling people to access and share data will lead to the democracy democratization of data and network effects will kick in this was essentially metcalfe's law for data bob metcalf was the inventor of ethernet ethernet he put forth that premise when we we both worked or the premise when we both worked for pat mcgovern at idg that the value of a network is proportional to the square of the number of its users or nodes on the network thought of another way the first connection isn't so valuable but the billionth connection is really valuable slootman's law if i may says the more people that have access to the data governed of course and the more data connections that can be shared or create sharing the more value will be realized from that data exponential value in fact okay but what is a super cloud super cloud is an architecture that taps the underlying services and primitives of hyperscale clouds to deliver incremental value above and beyond what's available from the public cloud provider a super cloud delivers capabilities through software consumed as services and can run on a single hyperscale cloud or span multiple clouds in fact to the degree that a super cloud can span multiple clouds and even on-premises workloads and hide the underlying complexity of the infrastructure supporting this work the more adoption and the more value will be realized now we've listed some examples of what we consider to be super clouds in the making snowflake is an example we use frequently frequently building a data cloud that spans multiple clouds and supports distributed data but governs that data centrally somewhat consistent with the data mesh approach that we've been talking about for quite some time goldman sachs announced at re invent this year a new data management cloud the goldman sachs financial cloud for data with amazon web services we're going to come back to that later nasdaq ceo adina friedman spoke at the day one keynote with adam silipsky of course the new ceo of aws and talked about the super cloud they're building they didn't use that term that's our term dish networks is building a super cloud to power 5g wireless networks united airlines is really in my view they're porting applications to aws as part of its digital transformation but eventually it will start building out a super cloud travel platform what was most significant about the united effort is the best practices they're borrowing from aws like small teams and moving fast but many others that we've listed here are on a super cloud journey just some of the folks we talked to at reinvent that are building clouds on top of clouds that are shown here cohesity building out a data management cloud focused on data protection and governance hashicorp announced its ipo at a 13 billion valuation building an it automation super cloud data bricks chaos search z-scaler z-scaler is building a security super cloud and many others that we spoke with at the event now we want to take a moment to talk about castles in the cloud it's a premise put forth by jerry chen and the team at greylock it's a really important piece of work that is building out a data set and categorizing the various cloud services to better understand where the cloud giants are investing where startups can participate and how companies can play in the castles that are being built that have been built by the hyperscalers and how they can cross the moats that have been dug and where innovation opportunities exist for other companies now frequently i'm challenged about our statements that there really are only four hyperscalers that exist in the world today aws microsoft google and alibaba while we recognize that companies like oracle have done a really excellent job of improving their clouds we don't consider companies like oracle ibm and other managed service providers as hyperscalers and one of the main data points that we use to defend our thinking is capex investment this was a point that was made in castles in the cloud there are many others that we look at elder kpi size of ecosystem partner acceleration enablement for partners feature sets etc but capex is a big one here's a chart from platform nomics a firm that is obsessed with cl with capex showing annual capex spend for five cloud companies amazon google microsoft ibm and oracle this data goes through 2019 it's annual spend and we've superimposed the direction for each of these companies amazon spent more than 40 billion dollars on capex in 2020 and will spend more than 50 billion this year sure there are some warehouses for the amazon retail business in there and there's other capital expenses in these numbers but the vast majority spent on building out its cloud infrastructure same with google and microsoft now oracle is at least increasing its cap x it's going to spend about 4 billion but it's de minimis compared to the cloud giants and ibm is headed in the other direction it's choosing to invest for instance 34 billion dollars in acquiring red hat instead of putting its capital into a cloud infrastructure look that's a very reasonable strategy but it underscores the gap okay another metric we look at is i as revenue here's an updated chart that we showed last month in our cloud update which at the time excluded alibaba's most recent quarter results so we've updated that very slight change it wasn't really material so you see the four hyperscalers and by the way they invested more than a hundred billion dollars in capex last year it's gonna be larger this year they'll collectively generate more than 120 billion dollars in revenue this year and they're growing at 41 collectively that is remarkable for such a large base of revenue and for aws the rate of revenue growth is accelerating it's the only hyperscaler that can say that that's unreal at their size i mean they're going to do more than 60 billion dollars in revenue this year okay so that's why we say there are only four hyperscalers but so what there are so many opportunities to build on top of the infrastructure that the three u.s giants especially are building as folks are really cautious about china at the moment so let's take a look at what some of the companies that we've been following are doing in the super cloud arena if you will this chart shows some etr data plotting net score or spending momentum on the vertical axis and market share or presence in the etr data set on the horizontal axis most every name on the chart is building some type of super cloud but let me start as we often do calling out aws and azure i guess they're already super clouds but they're not building necessarily on top of of of other people's clouds and there are a little bit you know microsoft does some of that certainly google's doing some of that amazon really bringing its cloud to the edge at this point it's not participating in multi-cloud actively anyway aws and azure they stand alone as the cloud leaders and you can debate what's included in azure in our previous chart on revenue attempts to strip out the microsoft sas business but this is a customer view they see microsoft as a cloud leader which it is so that's why its presence on the horizontal axis and its momentum is is you know very large and very strong stronger than even in aws in this view even though it's is revenue that we showed earlier microsoft is significantly smaller but they both have strong momentum on the vertical axis as shown by that red horizontal line anything above that remember is considered considered elevated that 40 percent or above now google cloud it's well behind these two to we kind of put a red dotted line around it but look at snowflake that blue circle i mean i realize we repeat ourselves often but snowflake continues to hold a net score in the mid to high 70s it held 80 percent for a long time it's getting much much bigger it's so hard to hold that and in 165 mentions in the survey which you can see in the inserted table it continues to expand its market's presence on the horizontal axis now all the technology companies that we track of all of them we feel snowflake's vision and execution on its data cloud and that strategy is most is the most prominent example of a super cloud truly every tech company every company should be paying attention to snowflakes moves and carving out unique value propositions for their customers by standing on the shoulders of cloud giants as ceo ed walsh likes to say now on the left hand side of the chart you can see a number of companies that we spoke with that are in various stages of building out their super clouds data bricks dot spot data robots z z scalar mentioned hashi you see elastic confluent they're all above the forty percent line and somewhat below that line but still respectable we see vmware with tanzu cohesity rubric and veeam and many others that we didn't necessarily speak with directly at reinvent and or they don't show up in the etr dataset now we've also called out cisco dell hpe and ibm we didn't plot them because there's so much other data in there that's not apples to apple but we want to call them up because they all have different points of view and are two varying degrees building super clouds but to be honest these large companies are first protecting their respective on-prem turf you can't blame them those are very large install basis now they're all adding as a service offerings which is cloud-like i mean they're behind way behind trying to figure out you know things like billing and they don't nearly have the ecosystem but they're going to fight rightly they're going to fight hard and compete with their respective portfolios with their channels and their vastly improved simplicity but when you speak to customers at re invent and these are not just startups we're talking to we're talking about customers of these enterprise tech companies these customers want to build on aws they look at aws as cloud and that is the cloud that they want to write to now they want to connect they're on-prem but they're still largely different worlds when you when you talk to these customers now they'll fully admit they can't or won't move everything out of their data centers but the vast vast majority of the customers i spoke with last week at reinvent have much more momentum around moving towards aws they're not repatriating as everybody's talking about or not everybody but many are talking about and yeah there's some recency bias because we just got back but the numbers that we shared earlier don't lie the trend is very clear now these large firms that we mentioned these incumbents in the tech industry these big enterprise tech giants they're starting to move in the super cloud direction and they will have much more credibility around multi-cloud than the hyperscalers but my honest view is that aws's lead is actually accelerating the gap in my opinion is not closing now i want to come back and dig into super cloud a little bit more around 2010 and 2011 we collaborated with two individuals who really shaped our thinking in the big data space peter goldmaker was a cell side analyst at common at the time and abi abhishek meta was with bank of america and b of a was transforming its data operations and avi was was leading that now peter was you know an analyst sharp and less at the time he said you know it's going to be the buyers of big data technology and those that apply big data to their operations who would create the most value he used an example of sap he said look you you couldn't have chosen that sap was going to lead an erp but if you could have figured out who which companies were going to apply erp to their business you would have made a lot of money investing so that was kind of one of his investment theses now he posited that the companies that would apply the big data technology the buyers if you will would create far more value than the cloud errors or the hortonworks or a collection of other number of big data players and clearly he was right in that regard now abi mehta was an example of that and he posited that ecosystems would evolve within vertical industries around data kind of going back to frank slootman's premise that in putting data at the core and that would power the next generation of value creation via data machine learning and business transformation and he was right and that's what we're seeing with the rise of super cloud now after the after the first reinvent we published a post seen on the right hand side of this chart on wikibon about the making of a new gorilla aws and we said the way to compete would be to take an industry focus or one way to compete with take an industry focus and become best to breed within that industry and we aligned really with abbey meta's point of view that industry ecosystems would evolve around data and offer opportunities for non-hyperscalers to compete now what we didn't predict at the time but are now seeing clearly emerge is that these super clouds are going to be built on top of aws and other hyperscale clouds makes sense goldman's financial cloud for data is taking a page out of aws it's pointing its proprietary data algorithms tools and processes at its clients just like amazon did with its technology and it's making these assets available as a service on top of the aws cloud a super cloud for financial services if you will they are relying on aws for infrastructure compute storage networking security and other services like sagemaker to power that super cloud but they're bringing their own ip to the table nasdaq and dish similarly bringing forth their unique value and as i said as i said earlier united airlines will in our view eventually evolve from migrating its apps portfolio to the cloud to building out a super cloud for travel what about your logo what's your super cloud strategy i'm sure you've been thinking about it or perhaps you're already well down the road i'd love to hear how you're doing it and if you see the trends the same or differently as we do okay that's it for now don't forget these episodes are all available as podcasts wherever you listen all you do is search breaking analysis podcast you definitely want to check out etr's website at etr.plus for all the survey data remember we publish a full report every week on wikibon.com and siliconangle.com you can email me if you want to get in touch with david.velante at siliconangle.com you can dm me at devolante on twitter you can comment on our linkedin posts this is dave vellante for the cube insights powered by etr have a great week stay safe be well and we'll see you next time [Music] you

>> From theCube studios in Palo Alto in Boston, bringing you data-driven insights from theCube in ETR. This is Breaking Analysis with Dave Vellante. >> New data suggests the tech spending will be higher than we previously thought for 2021. COVID learnings, a faster than expected vaccine rollout, productivity gains in the last 10 months, and broad-based cloud leverage lead us to raise our outlook for next year. We now expect a three to 5% increase in 2021 technology spending, roughly double our previously forecasted growth rate of 2%. Hello everyone and welcome to this week's we keep on Cube Insights powered by ETR. In this breaking analysis, we're going to share new spending data from ETR partners and take a preliminary look at which sectors and which companies are showing momentum heading into next year. Let's get right into it. The data is pointing to a strong 2021 rebound. A latest survey from ETR and the information from theCube Community suggests that the accelerated pace of the vaccine rollout pent up demand for normalcy and learnings from COVID will boost 2021 tech spending higher than previously anticipated. Now a key factor we've cited is that the forced March to digital transformation due to the pandemic created a massive proof of concept for what works and what doesn't in a digital business. CIOs are planning to bet on those sure things to drive continued productivity improvements and new business opportunities. Now, speaking of productivity, nearly 80% of respondents in the latest ETR survey indicate that productivity either stayed the same or improved over the past three months. Now of those, the vast majority, more than 80% cited improvements in productivity. This has been a common theme throughout the year. As well, the expectation among CIOs is that many workers will return to the office in the second half of the year, which we expect will drive new spending in the infrastructure needs of company HQs, which have been neglected over the past 10 months. Now, despite the expectation that many workers will return to the office, 2020 has shown us that working remotely, hey, it's here to stay, and a much larger number of employees are going to be permanently remote working than pre pandemic. ETR survey data shows that that number is going to be approximately double over the longterm. We'll look at some of that specific data. In addition, cloud computing, it became the staple of business viability in 2020. Those that were up the cloud adoption ramp, well, they benefited greatly, those that weren't well, they had to learn fast. Now, along with remote work cloud necessitated new thinking around network security, and as we've reported identity access management, endpoint security and cloud security with the beneficiaries. Companies like Okta, CrowdStrike, Zscaler, a number of others continue to ride this wave. Larger established security companies like Cisco, Palo Alto Networks, F5, Fortunate and others, they have major portions of their business that are benefiting from the tailwinds in the shift and network traffic, as a result of cloud and remote work. Now, despite all the momentum in the market and the expect of improvements in 2021, these tailwinds are not expected to be evenly distributed, far from it. We think Q4 is going to remain soft relative to last year and Q1 2021 is going to be flat, maybe up slightly. Remember the COVID impact was definitely felt in March of this year. So based on the earnings that we saw, there may be some upside in Q1, given that organizations are still being cautious in Q4, and really there's still some uncertainty in Q1. Let's look at some of the survey responses and you'll see why we're more optimistic than we've previously reported. This chart shows the responses to key questions around spending trajectories from the March, June, September, and December surveys of this year. Now it's no surprise that there's been little change in remote workers and limiting business travel. But look at the other categories, seeing a dramatic reduction in hiring freezes. The percentage of companies freezing new IT deployments continues to drop throughout the year. And then conversely, the percentage of companies accelerating new it deployments that's sharply up to 34% from the March low of 12%. And look at the headcount trends. The percentage of companies instituting layoffs. It continues its downward trajectory while accelerated hiring is now up to 17%. So there's a lot to be excited about in these results. Now let's look the remote worker trend. How do CIO see that shift in the near to midterm? This chart shows the work from home data and it's amazingly consistent from the September survey drill down. You can see CIO's is indicate that on average, 15 to 60% of workers were remote prior to the pandemic, and that jumped up to 72 to 73% currently, and is expected to stay in the high fifties until the summer of 2021. Thereafter, organizations expect that the number of employees that work remotely on a permanent basis is going to more than double to 34% long term. By the way, I've talked to a number of executives, CEOs, CIOs, and CFOs that expect that number to be higher than these especially in the technology sector. They expect more than half of their workers to be remote and are looking to consolidate facilities cost to save money. As we've said, cloud computing has been the most significant contributor to business resilience and digital transformation this year. So let's look at cloud strategies and see how CIOs expect those to evolve. This chart shows responses to how organizations see multi-cloud evolving. It's interesting to note the ETR call-out, which concludes that the narrative around multi-cloud multi-cloud is real, and it is. But I want to talk to you about a flip side to this notion in that, as many customers have, or are planning to increasingly concentrate workloads in the cloud. This actually makes some sense. Sure, virtually every major company uses multiple clouds, but more often than not, it concentrate work on a primary cloud. CIO strategies, they're not generally evenly distributed across clouds. The data shows that this is the case for less than 20% of the respondents, rather organizations are typically going to apply an 80, 20 or a 70, 30 rule for their multi-cloud approach. Meaning they pick a primary cloud on which most work is done, and then they use alternative clouds as either a hedge or maybe for specific workloads or maybe even data protection purposes. Now, if you think about it, optimizing on a primary cloud allows organizations to simplify their security and governance and consolidate their skills. At this point in the cloud evolution, it seems CIOs feel there's more value that is going to come from leveraging the cloud to change their operating models, and maybe broadly spreading the wealth to reduce risk or maybe cut costs, or maybe even to tap specialized capabilities. What's more in thinking about AWS and Microsoft respectively. Each can make a very strong case from MANO cloud. AWS has more features than any other cloud, and as such can handle most workloads. Microsoft can make a similar argument for its customers that have an affinity and a largest state of Microsoft software. The key for multi-cloud in our view will be the degree to which technology vendors can abstract the underlying cloud complexity and create a layer that floats above the clouds and adds incremental value. Snowflakes data cloud is one of the best examples of this, and we've covered that pretty extensively. Now, clearly VMware and Red Hat have aspirations at the infrastructure layer in a similar fashion. Pure storage, and NetApp are a couple of the largest storage players with similar visions. And then Qumulo and Clumio are two other examples with promising technologies, but they have a much smaller install base. Take a look at Cisco, Dell, IBM and HPE. They have a lot to gain and a lot to lose in this cloud game. So multi-cloud is an imperative for these leaders, but for them it's much more complicated because of the complexity and vastness of their portfolios. And notably Dell has VMware and IBM of course has Red Hat, which are key assets that can be leveraged for this multi-cloud game. HPE has a channel and a large install base, but all of these firms, they have to spread R&D much more thinly than some of these other companies that we mentioned for example. The bottom line is that multi-cloud has to be more than just plugging into an operating well on any of the clouds. It require... Which is by the way, this is mostly where we are today. It requires an incremental value proposition that solves a clear problem, and at the same time runs efficiently, meaning it takes advantage of cloud native services at scale. What sectors are showing momentum heading into 2021? And who are some of the names that are looking strong? We've reported a lot that cloud containers and container orchestration, machine intelligence and automation are by far the hottest sectors, the biggest areas of investment with the greatest spending momentum. Now we measure this in ETR parlance, remember by net score. But here's the good news, almost every other sector in the ETR taxonomy with the notable exception of IT outsourcing and IT consulting is showing positive spending momentum relative to previous surveys this year. Yeah, maybe not, it's not a shock, but it appears that the tech spending recovery will be broad-based. It's also worth noting that there are several vendors that stand out and we show a number of them here. CrowdStrike, Microsoft has had consistent performance in the dataset throughout this year. Okta, we called out those guys last year and they've clearly performed as you can see in their earnings reports. Pure storage, interestingly, big acceleration and a turnaround from last quarter in the dataset, and of course, snowflake has been off the charts as we reported many times. These guys are all seeing highly accelerated momentum. UiPath just announced its intent to IPO, AWS, Google, Zscaler, SailPoint, ServiceNow, and Elastic, these all continue to trend up. And so, there are some real positives that we're looking for a member of the ETR surveys, they're forward-looking. So we'll see, as we catch up next quarter. Now, before we wrap, I want to say a few words on security, and maybe it's a bit of a non-sequitur here, but I think it's relevant to the trends that we've been discussing, especially as we talk about moving to the cloud. And as you know, we've reported many times on the security space, basically updating you quarterly with our scenarios and the spending and the technology trends and highlighting our four-star companies. Four-star company's insecurity on those with both momentum and significant market presence. And last year we put CrowdStrike, Okta and Zscaler, and some others on the radar. And we've closely track the cyber business of larger companies with a security portfolio like Palo Alto and Cisco, and more recently, VMware has made some acquisitions. Now the government hacked that became news this week. It really underscores the importance of security. It remains the most challenging area for organizations because well, failure's not an option, skills are short, tools are abundant, the adversaries are very well-funded and extremely capable yet failure is common as we saw this week. And there's a misconception that cloud solves the security problem, and it's important to point out that it does not. Cloud is a shared responsibility model, meaning the cloud provider is going to secure the infrastructure for example, but it's up to you as the customer to configure things properly and deal with application security. It's ultimately on you. And the example of S3 is instructive because we've seen a number S3 breaches over the years where the customer didn't properly configure the S3 bucket. We're talking about companies like Honda and Capital One, not just small businesses that don't have the SecOps resources. And generally it was because a non-security person was configuring things. Maybe they were Or developers who are not focused on security, and perhaps permission set too broadly, and access was given to far too many people. Whatever the issue, it took some breaches and subsequent education to increase awareness of this problem and tighten it up. We see some similar trends occurring with new workloads, especially in cloud databases. It's becoming so easy to spin up new data warehouses for example, and we believe that there are exposures out there due the lack of awareness or inconsistent corporate governance being applied to these new data stores. As well, even though important areas like threat intelligence and database security are important, SecOps budgets are stretched thin. And when you ask companies where the priorities are, these fall lower down the list, these areas specifically have taken a back seat, the endpoint, identity and cloud security. And we bring this up because it's a potential blind spot as we saw this week with the US government hack. It was stealthy, it wasn't detected for many, many months. Who knows maybe even years. And not to be a buzzkill, but the point is, cloud enthusiasm has to be concompetent with security vigilant. Enough preaching, let's wrap up here. As we enter 2020, this year, we said the cloud was going to be the force that drove innovation along with data and AI. And as we look in the rear view mirror and put 2020 behind us, I know many of you want to do that, it was the cloud that enabled businesses to not only continue to operate, but to actually increase productivity. Nonetheless, we still see IT spending declines of four to 5% this year with an expectation of a tepid Q4 relative to the last year. We see Q1 slowly rebounding and kind of a swoosh, let me try that again, recovery in the subsequent quarters with tech spending rebounding in 2021 to a positive three to 5%, let's call it 4%. Now supporting us scenario, the pandemic forced a giant Petri dish for digital. And we see some real successes and learnings that organizations will apply in 2021 to bet on sure things. These are cloud, containers, AI, ML, machine intelligence pieces and automation. For sure, along with upticks for virtually every other sector of technology because spending has been so depressed. The two exceptions are outsourcing and IT consulting and related services which continue to be a drag on overall spending. Priorities must be focused on security and governance and further improvements in applying corporate edicts in a cloud world. We also see new data architectures emerging where domain knowledge becomes central to data platforms. We'll be covering this in more detail on top of the work that we've already done in this area. Now, automation is not only an opportunity, it's become a mandate. Yes, RPA, but also broader automation agendas be on point tools. And importantly, we're not talking about paving the cow path here by automating existing processes. Rather we're talking about rethinking processes across the entire organization for a new digital reality where many of these processes are being invented. The work of Erik Brynjolfsson and Andrew McAfee on the second machine age. It was pressured back in 2014 and the conclusions they drew, they're becoming increasingly important in the 2020s, meaning that look machines have always replaced humans throughout time. But for the first time in history, it's happening for cognitive functions, and a huge base of workers is going to be, or as being marginalized, unless they're retrained. Education and public policy that supports this transition is critical. And I for one would like to see a much more productive discussion that goes beyond the cult of break up big tech. Rather I'd like to see governments partner with big tech to truly do good and help drive the re-skilling of workers for the digital age. Now cloud remains the underpinning of the digital business mandate, but the path forward isn't really always crystal clear. This is evidenced by the virtual dead heat between those organizations that are consolidating workloads in a cloud workloads versus those that are hedging bets on a multi-cloud strategy. One thing is clear cloud is the linchpin for our growth scenarios and will continue to be the substrate for innovation in the coming decade. Remember, these episodes, they're all available as podcasts, wherever you listen, all you got to do is search Breaking Analysis podcast, and please subscribe to the series, appreciate that. Check out ETR's website at ETR.plus. We also publish full report every week on wikibond.com and siliconangle.com and get in touch with me at David.vallante, siliconangle.Com, you can DM me at D. Vellante. And please by all means comment on our LinkedIn posts. This is Dave Vellante for theCube Insights powered by ETR. Have a great week everybody, Merry Christmas, happy Hanukkah, happy Kwanzaa, or happy, whatever holiday you celebrate. Stay safe, be well, and we'll see you next time. (upbeat music)

(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.

>> Announcer: theCUBE presents HPE Discover 2022, brought to you by HPE. >> We're back at the formerly the Sands Convention Center, it's called the Venetian Convention Center now, Dave Vellante and John Furrier here covering day three, HPE Discover 2022, it's hot outside, it's cool in here, and we're going to heat it up with Rashmi Kumar, who's the Senior Vice President and CIO of Hewlett Packard Enterprise, great to see you face to face, it's been a while. >> Same here, last couple of years, we were all virtual. >> Yeah, that's right. So we've talked before about sort of your internal as-a-service transformation, you know, we do call it dog fooding, everybody likes to course correct and say, no, no, it's drinking your own champagne, is it really that pretty? >> It is, and the way I put it is, no pressure to my product teams, it's being customer zero. >> Right, take us through the acceleration on how everything's been going with you guys, obviously, the pandemic was an impact to certainly the CIO role and your team but now you've got GreenLake coming in and Antonio's big statement before the pandemic, by 2022 everything will be as a service and then everything went remote, VPNs and all this new stuff, how's it going? >> Yeah, so from business perspective, that's a great point to start that, right? Antonio promised in 2019 that HPE will be Everything-as-a-Service company and he had no view of what's going to happen with COVID. But guess what? So many businesses became digital and as-a-service during those two years, right? And now we came back this year, it was so exciting to be part of Discover when now we are Everything-as-a-Service. So great from business perspective but, when I look at our own transformation, behind the scene, what IT has been busy with and we haven't caught a breadth because of pandemic, we have taken care of all that change, but at the same time have driven our transformation to make HPE, edge to cloud platform as a service company. >> You know, I saw a survey, I referenced it earlier today, it was a survey, I think it was been by Couchbase, it was a CIO survey, so they asked, who was responsible at your organization for the digital transformation? And overwhelming, like 75% said, CIO, which surprised me 'cause, you know, in line with the business and so forth but in fact I thought, well, maybe, because of the forced march to digital that's what was top of their mind, so who is responsible for, and I know it's not just one person, for the digital transformation? Describe that dynamic. >> Yeah, so definitely it's not one person, but you do need that whole accountable, responsible, informed, right, in the context of digital transformation. And you call them CIO, you call them CDIO or CDO and whatnot but, end of the day, technology is becoming an imperative for a business to be successful and COVID alone has accelerated it, I'm repeating this maybe millions time if you Google it but, CIOs are best positioned because they connect the dots across organization. In my organization at HPE, we embarked upon this large transformation where we were consolidating 10 different ERPs, multiple master data system into one and it wasn't about doing digital which is e-commerce website or one technology, it was creating that digital foundation for the company then to transform that entire organization to be a physical product company to a digital product company. And we needed that foundation for us to get that code to cash experience, not only in our traditional business, but in our as-a-service company. >> So maybe that wasn't confirmation bias, I want to ask you about, we've been talking a lot about sustainability and I've made the comment that, if you go back, you know, 10, 12 years and you were CIO IT at that time, CIO really didn't care about the energy bill, that was paid for by facilities, they really didn't talk to each other much and that's completely changed, why has it changed? How should a CIO, how do your your peers think about energy costs today? >> Yeah, so, at some point look, ESG is the biggest agenda for companies, regulators, even kind of the watchers of ISS and Glass Lewis type thing and boards are becoming aware of it. If you look at 2-4% of greenhouse emission comes from infrastructure, specifically technology infrastructure, as part of this transformation within HPE, I also did what I call private cloud transformation. Remember, it's not data center transformation, it's private cloud transformation. And if you can take your traditional workload and cloudify it which runs on a GreenLake type platform, it's currently 30% more efficient than traditional way of handling the workload and the infrastructure but, we recently published our green living progress report and we talk about efficiency, by 2020 if you have achieved three times, the plan is to get to 30 times by 2050 where, infrastructure will not contribute to energy bill in turn the greenhouse emission as well. I think CIOs are responsible multifold on the sustainability piece. One is how they run their data center, make it efficient with GreenLake type implementations, demand from your hyperscaler to provide that, what Fidelma just launched, sustainability scorecard of the infrastructure, second piece is, we are the data gods in the company, right? We have access to all kinds of data, provide that to the product teams and have them, if we cannot measure, we cannot improve. So if you work with your product team, work with your BU leader, provide them data around greenhouse gas and how they're impacting a mission through their products and how can they make it better going forward, and that can be done through technology, right? All the measurements come from technology. So what technology we need to provide to our manufacturing lines so that they can monitor and improve on the sustainability front as well. >> You mentioned data, I wanted to bring that up 'cause I was going to bring that up in another top track here, data as an asset now is at play, so I get the data on the sustainability, feed that in, but as companies go to the cloud operating model, they go, hey, I got the hyperscalers, you call microscale, Amazon for instance, and you got on-premises data center, which is a large edge and you got the edge, the data control plane, and then the control plane and the data plane are always seem to be like the battle ground, I want to control the data plane, will customers own the data plane or will the infrastructure providers control that data plane? And how do you see that? Because we want to power the machine learning, so data plane control plane, it seems to be like the new middleware, what's your view on that? How do you look at that holistically? >> Yeah, so I'll start based on the hyperscaler conversation, right? And I had this conversation with one of the very big ones recently, or even our partner, SAP, when they talk about RISE, data center and how I host my application infrastructure, that's the lowest common denominator of our job. When I talk about CIOs being responsible for digital transformation, that means how do I make my business process more innovative? How do I make my data more accessible, right? So, if you look at data as an asset for the company, it's again, they're responsible, accountable. As CIO, I'm responsible to have it managed, have it on a technology platform, which makes it accessible by it and our business leader accountable to define the right metrics, right kind of KPIs, drive outcome from that data. IT organization, we are also too busy driving a lot of activities and today's world is going to bad business outcome. So with the data that I'm collecting, how do I enable my business leader to be able to drive business outcome through the use of the data? That's extremely important, and at HPE, we have achieved it, there are two ways, right? Now I have one single ERP, so all the data that I need for what I call operational reporting, get hindsight and insight is available at one place and they can drive their day to day business with that, but longer term, what's going to happen based on what happened, which I call insight to foresight comes from a integrated data platform, which I have control of, and you know, we are fragmenting it because companies now have Databox, Snowflake, AWS data analytics tool, Azure data analytics tool, I call it data torture. CIOs should get control of common set of data and enable their businesses to define better measurements and KPIs to be able to drive the data. >> So data's a crown jewel then, it's crown jewel not-- >> Can we double-click on that because, okay, so you take your ERP system, the consumers of data in the ERP system, they have the context that we've kind of operationalized those systems. We haven't operationalized our analytics systems in the same way, which is kind of a weird dynamic, and so you, right, I think correctly noted Rashmi that, we are creating all these stove pipes. Now, think I heard from you, you're gaining control of those stove pipes, but then how do you put data back in the hands of those line of business users without having to go through a hyper specialized analytics team? And that's a real challenge I think for data. >> It is challenge and I'll tell you, it's messy even in my world but, I have dealt with data long enough, the value lies in how do I take control of all stove pipes, bring it all together, but don't make it a data lake which is built out of multiple puddles, that data lake promise hasn't delivered, right? So the value lies in the conformed layer which then it's easier for businesses to access and run their analytics from, because they need a playground because all the answers they don't have, on the operation side, as you mentioned, we got it, right? It'll happen, but on the fore site side and deeper insight side based on driving the key metrics, two challenges; understanding what's the key metrics in KPI, but the second is, how to drive visibility and understanding of it. So we need to get technology out of the conversation, bring in understanding of the data into the conversation and we need to drive towards that path. >> As a business, you know, line of business person putting that hat on, I would love to have this conversation with my CIO because I would say, I just want self-service infrastructure and I want to have access to the data that I need, I know what metrics I need to run my business so now I want the technology to be just a technical detail, you take care of that and then somebody in the organization, probably not the line of business person wants to make sure that that data is governed and secure. So there's somebody else and that maybe is your responsibility, so how do you handle that real problem? So I think you're well on the track with GreenLake for self-serve infrastructure, right, how do you handle the sort of automated governance piece of it, make that computational? Yeah, so one thing is technology is important because that's bringing all the data together at one place with single version of truth. And then, that's why I say my sons are data scientist, by the way, I tell them that the magic happens at the intersection of technology knowledge, data knowledge, and business knowledge, and that's where the talent, which is very hard to find who can connect dots across these three kind of circles and focus on that middle where the value lies and pushing businesses to, because, you know, business is messy, I've worked on pharma companies, utilities, now technology, order does not mean revenue, right? There's a lot more that happen and pricing or chargeback, rebates, all that things, if somebody can kind of make sense out of it through incremental innovation, it's not like a big bang I know it all, but finding those areas and applying what you said, I call it the G word, governance, to make sure your source is right and then creating that conform layer then makes into the dashboard the right information about those types of metrics is extreme. >> And then bringing that to the ecosystem, now I just made it 10 times more complicated. >> Yeah, this is a great conversation, we on theCUBE interview one time we're talking about the old software days where shrink-wrap software be on the shelf, you wouldn't know if was successful until you looked at the sales data, well after the fact, now everything's instrumented, SaaS companies, you know exactly what the adoption is, either people like it or they don't, the data doesn't lie. So now companies are realizing, okay, I got data, I can instrument everything, your customers are now saying, I can get to the value fast now. So knowing what that value is is what everyone's talking about. How do you see that changing the data equation? >> Yeah, that's so true even for our business, right? If you talk to Fidelma today, who is our CTO, she's bringing together the platform and multiple platforms that we had so far to go to as-a-service business, right? Infosite, Aruba Central, GLCP, or now we call it it's all HPE GreenLake, but now this gives us the opportunity to really be a alongside customer. It's no more, I sold a box, I'll come back to you three years later for a refresh, now we are in touch with our customer real time through Telemetry data that's coming from our products and really understanding how our customers are reacting with that, right? And that's where we instantiated what we call is a federated data lake where, marketing, product, sales, all teams can come together and look at what's going on. Customer360, right? Data is locked in Salesforce from opportunity, leads, codes perspective, and then real time orders are locked in S4. The challenge is, how do we bring both together so that our sales people have on their fingertip whats the install base look like, how much business that we did and the traditional side and the GreenLake side and what are the opportunities here to support our customers? >> Real quick, I know we don't have a lot of time left, but I want to touch on machine learning, which basically feeds AI, machine learning, AI go together, it's only as good as the data you can provide to it. So to your point about exposing the data while having the stove pipes for compliance and governance, how do you architect that properly? You mentioned federated data lake and earlier you said the data lake promise hasn't come back, is it data meshes? What is the architecture to have as much available data to be addressed by applications while preserving the protection? >> Yeah, so, machine learning and AI, I will also add chatbots and conversational AI, right? Because that becomes the front end of it. And that's kind of the automation process promise in the data space, right? So, the point is that, if we talk about federated data lake around one capability which I'm talking about GreenLake consumption, right? So one piece is around, how do I get data cleanly? How do I relate it across various products? How do I create metrics out of it? But how do I make it more accessible for our users? And that's where the conversational AI and chatbot comes in. And then the opportunity comes in is around not only real time, but analytics, I believe Salesforce had a pitch called customer insight few years ago, where they said, we have so many of you on our platform, now I can combine all the data that I can access and want to give you a view of how every company is interacting with their customer and how you can improve it, that's where we want to go. And I completely agree, it ends up being clean data, governed data, secure data, but having that understanding of what we want to project out and how do I make it accessible for our users very seamlessly. >> Last question, what's your number one challenge right now in this post isolation world? >> Talent, we haven't talked about that, right? >> Got to get that out there. >> All these promises, right, the entire end to end foundational transformation, as-a-service transformation, talking about the promise of data analytics, we talked about governance and security, all that is possible because of the talent we have or we will have, and our ability to attract and retain them. So as CIO, I personally spend a lot of time, CEO, John Schultz, Antonio, very, very focused on creating that employee experience and what we call everything is edge for us, so edge to office initiative where we are giving them hybrid work capabilities, people are very passionate about purpose, so sustainability, quality, all these are big deal for them, making sure that senior leadership is focused on the right thing, so, hybrid working capability, hiring the right set of people with the right skill set and keeping them excited about the work we are doing, having a purpose, and being honest about it means I haven't seen a more authentic leader than Antonio, who opens up his keynote for this type of convention, with the purpose that he's very passionate about in current environment. >> Awesome, Rashmi, always great to have you on, wonderful to have you face to face, such a clear thinker in bringing your experience to our audience, really appreciate it. >> Thank you, I'm a big consumer of CUBE and look forward to having-- >> All right, and keep it right there, John and I will be back to wrap up with Norm Follett, from HPE discover 2022, you're watching theCUBE. 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>>Hello, buddy. And welcome back to the cubes. Coverage of Snowflake Data Cloud Summer 2020. We're tracking the rise of the data cloud and fresh off the keynotes. Hear Frank's Luqman, the chairman and CEO of Snowflake, and Anita Lynch, the vice president of data governance at Disney Streaming Services. Folks. Welcome E Need a Disney plus. Awesome. You know, we signed up early. Watched all the Marvel movies. Hamilton, the new Pixar movie Soul. I haven't gotten to the man DeLorean yet. Your favorite, but I really appreciate you guys coming on. Let me start with Frank. I'm glad you're putting forth this vision around the data cloud because I never liked the term Enterprise Data Warehouse. What you're doing is is so different from the sort of that legacy world that I've known all these years. But start with why the data cloud? What problems are you trying to solve? And maybe some of the harder challenges you're seeing? >>Yeah, I know. You know, we have We've come a long way in terms of workload execution, right? In terms of scale and performance and, you know, concurrent execution. We really taking the lid off sort of the physical constraints that that have existed on these types of operations. But there's one problem, uh, that were not yet, uh solving. And that is the silo ing and bunkering of data. Essentially, you know, data is locked in applications. It's locked in data centers that's locked in cloud cloud regions incredibly hard for for data science teams to really, you know, unlocked the true value of data. When you when you can address patterns that that exists across data set. So we're perpetuate, Ah, status we've had for for ever since the beginning off computing. If we don't start Thio, crack that problem now we have that opportunity. But the notion of a data cloud is like basically saying, Look, folks, you know, we we have to start inside, lowing and unlocking the data on bring it into a place where we can access it. Uh, you know, across all these parameters and boundaries that have historically existed, it's It's very much a step level function. Customers have always looked at things won't workload at that time. That mentality really has to go. You really have to have a data cloud mentality as well as a workload orientation towards towards managing data. Yeah, >>Anita is great here in your role at Disney, and you're in your keynote and the work. You're doing the governance work, and you're you're serving a great number of stakeholders, enabling things like data sharing. You got really laser focused on trust, compliance, privacy. This idea of a data clean room is really interesting. You know, maybe you can expand on some of these initiatives here and share what you you're seeing as some of the biggest challenges to success. And, of course, the opportunities that you're unlocking. >>Sure. I mean, in my role leading data to governance, it's really critical to make sure that all of our stakeholders not only know what data is available and accessible to them, they can also understand really easily and quickly whether or not the data that they're using is for the appropriate use case. And so that's a big part of how we scale data governance. And a lot of the work that we would normally have to do manually is actually done for us through the data. Clean rooms. >>Thank you for that. I wonder if you could talk a little bit more about the role of data and how your data strategy has evolved and maybe discuss some of the things that Frank mentioned about data silos. And I mean, obviously you can relate to that having been in the data business for a while, but I wonder if you could elucidate on that. >>Sure, I mean data complexities air going to evolve over time in any traditional data architecture. Er, simply because you often have different teams at different periods in time trying thio, analyze and gather data across Ah, whole lot of different sources. And the complexity that just arises out of that is due to the different needs of specific stakeholders, their time constraints. And quite often, um, it's not always clear how much value they're going to be able to extract from the data at the outset. So what we've tried to do to help break down the silos is allow individuals to see up front how much value they're going to get from the data by knowing that it's trustworthy right away. By knowing that it's something that they can use in their specific use case right away, and by ensuring that essentially, as they're continuing to kind of scale the use cases that they're focused on. They're no longer required. Thio make multiple copies of the data, do multiple steps to reprocess the data. And that makes all the difference in the world, >>for sure. I mean, copy creep, because it be the silent killer. Frank, I followed you for a number of years. You know, your big thinker. You and I have had a lot of conversations about the near term midterm and long term. I wonder if you could talk about you know, when you're Kino. You talk about eliminating silos and connecting across data sources, which really powerful concept. But really only if people are willing and able to connect and collaborate. Where do you see that happening? Maybe What are some of the blockers there? >>Well, there's there's certainly, ah natural friction there. I still remember when we first started to talk to to Salesforce, you know, they had discovered that we were top three destination off sales first data, and they were wondering, you know why that was. And and the reason is, of course, that people take salesforce data, push it to snowflake because they wanna overlay it with what data outside of Salesforce. You know, whether it's adobe or any other marketing data set. And then they want to run very highly skilled processes, you know, on it. But the reflexes in the world of SAS is always like, no, we're an island were planning down to ourselves. Everybody needs to come with us as opposed to we We go, you know, to a different platform to run these type of processes. It's no different for the for the public club. Venter Day didn't mean they have, you know, massive moats around there. Uh, you know, their stories to, you know, really prevent data from from leaving their their orbit. Eso there is natural friction in in terms off for this to happen. But on the other hand, you know, there is an enormous need, you know, we can't deliver on on the power and potential of data unless we allow it to come together. Uh, snowflake is the platform that allows that to happen. You know, we were pleased with our relationship with Salesforce because they did appreciate you know why this was important and why this was necessary. And we think you know, other parts of the industry will gradually come around to it as well. So the the idea of a data cloud has really come, right? People are recognizing, you know, why does this matters now? It's not gonna happen overnight, And there's a step global function of very big change in mentality and orientation. You know, >>it's almost as though the SAS ification of our industries sort of repeated some of the application silos, and you build a hardened top around it. All the processes are hardened around it, and Okay, here we go. And you're really trying to break that, aren't you? Yeah, Exactly. Anita. Again, I wanna come back to this notion of governance. It's so it's so important. It's the first role in your title, and it really underscores the importance of this. Um, you know, Frank was just talking about some of the hurdles, and and this is this is a big one. I mean, we saw this in the early days of big data. Where governance was this after thought it was like, bolted on kind of wild, Wild West. I'm interested in your governance journey, and maybe you can share a little bit about what role Snowflake has played there in terms of supporting that agenda. Bond. Kind of What's next on that journey? >>Sure. Well, you know, I've I've led data teams in a numerous, uh, in numerous ways over my career. This is the first time that I've actually had the opportunity to focus on governance. And what it's done is allowed for my organization to scale much more rapidly. And that's so critically important for our overall strategy as a company. >>Well, I mean a big part of what you were talking about, at least my inference in your your talk was really that the business folks didn't have to care about, you know, wonder about they cared about it. But they're not the wonder about and and about the privacy, the concerns, etcetera. You've taken care of all that. It's sort of transparent to them. Is that >>yeah, right. That's right. Absolutely. So we focus on ensuring compliance across all the different regions where we operate. We also partner very heavily with our legal and information security teams. They're critical to ensuring, you know, that we're able Thio do this. We don't We don't do it alone. But governance includes not just, you know, the compliance and the privacy. It's also about data access, and it's also about ensuring data quality. And so all of that comes together under the governance umbrella. I also lead teams that focus on things like instrumentation, which is how we collect data. We focus on the infrastructure and making sure that we've architected for scale and all of these air really important components of our strategy. >>I got. So I have a question. Maybe each of you can answer. I I sort of see this our industry moving from, you know, products. So then the platforms and platforms even involving into ecosystems. And then there's this ecosystem of of data. You guys both talked a lot about data sharing. But maybe Frank, you could start in Anita. You can add on to Frank's answer. You're obviously both both passionate about the use of of data and trying to do so in a responsible way. That's critical, but it's also gonna have business impact. Frank, where's this passion come from? On your side. And how are you putting in tow action in your own organization? >>Well, you know, I'm really gonna date myself here, but, you know, many, many years ago, you know, I saw the first glimpse off, uh, multidimensional databases that were used for reporting. Really, On IBM mainframes on debt was extraordinarily difficult. We didn't even have the words back then. In terms of data, warehouses and business. All these terms didn't exist. People just knew that they wanted to have, um, or flexible way of reporting and being able Thio pivot data dimensionally and all these kinds of things. And I just whatever this predates, you know, Windows 3.1, which, really, you know, set off the whole sort of graphical in a way of dealing with systems which there's not a whole generations of people that don't know any different. Right? So I I've lived the pain off this problem on sort of been had a front row seat to watching this This transpire over a very long period of time. And that's that's one of the reasons um, you know why I'm here? Because I finally seen, you know, a glimpse off, you know, also as an industry fully fully just unleashing and unlocking the potential were not in a place where the technology is ahead of people's ability to harness it right, which we've We've never been there before, right? It was always like we wanted to do things that technology wouldn't let us. It's different now. I mean, people are just heads are spinning with what's now possible, which is why you see markets evolved very rapidly right now. Way we were talking earlier about how you can't take, you know, past definitions and concepts and apply them to what's going on the world. The world's changing right in front of your eyes right now. >>Sonita. Maybe you could add on to what Frank just said and share some of the business impacts and and outcomes that air notable since you're really applied your your love of data and maybe maybe touch on culture, your data culture. You know any words of wisdom for folks in the audience who might be thinking about embarking on a data cloud journey similar to what you've been on? >>Yeah, sure, I think for me. I fell in love with technology first, and then I fell in love with data, and I fell in love with data because of the impact the data can have on both the business and the technology strategy. And so it's sort of that nexus, you know, between all three and in terms of my career journey and and some of the impacts that I've seen I mean, I think with the advent of the cloud, you know before, Well, how do I say that before the cloud actually became, you know, so prevalent in such a common part of the strategy that's required? It was so difficult, you know, so painful. It took so many hours to actually be able to calculate, you know, the volumes of data that we had. Now we have that accessibility, and then on top of it with the snowflake data cloud, it's much more performance oriented from a cost perspective because you don't have multiple copies of the data, or at least you don't have toe have multiple copies of the data. And I think moving beyond some of the traditional mechanisms for for measuring business impact has has only been possible with the volumes of data that we have available to us today. And it's just it's phenomenal to see the speed at which we can operate and really, truly understand our customers, interests and their preferences, and then tailor the experiences that they really want and deserve for them. Um, it's It's been a great feeling. Thio, get to this point in time. >>That's fantastic. So, Frank, I gotta ask you if you're still in your spare time, you decided to write a book? I'm loving it. Um, I don't have a signed copy, so I'm gonna have to send it back and have you sign it. But you're I love the inside baseball. It's just awesome. Eso really appreciate that. So But why did you decide to write a book? >>Well, there were a couple of reasons. Obviously, we thought it was an interesting tale to tell for anybody you know who is interested in, You know what's going on. How did this come about, You know, where the characters behind the scenes and all this kind of stuff. But, you know, from a business standpoint, because this is such a step function, it's so non incremental. We felt like, you know, we really needed quite a bit of real estate to really lay out what the full narrative and context is on. Do you know we thought books titled The Rise of the Data Cloud. That's exactly what it ISS and We're trying to make the case for that mindset, that mentality, that strategy. Because all of us, you know, I think is an industry or were risk off persisting, perpetuating, You know, where we've been since the beginning off computing. So we're really trying to make a pretty forceful case for Look, you know, there is an enormous opportunity out there, The different choices you have to make along the way. >>Guys, we got to leave it there. Frank. I know you and I are gonna talk again. Anita. I hope we have a chance to meet face to face and and talking the Cube live someday. You're phenomenal, guest. And what a great story. Thank you both for coming on. And thank you for watching. Keep it right there. You're watching the Snowflake Data Cloud Summit on the Cube.

>> Hello everybody. And welcome back to, theCUBE coverage of the Snowflake Data Cloud Summit 2020. We're tracking the rise of the Data Cloud, and fresh off the keynotes here, Frank Slootman, the Chairman and CEO of Snowflake and Anita Lynch, the Vice President of data governance at Disney streaming services. Folks Welcome. >> Thank you >> Thanks for having us Dave. >> Anita Disney plus awesome. You know, we signed up early, watched all the Marvel movies, Hamilton, the new Pixar movie soul. I haven't gotten it to the Mandalorian yet, your favorite. But really appreciate you guys coming on. Let me start with Frank. I'm glad you're putting forth this vision around the Data Cloud, because I never liked the term enterprise data warehouse. What you're doing is so different from the sort of that legacy world that I've known all these years. But start with why the Data Cloud? What problems are you trying to solve? And maybe some of the harder challenges you're seeing. >> Yeah, you know, we have a, we've come a long way in terms of workload execution. Right? In terms of scale and performance, and concurrent execution. We've really taken the lid off, sort of the physical constraints that have existed on these type of operations. But there's one problem that we're not yet solving, and that is the siloing and bunkering of data. And essentially, data is locked in applications, it's locked in data centers, it's locked in cloud, cloud regions. Incredibly hard for data science teams to really unlock the true value of data, when you can't address patterns that exist across data sets. So where we perpetuate a status we've had for forever since the beginning of computing. If we don't start to crack that problem now we have that opportunity. But the notion of a Data Cloud is like basically saying, "Look folks, we have to start on siloing and unlocking the data, and bring it into a place, where we can access it across all these perimeters, and boundaries that have historically existed. It's very much a step level function. Like the customers have always looked at things, one workload at a time, that mentality really has to go. You really have to have a Data Cloud mentality, as well as a workload orientation towards managing data. >> Anita, it was great hearing your role at Disney and in your keynote, and the work you're doing, the governance work. and you're serving a great number of stakeholders, enabling things like data sharing. You got really laser focused on trust, compliance, privacy. This idea of a data clean room is really interesting. Maybe you can expand on some of these initiatives here, and share what you're seeing as some of the biggest challenges to success, and of course, the opportunities that you're unlocking. >> Sure. In my role leading data governance, it's really critical to make sure that all of our stakeholders not only know what data is available and accessible to them. They can also understand really easily and quickly, whether or not the data that they're using is for the appropriate use case. And so that's a big part of how we scale data governance, and a lot of the work that we would normally have to do manually is actually done for us through the data clean rooms. >> Thank you for that. I wonder if you could talk a little bit more about the role of data and how your data strategy has evolved and maybe discuss some of the things that Frank mentioned about data silos. And I mean, obviously you can relate to that having been in the data business for a while, but I wonder if you can elucidate on that. >> Sure. I mean, data complexities are going to evolve over time in any traditional data architecture simply because you often have different teams at different periods and time trying to analyze and gather data across a whole lot of different sources. And the complexity that just arises out of that is due to the different needs of specific stakeholders. There are time constraints and quite often, it's not always clear how much value they're going to be able to extract from the data at the outset. So what we've tried to do to help break down those silos is allow individuals to see upfront how much value they're going to get from the data by knowing that it's trustworthy right away. By knowing that it's something that they can use in their specific use case right away. And by ensuring that essentially as they're continuing to kind of scale the use cases that they're focused on, they're no longer required to make multiple copies of the data, do multiple steps to reprocess the data. And that makes all the difference in the world. >> Yeah, for sure. I'm a copy Creek because it'd be the silent killer. Frank I followed you for a number of years, you're a big thinker, you and I have had a lot of conversations about the near-term, mid-term and long-term, I wonder if you could talk about, in your keynote you're talking about eliminating silos and connecting across data sources. Which is really powerful concept but really only if people are willing and able to connect and collaborate. Where do you see that happening? Maybe what are some of the blockers there? >> Well, there's certainly a natural friction there. I still remember when we first started to talk to, Salesforce, you know, they had discovered that we were a top three destination of Salesforce data and they were wondering why that was, and the reason is of course, that people take Salesforce data push it to snowflake because they want to overlay it with what data outside of Salesforce. Whether it's Adobe or any other marketing dataset. And then they want to run very highly scaled processes on it. But the reflexes in the world of SaaS is always like no, we're an Island, we're a planet down to ourselves. Everybody needs to come with us, as opposed to we go to a different platform to run these types of processes. It's no different for the public cloud vendor. They didn't only, they have massive moats around their storage to really prevent data from leaving their orbit. So there is natural friction in terms for this to happen. But on the other hand there is an enormous need. We can't deliver on the power and potential of data unless we allow it to come together. Snowflake is the platform that allows that to happen. We were pleased with our relationship with Salesforce because they did appreciate why this was important and why this was necessary. And we think, other parts of the industry will gradually come around to it as well. So the idea of a Data Cloud has really come, right. When people are recognizing why this matters now. It's not going to happen overnight. It is a step while will function a very big change in mentality and orientation. >> Yeah. It's almost as though the the SaaS suffocation of our industry sort of repeated some of the application silos and you build a hardened top around it, all the processes are hardened around it and okay, here we go. And you're really trying to break that, aren't you? >> Yep, exactly. >> Anita, again, I want to come back to this notion of governance. It's so it's so important. It's the first role in your title and it really underscores the importance of this. You know, Frank was just talking about some of the hurdles and this is a big one. I mean, we saw this in the early days of big data where governance was just afterthought. It was like bolted on the kind of wild wild West. I'm interested in your governance journey. And maybe you can share a little bit about what role snowflake has played there in terms of supporting that agenda and kind of what's next on that journey. >> Sure. Well, I've led data teams in numerous ways over my career. This is the first time that I've actually had the opportunity to focus on governance and what it's done is allowed for my organization to scale much more rapidly. And that's so critically important for our overall strategy as a company. >> Well, I mean, a big part of what you were talking about at least my inference in your talk was really that the business folks didn't have to care about, you know, wonder about they cared about it, but they don't have to wonder about, and about the privacy concerns, et cetera. You've taken care of all that it's sort of transparent to them. Is that right?| >> Yea That's right absolutely. So we focus on ensuring compliance across all of the different regions where we operate. We also partner very heavily with our legal and information security teams. They're critical to ensuring that we're able to do this. we don't do it alone. But governance includes not just the compliance and the privacy, it's also about data access, and it's also about ensuring data quality. And so all of that comes together under the governance umbrella. I also lead teams that focus on things like instrumentation, which is how we collect data. We focus on the infrastructure and making sure that we've architected for scale and all of these are really important components of our strategy. >> I got a...So I have a question maybe each of you can answer. I sort of see this, our industry moving from products, to then, to platforms and platforms even evolving into ecosystems. And then there's this ecosystem of data. You guys both talked a lot about data sharing but maybe Frank, you can start, Anita you can add on to Frank's answer. You're obviously both passionate about the use of data and trying to do so in a responsible way. That's critical but it's also going to have business impact. Frank, where's this passion come from on your side. And how are you putting into action in your own organization? >> Well, you know I'm really going to date myself here, but many, many years ago, I saw the first glimpse of multidimensional databases that were used for reporting really on IBM mainframes. And it was extraordinarily difficult. We didn't even have the words back then in terms of data warehouses and business. All these terms didn't exist. People just knew that they wanted to have a more flexible in way of reporting and being able to pivot data dimensionally and all these kinds of things. And I just bought whatever this predates windows 3.1, which really, set off the whole sort of graphical, way of dealing with systems which there's now a whole generations of people that don't know any different right? So I've lived the pain of this problem and sort of had a front row seat to watching this transpire over a very long period of time. And that's one of the reasons, why I'm here, because I finally seen, a glimpse of, I also, as an industry fully, fully just unleashing and unlocking to potential. We're now in a place where the technology is ahead of people's ability to harness it. Which we've never been there before. It was always like, we wanted to do things that technology wouldn't let us. It's different now. I mean, people are just, their heads are spinning with what's now possible, which is why you see markets evolve, very rapidly right now we were talking earlier about how you can't take past definitions and concepts and apply them to what's going on in the world. because the world's changing right in front of your eyes right now. >> So Anita maybe you could add on to what Frank just said and share some of the business impacts and outcomes that are notable since you've really applied your your love of data and maybe, maybe touch on, on culture. Data culture, any words of wisdom for folks in the audience who might be thinking about embarking on a Data Cloud journey, similar to what you've been on. >> Yeah sure. I think for me, I fell in love with technology first and then I fell in love with data. And I fell in love with data because of the impact that data can have on both the business and the technology strategy. And so it's sort of that nexus, between all three. And in terms of my career journey and some of the impacts that I've seen. I mean, I think with the advent of the cloud, before, well, how do I say that. Before the cloud actually became so prevalent and such a common part of the strategy that's required it was so difficult, you know, so painful. It took so many hours to actually be able to calculate the volumes of data that we had. Now we have that accessibility, and then on top of it, with the snowflake Data Cloud it's much more performance oriented from a cost perspective because you don't have multiple copies of the data, or at least you don't have to have multiple copies of the data. And I think moving beyond some of the traditional mechanisms for for measuring business impact, has only been possible with the volumes of data that we have available to us today. And it's just, it's phenomenal to see the speed at which we can operate. And really, truly understand our customer's interests and their preferences and then tailor the experiences that they really want and deserve for them. It's, been a great feeling to get to this point in time. >> That's fantastic. So, Frank, I got to ask you this. So in your spare time you decided to write a book, I'm loving it. I don't have a signed copy so I'm going to have to send it back and have you sign it. But, and you're, I love the inside baseball. It's just awesome. So really appreciate that. So, but why did you decide to write a book? >> Well, there were a couple of reasons, obviously we thought of as an interesting tale to tell for anybody, who is interested in what's going on, how did this come about? Who are the characters behind the scenes and all this stuff. But from a business standpoint because this is such a step function it's so non incremental, we felt like, we really needed quite a bit of real estate to really lay out what the full narrative and context is. And, we thought, the books titled the "Rise of the Data Cloud." That's exactly what it is. And we're trying to make the case for that mindset, that mentality, that strategy because all of us, I think as an industry, were at risk of, persisting, perpetuating where we've been since the beginning of computing. So we're really trying to make a pretty forceful case for a look. There's an enormous opportunity out there but there's some choices you have to make along the way. >> Guys, we got to leave it there. Frank, I know you and I are going to talk again Anita, I hope we have a chance to meet face to face and talk in theCUBE live someday. You're phenomenal guests and what a great story. Thank you both for coming on. And thank you for watching. Keep it right there. You're watching the, Snowflake Data Cloud Summit, on theCUBE.

>>Hello, buddy. And welcome back to the cubes. Coverage of Snowflake Data Cloud Summer 2020. We're tracking the rise of the data cloud and fresh off the keynotes. Hear Frank's Luqman, the chairman and CEO of Snowflake, and Anita Lynch, the vice president of data governance at Disney Streaming Services. Folks. Welcome E Need a Disney plus. Awesome. You know, we signed up early. Watched all the Marvel movies. Hamilton, the new Pixar movie Soul. I haven't gotten to the man DeLorean yet. Your favorite, but I really appreciate you guys coming on. Let me start with Frank. I'm glad you're putting forth this vision around the data cloud because I never liked the term Enterprise Data Warehouse. What you're doing is is so different from the sort of that legacy world that I've known all these years. But start with why the data cloud? What problems are you trying to solve? And maybe some of the harder challenges you're seeing? >>Yeah. You know, we have We've come a long way in terms of workload, execution, right? In terms of scale and performance and concurrent execution. We really taking the lid off. Sort of the physical constraints that that have existed on these types of operations. But there's one problem, uh, that were not yet, uh solving. And that is the silo ing and bunkering of data essentially in the data is locked in applications. It's locked in data centers. It's locked in cloud cloud regions incredibly hard for for data science teams to really unlock the true value of data when you when you can address patterns that that exists across data set. So we're perpetuate, uh, status we've had for for ever since the beginning off computing. If we don't start Thio, crack that problem now we have that opportunity. But the notion of a data cloud is like basically saying, Look, folks, you know, we we have to start inside, lowing and unlocking the data on bring it into a place where we can access it. Uh, you know, across all these parameters and boundaries that have historically existed, it's very much a step level function. Customers have always looked at things won't workload at that time. That mentality really has to go. You really have to have a data club mentality as well as a workload orientation towards towards managing data. >>Anita is great here in your role at Disney and you're in your keynote and the work you're doing the governance work and you're you're serving a great number of stakeholders, enabling things like data sharing. You got really laser focused on trust, compliance, privacy. This idea of a data clean room is really interesting. You know, maybe you can expand on some of these initiatives here and share what you're seeing as some of the biggest challenges to success. And, of course, the opportunities that you're unlocking. >>Sure, I mean, in my role leading data to governance, it's really critical to make sure that all of our stakeholders not only know what data is available and accessible to them, they can also understand really easily and quickly whether or not the data that they're using is for the appropriate use case. And so that's a big part of how we scale data governance. And a lot of the work that we would normally have to do manually is actually done for us through the data Clean rooms. >>Thank you for that. I wonder if you could talk a little bit more about the role of data and how your data strategy has evolved and maybe discuss some of the things that Frank mentioned about data silos. And I mean, obviously you can relate to that having been in the data business for a while, but I wonder if you could elucidate on that. >>Sure, I mean data complexities air going to evolve over time in any traditional data architecture. Er, simply because you often have different teams at different periods in time trying thio, analyze and gather data across Ah, whole lot of different sources. And the complexity that just arises out of that is due to the different needs of specific stakeholders, their time constraints. And quite often, um, it's not always clear how much value they're gonna be able to extract from the data at the outset. So what we've tried to do to help break down the silos is allow individuals to see up front how much value they're going to get from the data by knowing that it's trustworthy right away. By knowing that it's something that they can use in their specific use case right away, and by ensuring that essentially, as they're continuing to kind of scale, the use cases that they're focused on their no longer required Thio make multiple copies of the data, do multiple steps to reprocess the data. And that makes all the difference in the world, >>for sure. I mean, copy creep, because it be the silent killer. Frank, I've followed you for a number of years. Your big thinker. You and I have had a lot of conversations about the near term midterm and long term. I wonder if you could talk about you know, when you're Kino. You talk about eliminating silos and connecting across data sources, which really powerful concept. But really only if people are willing and able to connect and collaborate. Where do you see that happening? Maybe What are some of the blockers there? >>Well, there's there's certainly, ah, natural friction there. I still remember when we first started to talk to to Salesforce, you know, they had discovered that we were top three destination off sales first data, and they were wondering why that was. And the reason is, of course, that people take salesforce data, push it to snowflake because they wanna overlay it with what data? Outside of Salesforce, you know, whether it's adobe or any other marketing data set and then they want to run very highly skilled processes, you know, on it. But the reflexes in the world of SAS is always like, No, we're an island were planning down to ourselves. Everybody needs to come with us as opposed to we We go, you know, to a different platform to run these type of processes. It's no different for the for the public club. Better day didn't mean they have, you know, massive moats around there. Uh, you know, their stories to, you know, really prevent data from from leaving their their orbit. Eso there is natural friction in, uh, in terms off for this to happen. But on the other hand, you know, there is an enormous need, you know, we can't deliver on on the power and potential of data unless we allow it to come together. Uh, snowflake is the platform that allows that to happen. Uh, you know, we were pleased with our relationship with Salesforce because they did appreciate you know why this was important and why this was necessary. And we think you know, other parts of the industry will gradually come around to it as well. So the the idea of a data cloud has really come, right? Uh, people are recognizing, you know, why does this matter now? It's not gonna happen overnight. There's a step global function of very big change in mentality and orientation. >>Yeah. It's almost as though the SAS ification of our industry sort of repeated some of the application silos and you build a hardened top around it. All the processes are hard around. OK, here we go. And you're really trying to break that, aren't you? Yeah, Exactly. Anita. Again, I wanna come back to this notion of governance. It's so it's so important. It's the first role in your title, and it really underscores the importance of this. Um, you know, Frank was just talking about some of the hurdles, and this is this is a big one. I mean, we saw this in the early days of big data. Where governance was this after thought it was like, bolted on kind of wild, Wild West. I'm interested in your governance journey. And maybe you could share a little bit about what role snowflake has played there in terms of supporting that agenda. Bond. Kind of What's next on that journey? >>Sure. Well, you know, I've I've led data teams in a numerous, uh, in numerous ways over my career. This is the first time that I've actually had the opportunity to focus on governance. And what it's done is allowed for my organization to scale much more rapidly. And that's so critically important for our overall strategy as a company. >>Well, I mean a big part of what you were talking about. At least my inference in your talk was really that the business folks didn't have to care about, you know, wonder about they cared about it. But they're not the wonder about and and about the privacy, the concerns, etcetera. You've taken care of all that. It's sort of transparent to them. Is that >>yeah, right. That's right. Absolutely So we focus on ensuring compliance across all of the different regions where we operate. We also partner very heavily with our legal and information security teams. They're critical to ensuring, you know, that were ableto do this. We don't we don't do it alone. But governance includes not just, you know, the compliance and the privacy. It's also about data access, and it's also about ensuring data quality. And so all of that comes together under the governance umbrella. I also lead teams that focus on things like instrumentation, which is how we collect data. We focus on the infrastructure and making sure that we've architected for scale and all of these air really important components of our strategy. >>I got. So I have a question. Maybe each of you can answer. I I sort of see this our industry moving from, you know, products toe, then two platforms and platforms, even involving into ecosystems. And then there's this ecosystem of data. You guys both talked a lot about data sharing, But maybe Frank, you could start in Anita. You can add on to Frank's answer. You're obviously both both passionate about the use of data and trying to do so in a responsible way. That's critical, but it's also gonna have business impact. Frank, where's this passion come from? On your side. And how are you putting in tow action in your own organization? >>Well, you know, I'm really gonna date myself here, but, you know, uh, many, many years ago, uh, I saw the first glimpse off, uh, multidimensional databases that were used for reporting really on IBM mainframes on git was extraordinarily difficult. We didn't even have the words back then in terms of data, warehouses and all these terms didn't exist. People just knew that they wanted to have, um, or flexible way of reporting and being able Thio pivot data dimensionally and all these kinds of things. And I just whatever this predates, you know, Windows 3.1, which really set off the whole sort of graphical in a way of dealing with systems which there's not a whole generations of people that don't know any different, Right? So I I've lived the pain off. This problem on sort of had a front row seat to watching this this transpire over a very long period of time. And that's that's one of the reasons you know why I'm here. Because I finally seen a glimpse off. You know, I also as an industry fully fully just unleashing and unlocking the potential were not in a place where the technology is ahead of people's ability to harness it right, which we've never been there before, right? It was always like we wanted to do things that technology wouldn't let us. It's different now. I mean, people are just heads are spinning with what's now possible, which is why you see markets evolved very rapidly right now. We were talking earlier about how you can't take, you know, past definitions and concepts and apply them to what's going on the world. The world's changing right in front of your eyes right now. >>Sonita. Maybe you could add on to what Frank just said and share some of the business impacts and and outcomes that are notable since you're really applied your your love of data and maybe maybe touch on culture, data, culture, any words of wisdom for folks in the audience who might be thinking about embarking on a data cloud journey similar to what you've been on? >>Yeah, sure, I think for me. I fell in love with technology first, and then I fell in love with data, and I fell in love with data because of the impact the data can have on both the business and the technology strategy. And so it's sort of that nexus, you know, between all three and in terms of my career journey and some of the impacts that I've seen. I mean, I think with the advent of the cloud you know before. Well, how do I say that before the cloud actually became, you know, so prevalent and such a common part of the strategy that's required It was so difficult, you know, so painful. It took so many hours to actually be able to calculate, you know, the volumes of data that we had. Now we have that accessibility, and then on top of it with the snowflake data cloud, it's much more performance oriented from a cost perspective because you don't have multiple copies of the data, or at least you don't have toe have multiple copies of the data. And I think moving beyond some of the traditional mechanisms for for measuring business impact has has only been possible with the volumes of data that we have available to us today. And it's just it's phenomenal to see the speed at which we can operate and really, truly understand our customers, interests and their preferences, and then tailor the experiences that they really want and deserve for them. Um, it's it's been a great feeling. Thio, get to this point in time. >>That's fantastic. So, Frank, I gotta ask you if you're still in your spare time. You decided to write a book? I'm loving it. Um, I don't have a signed copy, so I'm gonna have to send it back and have you sign it. But your love, the inside baseball, it's just awesome. Eso really appreciate that. So but why did you decide to write a book? >>Well, there were a couple of reasons. Obviously, uh, we thought it was an interesting tale to tell for anybody who's interested in, you know what's going on. How did this come about, You know, where the characters behind the scenes and all this kind of stuff. But, you know, from a business standpoint, because this is such a step function, it's so non incremental. We felt like, you know, we really needed quite a bit of real estate to really lay out what the full narrative in context is on. Do you know, we thought books titled The Rise of the Data Cloud. That's exactly what it iss. And we're trying to make the case for that mindset, that mentality, that strategy. Uh, because all of us, you know, I think it's an industry were risk off, you know, persisting, perpetuating. Uh, you know, where we've been since the beginning off computing. So we're really trying to make a pretty forceful case for Look, there's an enormous opportunity out there. The different choices you have to make along the way. >>Guys, we got to leave it there. Frank. I know you and I are gonna talk again. Anita. I hope we have a chance to meet face to face and and talking the Cube live someday. You're phenomenal guests. And what a great story. Thank you both for coming on. Thank you. All right, you're welcome. And keep it right there, buddy. We'll be back for the next guest right after this short break and we're clear. All right. Not bad.

>> Narrator: From "theCUBE" studios in Palo Alto, in Boston, connecting with thought leaders all around the world, this is "theCUBE" conversation. >> Hi everybody, this is Dave Vellante. and as you know, we've been tracking the next generation of cloud. Sometimes we call it cloud 2.0, Frank Slootman is here to really unpack this with me. Frank, great to see you. Thanks for coming on. >> Yeah, you as well Dave, good to see you. >> Yeah so, obviously hot off your IPO, a lot of buzz around that, that's fine. We could talk about that, but I really want to talk about the future. What, before we get off the IPO though, was something you told me when you were CEO of ServiceNow. You said, "Hey, we're priced to perfection." So it looks like Snowflake is going to be priced to perfection, it's a marathon though. You made that clear. I presume it's not any different here for you. >> Well, I think, you know, the ServiceNow journey was different in the sense that we were kind of underdogs and people sort of discovered over the years, the full potential of the company. And, I think with Snowflake, they pretty much just discovered it day one (laughs). It's a little bit more, sometimes it's nice to be an underdog or a bit of an overdog in this particular scenario, but yeah, it is what it is. And, it's all about execution, delivering the results, being great with our customers and, hopefully the (indistinct) where they may at that point. >> Yeah, you're a poorly kept secret at this point, Frank, after a while. I've got some excerpts of your book that I've been reading and of course I've been following your career since the 2000's. You're off sailing. You mentioned in your book that you were kind of retired, you were done, and then you got sucked back in. Now, why? Are you in this for the sport? What's the story here? >> Actually, that's not a bad way of characterizing it. I think I am in it, for the sport, the only way to become the best version of yourself is to be under the gun, every single day. And that's certainly what we are. It sort of has its own rewards, building great products, building great companies, regardless of what the spoils may be, it has its own reward. It's hard for people like us to get off the field and hang it up, so here we are. >> You're putting forth this vision now, the data cloud, which obviously it's good marketing, but I'm really happy because I don't like the term enterprise data warehouse. I don't think it reflects, what you're trying to accomplish. EDW, it's slow, only a few people really know how to use it, the time value of data is gone by the time, your business is moving faster than the data in EDW. And it really became, as the savior because of Sarbanes-Oxley, that's really, what became of a reporting mechanism. So I have never seen what you guys are doing as EDW. So I want you to talk about the data cloud, I want to get into the vision a little bit and maybe challenge you on a couple things so our audience can better understand it. >> Yeah, so the notion of a data cloud is actually a type of cloud that we haven't had. Data has been fragmented and locked up in a million different places, in different clouds, different cloud regions, obviously, on premise. And for data science teams, they're trying to drive analysis across datasets, which is incredibly hard, which is why a lot of this resorts to programming and things of that sort. It's hardly scalable because the data is not optimized, the economics are not optimized, there's no governance model and so on. But the data cloud is actually the ability to loosely couple and lightly federate data, regardless of where it is, so it doesn't have scale limitations or performance limitations the way traditional data warehouses have had it. So we really have a fighting chance of really killing the silos and unlocking the bunkers and allowing the full promise of data sciences and ML and AI to really happen. A` lot of the analysis that happens on data is on a single dataset because it's just too damn hard to drive analysis across multiple datasets. When we talk to our customers, they have very precise designs on what they're trying to do. They say, "Look, we are trying to discover through deep learning what the patterns are that lead to transactions, whether it's... If you're a streaming company, maybe it's that you're signing up for a channel or you're buying a movie or whatever it is. What is the pattern of datapoints that leads us to that desired outcome?" Once you have a very accurate description of the data relationships that result in that outcome, you can then search for it and scale it tens of million times over. That's what digital enterprises do, right? So in order to discover these patterns, enrich the data to the point where the patterns become incredibly predictive, that's what Snowflake is for, right? But it requires a completely federated data model because you're not going to find a data pattern in a single dataset, per se, right? So that's what it's all about. The outcomes of a data cloud are very, very closely related to the business outcomes that the user is seeking, right? It's not some infrastructure process. that has a very remote relationship with business outcome. This is very, very closely related. >> So it doesn't take a brain surgeon to look at the trillionaires' club. (chuckles) So I can see that. I can see the big trillion dollars, Apple, $2 trillion market cap companies, they get data at the core. Whereas most companies, most incumbents, it might be a bottling plant at the core or some manufacturing or some of the process, and they put data rounded in these silos. It seems like you're trying to really bring that innovation and put data at the core and you've got an architecture to do that. You're talking about your multi cluster shared storage architecture. You mentioned data sharing. Will this, in your opinion enable, for instance, incumbents to do what a lot of the startups were able to do with the cloud days. hey got access to data centers which they couldn't have before the cloud. Are you trying to do something similar with data? >> Yeah, so obviously there's no doubt that the cloud is a critical enabler. This wouldn't be happening without it. At the same time, the trails that have been blazed by Alexa, Facebook and Google. The reason that those enterprises are so extraordinarily valuable is because of what they know through data and how they can monetize what they know through data. But that power is now becoming available to every single enterprise out there, right. Because the data platforms and the underlying cloud capabilities, we are now delivering that to anybody who wants it. Now you still need to have strong data engineering, data science capabilities. It's not like falling off of a log, but fundamentally those capabilities are now broadly accessible in the marketplace. >> So we talking up front about some of the differences between what you've done early in your career, like I said, you're the worst kept secret, Data Domain I would say it was somewhat of a niche market. You blew it up until it was very disruptive, but it was somewhat limited in what could be done. And maybe some of that limitation, you know, wouldn't have occurred if you stayed an independent company. ServiceNow, you mopped the table up 'cause you really had no competition there. Not the case here. You've got some of the biggest competitors in the world. So talk about that and what gives you confidence that you can continue to dominate? >> It's actually interesting that you bring up these companies. Data Domain, it was a scenario where we were constrained on market and we were a data backup company as you recall, we needed to move into backup software, needed to move into primary storage. While we knew it, we couldn't execute on it because it took tremendous resources which, back in the day, it was much harder than what it is right now. So we ended up selling the company to EMC and now part of Dell, but we're left with some trauma from that experience in the sense that, why couldn't we execute on that transformation? So coming to ServiceNow, we were extremely, and certainly me personally, extremely attuned to the challenges that we had endured in our prior company, and one of the reasons why you saw ServiceNow break out at scale, at tremendous growth rates is because of what we learned from the prior journey. We were not going to ever get caught again in the situation where we could not sustain our markets and sustain our growth. So ServiceNow is very much, the execution model, very much a reaction to what we had encountered in the prior company. Now coming into Snowflake a totally different deal because not only is this a large market this is a developing market. I think you've pointed out in some of your broadcasting, that this market is very much influx. And the reason is that technology is now capable of doing things for people and enterprises that they could never do before. So people are spending way more resources than they ever thought possible on these new capabilities. So you can't think in terms of static markets and static data definitions, it means nothing. Okay, these things are so in transition right now. It's very difficult for people to scope the scale of this opportunity. >> Yeah, I want to understand your thinking around and, you know, I've written about the TAM and can Snowflake grow into it's valuation and the way I drew it, I said, okay, I've got data lakes and you've got an enterprise data warehouse, that's pretty well understood but I called it data as a service company the closest analogy to your data cloud. And then even beyond that when you start bringing in the Edge and real time data. Talk about how you're thinking about that TAM what you have to do to participate. Do you have to bring adjacent capabilities? Or is it this read data sharing that will get you there? In other words, you're not like a transaction system. You hear people talking about converged databases. You're going to talk about real time inference at the Edge that today anyway, isn't what Snowflake is about. Does that vision of data sharing in the data cloud, does that allow you to participate in that massive multi hundred billion dollar TAM that I laid out and probably others as well? >> Yeah, well, it's always difficult to define markets based on historical concept that probably not going to apply a whole lot or for much longer. I mean the way we think of it is that data is the beating heart of the digital enterprise and digital enterprises today, what are you looking at people like the car door dash or so on. They were built from the ground up to be digital enterprises. And data is the beating heart of their operation, data operations is their manufacturing if you will. Every other enterprise out there is working very hard to become digital or part digital and is going to learn to develop a data platform like what we're talking about here to data cloud as well as the expertise in terms of data engineering and data sciences to really fully become a digital enterprise, right? So we view data as driving operations, all the all the digital enterprise, that's really what it is, right? And it's completely data driven end-to-end. There's no people involved and the people are developing and supporting the process but in the execution, it is end-to-end data driven. Meaning that data is the signal that initiates the process he's taking, but as they're, as they're being detected, and then they fully execute the entire machinery, programmatic machinery if you will, all of the processes have been designed. Now for example, I may fit a certain pattern, that leads to some transactional law context, but that's not fully completed, that pattern until I click on some link and all of a sudden, poof, I have become a prime prospect. System detects that in the real time and then unleashes all its outreach and capabilities to get me to transact. You and I are experiencing this every day. When we're, when we're online, you just may not fully realize (laughs) that that's what's happening behind the scenes. That's really what this is all about. So to me, this is sort of the new online transaction processing is an end to end data digital process that is continually acquiring, analyzing and acting on data. >> Well, you've talked about the time, time value of, of data. It loses value over time. And to the extent that you can actually affect decisions, maybe prior, before you lose the customer, before you lose the patient, even even more importantly, or before you lose the battle. There's all kinds of mental models that you can apply this. So automation is a key part of that and then again, I think a lot of people, like you said, if you just try to look at historical markets, you can't really squint through those and apply them. You really have to open up your mind and think about the new possibilities. And so I could see >> Exactly. >> Your component of automation. I see what's happening in the RPA space, and I could see these just massive opportunities to really change society, change business. Your last thoughts. >> While there's just no scenario that I can envision where data is not completely core and central to a digital enterprise period. >> Yeah, I think I really do think Frank, your vision is misunderstood somewhat. I think people say, "Okay hey, we'll bet on Slootman, "Scott Pelley, the team." That's great to do that, but I think this is going to unfold in a way that people maybe haven't predicted and maybe you guys yourselves and your founders you know haven't, aren't able to predict as well, but you've got that good, strong architectural philosophy that you're pursuing and it just kind of feels right, doesn't it? >> One of the harder conversations and the this is one of the reasons why we also wrote our book "The Rise of the Data Cloud" is to convey to the marketplace that this is not an incremental evolution. It is just not sort of building on the past. There is a real step function here. And the way to think about it is that typically enterprises and institutions will look at a platform like Snowflake from a workload context. In other words, I have this business, I have this workload, which is very much historically defined by the way, and then they benchmark us against what they're already doing on some legacy platform and they decide, "Yeah, this is a good fit, we're going to put Snowflake here, maybe there." But, it's still very workload centric which means that we are, essentially, perpetuating the mentality of the past. We were doing it one workload at a time, we're creating the new silos and the new bunkers of data in the process. And we're really not approaching this with the level of vision that the data scientists really require to drive maximum benefit from data. So our argument, and this is not an easy argument, is to say to CIOs and any other C-level person that wants to listen and say, "Look, just thinking about operational context and operational excellence, it's like we have to have a platform that allows use unfettered access to the data that we may need to bring the analytical power to." If you have to bring analytical power to a diversity of datasets, how are we going to do that? The data lives in 500 different places, it's just not possible, other than with insane amounts of programming and complexity and then we don't have the performance and we don't have the economics and we don't have the governance and so on. So you really want to set yourself up with a data cloud so that you can unleash your data science capabilities, your machine learning, your deep learning capabilities and then really get the full throttle advantage of what the technology can do. If you going to perpetuate the silo-ing and bunkering of data by doing it one workload at a time, five, 10 years from now, we're having the same conversations we've been having over the last 40 years. >> Yeah, operationalizing your data is going to require busting down those silos and it's going to require something like the data cloud to really power that to the next decade and beyond. Frank Slootman, thanks so much for coming to "theCUBE" and helping us do a preview here of what's to come. >> You bet Dave, thanks. >> All right, thank you for watching everybody. This is Dave Vellante from the "theCUBE". We'll see you next time.

>> Narrator: From around the globe, it's theCUBE with digital coverage of Women Transforming Technology. Brought to you by VMware. >> Welcome to theCUBE, I'm Lisa Martin covering the fifth annual Women Transforming Technology. The first year that this event has gone completely digital. We're very pleased to welcome back to theCUBE one of our favorite alumni, the Chief People Officer of VMware, Betsy Sutter. Betsy, welcome back! >> Oh, thank you, Lisa. It's great to see you and it's great to be back. Love this time of year. >> Likewise, me too. And you know, I've had the great opportunity and pleasure of covering WT2 for theCUBE the last few years so I know walking into that courtyard area in Palo Alto, VMware's headquarters, you feel the energy and the excitement, and it's really genuine. And so, knowing that you had to pivot a couple you know, eight weeks or so ago or more, to convert what is such an engaging in-person experience to digital, hard decision, the right decision, but huge in terms of the number of attendees. Tell us a little bit about that process of taking We Rise digital. >> Yeah, you know, it was a pretty quick decision. At VMWare, we were starting to virtualize some other events, and so in realtime, we said, "let's go ahead "and virtualize Women Transforming Technology 2020." And so, when we immediate, flipped to that mode, things started to really open up. The possibilities became pretty interesting. And so honestly, we did not imagine you know, the people attending would grow from roughly thousands to over 5,000. And that's what digitalizing the event, virtualizing the event did. And it was super fun to use technology to make it so much more inclusive and accessible for people around the world. I'm sure you've heard that we had over 5,000 people from over 500 companies represented from 30 different countries. So that was amazing in its own right. >> One of the things that I think was a great advantage knowing that this was the fifth one, but that you had the opportunity to build the community, and such a strong, tight-knight community over the last few years, I think was probably a great facilitator of the event being so much bigger digitally. But when I spoke with a number of your speakers, everybody said, and I saw the Twitter stream, that the engagement, it wasn't like they were watching a video. It was really interactive, and that is hard to achieve with digital. >> Yeah, you know, what I love about the technology was that there were chat rooms, and there were Q&A rooms. And so, there was a lot of back and forth in realtime, even while the speakers were talking. You could sort of multitask, and the speakers were really, really fun to interact with that way as well. And it's super fun to see people in their home environments. You know, it's a just a little bit more information about them, and they seem a little bit more relaxed too, so it was tremendous. Watching Laura Dern, who is an activist and an obviously a very famous actress, in her own home talking to us about the issues she's faced as a woman in her industry, and then moving to another woman named Kathryn Finney, who is the CEO of digitalundivided, in her home with all the activity, she had a four-year old sort of in the background, was super fun and really landed their conversations with us even more solidly. It was a great day. >> I heard that throughout Twitter that people really felt that there was a personal connection. Lot of people talking about, I'm sitting here zooming with Laura Dern, what are you doing today? And some of the things that she said about, you know, you don't have to stay in your own swimlane. That resonated with me and I think with your community very well. >> You know,the diversity, the eclecticness of the women that were able to join from around the world and from many different industries, but you know, technical women, women in tech, was, it just up-leveled everything and it fit into the theme of the conference which was "We Rise", because you know, you're trying to rise as an individual, but there we were rising as a collective for a full day, and the workshops were super fun. I mean I participated in a number of 'em, and I literally went through a workshop with I don't know how many women, but you know, I was drawing on paper then engaging on the screen, then chatting, using the Q&A feature. It was a really dynamic day. I'm wondering now if we'll ever go back, honestly. >> Right, well I was already thinking, "Wow, you can take WT to global and do original events." And there's so much opportunity right now. Tremendous amount of challenge but on the same time, there is a lot of opportunity. In fact, when I was speaking with Sharmain (mumbles) yesterday, it was amazing that she was talking about, you know, right now, like the percentage increase, in people actually reading email because they have more time to, the commute time is gone. And so her advice to be really vivid, in making yourself visual, in terms of how you communicate, and evaluate your role and how you can add new value during this challenging time and I thought that was such a powerful message because we do need to look at what opportunities are we going to be able to uncover? There will be certain things that will go away, to your point, maybe we do digital because we can engage, we can interact and we can reach a bigger audience and learn from more people. >> Yeah, I think that's spot on. I couldn't have said that better. And you could really feel it that day and then the response from both the attendees, but even the keynote speakers, both Laura and Kathryn reaching back to us and talking about the experience they had. It was a pretty uplifting day, I'm still flying pretty high from it. And it was Cinco de Mayo so there had to have been at least margaritas, skinny margaritas, maybe, you know, virgin margaritas. But something there to celebrate an accomplishment of doing something in a short period of undertaking that community and being able to push the energy through the screen is awesome. I'd love to understand, you've been the Chief People Officer at the VMware for a while, the COVID crisis is so challenging in every aspect of life. We often talk about disruption, you know, in technology, a technology disruptor, you know, video streaming was a technology disruptor and Uber was a disruptor to transportation and the taxi service, but now the disruption is an unseen, scary thing and so the emotional impact, people are talking and a number of your folks I spoke to as well said it's hard to be motivated but it's important to acknowledge that I don't feel so motivated today for managers to be able to have that check-in with our employees and our teams. Tell me a little bit about the culture of VMware and how maybe the "We Rise" theme is really kind of, pervasive across VMware right now. >> Yeah, you know, one of the things that I believe and that I've seen in the people business is that more and more people join communities, they join companies but they join communities and communities come together based on you know, their actions, their ideas, their behaviors and what I've seen in terms of VMware's response to COVID-19 has been pretty remarkable. I think at first, you know, we were in crisis mode, sort of going in triage mode about what we do to keep our people feeling safe and healthy. But now we're sort of in a mode of "okay, there's a lot of opportunity that this presents." Now, we are very very fortunate, very blessed to be in the industry that we're in, and a lot of what we do and build and provide for our customers and partners fits into this new business model of working distributedly, so there's been some highs and some lows as we've navigated. First and foremost, we've just put our employees first and their health and safety, making sure that they're comfortable is just been top of mind for us. We just did a small sentiment survey, six questions. Because about two weeks ago, I realized, "I wonder if we really know how people are feeling about this?" And one of the things that came through, I'll say this, out of 32,000 people within 24 hours, over 10,000 people responded to this six question survey, they wanted to tell us how they were doing. But over 70% said they felt, if not the same amount of connection but more connection with each other working in a distributed fashion. And I think COVID-19's brought that alive. That we're going to work in a new way, it's a new business model and so we're doing it at VMware and then we're really pleased that we can offer that to our customers and partners around the globe. >> You know, I'm glad that you talked about the employee experience because obviously, with any business, customers are critical to the life, blood of that business. But equally important, if not sometimes more impactful to the revenue of an organization is the employee experience and being productive day in and day out. And that, if the employee experience is, I think, I don't know, you can't have a good customer experience without a good employee experience. And to (mumbles) that focus is key. So it must have been really nice for the VMware employees to go, "they're wanting to know how I feel right now." That's huge for people to know, the executive team genuinely cares. >> Yeah, you know, Lisa, we have really amped up our communications. We have done more town halls, whether it's to our management community our leadership and executive community or to the whole company. Yesterday alone, I think I did six town halls and two ask-me-anythings just to make sure we know it's on top of people's minds, what's important to them and that's kind of the new normal. And it's so much easier, right? I'm not trying to get to places, I'm just kind of clicking on a button and I'm all of a sudden talking to the employees in India. And you know, when I talk to my colleagues in other industries, like, Beth Axelrod or Tracey Ballow, that are in the you know, the Marriott and the Air BnB industries, their challaneges are so different. And what they're facing in this short-term, in the medium term. VMware is in a position where we can really help these businesses and at the core of that is really, how well our employees are doing and so that's been our focus. >> One of the things that I also talked about yesterday with Jo Miller, the CEO of Be Leaderly, was the difference between a mentor and a sponsor. And I had never even understood that they were two different things until WT2. And so, I thought, you know, we all know about mentors, we talk about that all the time. But I, she was really, I think it's an important message for your audience and ours to understand the difference and she said, "people are often over-mentored and under-sponsored." And so I thought, well, "I want to understand VMware's culture of sponsorship." Tell me what's going on in that respect. >> Yeah, we're, well, I agree with everything that you said on the mentorship side and so what we've instituted on the mentorship side at VMware's reverse mentorship. So every executive at VMware has a reverse mentor, so that they can learn something that they might not be thinking about. And whether it's a reverse mentor who happens to be, if you're a man, who happens to be a woman, or if you want to engage with the under-represented minority, or if you just want to learn about the different aspect of the business, we're big on reverse mentoring. On the sponsorship side, we do do that. And that's a really important aspect to any company's culture if you're trying to cultivate talent. And sponsorship is really championship, right? And I know I champion a lot of people, a lot of the talent around the company and it's very different than maybe coaching, advicing, and interacting in that venue. It's more about, what's the right opportunity for this person? When I'm in the board room, or when I'm in the executive staff meeting, actually advocating for that person, and I'm fierce about that. Especially for women right now at VMware, and it's just important. And a lot of people are starting to adopt that mindset because there's a lot more power and influence in having sponsorship behind you than having mentorship. >> I completely agree. Are you saying that, you know, we often talk about the hard skills and then the soft skills. And I always think soft is the wrong word but I keep forgetting to look it up on the thesaurus to get a better word. Because right now, I think, more important than ever, looking at someone who might have all of the hard skills to be on this the track to the c-suite,  but the importance of authenticity and empathy, I think now are under a microscope. We talked a lot about that too with some of your guests, tell me little bit about those kinds of conversations, that came up during the interactive sessions with WT2. >> Yeah, well, you know, this is one of the blessings that's come out of COVID-19, and this pandemic is that people are starting to see, because everyone's impacted by this and not just in one way, but in multiple ways. So, there's really this once in a lifetime opportunity, at least as far as what I've seen in my lifetime, to seize this heightened level of compassion and empathy for all the people around you in terms of what we're doing. At WT2, I saw it a lot in terms of the quality of the conversations that were happening virtually and sometimes with the key notes and the guest speakers, with the audience, there was always a lead-in with compassion and empathy in terms of all of us. All of us, no matter where you are in the world, or no matter what you're doing, adjusting to what we're calling this new normal. And there's a new business normal but the new normal on the personal side I think is going to take a little bit longer, right? In terms of what people are managing. But in the business world, I think you know, people are starting to re-bound and rebuild, they're honing those skills, and they're going to be wiser and better because of it. But at the heart of it all is, as you said, a lot more compassion and empathy 'cause never before, have we all kind of gone through something quite so traumatic as COVID-19. >> Traumatic and surreal. And you know, we are all in this same storm and I think there's a level of comfort there, that I know I feel with knowing, okay, everyone is going to be feeling this rollercoaster at some point. Some days you're here, some days you're here. But we're all in this, whether you're, you know, in your role, or Pat Gelsinger or an individual contributor role, we're all in the same sea. Betsy, congratulations on a successful fifth WT2, first digital. I'm so glad the theCUBE and myself was able to participate digitally. It's always one of my favorite events every year and I look forward to seeing you again soon, which I soon will be digitally, but I look forward to it. >> Lisa, thank you so much and thanks for all of your sponsorship and mentorship with WT2 over the years too. Thank you. >> All right, you too. That was Betsy Sutter, I'm Lisa Martin. You're watching theCUBE's coverage of Women Transforming Technology 2. Thanks for watching, see you next time. (soft music)

>>Yeah, >>from the Cube Studios in Palo Alto and Boston. It's the Cube covering IBM. Think brought to you by IBM. >>Welcome back, everybody. This is Dave Vellante of the Cube, and you're watching our continuous coverage of the IBM think Digital 2020 experience. And we're really pleased to have Rob High here. He's not only an IBM fellow bodies. He runs the vice president CTO of the IBM Edge Computing Initiative. Rob, thanks so much for coming on the Cube. Good to see you. Which we're face to face, but yeah, that time to be safe and healthy, I guess. And did so edge obviously hot topic. Everybody has this sort of point of view would be interested in how IBM looks at edge. You define it and what your thoughts are on. It's evolution. >>Yeah, well, you know, there's ah really kind of two fairly distinct ways of thinking about the edge of the telcos. Our, ah, you know, they're creating edge capabilities in their own network facilities. We call that the network edge on the other side of the edge they that I think matters a lot to our enterprise businesses is there's remote on premise locations where they actually perform the work that they do, where the majority of people are, where the data that actually gets created is first formed and where the actions that they need to operate on are being taken. That is a lot of interest, because if we can move work workloads, Iot workloads to where that data is being created, where those actions are being taken Uh, not only can we dramatically reduce the late and see to those decisions, uh, but we can also ensure continuous operations and the failed in the presence of perhaps network failures. We can manage the growth of increasing demand for network bandwidth as Maura born data gets created and we can optimize the efficiency of both the business operations as well as the I t operations before that. So for us edge computing at the end of the day is about movie work where the data and the actions are being taken >>well, so this work from home, you know, gives a result of this pandemic is kind of creating a new stresses on networks and people are putting, you know, pouring money actually into beefing up that infrastructure is sort of an extension of what we used to think about edge. But I wonder if you could talk about some of the industries and the use cases that you guys we are seeing and notwithstanding, though assay that >>work from home pivot. Yeah, absolutely. So I mean, look, we have seen ah, the need for placing workloads close to where it is being created and where actions have been picking in virtually every industry, the ones that are probably easier for us to think about and more common in terms of our mindset. Our is manufacturing. If you think about all the things that go on in a factory floor that need to be able to perform analytic in, uh, in the equipment and the processes that are performing in the affection for, If you think, for example, production quality. Uh, you know, if you've got a machine that's putting out parts and maybe it's welding seams on metal boxes, uh, you know, you want to be able to look at the quality of that seem at the moment that is being performed, so that if there are any problems, you can remediate that immediately rather than having that box move on down the line and find that you know the quality issues they were created earlier on now have exacerbated in other ways. Um, you know, so quality, productive quality. Ah, inspection production optimization in our world of Covic Cover 19 and worker safety and getting workers back to work and ensuring that you know people wearing the masks and are exercising social distancing. This is on the factory floor. Worker Insight is another major use case that we're seeing surface of lake with a lot of interest in using whether that's infrared cameras or Bluetooth beacons or infrared cameras. Any variety of devices that could be employed in the work area to help ensure that factories are operating efficiently, that workers are safe. Ah, and whether that's in a factor situation or even in an office situation or e a r in a warehouse or distribution center. And all these scenarios the the utility, the edge computing to bring to those use cases is tremendous. >>And a lot of these devices are unattended or infrequently attended. I always use the windmill example. Um, you know, you don't want to have to do a truck roll to figure out you know what the dynamics are going on, that at the windmill s, so I can instrument that. But what about the management of those devices you know from an autonomous standpoint? And and are you? What are you doing? Or are you doing anything in the autonomous managed space? >>Yeah. In fact, that's really kind of key here, because when you think about the scale, the diversity and the dynamic dynamism of equipment in these environments And as you point out, Dave, you know the lack of I t resource lack of skills on the factory floor, or even in the retail store or hotel or distribution center or any of these environments. The situation is very similar. You can't simply manage getting the right workloads to the right place at the right time. In sort of the traditional approach is, you have to really think about another autonomous approach to management and, you know, let the system the side for you. What software needs to be placed out there? Which software to put their If it's an analytic algorithm, what models to be associated with that software and getting to the right place at the right place at the right Time is a key Part of what we do in this thing that we call IBM Edge application manager is that product that we're really kind of bringing to market right now in the context of edge computing that facilitates this idea of autonomous management. >>You know, I wonder if you could comment Robb on just sort of the approach that you're taking with regard to providing products and services. I mean, we've seen a lot of, uh, situations where people are just essentially packing, packaging traditional, you know, compute and storage devices and sort of throwing it over the fence at the edge. Uh, and saying, Hey, here's our edge computing solution and another saying there's not a place for that. Maybe that will help flatten the network and, you know, provide Ah, gateway for storing on maybe processing information. But it seems to us that that that a bottoms up approach is going to be more appropriate. In other words, you've got engineers, you know who really understand operations, technology, people, maybe a new breed of developers emerging. How do you see the evolution you know of products and services and architectures at >>the edge? Yeah, so First of all, let me say IBM is taking a really pretty broad approach to edge computing we have. What I just described is IBM Edge Application Manager, which is the if you will the platform or the infrastructure on which we can manage the appointment of workloads out to the edge. But then add to that we do have a whole variety of edge and Nevil enabled applications that are being created are global service of practices and our AI applications business all are creating, um, variations of their product specific to address and exploit edge computing and to bring that advantage to the business. And of course, then we also have global services Consulting, which is a set of skilled resource, is who know we understand the transformations that business need to go through when they went, take advantage of edge computing and how to think about that in the context of both their journey to the cloud as well as now in this case, the edge. But also then how to go about implementing and delivering that, uh and then firmly further managing that now you know, coupled out then with at the end of the day you're also going to need the equipment, the devices, whether that is an intelligent automobile or other vehicle, whether that is an appellate, a robot or a camera, Um, or if those things are not intelligent. But you want to bring intelligence to them that how you augment that with servers and other forms of cluster computing that resides resident with the device. All of those are going to require participation from a very broad ecosystem. So we've been working with partners of whether that is vendors who create hardware and enabling that hardware in certifying that hardware to work with our management infrastructure or whether those are people who bring higher order services to the table that provide support for, let's, say, data cashing and facilitating the creation of applications, or whether those are device manufacturers that are embedding compute in their device equipment. All of that is part of our partnership ecosystem, Um, and then finally, you know, I need to emphasize that, you know, the world that we operate in is so vast and so large. There are so many edge devices in the marketplace, and that's growing so rapidly, and so many participants in that likewise There are a lot of other contributors to this ecosystem that we call edge computing. And so for all of those reasons, we have grounded IBM education manager on open source. We created an open source project called Open Rise, and we've been developing that, actually now, for about 4.5 years just recently, the Linux Foundation has adopted Stage one adoption of Open arising as part of its Lennox Foundation edge LF edge, uh, Reg X Foundry project. And so we think this is key to building out, Um, a ecosystem of partners who want to both contribute as well consumed value and create ecosystems around this common idea of how we manage the edge. >>Yeah, I'm glad you brought up the ecosystem, and it's too big for any one company toe to go it alone. But I want to tap your brain on just sort of architectures. And there's so many diverse use cases, you know, we don't necessarily see one uber architecture emerging, but there are some characteristics that we think are important at the edge you mentioned sort of real time or near real time. In many cases, it has to be real time you think about autonomous vehicles? Um, yeah. A lot of the data today is analog, and maybe it doesn't have to be digitized, but much of it will be, um, it's not all gonna be sent back to the cloud. It may not all have to be persisted. So we've envisioned this sort of purpose built, you know, architecture for certain use cases that can support real time. That maybe have, you know, arm based processors. Ah, or other alternative processors there that can do real time analytics at the edge and maybe sending portions of the data back. How do you see the architectures evolving from a technologist? >>Well, so certainly one of the things that we see at the edge is a tremendous premium being placed on things like energy consumption. So architectures they're able to operate efficiently with less power is ah is certainly an advantage to any of those architectures that are being brought aboard. Um, clearly, you know x 86 is a dominant architecture in any information technology endeavor. More specifically at the edge. We're seeing the emergence of lot of arm based architecture chips out there. In fact, I would guess that the majority of the edge devices today are not being created with, um, arm architectures, but it's the you know, but some of this is about the underlying architecture of the compute. But also then the augmentation of that compute the the compute Thea the CP use with other types of processing units. Whether those GPS, of course, we're seeing, you know, a number of deep use being created that are designed to be low power consuming, um, and have a tremendous amount of utility at the edge. There are alternate processing units, architectures that have been designed specifically for AI model based analytics. Uh, things like TP use and infuse and and, uh, and set around, which are very purpose built for certain kinds of intellect. And we think that those are starting to surface and become increasingly important. And then on the flip side of this is both the memory storage in network architectures which are sort of exotically different. But at least in terms of capacity, um have quite variability. Specifically, five G, though, is emerging and five g. While it's not necessarily the same computing, there is a lot of symbolism between edge and five G and the kinds of use cases that five G envisions are very similar to those that we've been talking about in the edge world as well. >>Rob, I want to ask you about sort of this notion of program ability at the edge. I mean, we've seen the success of infrastructure as code. Um, how do you see program ability occurring at the edge in terms of fostering innovation and maybe new developer bottles or maybe existing developer models at the edge? Yeah, >>we found a lot of utility in sort of leveraging what we now think of as cloud computing or cloud computing models. Uh, you know, the idea of continue ization extends itself very easily into the edge. Whether that is running a container in a docker runtime, let's say on an edge device which is, you know, resource constrained and purpose built and needs to focus on sort of a very small footprint or even edge clusters edge servers where we might be running a cluster of containers using our kubernetes platform called open shift. Um, you know the course of practices of continuous integration, continuous delivery. What we write a Otherwise think of his Dev ops. Ah, and, of course, the benefits they continue. Realization brings to the idea of component architectures. Three. Idea of loose coupling. The separation of concerns, the ability to mix and match different service implementations to be opposed. Your application are all ideas that were matured in the cloud world but have a lot of utility in the edge world. Now we actually call it edge native programming. But you can think of that as being mostly cloud native programming, with a further extension that there are certain things you have to be aware of what you're building for the edge. You have to recognize that resource is air limited. Unlike the cloud where we have this notion of infinite resource, you don't have that at the edge. Find and constrained resources. Be worried about, you know, Layton sees and the fact that there is a network that separates the different services and that network can be and reliable. It can introduce his own forms of Layton sees it, maybe bandwidth constrained and those air issues that you now have to factor into your thinking as you build out the logic of your application components. But I think by building on the cloud native programming about me paradigm. You know, we get to exercise sort of all of the skills that have been developing and maturing in the cloud world. Now, for the edge >>that makes sense. My last question is around security. I mean, I've often sort of tongue in cheek said, you know, building a moat around the castle doesn't work anymore. The queen i e. The data has left the castle. She's everywhere. So what about the security model? I mean, I feel like the edge is moving so fast you feel confident or what gives you confidence >>that we can secure the edge. You know, the edges does introduce some very interesting and challenging concerns with respect to security because, frankly, the compute is out there in the wild. You know, you've got computers in the store you've got, you know, people walking around the kiosks you have in the manufacturing site, you know, workers that are, you know, in the midst of all of this compute capability and so the attack surface is substantially bigger. And that's been a big focus for us, is how to the only way validate in 30 of the software that was But it also takes advantage of one of the key characters with edge computing to bring to the table, which is, if you think about it. You know, when you've got personal and private information being entered into quote system, the more often you move that personal private data around, and certainly the more that you move it to a central location and aggregate that with other data, the more of a target becomes more vulnerable, exposed that data becomes and by using edge computing, which moves the workloads out to the edge where that did has been created in some sense, you can process on it there and then move it back. They need central location, you don't have to aggregate it. And that actually in itself is a counterbalance of all of the other issues that we also describe about security by essentially not moving the personal privacy and in protecting by keeping it exactly where it began. >>You know, Rob, this is an exciting topic. Is a huge opportunity for IBM and Ginny in and talk about the trillion dollar opportunity and hybrid cloud and the Edge is a multi $1,000,000,000 opportunity for IBM and, uh So you just got to go get her done. But I really appreciate you coming on the Cube and sharing your insights. That awesome topic in the best interest of the David. Yeah. Thank you. Thank you for the thank you. Stay safe and thank you for watching everybody. This is Dave Volante for the Cube. This is our coverage of IBM. Think 2020 the digital. Think >>we'll be right back after this short break? >>Yeah, yeah, yeah, yeah.

>> from Santa Clara, California It's the Cube covering techno ovation World Pitch Summit 2019 Brought to You by Silicon Angle Media Now here's Sonia to Gari >> Hi and welcome to the Cube. I'm your host, >> Sonia to Gari, and we're here at Oracle's >> Agnew's campus in Santa Clara, California covering techno vacations. World Pitch Summit 2019 a pitch competition in which girls from around the world developed mobile lapse in order to create positive change >> in the world with us. Today we have team >> powerful daisies from Brazil. Um, and their acts called safe tears. So their members are on a Toronado. Uh, Clara Patan. Um, Anna Julia Uh, Giacomelli um Emmanuel Amara Skin and Julie Carr Bio. Welcome to the Cuban. Congratulations on your being finalists. Thank you. So your app safe tears tell us more about that. >> So our APP is a suicide prevention app in which its user gets his own glass of blue feelings, where to use their ads or remove tears accordingly with his feelings. So if the user said they had tears any, they're happy they take theirs out. >> Wow, that's amazing. So can you tell us how someone would use Thea >> So let's say I'm set. So I go to the app and I at use. So add those as my 2% rise is the absolute send motivational messages to me like saying go talk to somebody over find help and also encouraging me to be to know, to get better. And if I'm happy, I take tourists out and I get messages like congratulating me too because I'm doing better. >> So is there like a graph of your improvement of how you feel some days you feel the other days >> we would like to implement dead in your future. But right now, in this version of the app that is not available >> OK, well, yeah, that would be a great thing, Thio. So how did you come up with this idea? >> So in our community, there was a lot of suicide cases and off course with friends and family, and it was something that really needed more help. So we went Thio lecture about suicide, and the woman said that we are like a glass of water. We we feel that up and then one day all the water gets out and then somebody you know tries to suicide themselves. So we wanted this person to thio like realize that she's getting wars so she can find help before anything bad happens. >> And I know that sometimes giving advice to someone who's depressed can be very tricky. And you have to make sure saying the right thing. So how did you find out what kind of advice to give in your app? >> Yeah, we had help over school psychologist. So she was there with those the whole time we were developing and she helped us do Every single message is that the absense to the person is, you know, viewed by >> her And have you seen has anyone used the app and has felt better? Any success stories >> they're hesitant to launch, But we did tested it and people really liked it and thought that they would use it. >> That's amazing. So how >> did you all meet and why did >> you decide to join techno vacation? >> So we were from the same school from different classes where we're from the same school. So we met there and our teacher showed us the documentary code girl and their inspired us to join techno vacation because we thought it would be a cool experience. >> And so how detective ation help you achieve your goals and make your act better. >> So without techno vacation, of course, we couldn't be here and get all this experience in learning's to improve our app. So it's helping a lot. >> And, um, can you tell us more specifically like, what skills have you learned from Tekken? Ovation. >> Like programming, big public speaking and about business. We learn a lot like doing the business plan about marketing and publicity and all that. And I heard you >> guys had an amazing week this week. You went to whoever you saw Golden Gate Bridge. Can you tell us more? About what? The highlights of the wiki pad? >> Yeah, we went to Webber, of course. And we talked to people there. He was amazing. Talk to employees and see how is life there. And also we went to the Golden Bridge and we crossed the bridge. It was a Bahar, you know, we're not used to exercising. Right? And last night we had a dance party. What? She was really fun and we got to interact with people from all over the world and it was amazing. >> That's so great. Well, thank you so much for coming on. I'm so looking forward to seeing your app in the APP store one day. And congratulations. And good luck for the pitch tonight. >> Thank you so much. This has been team >> powerful daisies from Brazil. This'd the Cube. We'll see you next time.

>> Live from Las Vegas, it's theCUBE. Covering AWS re:Invent 2018. Brought to you by Amazon web services, intel, and their ecosystem partners. >> Welcome back everyone to theCUBE's live coverage of AWS re:Invent, here at the Venetian in Las Vegas. I'm your host, Rebecca Knight, along with my co-host John Furrier. We're joined by Clement Pang. He is the co-founder of Wavefront by VMware. Welcome. >> Thank you Thank you so much. >> It's great to have you on the show. So, I want you tell our viewers a little bit about Wavefront. You were just purchased by VMware in May. >> Right. >> What do you do, what is Wavefront all about? >> Sure, we were actually purchased last year in May by VMware, yeah. We are an operational analytics company, so monitoring, I think is you could say what we do. And the way that I always introduce Wavefront is kind of a untold secret of Silicon Valley. The reason I said that is because in the, well, just look at the floor. You know, there's so many monitoring companies doing logs, APM, metrics monitoring. And if you really want to look at what do the companies in the Valley really use, right? I'm talking about companies such as Workday, Watts, Groupon, Intuit, DoorDash, Lyft, they're all companies that are customers of Wavefront today. So they've obviously looked at all the tools that are available on the market, on the show floor, and they've decided to be with Wavefront, and they were with us before the acquisition, and they're still with us today, so. >> And they're the scale-up guys, they have large scale >> That's right, yeah, container, infrastructure, running clouds, hybrid clouds. Some of them are still on-prem data centers and so we just gobble up all that data. We are platform, we're not really opinionated about how you get the data. >> You call them hardcore devops. >> Yes, hardcore devops is the right word, yeah. >> Pushing the envelope, lot of new stuff. >> That's right. >> Doing their own innovation >> So even serverless and all the ML stuff that that's been talked about. They're very pioneering. >> Alright, so VMware, they're very inquisitive on technology, very technology buyers. Take a minute to explain the tech under the covers. What's going on. >> Sure, so Wavefront is a at scale time series database with an analytics engine on top of it. So we have actually since expanded beyond just time series data. It could be distributed histograms, it could be tracing, it includes things like events. So anything that you could gather up from your operation stack and application metrics, business metrics, we'll take that data. Again, I just said that we are unopinionated so any data that you have. Like sometimes it could be from a script , it could be from your serverless functions. We'll take that data, we'll store it, we'll render it and visualize it and of course we don't have people looking at charts all day long. We'll alert you if something bad is going on. So teams just really allow the ability to explore the data and just to figure out trends, correlations and just have a platform that scales and just runs reliably. >> With you is Switzerland. >> Yeah, basically I think that's the reason why VMware is very interested, is cause we work with AWS, work with Azure, work with GCP and soon to be AliCloud and IBM, right. >> Talk about why time series data is now more on board. We've got, we've had this conversation with Smug, we saw the new announcement by Amazon. So 'cause if you 're doing real-time, time matters and super important. Why is it important now, why are people coming to the realization as the early adopters, the pioneers. >> That's right, I think I used to work at Google and I think Google, very early on I realized that time series is a way to understand complex systems, especially if you have FMR workloads and so I think what companies have realized is that logs is just very voluminous, it's very difficulty to wield and then traditional APM products, they tend to just show you what they want to show you, like what are the important paying points that you should be monitoring and with Wavefront, it's just a tool that understands time series data and if you think about it, most of the data that you gather out of your operational environment is timer series data. CPU, memory, network, how many people logging in, how many errors, how many people are signing up. We certainly have our customer like Lyft. You know, how many of you are getting Rise, how many credit cards are off. You know all of that information drives, should we pay someone because a certain city, nobody is getting picked up and that's kind of the dimension that you want to be monitoring on, not on the individual like, okay this base, no network even though we monitor those of course. >> You know, Clement, I got to talk to you about the supporting point because we've been covering real time, we've been covering IoT, we've been doing a ton of stuff around looking at the importance of data and having data be addressable in real-time. And the database is part of the problem and also the overall architecture of the holistic operating environment. So to have an actual understanding of time series is one. Then you actually got to operationalize it. Talk about how customers are implementing and getting value out of time series data and how they differentiate that with data leagues that they might spin up as well as the new dupe data in it. Some might not be valuable. All this is like all now coming together. How do people do that? >> So I think there were a couple of dimensions to that. So it's scalability is a big piece. So you have to be able to take in enormous amount of data, (mumbles) data leagues can do that. It has to be real-time, so our latency from ingestion to maturalization on a chart is under our second So if you're a devops team, you're spinning up containers, you can't go blind for even 10 seconds or else you don't know what's going on with your new service that you just launched. So real-time is super important and then there's analytics. So you can't, you can see all the data in real-time but if it's like millions of time series coming in, it's like the matrix, you need to have some way to actually gather some insights out of that data. SO I think that's what we are good at. >> You know a couple of years ago, we were doing Open Compute, a summit that Facebook puts on, you eventually worked with Google so I see he's talking about the cutting edge tech companies. There's so much data going onto the scale, you need AI, you got to have machines so some of the processing, you can't have this manual process or even scrips, you got to have machines that take care of it. Talk about the at-scale component because as the tsunami of data continues to grow, I mean Amazon's got a satellite, Lockheed Martin, that's going to light up edge computing, autonomous vehicles, pentabytes moving to the cloud, time series matters. How do people start thinking about machine learning and AI, what do you guys do. >> So I think post-acquisition I would say, we really double down on looking at AI and machine learning in our system. We, because we don't down sample any of the data that we collect, we have actually the raw data coming in from weather sensors, from machines, from infrastructure, from cloud and we just is able to learn on that because we understand incidence, we understand anomalies. So we can take all of that data and punch it through different kinds of algorithms and figures out, maybe we could just have the computer look at the incoming time series data and tell you if its anomalist, right. The holy grail for VMware I think, is to have a self-driving data center and what that means is you have systems that understands, well yesterday there was a reinforcement learning announcement by Amazon. How do we actually apply those techniques so that we have the observability piece and then we have some way to in fact change against the environment and then we figure out, you know, just let the computer just do it. >> I love this topic, you should come into our studio, if I'm allowed to, we'll do a deep dive on this because there's so many implications to the data because if you have real-time data, you got to have the streaming data come in, you got to make sense of it. The old networking days, we call it differentiate services. You got to differentiate of the data. Machine learning, if the data's good, it works great, but data sucks, machine learning doesn't go well so if I want that dynamic of managing the data so you don't have to do all this cleaning. How do people get that data verified, how do they set up the machine learning. >> Sure, it still required clean data because I mean, it's garbage in, garbage out >> Not dirty data >> So, but the ability for us, for machine learning in general to understand anything in a high dimensional space is for it to figure out, what are the signals from a lot of the noise. A human may require to be reduces in dimensionality so that they could understand a single line, a single chart that they could actually have insights out of. Machines can technically look at hundreds or even tens of thousands of series and figures out, okay these are the two that are the signals and these are the knobs that I could turn that could affect those signals. So I think with machine learning, it actually helps with just the voluminous nature of the data that we're gathering. And figuring out what is the signal from the noise. >> It's a hard problem. So talk about the two functionalities you guys just launched. What's the news, what are you doing here at AWS. >> So the most exciting thing that we launched is our distributed tracing offering. We call it a three-dimensional micro service observability. So we're the only platform that marry metrics, histograms and distributed tracing in a single platform offering. So it's certainly at scale. As I said, it's reliable, it has all the analytical capabilities on top of it, but we basically give you a way to quickly dive down into a problem and realize what the root cause is and to actually see the actual request at it's context. Whether it's troubleshooting , root cause analysis, performance optimization. So it's a single shop kind of experience. You put in our SDK, it goes ahead and figures out, okay you're running Java, you're running Jersey or Job Wizard or Spring Boot and then it figures out, okay these are the key metrics you should be looking at. If there are any violations, we show you the actual request including multiple services that are involved in that request and just give you an out of the box turn keyway to understand at scale, microservice deployments, where are the pain points, where is latency coming from, where are the errors coming from. So that's kind of our first offering that we're launching. Same pricing mode, all that. >> So how are companies going to use this? What kind of business problem is this solving. >> So as the world transitions to a deployment architecture that mostly consists of Microservices, it's no longer a monolytic app, it's no longer an end-tier application. There are a lot of different heterogeneous languages, frameworks are involved, or even AWS. Cloud services, SAS services are involved and you just have to have some way to understand what is goin on. The classic example I have is you could even trace things like an actual order and how it goes through the entire pipeline. Someone places the orders, a couple days later there's someone who, the orders actually get shipped and then it gets delivered. You know, that's technically a trace. It could be that too. You could send that trace to us but you want to understand, so what are the different pieces that was involved. It could be code or it could be like a vendor. I could be like even a human process. All of that is a distributed tracing atom and you could actually send it to Wavefront and we just help you stitch that picture together so you could understand what's really going on. >> What's next for you guys. Now you're part of VMware. What's the investment area, what are you guys looking at building, what's the next horizon? >> So I think, obviously the (mumbles) tracing, we still have a lot to work on and just to help teams figure out, what do they want to see kind of instantly from the data that we've gathered. Again, we just have gathered data for so long, for so many years and at the full resolution so why can't we, what insights can develop out of it and then as I said, we're working on AI and ML so that's kind of the second launch offering that we have here where you know, people have been telling us, it's great to have all the analytics but if I don't have any statistical background to anything like that, can you just tell me, like, I have a chart, a whole bunch of lines, tell me just what I should be focusing on. So that's what we call the AI genie and so you just apply, call it a genie I guess, and then you would basically just have the chart show you what is going wrong and the machines that are going wrong, or maybe a particular service that's going wrong, a particular KPI that's in violation and you could just go there and figure out what's-- >> Yeah, the genie in the bottle. >> That's right (crosstalk) >> So final question before we go. What's it like working for VMware start-up culture. You raised a lot of money doing your so crunch based reports. VMware's cutting edge, they're a part with Amazon, bit turn around there, what's it like there? >> It's a very large company obviously, but they're, obviously as with everything, there's always some good points and bad points. I'll focus on the good. So the good things are there's just a lot of people, very smart people at VMware. They've worked on the problem of virtualization which was, as a computer scientist, I just thought, that's just so hard. How do you run it like the matrix, right, it's kind of like and a lot of very smart people there. A lot of the stuff that we're actually launching includes components that were built inside VMware based on their expertise over the years and we're just able to pull, it's just as I said, a lot of fun toys and how do we connect all of that together and just do an even better job than what we could have been as we were independent. >> Well congratulations on the acquisition. VMware's got the radio event we've covered. We were there, you got a lot of engineers, a lot of great scientists so congratulations. >> Thank you so much. >> Great, Clement thanks so much for coming on theCUBE. >> Thank you so much Rebecca. >> I'm Rebecca Knight for John Furrier. We will have more from AWS re:Invent coming up in just a little bit. (light electronic music)

>> Live, from Las Vegas, it's the Cube, covering AWS re:Invent 2018, brought to you by Amazon Web Services, Intel, and their ecosystem partners. >> Hey welcome back everyone, live here in Las Vegas for AWS re:Invent 2018, all the action is happening for Amazon Web Services. I'm John Furrier, Dave Vellante, Dave six years covering Amazon, great opportunity, a lot of news, Red Hat is a big part of it, Mike Ferris is here. Vice President, Technical Business Development for Red Hat, welcome back, good to see you. >> Likewise. >> A lot's going with you guys since our Red Hat Summit days in San Francisco just a few months ago. >> Yeah. >> Big news hit. >> Yeah. >> The bomb around the world, the rock that hit the ground really hard, shook everyone up, surprised everyone including me, I'm like "Wow, IBM and Red Hat". What an interesting relationship, obviously the history with IBM has been good. Talk about the announcement with IBM because this is huge. Of course, big numbers, but also impact wise pretty big. >> Yeah, it's exciting times right? And if you kind of look at, you know, from the perspective of Red Hat in this, this will allow us to really scale and accelerate what we've already been doing for the past, you know, since really the 1994 era when Red Hat was founded and, you know, it kind of validates a lot of what we've put into open source and enterprise customers since then. You know, we really see a couple of key outtakes from this, one of which is, certainly it's going to give us the resources to be able to really grow with the scale that we need to. It's also going to allow us to invest more in open source in emerging areas, bring the value of scale and certainly choice and flexibility to more customers, and then ultimately kind of the global advantage of hybrid and multi cloud, we'll be able to reach more partners and customers everywhere, and it puts us several years ahead of where we have been and where we would have been frankly, and ultimately our intent is that with IBM we'll become the leading hybrid and multi cloud provider overall. >> Yeah, Jimmy and Jim Whitehurst kind of ruined our Sunday, we were sitting down to watch football and he's like got the announcement. And then Jimmy kept saying "It's not backend loaded, it's not backend loaded" and then you start to realize, wow, IBM has an enormous business of managing applications that need to be modernized and OpenShift is obviously a great place to do that so, it's got to be super exciting for you guys to have that giant new opportunity to go after as well as global scale that you didn't have before. >> And, you know, this extends the stuff that we did, announced in May at Red Hat Summit with IBM where we really focused on how do we take WebSphere DB2MQ, running on IBM cloud private, running on OpenShift, and make that the hybrid choice. And so it's a natural extension of what we've already been doing and it gives us a lot more resources than we would have otherwise. >> This is good, coming into the next segment I want to chat about is RHEL, and what people might not understand from the announcment is the synergy you guys have with IBM because, being a student of Red Hat, being just in the industry when you guys were rebels, open source, second tier citizen, and the enterprise, the adoption then became tier one service. I mean you guys have, level of service, 17 years or something, huge numbers, but remember where it all started. And then you became a tier one supplier to almost all the enterprises, so you're actually a product company as well as a huge open source player. That's powerful and unique. >> Absolutely, even if you look at kind of what Amazon is doing this week and have been doing over the years, they're a huge value ad provider of open source technology as well, and one of the statements that we've always made is, the public cloud would not exist if not for Linux and open source, and so everything has been based upon that. There's one provider that doesn't use Linux as the base of their platform but certainly as we've taken the in roads into the enterprise, you know, I was there when it started with just turning Red Hat Enterprise Linux on and then bringing it from the edge of network into the data center and talking about major providers like Oracle, HP, Del, IBM as part of that. Now, we're looking at "Is it a de facto standard?", and everyone including Amazon and all of it's competitors are really invested heavily in the open source world. >> And so, let's talk about the impact to the products, okay so one of the things that has come up, at least on my Twitter feed and the conversations is, okay, it's going to take some time to close the deal, you're still Red Hat, you're still doing your things, what's the impact to the customers and to the ecosystem in your mind? How are you guys talking about that right now? Obviously, it's more of the same, keep the Red Hat same, unique, independent, what new thing is going to come out of it? >> So, to be clear, the deal has not closed, right, so there's not a lot we're going to say otherwise. >> A year away, you got a lot of work to do. >> Our focus is what it always has been. Let's build the best enterprise products using the open source development model and make those available across all public and hybrid cloud environments. >> At a certain level, that's enterprise, multi-year, old Red Hat, same Red Hat model, alright. >> But let me follow up on that, because you're a believer in multi cloud, we're a believer in, whatever you call it, multiple clouds, customers are going to use multiple clouds. We believe that, you believe that, it seems like Amazon has a slightly different perspective on that, >> Cause they're one cloud. >> in that this greater value, right cause they're one cloud, there's greater value, but it seems like the reality when you talk to customers is, we're not just one company, we've got different divisions, and eventually we've got to bring those together in some kind of extraction layer. That's what you guys want to be, right? So, your perspectives on multi cloud? >> Absolutely, so, each individual department, each project, each developer, in all of these major enterprises, you know, has a different vantage, and yes, there are corporate standards, golden masters of RHEL that get produced, everybody's supposed to be using, but you know, the practicalities of how you develop software, especially in the age of dev ops and containers and moving forward is actually, you have to have the choice necessary to meet your specific needs, and while we will absolutely do everything we can to make sure that things are consistent, I mean, we started this with RHEL consistency, on and off premise, when we did the original Amazon relationship. The point is, you need to be able to give people the flexibility and choice that they desire, regardless of what area of the company that they're in, and that's going to be the focus, regardless of whether it's Microsoft, Amazon, Google, IBM clouds, international clouds with Alibaba, it's all the same to us and we have to make sure it's there. >> What's always great about the cloud shows, especially this one, it's one of my favorites, because it really is dev ops deep in the mindset culture. As you see AI and machine learning start to get powered by all these great resources, computer, et cetera, the developer is going crazy, there's going to be another renaissance in software development, and then you got things like Kubernetes and containers now mainstream. Kubernetes almost, I say, de facto standard. >> Yeah. >> Absolutely happened, you guys had a big part of making that happen. People are now agreeing on things, so the formation's coming together pretty quickly, you're seeing the growth, we're hearing terms like "co-creation", "co-opetition", those are signals for a large rising tide, your thoughts? >> So, it's interesting, we were an early investor in Kubernetes, we actually launched OpenShift prior to Kubernetes, and then we adopted it and made a shift of our platform before it was too late. We did the same thing with hypervisors when we moved from Zen to KBM, but this overall approach is, once we see the energy happening both in the community and the early customers, then you see the partners start to come on board, it becomes the de facto standard, it's really crucial for us as an open source company to make sure we follow those trends, and then we help mature them across the business ecosystem, and that's something we've loved being able to engage with. I mean, Google certainly instigated the Kubernetes movement, but then it starts to propagate, just like on the open stack side, it came out of Rackspace and Nasa and then moved on to different areas and so, you know, our focus is, how do we continue that choice and that evolution overall? >> How would you talk about the impact of Kubernetes if someone says "Hey Mike, what's the real impact, what is it going to accomplish at the end of the day?" What's your view of that? >> It will have the same impact that the Linux current standardization has had, you know, but in this case for micro services and application packaging and being able to do dev ops much more efficiently across heterogeneous platforms. >> Does it make it easier or less painful or does it go away? Is it automated under the covers? I mean, this is a big, awesome opportunity. >> So the orchestration capabilities of Kubernetes combined with all the other tools that surround key container platforms like OpenShift, really give that developer the full life cycle environment to be able to take something from concept through deployment, and onto the maintenance phases, and you know, what we end up doing is we look at, okay the technologies are there, what value ads to we have around that to make sure that a customer and a developer cn actually maintain this thing long term and keep their enterprise applications up? >> So, security for example. >> Security is a great example, right? How do we make sure that every container that gets deployed on Kubernetes platforms or by Kubernetes platforms, that every container that's deployed which, keep in mind, is an operating system, it has an operating system in the container itself, how is that kept up to date? How do you make sure that when the next security errata is released, from us or a different vendor possibly, how do you make sure that that container is secure? And, you know, we've done a lot in our registry as well as our catalog to make sure that all of our partners and customers can see their containers, know what grade they have in a security context, and be able to grow that. That's one of the core things that we see adding into this Kubernetes value and authorization level. >> It's not a trivial technical problem either. >> No. >> Sometimes micro services aren't so micro. >> It's been part of what we've for RHEL from the start, it's been how do we bring that enterprise value into technology that is maturing out of the open source community and make that available to customers? >> Yeah, one of the key things you guys, first of all, OpenShift has been phenomenal, you guys did a great job with that, been watching that grow, but I think a real seminal moment was the CoreOS acquisition. >> Sure. >> That was a real turbo boost for you guys, great acquisition, fits in with the culture, and then Kubernetes just lifted from that, that was the point but, at the timing of all this, Kubernetes gets mainstream lift, people recognize that the standardization it is a good thing, and then, boom, developers are getting engaged. >> Yeah, and if you see what the CoreOS environment has brought us from over their updates for our platforms, to being able to talk about a registry in the environment. Being able to say that, is kind of additive to this overall messaging, it really rounds out the offering for us, and allows us to participate even more deeply in the communities as well. >> Well, we're looking forward to keeping you covered, we love Kubernetes, we've got a special report called "Kubernetes Special Report" on siliconangle.com, it's called "The Rise Of Kubernetes", it's a dedicated set of content, we're publishing a lot on Kubernetes. Final question I want to get to you because I think it's super important, what's the relationship you have with AWS? And take some time to explain the partnership, how many years, what you guys are doing together, I know you're actively involved, so take a minute. >> It is somewhat blurry, it's been a long time, so 2007 era is when we started in depth with them, and I can remember the early days, actually in the development of S3, prior to EC2, being able to say alright, what is this thing and how does Red Hat participate in this? And I think, yesterday Terry Wiese even mentioned that we were one of the first partners to actually engage in the consumption model and, you know, claiming partial credit for out 34 billion dollar valuation that we just got announced. But, you know, overall the relationship really spawned out of that, how do we help build a cloud and how do we help offer our products to our customers in a more flexible way? And so that snowballed over the years from just early adopters being able to play with it to now where you see it's many many millions of dollars that are being generated in customers and they think, in the hundreds of millions of hours of our products being consumed, at least within a month if not shorter timeframes, every time period we have. >> You know that's an unsung benefit that people might not know about with Red Hat is that, you guys are in early markets because, one, everyone uses Linux pretty much these days for anything core, meaningful. And you listen to community, and so you guys are always involved in big moving things, cloud, Amazon, 2007, it was command line back then. >> Yeah. >> It wasn't even, I think RightScale just came online that year, so you remember. You guys are always in all these markets so it's a good indicator, you guys are a bellwether, I think it's a good beacon to look at. >> And we do this, certainly on the container space, and the middleware space, and the storage space, you know, we replicate this model and, including in management, about how do we actually invest in the right places where we see the industry and communities going so we can actually help those? >> And you're very partner friendly, you bring a lot to the table, I love the open source ethos, I think that's the future. The future of that ethos of contributing to get value downstream is going to be a business practice, not just software, so you guys are a big part of the industry on that and I want to give you guys props for that. Okay, more Cube coverage here in Las Vegas, AWS Reinvent, after this short break, more live coverage, I'm John Furrier, Dave Vellante, we'll be right back. (electronic music)

>> Narrator: Live from Boston, Massachusetts, this is theCUBE, covering Spark Summit East 2017. Brought to you by Databricks. Now, here are your hosts, Dave Vellante and George Gilbert. >> Welcome back to day two in Boston where it is snowing sideways here. But we're all here at Spark Summit #SparkSummit, Spark Summit East, this is theCUBE. Sound like an Anglo flagship product. We go out to the event, we program for our audience, we extract the signal from the noise. I'm here with George Gilbert, day two, at Spark Summit, George. We're seeing the evolution of so-called big data. Spark was a key part of that. Designed to really both simplify and speed up big data oriented transactions and really help fulfill the dream of big data, which is to be able to affect outcomes in near real time. A lot of those outcomes, of course, are related to ad tech and selling and retail oriented use cases, but we're hearing more and more around education and deep learning and affecting consumers and human life in different ways. We're now 10 years in to the whole big data trend, what's your take, George, on what's going on here? >> Even if we started off with ad tech, which is what most of the big internet companies did, we always start off in any new paradigm with one application that kind of defines that era. And then we copy and extend that pattern. For me, on the rethinking your business the a McGraw-Hill interview we did yesterday was the most amazing thing because they took, what they had was a textbook business for their education unit and they're re-thinking the business, as in what does it mean to be an education company? And they take cognitive science about how people learn and then they take essentially digital assets and help people on a curriculum, not the centuries old sort of teacher, lecture, homework kind of thing, but individualized education where the patterns of reinforcement are consistent with how each student learns. And it's not just a break up the lecture into little bits, it's more of a how do you learn most effectively? How do you internalize information? >> I think that is a great example, George, and there are many, many examples of companies that are transforming digitally. Years and years ago people started to think about okay, how can I instrument or digitize certain assets that I have for certain physical assets? I remember a story when we did the MIT event in London with Andy MacAfee and Eric Binyolsen, they were giving the example of McCormick Spice, the spice company, who digitized by turning what they were doing into recipes and driving demand for their product and actually building new communities. That was kind of an interesting example, but sort of mundane. The McGraw-Hill education is massive. Their chief data scientist, chief data scientist? I don't know, the head of engineering, I guess, is who he was. >> VP of Analytics and Data Science. >> VP of Analytics and Data Science, yeah. He spoke today and got a big round of applause when he sort of led off about the importance of education at the keynote. He's right on, and I think that's a classic example of a company that was built around printing presses and distributing dead trees that is completely transformed and it's quite successful. Over the last only two years brought in a new CEO. So that's good, but let's bring it back to Spark specifically. When Spark first came out, George, you were very enthusiastic. You're technical, you love the deep tech. And you saw the potential for Spark to really address some of the problems that we faced with Hadoop, particularly the complexity, the batch orientation. Even some of the costs -- >> The hidden costs. >> Associated with that, those hidden costs. So you were very enthusiastic, in your mind, has Spark lived up to your initial expectations? >> That's a really good question, and I guess techies like me are often a little more enthusiastic than the current maturity of the technology. Spark doesn't replace Hadoop, but it carves out a big chunk of what Hadoop would do. Spark doesn't address storage, and it doesn't really have any sort of management bits. So you could sort of hollow out Hadoop and put Spark in. But it's still got a little ways to go in terms of becoming really, really fast to respond in near real time. Not just human real time, but like machine real time. It doesn't work sort of deeply with databases yet. It's still teething, and sort of every release, which is approximately every 12 to 18 months, it gets broader in its applicability. So there's no question sort of everyone is piling on, which means that'll help it mature faster. >> When Hadoop was first sort of introduced to the early masses, not the main stream masses, but the early masses, the profundity of Hadoop was that you could leave data in place and bring compute to the data. And people got very excited about that because they knew there was so much data and you just couldn't keep moving it around. But the early insiders of Hadoop, I remember, they would come to theCUBE and everybody was, of course, enthusiastic and lot of cheerleading going on. But in the hallway conversations with Hadoop, with the real insiders you would have conversations about, people are going to realize how much this sucks some day and how hard this is and it's going to hit a wall. Some of the cheerleaders would say, no way, Hadoop forever. Now you've started to see that in practice. And the number of real hardcore transformations as a result of Hadoop in and of itself have been quite limited. The same is true for virtually, most anyway, technology, not any technology. I'd say the smartphone was pretty transformative in and of itself, but nonetheless, we are seeing that sort of progression and we're starting to see a lot of the same use cases that you hear about like fraud detection and retargeting as coming up again. I think what we're seeing is those are improving. Like fraud detection, I talked yesterday about it used to be six months before you'd even detect fraud, if you ever did. Now it's minutes or seconds. But you still get a lot of false positives. So we're going to just keep turning that crank. Mike Gualtieri today talked about the efficacy of today's AI and he gave some examples of Google, he showed a plane crash and he said, it said plane and it accurately identified that, but also the API said it could be wind sports or something like that. So you can see it's still not there yet. At the same time, you see things like Siri and Amazon Alexa getting better and better and better. So my question to you, kind of long-winded here, is, is that what Spark is all about? Just making better the initial initiatives around big data, or is it more transformative than that? >> Interesting question, and I would come at it with a couple different answers. Spark was a reaction to you can't, you can't have multiple different engines to attack all the different data problems because you would do a part of the analysis here, push it into a disk, pull it out of a disk to another engine, all of that would take too long or be too complex a pipeline to go from end to the other. Spark was like, we'll do it all in our unified engine and you can come at it from SQL, you can come at it from streaming, so it's all in one place. That changes the sophistication of what you can do, the simplicity, and therefore how many people can access it and apply it to these problems. And the fact that it's so much faster means you can attack a qualitatively different setup of problems. >> I think as well it really underscores the importance of Open Source and the ability of the Open Source community to launch projects that both stick and can attract serious investment. Not only with IBM, but that's a good example. But entire ecosystems that collectively can really move the needle. Big day today, George, we've got a number of guests. We'll give you the last word at the open. >> Okay, what I thought, this is going to sound a little bit sort of abstract, but a couple of two takeaways from some of our most technical speakers yesterday. One was with Juan Stoyka who sort of co-headed the lab that was the genesis of Spark at Berkeley. >> AMPLabs. >> The AMPLab at Berkeley. >> And now Rise Labs. >> And then also with the IBM Chief Data Officer for the Analytics Unit. >> Seth Filbrun. >> Filbrun, yes. When we look at what's the core value add ultimately, it's not these infrastructure analytic frameworks and that sort of thing, it's the machine learning model in its flywheel feedback state where it's getting trained and re-trained on the data that comes in from the app and then as you continually improve it, that was the whole rationale for Data Links, but not with models. It was put all the data there because you're going to ask questions you couldn't anticipate. So here it's collect all the data from the app because you're going to improve the model in ways you didn't expect. And that beating heart, that living model that's always getting better, that's the core value add. And that's going to belong to end customers and to application companies. >> One of the speakers today, AI kind of invented in the 50s, a lot of excitement in the 70s, kind of died in the 80s and it's coming back. It's almost like it's being reborn. And it's still in its infant stages, but the potential is enormous. All right, George, that's a wrap for the open. Big day today, keep it right there, everybody. We got a number of guests today, and as well, don't forget, at the end of the day today George and I will be introducing part two of our WikiBon Big Data forecast. This is where we'll release a lot of our numbers and George will give a first look at that. So keep it right there everybody, this is theCUBE. We're live from Spark Summit East, #SparkSummit. We'll be right back. (techno music)