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

>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR, This is "Breaking Analysis with Dave Vellante". >> Black Hat 22 was held in Las Vegas last week, the same time as theCUBE Supercloud event. Unlike AWS re:Inforce where words are carefully chosen to put a positive spin on security, Black Hat exposes all the warts of cyber and openly discusses its hard truths. It's a conference that's attended by technical experts who proudly share some of the vulnerabilities they've discovered, and, of course, by numerous vendors marketing their products and services. Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR. In this "Breaking Analysis", we summarize what we learned from discussions with several people who attended Black Hat and our analysis from reviewing dozens of keynotes, articles, sessions, and data from a recent Black Hat Attendees Survey conducted by Black Hat and Informa, and we'll end with the discussion of what it all means for the challenges around securing the supercloud. Now, I personally did not attend, but as I said at the top, we reviewed a lot of content from the event which is renowned for its hundreds of sessions, breakouts, and strong technical content that is, as they say, unvarnished. Chris Krebs, the former director of Us cybersecurity and infrastructure security agency, CISA, he gave the keynote, and he spoke about the increasing complexity of tech stacks and the ripple effects that that has on organizational risk. Risk was a big theme at the event. Where re:Inforce tends to emphasize, again, the positive state of cybersecurity, it could be said that Black Hat, as the name implies, focuses on the other end of the spectrum. Risk, as a major theme of the event at the show, got a lot of attention. Now, there was a lot of talk, as always, about the expanded threat service, you hear that at any event that's focused on cybersecurity, and tons of emphasis on supply chain risk as a relatively new threat that's come to the CISO's minds. Now, there was also plenty of discussion about hybrid work and how remote work has dramatically increased business risk. According to data from in Intel 471's Mark Arena, the previously mentioned Black Hat Attendee Survey showed that compromise credentials posed the number one source of risk followed by infrastructure vulnerabilities and supply chain risks, so a couple of surveys here that we're citing, and we'll come back to that in a moment. At an MIT cybersecurity conference earlier last decade, theCUBE had a hypothetical conversation with former Boston Globe war correspondent, Charles Sennott, about the future of war and the role of cyber. We had similar discussions with Dr. Robert Gates on theCUBE at a ServiceNow event in 2016. At Black Hat, these discussions went well beyond the theoretical with actual data from the war in Ukraine. It's clear that modern wars are and will be supported by cyber, but the takeaways are that they will be highly situational, targeted, and unpredictable because in combat scenarios, anything can happen. People aren't necessarily at their keyboards. Now, the role of AI was certainly discussed as it is at every conference, and particularly cyber conferences. You know, it was somewhat dissed as over hyped, not surprisingly, but while AI is not a panacea to cyber exposure, automation and machine intelligence can definitely augment, what appear to be and have been stressed out, security teams can do this by recommending actions and taking other helpful types of data and presenting it in a curated form that can streamline the job of the SecOps team. Now, most cyber defenses are still going to be based on tried and true monitoring and telemetry data and log analysis and curating known signatures and analyzing consolidated data, but increasingly, AI will help with the unknowns, i.e. zero-day threats and threat actor behaviors after infiltration. Now, finally, while much lip service was given to collaboration and public-private partnerships, especially after Stuxsnet was revealed early last decade, the real truth is that threat intelligence in the private sector is still evolving. In particular, the industry, mid decade, really tried to commercially exploit proprietary intelligence and, you know, do private things like private reporting and monetize that, but attitudes toward collaboration are trending in a positive direction was one of the sort of outcomes that we heard at Black Hat. Public-private partnerships are being both mandated by government, and there seems to be a willingness to work together to fight an increasingly capable adversary. These things are definitely on the rise. Now, without this type of collaboration, securing the supercloud is going to become much more challenging and confined to narrow solutions. and we're going to talk about that little later in the segment. Okay, let's look at some of the attendees survey data from Black Hat. Just under 200 really serious security pros took the survey, so not enough to slice and dice by hair color, eye color, height, weight, and favorite movie genre, but enough to extract high level takeaways. You know, these strongly agree or disagree survey responses can sometimes give vanilla outputs, but let's look for the ones where very few respondents strongly agree or disagree with a statement or those that overwhelmingly strongly agree or somewhat agree. So it's clear from this that the respondents believe the following, one, your credentials are out there and available to criminals. Very few people thought that that was, you know, unavoidable. Second, remote work is here to stay, and third, nobody was willing to really jinx their firms and say that they strongly disagree that they'll have to respond to a major cybersecurity incident within the next 12 months. Now, as we've reported extensively, COVID has permanently changed the cybersecurity landscape and the CISO's priorities and playbook. Check out this data that queries respondents on the pandemic's impact on cybersecurity, new requirements to secure remote workers, more cloud, more threats from remote systems and remote users, and a shift away from perimeter defenses that are no longer as effective, e.g. firewall appliances. Note, however, the fifth response that's down there highlighted in green. It shows a meaningful drop in the percentage of remote workers that are disregarding corporate security policy, still too many, but 10 percentage points down from 2021 survey. Now, as we've said many times, bad user behavior will trump good security technology virtually every time. Consistent with the commentary from Mark Arena's Intel 471 threat report, fishing for credentials is the number one concern cited in the Black Hat Attendees Survey. This is a people and process problem more than a technology issue. Yes, using multifactor authentication, changing passwords, you know, using unique passwords, using password managers, et cetera, they're all great things, but if it's too hard for users to implement these things, they won't do it, they'll remain exposed, and their organizations will remain exposed. Number two in the graphic, sophisticated attacks that could expose vulnerabilities in the security infrastructure, again, consistent with the Intel 471 data, and three, supply chain risks, again, consistent with Mark Arena's commentary. Ask most CISOs their number one problem, and they'll tell you, "It's a lack of talent." That'll be on the top of their list. So it's no surprise that 63% of survey respondents believe they don't have the security staff necessary to defend against cyber threats. This speaks to the rise of managed security service providers that we've talked about previously on "Breaking Analysis". We've seen estimates that less than 50% of organizations in the US have a SOC, and we see those firms as ripe for MSSP support as well as larger firms augmenting staff with managed service providers. Now, after re:Invent, we put forth this conceptual model that discussed how the cloud was becoming the first line of defense for CISOs, and DevOps was being asked to do more, things like securing the runtime, the containers, the platform, et cetera, and audit was kind of that last line of defense. So a couple things we picked up from Black Hat which are consistent with this shift and some that are somewhat new, first, is getting visibility across the expanded threat surface was a big theme at Black Hat. This makes it even harder to identify risk, of course, this being the expanded threat surface. It's one thing to know that there's a vulnerability somewhere. It's another thing to determine the severity of the risk, but understanding how easy or difficult it is to exploit that vulnerability and how to prioritize action around that. Vulnerability is increasingly complex for CISOs as the security landscape gets complexified. So what's happening is the SOC, if there even is one at the organization, is becoming federated. No longer can there be one ivory tower that's the magic god room of data and threat detection and analysis. Rather, the SOC is becoming distributed following the data, and as we just mentioned, the SOC is being augmented by the cloud provider and the managed service providers, the MSSPs. So there's a lot of critical security data that is decentralized and this will necessitate a new cyber data model where data can be synchronized and shared across a federation of SOCs, if you will, or mini SOCs or SOC capabilities that live in and/or embedded in an organization's ecosystem. Now, to this point about cloud being the first line of defense, let's turn to a story from ETR that came out of our colleague Eric Bradley's insight in a one-on-one he did with a senior IR person at a manufacturing firm. In a piece that ETR published called "Saved by Zscaler", check out this comment. Quote, "As the last layer, we are filtering all the outgoing internet traffic through Zscaler. And when an attacker is already on your network, and they're trying to communicate with the outside to exchange encryption keys, Zscaler is already blocking the traffic. It happened to us. It happened and we were saved by Zscaler." So that's pretty cool. So not only is the cloud the first line of defense, as we sort of depicted in that previous graphic, here's an example where it's also the last line of defense. Now, let's end on what this all means to securing the supercloud. At our Supercloud 22 event last week in our Palo Alto CUBE Studios, we had a session on this topic on supercloud, securing the supercloud. Security, in our view, is going to be one of the most important and difficult challenges for the idea of supercloud to become real. We reviewed in last week's "Breaking Analysis" a detailed discussion with Snowflake co-founder and president of products, Benoit Dageville, how his company approaches security in their data cloud, what we call a superdata cloud. Snowflake doesn't use the term supercloud. They use the term datacloud, but what if you don't have the focus, the engineering depth, and the bank roll that Snowflake has? Does that mean superclouds will only be developed by those companies with deep pockets and enormous resources? Well, that's certainly possible, but on the securing the supercloud panel, we had three technical experts, Gee Rittenhouse of Skyhigh Security, Piyush Sharrma who's the founder of Accurics who sold to Tenable, and Tony Kueh, who's the former Head of Product at VMware. Now, John Furrier asked each of them, "What is missing? What's it going to take to secure the supercloud? What has to happen?" Here's what they said. Play the clip. >> This is the final question. We have one minute left. I wish we had more time. This is a great panel. We'll bring you guys back for sure after the event. What one thing needs to happen to unify or get through the other side of this fragmentation and then the challenges for supercloud? Because remember, the enterprise equation is solve complexity with more complexity. Well, that's not what the market wants. They want simplicity. They want SaaS. They want ease of use. They want infrastructure risk code. What has to happen? What do you think, each of you? >> So I can start, and extending to the previous conversation, I think we need a consortium. We need a framework that defines that if you really want to operate on supercloud, these are the 10 things that you must follow. It doesn't matter whether you take AWS, Slash, or TCP or you have all, and you will have the on-prem also, which means that it has to follow a pattern, and that pattern is what is required for supercloud, in my opinion. Otherwise, security is going everywhere. They're like they have to fix everything, find everything, and so on and so forth. It's not going to be possible. So they need a framework. They need a consortium, and this consortium needs to be, I think, needs to led by the cloud providers because they're the ones who have these foundational infrastructure elements, and the security vendor should contribute on providing more severe detections or severe findings. So that's, in my opinion, should be the model. >> Great, well, thank you, Gee. >> Yeah, I would think it's more along the lines of a business model. We've seen in cloud that the scale matters, and once you're big, you get bigger. We haven't seen that coalesce around either a vendor, a business model, or whatnot to bring all of this and connect it all together yet. So that value proposition in the industry, I think, is missing, but there's elements of it already available. >> I think there needs to be a mindset. If you look, again, history repeating itself. The internet sort of came together around set of IETF, RSC standards. Everybody embraced and extended it, right? But still, there was, at least, a baseline, and I think at that time, the largest and most innovative vendors understood that they couldn't do it by themselves, right? And so I think what we need is a mindset where these big guys, like Google, let's take an example. They're not going to win at all, but they can have a substantial share. So how do they collaborate with the ecosystem around a set of standards so that they can bring their differentiation and then embrace everybody together. >> Okay, so Gee's point about a business model is, you know, business model being missing, it's broadly true, but perhaps Snowflake serves as a business model where they've just gone out and and done it, setting or trying to set a de facto standard by which data can be shared and monetized. They're certainly setting that standard and mandating that standard within the Snowflake ecosystem with its proprietary framework. You know, perhaps that is one answer, but Tony lays out a scenario where there's a collaboration mindset around a set of standards with an ecosystem. You know, intriguing is this idea of a consortium or a framework that Piyush was talking about, and that speaks to the collaboration or lack thereof that we spoke of earlier, and his and Tony's proposal that the cloud providers should lead with the security vendor ecosystem playing a supporting role is pretty compelling, but can you see AWS and Azure and Google in a kumbaya moment getting together to make that happen? It seems unlikely, but maybe a better partnership between the US government and big tech could be a starting point. Okay, that's it for today. I want to thank the many people who attended Black Hat, reported on it, wrote about it, gave talks, did videos, and some that spoke to me that had attended the event, Becky Bracken, who is the EIC at Dark Reading. They do a phenomenal job and the entire team at Dark Reading, the news desk there, Mark Arena, whom I mentioned, Garrett O'Hara, Nash Borges, Kelly Jackson, sorry, Kelly Jackson Higgins, Roya Gordon, Robert Lipovsky, Chris Krebs, and many others, thanks for the great, great commentary and the content that you put out there, and thanks to Alex Myerson, who's on production, and Alex manages the podcasts for us. Ken Schiffman is also in our Marlborough studio as well, outside of Boston. Kristen Martin and Cheryl Knight, they help get the word out on social media and in our newsletters, and Rob Hoff is our Editor-in-Chief at SiliconANGLE and does some great editing and helps with the titles of "Breaking Analysis" quite often. Remember these episodes, they're all available as podcasts, wherever you listen, just search for "Breaking Analysis Podcasts". I publish each on wikibon.com and siliconangle.com, and you could email me, get in touch with me at david.vellante@siliconangle.com or you can DM me @dvellante or comment on my LinkedIn posts, and please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, and we'll see you next time on "Breaking Analysis". (upbeat music)

>>Okay, we're back. My name is Dave Valante and this is the Cube's coverage of AWS storage day. You know, coming off of reinforc I wrote the, the cloud was a new layer of defense. In fact, the first line of defense in a cyber security strategy. And that brings new thinking and models for protecting data, data protection, specifically, traditionally thought of as backup and recovery, it's become a critical adjacency to security and a component of a comprehensive cybersecurity strategy. We're here in our studios outside of Boston with two cube alums, and we're gonna discuss this in other topics. Wayne do so is the vice president for AWS storage edge and data services, and Nancy Wong as general manager of AWS backup and data protection services, guys. Welcome. Great to see you again. Thanks for coming on. Of >>Course, always a pleasure, Dave. Good to >>See you, Dave. All right. So Wayne, let's talk about how organizations should be thinking about this term data protection. It's an expanding definition, isn't >>It? It is an expanding definition. They, last year we talked about data and the importance of data to companies. Every company is becoming a data company, you know, da the amount of data they generate, the amount of data they can use to create models, to do predictive analytics. And frankly, to find ways of innovating is, is grown rapidly. And, you know, there's this tension between access to all that data, right? Getting the value out of that data. And how do you secure that data? And so this is something we think about with customers all the time. So data durability, data protection, data resiliency, and, you know, trust in their data. If you think about running your organization on your data, trust in your data is so important. So, you know, you gotta trust where you're putting your data. You know, people who are putting their data on a platform need to trust that platform will in fact, ensure it's durability, security, resiliency. >>And, you know, we see ourselves AWS as a partner in securing their data, making their data dur durable, making their data resilient, right? So some of that responsibility is on us. Some of that is on so shared responsibility around data protection, data resiliency. And, you know, we think about forever, you know, the notion of, you know, compromise of your infrastructure, but more and more people think about the compromise of their data as data becomes more valuable. And in fact, data is a company's most valuable asset. We've talked about this before. Only second to their people. You know, the people that are most valuable asset, but right next to that is their data. So really important stuff. >>So Nancy, you talked to a lot of customers, but by the way, it always comes back to the data. We've saying this for years, haven't we? So you've got this expanding definition of data protection, you know, governance is in there. You, you think about access cetera. When you talk to customers, what are you hearing from them? How are they thinking about data protection? >>Yeah. So a lot of the customers that Wayne and I have spoken to often come to us seeking thought leadership about, you know, how do I solve this data challenge? How do I solve this data sprawl challenge, but also more importantly, tying it back to data protection and data resiliency is how do I make sure that data is secure, that it's protected against, let's say ransomware events, right. And continuously protected. So there's a lot of mental frameworks that come to mind and a very popular one that comes up in quite a few conversations is this cybersecurity framework, right? And from a data protection perspective is just as important to protect and recover your data as it is to be able to detect different events or be able to respond to those events. Right? So recently I was just having a conversation with a regulatory body of financial institutions in Europe, where we're designing a architecture that could help them make their data immutable, but also continuously protected. So taking a step back, that's really where I see AWS's role in that we provide a wide breadth of primitives to help customers build secure platforms and scaffolding so that they can focus on building the data protection, the data governance controls, and guardrails on top of that platform. >>And, and that's always been AWS's philosophy, you know, make sure that developers have access to those primitives and APIs so that they can move fast and, and essentially build their own if that that's in fact what they wanna do. And as you're saying, when data protection is now this adjacency to cyber security, but there's disaster recoveries in there, business continuance, cyber resilience, et cetera. So, so maybe you could pick up on that and sort of extend how you see AWS, helping customers build out those resilient services. >>Yeah. So, you know, two core pillars to a data protection strategy is around their data durability, which is really an infrastructure element. You know, it's, it's, it's, it's by and large the responsibility of the provider of that infrastructure to make sure that data's durable, cuz if it's not durable, everything else doesn't matter. And then the second pillar is really about data resiliency. So in terms of security, controls and governance, like these are really important, but these are shared responsibility. Like the customers working with us with the services that we provide are there to architect the design, it's really human factors and design factors that get them resiliency, >>Nancy, anything you would add to what Wayne just said. >>Yeah, absolutely. So customers tell us that they want always on data resiliency and data durability, right? So oftentimes in those conversations, three common themes come up, which is they want a centralized solution. They want to be able to transcribe their intent into what they end up doing with their data. And number three, they want something that's policy driven because once you centralize your policies, it's much better and easier to establish control and governance at an organizational level. So keeping that in mind with policy as our interface, there's two managed AWS solutions that I recommend you all check out in terms of data resiliency and data durability. Those are AWS backup, which is our centralized solution for managing protection recovery, and also provides an audit audit capability of how you protect your data across 15 different AWS services, as well as on-premises VMware and for customers whose mission critical data is contained entirely on disk. We also offer AWS elastic disaster recovery services, especially for customers who want to fail over their workloads from on premises to the cloud. >>So you can essentially centralize as a quick follow up, centralize the policy. And like I said, the intent, but you can support a federated data model cuz you're building out this massive, you know, global system, but you can take that policy and essentially bring it anywhere on the AWS cloud. Is that >>Right? Exactly. And actually one powerful integration I want to touch upon is that AWS backup is natively integrated with AWS organizations, which is our defacto multi account federated organization model for how AWS services work with customers, both in the cloud, on the edge, at the edge and on premises. >>So that's really important because as, as we talk about all the time on the cube, this notion of a, a decentralized data architecture data mesh, but the problem is how do you ensure governance and a federated model? So we're clearly moving in that direction. Wayne, I want to ask you about cyber as a board level discussion years ago, I interviewed Dr. Robert Gates, you know, former defense secretary and he sat on a number of boards and I asked him, you know, how important and prominent is security at the board level? Is it really a board level discussion? He said, absolutely. Every time we meet, we talk about cyber security, but not every company at the time, this was kind of early last decade was doing that. That's changed now. Ransomware is front and center. Hear about it all the time. What's AWS. What's your thinking on cyber as a board level discussion and specifically what are you guys doing around ran ransomware? >>Yeah. So, you know, malware in general, ransomware being a particular type of malware. Sure. It's a hot topic and it continues to be a hot topic. And whether at the board level, the C-suite level, I had a chance to listen to Dr. Gates a couple months ago and super motivational, but we think about ransomware and the same way that our customers do. Right? Cause all of us are subject to an incident. Nobody is immune to a ransomware incident. So we think very much the same way. And you, as Nancy said, along the lines of the, this framework, we really think about, you know, how do customers identify their critical access? How do they plan for protecting those assets, right? How do they make sure that they are in fact protected? And if they do detect the ransomware event and ransomware events come from a lot of different places, like there's not one signature, there's not one thumbprint, if you would for ransomware. >>So it's, it's, there's really a lot of vigilance that needs to be put in place, but a lot of planning that needs to be put in place. And once that's detected and a, a, we have to recover, you know, we know that we have to take an action and recover having that plan in place, making sure that your assets are fully protected and can be restored. As you know, ransomware is a insidious type of malware. You know, it sits in your system for a long time. It figures out what's going on, including your backup policies, your protection policies, and figures out how to get around those with some of the things that Nancy talked about in terms of air gaping, your capabilities, being able to, if you would scan your secondary, your backup storage for malware, knowing that it's a good copy. And then being able to restore from that known good copy in the event of an incident is critical. So we think about this for ourselves and the same way that we think about these for our customers. You gotta have a great plan. You gotta have great protection and you gotta be ready to restore in the case of an incident. And we wanna make sure we provide all the capabilities to do >>That. Yeah. So I'll glad you mentioned air gaping. So at the recent re reinforce, I think it was Kurt kufeld was speaking about ransomware and he didn't specifically mention air gaping. I had to leave. So I might have, I might have missed it cause I was doing the cube, but that's a, that's a key aspect. I'm sure there were, were things on the, on the deep dives that addressed air gaping, but Nancy look, AWS has the skills. It has the resources, you know, necessary to apply all these best practices and, you know, share those with customers. But, but what specific investments is AWS making to make the CISO's life easier? Maybe you could talk about that. >>Sure. So following on to your point about the reinforced keynote, Dave, right? CJ Boes talked about how the events of a ransomware, for example, incident or event can take place right on stage where you go from detect to respond and to recover. And specifically on the recovery piece, you mentioned AWS backup, the managed service that protects across 15 different AWS services, as well as on-premises VMware as automated recovery. And that's in part why we've decided to continue that investment and deliver AWS backup audit manager, which helps customers actually prove their posture against how their protection policies are actually mapping back to their organizational controls based on, for example, how they TA tag their data for mission criticality or how sensitive that data is. Right. And so turning to best practices, especially for ransomware events. Since this is very top of mind for a lot of customers these days is I will, will always try to encourage customers to go through game day simulations, for example, identifying which are those most critical applications in their environment that they need up and running for their business to function properly, for example, and actually going through the recovery plan and making sure that their staff is well trained or that they're able to go through, for example, a security orchestration automation, recovery solution, to make sure that all of their mission critical applications are back up and running in case of a ransomware event. >>Yeah. So I love the game day thing. I mean, we know, well just the, in the history of it, you couldn't even test things like disaster recovery, right? Because it was too dangerous with the cloud. You can test these things safely and actually plan out, develop a blueprint, test your blueprint. I love the, the, the game day >>Analogy. Yeah. And actually one thing I'd love to add is, you know, we talked about air gaping. I just wanna kind of tie up that statement is, you know, one thing that's really interesting about the way that the AWS cloud is architected is the identity access and management platform actually allows us to create identity constructs, that air gap, your data perimeter. So that way, when attackers, for example, are able to gain a foothold in your environment, you're still able to air gap your most mission critical and also crown jewels from being infiltrated. >>Mm that's key. Yeah. We've learned, you know, when paying the ransom is not a good strategy, right? Cuz most of the time, many times you don't even get your data back. Okay. So we, we're kind of data geeks here. We love data and we're passionate about it on the cube AWS and you guys specifically are passionate about it. So what excites you, Wayne, you start and then Nancy, you bring us home. What excites you about data and data protection and why? >>You know, we are data nerds. So at the end of the day, you know, there's this expressions we use all the time, but data is such a rich asset for all of us. And some of the greatest innovations that come out of AWS comes out of our analysis of our own data. Like we collect a lot of data on our operations and some of our most critical features for our customers come out of our analysis, that data. So we are data nerds and we understand how businesses view their data cuz we view our data the same way. So, you know, Dave security really started in the data center. It started with the enterprises. And if we think about security, often we talk about securing compute and securing network. And you know, if you, if you secured your compute, you secured your data generally, but we've separated data from compute so that people can get the value from their data no matter how they want to use it. And in doing that, we have to make sure that their data is durable and it's resilient to any sort of incident and event. So this is really, really important to us. And what do I get excited about? You know, again, thinking back to this framework, I know that we as thought leaders alongside our customers who also thought leaders in their space can provide them with the capabilities. They need to protect their data, to secure their data, to make sure it's compliant and always, always, always durable. >>You know, it's funny, you'd say funny it's it's serious actually. Steven Schmidt at reinforc he's the, the, the chief security officer at Amazon used to be the C C ISO of AWS. He said that Amazon sees quadrillions of data points a month. That's 15 zeros. Okay. So that's a lot of data. Nancy bring us home. What's what excites you about data and data protection? >>Yeah, so specifically, and this is actually drawing from conversations that I had with multiple ISV partners at AWS reinforc is the ability to derive value from secondary data, right? Because traditionally organizations have really seen that as a call center, right? You're producing secondary data because most likely you're creating backups of your mission critical workloads. But what if you're able to run analytics and insights and derive insights from that, that secondary data, right? Then you're actually able to let AWS do the undifferentiated heavy lifting of analyzing that secondary data state. So that way us customers or ISV partners can build value on the security layers above. And that is how we see turning cost into value. >>I love it. As you're taking the original premise of the cloud, taking away the under heavy lifting for, you know, D deploying, compute, storage, and networking now bringing up to the data level, the analytics level. So it continues. The cloud continues to expand. Thank you for watching the cubes coverage of AWS storage day 2022.

[Music] okay we're back my name is dave vellante and this is thecube's coverage of aws storage day you know coming off of reinforce i wrote that the cloud was a new layer of defense in fact the first line of defense in a cyber security strategy that brings new thinking and models for protecting data data protection specifically traditionally thought of as backup and recovery it's become a critical adjacency to security and a component of a comprehensive cyber security strategy we're here in our studios outside of boston with two cube alums and we're going to discuss this and other topics wayne dusso is the vice president for aws storage edge and data services and nancy wong as general manager of aws backup and data protection services guys welcome great to see you again thanks for coming on of course always a pleasure dave good to see you dave all right so wayne let's talk about how organizations should be thinking about this term data protection it's an expanding definition isn't it it is an expanded definition dave last year we talked about uh data and the importance of data to companies every company um is becoming a data company uh you know the amount of data they generate uh the amount of data they can use to uh create models to do predictive analytics and frankly uh to find ways of innovating uh is is growing uh rapidly and you know there's this tension between access to all that data right getting the value out of that data and how do you secure that data and so this is something we think about with customers all the time so data durability data protection data resiliency and you know trust in their data if you think about running your organization on your data trust in your data is so important so you know you got to trust where you're putting your data you know people who are putting their data on a platform need to trust that platform will in fact ensure its durability security resiliency and you know we see ourselves uh aws as a partner uh in securing their data making their data they're built durable making their data resilient all right so some of that responsibility is on us some of that is on amazon responsibility around data protection data resiliency and you know um we think about forever you know the notion of um you know compromise of your infrastructure but more and more people think about the compromise of their data as data becomes more valuable in fact data is a company's most valuable asset we've talked about this before only second to their people you know the people who are the most valuable asset but right next to that is their data so really important stuff so nancy you talk to a lot of customers but by the way it always comes back to the data we've been saying this for years haven't we so you've got this expanding definition of data protection you know governance is in there you think about access etc when you talk to customers what are you hearing from them how are they thinking about data protection yeah so a lot of the customers that wayne and i have spoken to often come to us seeking thought leadership about you know how do i solve this data challenge how do i solve this data sprawl challenge but also more importantly tying it back to data protection and data resiliency is how do i make sure that data is secure that it's protected against let's say ransomware events right and continuously protected so there's a lot of mental frameworks that come to mind and a very popular one that comes up in quite a few conversations is in this cyber security framework right and from a data protection perspective it's just as important to protect and recover your data as it is to be able to detect different events or be able to respond to those events right so recently i was just having a conversation with a regulatory body of financial institutions in europe where we're designing a architecture that could help them make their data immutable but also continuously protected so taking a step back that's really where i see aws's role in that we provide a wide breadth of primitives to help customers build secure platforms and scaffolding so that they can focus on building the data protection the data governance controls and guardrails on top of that platform and that's always been aws philosophy make sure that developers have access to those primitives and apis so that they can move fast and essentially build their own if that that's in fact what they want to do and as you're saying when data protection is now this adjacency to cyber security but there's disaster recoveries in there business continuance cyber resilience etc so so maybe you could pick up on that and sort of extend how you see aws helping customers build out those resilient services yeah so you know two uh core pillars to a data protection strategy is around their data durability which is really an infrastructural element you know it's it's it's by and large the responsibility of the provided that infrastructure to make sure that data is durable because if it's not durable and everything else doesn't matter um and the second pillar is really about data resiliency so in terms of security controls and governance like these are really important but these are a shared responsibility like the customers working with us with the services that we provide are there to architect the design it's really human factors and design factors that get them resiliency nancy anything you would add to what wayne just said yeah absolutely so customers tell us that they want always on data resiliency and data durability right so oftentimes in those conversations three common themes come up which is they want a centralized solution they want to be able to transcribe their intent into what they end up doing with their data and number three they want something that's policy driven because once you centralize your policies it's much better and easier to establish control and governance at an organizational level so keeping that in mind with policy as our interface there's two managed aws solutions that i recommend you all check out in terms of data resiliency and data durability those are aws backup which is our centralized solution for managing protection recovery and also provides an audit audit capability of how you protect your data across 15 different aws services as well as on-premises vmware and for customers whose mission-critical data is contained entirely on disk we also offer aws elastic disaster recovery services especially for customers who want to fail over their workloads from on-premises to the cloud so you can essentially centralize as a quick follow-up centralize the policy and as you said the intent but you can support a federated data model because you're building out this massive you know global system but you can take that policy and essentially bring it anywhere on the aws cloud is that right exactly and actually one powerful integration i want to touch upon is that aws backup is natively integrated with aws organizations which is our de facto multi-account federated organization model for how aws services work with customers both in the cloud on the edge at the edge and on premises so that's really important because as we talk about all the time on the cube this notion of a decentralized data architecture data mesh but the problem is how do you ensure governance in a federated model so we're clearly moving in that direction when i want to ask you about cyber as a board level discussion years ago i interviewed dr robert gates you know former defense secretary and he sat on a number of boards and i asked him you know how important and prominent is security at the board level is it really a board level discussion he said absolutely every time we meet we talk about cyber security but not every company at the time this was kind of early last decade was doing that that's changed um now ransomware is front and center hear about it all the time what's aws what's your thinking on cyber as a board level discussion and specifically what are you guys doing around ransomware yeah so you know malware in general ransomware being a particular type of malware um it's a hot topic and it continues to be a hot topic and whether at the board level the c-suite level um i had a chance to listen to uh dr gates a couple months ago and uh it was super motivational um but we think about ransomware in the same way that our customers do right because all of us are subject to an incident nobody is uh uh immune to a ransomware incident so we think very much the same way and as nancy said along the lines of the nist framework we really think about you know how do customers identify their critical access how do they plan for protecting those assets right how do they make sure that they are in fact protected and if they do detect a ransomware event and ransomware events come from a lot of different places like there's not one signature there's not one thumb print if you would for ransomware so it's it's there's really a lot of vigilance uh that needs to be put in place but a lot of planning that needs to be put in place and once that's detected and a we have to recover you know we know that we have to take an action and recover having that plan in place making sure that your assets are fully protected and can be restored as you know ransomware is a insidious uh type of malware you know it sits in your system for a long time it figures out what's going on including your backup policies your protection policies and figures out how to get around those with some of the things that nancy talked about in terms of air gapping your capabilities being able to if you would scan your secondary your backup storage for malware knowing that it's a good copy and then being able to restore from that known good copy in the event of an incident is critical so we think about this for ourselves in the same way that we think about these for our customers you've got to have a great plan you've got to have great protection and you've got to be ready to restore in the case of an incident and we want to make sure we provide all the capabilities to do that yeah so i'm glad you mentioned air gapping so at the recent reinforce i think it was kurt kufeld was speaking about ransomware and he didn't specifically mention air gapping i had to leave so i might i might have missed it because i'm doing the cube but that's a that's a key aspect i'm sure there were things in the on the deep dives that addressed air gapping but nancy look aws has the skills it has the resources you know necessary to apply all these best practices and you know share those as customers but but what specific investments is aws making to make the cso's life easier maybe you could talk about that sure so following on to your point about the reinforced keynote dave right cj moses talked about how the events of a ransomware for example incident or event can take place right on stage where you go from detect to respond and to recover and specifically on the recover piece he mentioned aws backup the managed service that protects across 15 different aws services as well as on-premises vmware as automated recovery and that's in part why we've decided to continue that investment and deliver aws backup audit manager which helps customers actually prove their posture against how their protection policies are actually mapping back to their organizational controls based on for example how they tag their data for mission criticality or how sensitive that data is right and so turning to best practices especially for ransomware events since this is very top of mind for a lot of customers these days is i will always try to encourage customers to go through game day simulations for example identifying which are those most critical applications in their environment that they need up and running for their business to function properly for example and actually going through the recovery plan and making sure that their staff is well trained or that they're able to go through for example a security orchestration automation recovery solution to make sure that all of their mission critical applications are back up and running in case of a ransomware event yeah so i love the game date thing i mean we know well just in the history of it you couldn't even test things like disaster recovery be right because it was too dangerous with the cloud you can test these things safely and actually plan out develop a blueprint test your blueprint i love the the game day analogy yeah and actually one thing i love to add is you know we talked about air gapping i just want to kind of tie up that statement is you know one thing that's really interesting about the way that the aws cloud is architected is the identity access and management platform actually allows us to create identity constructs that air gap your data perimeter so that way when attackers for example are able to gain a foothold in your environment you're still able to air gap your most mission critical and also crown jewels from being infiltrated that's key yeah we've learned you know when paying the ransom is not a good strategy right because most of the time many times you don't even get your data back okay so we we're kind of data geeks here we love data um and we're passionate about it on the cube aws and you guys specifically are passionate about it so what excites you wayne you start and then nancy you bring us home what excites you about data and data protection and why you know we are data nerds uh so at the end of the day um you know there's there's expressions we use all the time but data is such a rich asset for all of us some of the greatest innovations that come out of aws comes out of our analysis of our own data like we collect a lot of data on our operations and some of our most critical features for our customers come out of our analysis that data so we are data nerds and we understand how businesses uh view their data because we view our data the same way so you know dave security really started in the data center it started with the enterprises and if we think about security often we talk about securing compute and securing network and you know if you if you secured your compute you secured your data generally but we've separated data from compute so that people can get the value from their data no matter how they want to use it and in doing that we have to make sure that their data is durable and it's resilient to any sort of incident event so this is really really important to us and what do i get excited about um you know again thinking back to this framework i know that we as thought leaders alongside our customers who also thought leaders in their space can provide them with the capabilities they need to protect their data to secure their data to make sure it's compliant and always always always durable you know it's funny you'd say it's not funny it's serious actually steven schmidt uh at reinforce he's the the chief security officer at amazon used to be the c c iso of aws he said that amazon sees quadrillions of data points a month that's 15 zeros okay so that's a lot of data nancy bring us home what's what excites you about data and data protection yeah so specifically and this is actually drawing from conversations that i had with multiple isv partners at aws reinforce is the ability to derive value from secondary data right because traditionally organizations have really seen that as a cost center right you're producing secondary data because most likely you're creating backups of your mission critical workloads but what if you're able to run analytics and insights and derive insights from that secondary data right then you're actually able to let aws do the undifferentiated heavy lifting of analyzing that secondary data as state so that way you as customers or isv partners can build value on the security layers above and that is how we see turning cost into value i love it you're taking the original premise of the cloud taking away the undifferentiated heavy lifting for you know deploying compute storage and networking now bringing up to the data level the analytics level so it continues the cloud continues to expand thank you for watching thecube's coverage of aws storage day 2022

(electronic music) >> Hello everybody, welcome back to Boston. This is Dave Vellante and you're watching theCUBE's continuous coverage of AWS re:Inforce 2022. We're here at the convention center in Boston where theCUBE got started in May of 2010. I'm really excited. Lena Smart is here, she's the chief information security officer at MongoDB rocket ship company We covered MongoDB World earlier this year, June, down in New York. Lena, thanks for coming to theCUBE. >> Thank you for having me. >> You're very welcome, I enjoyed your keynote yesterday. You had a big audience, I mean, this is a big deal. >> Yeah. >> This is the cloud security conference, AWS, putting its mark in the sand back in 2019. Of course, a couple of years of virtual, now back in Boston. You talked in your keynote about security, how it used to be an afterthought, used to be the responsibility of a small group of people. >> Yeah. >> You know, it used to be a bolt on. >> Yep. >> That's changed dramatically and that change has really accelerated through the pandemic. >> Yep. >> Just describe that change from your perspective. >> So when I started at MongoDB about three and a half years ago, we had a very strong security program, but it wasn't under one person. So I was their first CISO that they employed. And I brought together people who were already doing security and we employed people from outside the company as well. The person that I employed as my deputy is actually a third time returnee, I guess? So he's worked for, MongoDB be twice before, his name is Chris Sandalo, and having someone of that stature in the company is really helpful to build the security culture that I wanted. That's why I really wanted Chris to come back. He's technically brilliant, but he also knew all the people who'd been there for a while and having that person as a trusted second in command really, really helped me grow the team very quickly. I've already got a reputation as a strong female leader. He had a reputation as a strong technical leader. So us combined is like indestructible, we we're a great team. >> Is your scope of responsibility, obviously you're protecting Mongo, >> Yeah. >> How much of your role extends into the product? >> So we have a product security team that report into Sahir Azam, our chief product officer. I think you even spoke to him. >> Yeah, he's amazing. >> He's awesome, isn't he? He's just fabulous. And so his team, they've got security experts on our product side who are really kind of the customer facing. I'm also to a certain extent customer facing, but the product folks are the absolute experts. They will listen to what our customers need, what they want, and together we can then work out and translate that. I'm also responsible for governance risk and compliance. So there's a large portion of our customers that give us input via that program too. So there's a lot of avenues to allow us to facilitate change in the security field. And I think that's really important. We have to listen to what our customers want, but also internally. You know, what our internal groups need as well to help them grow. >> I remember last year, Re:invent 2021, I was watching a talk on security. It was the, I forget his name, but it was the individual who responsible for data center security. And one of the things he said was, you know, look it's not at the end of the day, the technology's important but it's not the technology. It's how you apply the tools and the practices and the culture- >> Right. That you build in the organization that will ultimately determine how successful you are at decreasing the ROI for the bad guys. >> Yes. >> Let's put it that way. So talk about the challenges of building that culture, how you go about that, and how you sustain that cultural aspect. >> So, I think having the security champion program, so that's just, it's like one of my babies, that and helping underrepresented groups in MongoDB kind of get on in the tech world are both really important to me. And so the security champion program is purely voluntary. We have over a hundred members. And these are people, there's no bar to join. You don't have to be technical. If you're an executive assistant who wants to learn more about security, like my assistant does, you're more than welcome. Up to, we actually people grade themselves, when they join us, we give them a little tick box. Like five is, I walk in security water. One is, I can spell security but I'd like to learn more. Mixing those groups together has been game changing for us. We now have over a hundred people who volunteer their time, with their supervisors permission, they help us with their phishing campaigns, testing AWS tool sets, testing things like queryable encryption. I mean, we have people who have such an in-depth knowledge in other areas of the business that I could never learn, no matter how much time I had. And so to have them- And we have people from product as security champions as well, and security, and legal, and HR, and every department is recognized. And I think almost every geographical location is also recognized. So just to have that scope and depth of people with long tenure in the company, technically brilliant, really want to understand how they can apply the cultural values that we live with each day to make our security program stronger. As I say, that's been a game changer for us. We use it as a feeder program. So we've had five people transfer from other departments into the security and GRC teams through this Champions program. >> Makes a lot of sense. You take somebody who walks on water in security, mix them with somebody who really doesn't know a lot about it but wants to learn and then can ask really basic questions, and then the experts can actually understand better how to communicate. >> Absolutely. >> To that you know that 101 level. >> It's absolutely true. Like my mom lives in her iPad. She worships her iPad. Unfortunately she thinks everything on it is true. And so for me to try and dumb it down, and she's not a dumb person, but for me to try and dumb down the message of most of it's rubbish, mom, Facebook is made up. It's just people telling stories. For me to try and get that over to- So she's a one, and I might be a five, that's hard. That's really hard. And so that's what we're doing in the office as well. It's like, if you can explain to my mother how not everything on the internet is true, we're golden. >> My mom, rest her soul, when she first got a- we got her a Macintosh, this was years and years and years ago, and we were trying to train her over the phone, and said, mom, just grab the mouse. And she's like, I don't like mice. (Lena laughs) There you go. I know, I know, Lena, what that's like. Years ago, it was early last decade, we started to think about, wow, security really has to become a board level item. >> Yeah. >> And it really wasn't- 2010, you know, for certain companies. But really, and so I had the pleasure of interviewing Dr. Robert Gates, who was the defense secretary. >> Yes. >> We had this conversation, and he sits on a number, or sat on a number of boards, probably still does, but he was adamant. Oh, absolutely. Here's how you know, here. This is the criticality. Now it's totally changed. >> Right. >> I mean, it's now a board level item. But how do you communicate to the C-Suite, the board? How often do you do that? What do you recommend is the right regime? And I know there's not any perfect- there's got to be situational, but how do you approach it? >> So I am extremely lucky. We have a very technical board. Our chairman of the board is Tom Killalea. You know, Amazon alum, I mean, just genius. And he, and the rest of the board, it's not like a normal board. Like I actually have the meeting on this coming Monday. So this weekend will be me reading as much stuff as I possibly can, trying to work out what questions they're going to ask me. And it's never a gotcha kind of thing. I've been at board meetings before where you almost feel personally attacked and that's not a good thing. Where, at MongoDB, you can see they genuinely want us to grow and mature. And so I actually meet with our board four times a year, just for security. So we set up our own security meeting just with board members who are specifically interested in security, which is all of them. And so this is actually off cadence. So I actually get their attention for at least an hour once a quarter, which is almost unheard of. And we actually use the AWS memo format. People have a chance to comment and read prior to the meeting. So they know what we're going to talk about and we know what their concerns are. And so you're not going in like, oh my gosh, what what's going to happen for this hour? We come prepared. We have statistics. We can show them where we're growing. We can show them where we need more growth and maturity. And I think having that level of just development of programs, but also the ear of the board has has helped me mature my role 10 times. And then also we have the chance to ask them, well what are your other CISOs doing? You know, they're members of other boards. So I can say to Dave, for example, you know, what's so-and-so doing at Datadog? Or Tom Killelea, what's the CISO of Capital One doing? And they help me make a lot of those connections as well. I mean, the CISO world is small and me being a female in the world with a Scottish accent, I'm probably more memorable than most. So it's like, oh yeah, that's the Irish girl. Yeah. She's Scottish, thank you. But they remember me and I can use that. And so just having all those mentors from the board level down, and obviously Dev is a huge, huge fan of security and GRC. It's no longer that box ticking exercise that I used to feel security was, you know, if you heated your SOC2 type two in FinTech, oh, you were good to go. You know, if you did a HERC set for the power industry. All right, right. You know, we can move on now. It's not that anymore. >> Right. It's every single day. >> Yeah. Of course. Dev is Dev at the Chario. Dev spelled D E V. I spell Dave differently. My Dave. But, Lena, it sounds like you present a combination of metrics, so, the board, you feel like that's appropriate to dig into the metrics. But also I'm presuming you're talking strategy, potentially, you know, gaps- >> Road roadmaps, the whole nine yards. Yep. >> What's the, you know, I look at the budget scenario. At the macro level, CIOs have told us, they came into the year saying, hey we're going to grow spending at the macro, around eight percent, eight and a half percent. That's dialed down a little bit post Ukraine and the whole recession and Fed tightening. So now they're down maybe around six percent. So not dramatically lower, but still. And they tell us security is still the number one priority. >> Yes. >> That's been the case for many, many quarters, and actually years, but you don't have an unlimited budget. >> Sure >> Right. It's not like, oh, here is an open checkbook. >> Right. >> Lena, so, how does Mongo balance that with the other priorities in the organization, obviously, you know, you got to spend money on product, you got to spend money and go to market. What's the climate like now, is it, you know continuing on in 2022 despite some of the macro concerns? Is it maybe tapping the brakes? What's the general sentiment? >> We would never tap the breaks. I mean, this is something that's- So my other half works in the finance industry still. So we have, you know, interesting discussions when it comes to geopolitics and financial politics and you know, Dev, the chairman of the board, all very technical people, get that security is going to be taken advantage of if we're seeing to be tapping the brakes. So it does kind of worry me when I hear other people are saying, oh, we're, you know, we're cutting back our budget. We are not. That being said, you also have to be fiscally responsible. I'm Scottish, we're cheap, really frugal with money. And so I always tell my team: treat this money as if it's your own. As if it's my money. And so when we're buying tool sets, I want to make sure that I'm talking to the CISO, or the CISO of the company that's supplying it, and saying are you giving me the really the best value? You know, how can we maybe even partner with you as a database platform? How could we partner with you, X company, to, you know, maybe we'll give you credits on our platform. If you look to moving to us and then we could have a partnership, and I mean, that's how some of this stuff builds, and so I've been pretty good at doing that. I enjoy doing that. But then also just in terms of being fiscally responsible, yeah, I get it. There's CISOs who have every tool that's out there because it's shiny and it's new and they know the board is never going to say no, but at some point, people will get wise to that and be like, I think we need a new CISO. So it's not like we're going to stop spending it. So we're going to get someone who actually knows how to budget and get us what the best value for money. And so that's always been my view is we're always going to be financed. We're always going to be financed well. But I need to keep showing that value for money. And we do that every board meeting, every Monday when I meet with my boss. I mean, I report to the CFO but I've got a dotted line to the CTO. So I'm, you know, I'm one of the few people at this level that's got my feet in both camps. You know budgets are talked at Dev's level. So, you know, it's really important that we get the spend right. >> And that value is essentially, as I was kind of alluding to before, it's decreasing the value equation for the hackers, for the adversary. >> Hopefully, yes. >> Right? Who's the- of course they're increasingly sophisticated. I want to ask you about your relationship with AWS in this context. It feels like, when I look around here, I think back to 2019, there was a lot of talk about the shared responsibility model. >> Yes. >> You know, AWS likes to educate people and back then it was like, okay, hey, by the way, you know you got to, you know, configure the S3 bucket properly. And then, oh, by the way, there's more than just, it's not just binary. >> Right, right. >> There's other factors involved. The application access and identity and things like that, et cetera, et cetera. So that was all kind of cool. But I feel like the cloud is becoming the first line of defense for the CISO but because of the shared responsibility model, CISO is now the second line of defense >> Yes. Does that change your role? Does it make it less complicated in a way? Maybe, you know, more complicated because you now got to get your DevSecOps team? The developers are now much more involved in security? How is that shifting, specifically in the context of your relationship with AWS? >> It's honestly not been that much of a shift. I mean, these guys are very proactive when it comes to where we are from the security standpoint. They listen to their customers as much as we do. So when we sit down with them, when I meet with Steve Schmidt or CJ or you know, our account manager, its not a conversation that's a surprise to me when I tell them this is what we need. They're like, yep, we're on that already. And so I think that relationship has been very proactive rather than reactive. And then in terms of MongoDB, as a tech company, security is always at the forefront. So it's not been a huge lift for me. It's really just been my time that I've taken to understand where DevSecOps is coming from. And you know, how far are we shifting left? Are we actually shifting right now? It's like, you know, get the balance, right? You can't be too much to one side. But I think in terms of where we're teaching the developers, you know, we are a company by developers for developers. So, we get it, we understand where they're coming from, and we try and be as proactive as AWS is. >> When you obviously the SolarWinds hack was a a major mile- I think in security, there's always something in the headlines- >> Yes. But when you think of things like, you know, Stuxnet, you know, Log4J, obviously Solarwinds and the whole supply chain infiltration and the bill of materials. As I said before, the adversary is extremely capable and sophisticated and you know, much more automated. It's always been automated attacks, but you know island hopping and infiltrating and self-forming malware and really sophisticated techniques. >> Yep. >> How are you thinking about that supply chain, bill of materials from inside Mongo and ultimately externally to your customers? >> So you've picked on my third favorite topic to talk about. So I came from the power industry before, so I've got a lot of experience with critical infrastructure. And that was really, I think, where a lot of the supply chain management rules and regulations came from. If you're building a turbine and the steel's coming from China, we would send people to China to make sure that the steel we were buying was the steel we were using. And so that became the H bomb. The hardware bill of materials, bad name. But, you know, we remember what it stood for. And then fast forward: President Biden's executive order. SBOs front and center, cloud first front and center. It's like, this is perfect. And so I was actually- I actually moderated a panel earlier this year at Homeland Security Week in DC, where we had a sneak CISA, So Dr. Allen Friedman from CISA, and also Patrick Weir from OWASP for the framework, CISA for the framework as well, and just the general guidance, and Snake for the front end. That was where my head was going. And MongoDB is the back-end database. And what we've done is we've taken our work with Snake and we now have a proof of concept for SBOs. And so I'm now trying to kind of package that, if you like, as a program and get the word out that SBOs shouldn't be something to be afraid of. If you want to do business with the government you're going to have to create one. We are offering a secure repository to store that data, the government could have access to that repository and see that data. So there's one source of truth. And so I think SBOs is going to be really interesting. I know that, you know, some of my peers are like, oh, it's just another box to tick. And I think it's more than that. I definitely- I've just, there's something percolating in the back of my mind that this is going to be big and we're going to be able to use it to hopefully not stop things like another Log4j, there's always going to be another Log4j, we know that. we don't know everything, the unknown unknown, but at least if we're prepared to go find stuff quicker than we were then before Log4j, I think having SBOs on hand, having that one source of truth, that one repository, I think is going to make it so much easier to find those things. >> Last question, what's the CISO's number one challenge? Either yours or the CISO, generally. >> Keeping up with the fire hose that is security. Like, what do you pick tomorrow? And if you pick the wrong thing, what's the impact? So that's why I'm always networking and talking to my peers. And, you know, we're sometimes like meerkats, you know. there's meerkats, you see like this, it's like, what do we talk about? But there's always something to talk about. And you just have to learn and keep learning. >> Last question, part B. As a hot technology company, that's, you know, rising star, you know not withstanding the tech lash and the stock market- >> Yeah. >> But Mongo's growing, you know, wonderfully. Do you find it easier to attract talent? Like many CISOs will say, you know, lack of talent is my biggest, biggest challenge. Do you find that that's not the challenge for you? >> Not at all. I think on two fronts, one, we have the champions program. So we've got a whole internal ecosystem who love working there. So the minute one of my jobs goes on the board, they get first dibs at it. So they'd already phoning their friends. So we've got, you know, there's ripple effects out from over a hundred people internally. You know, I think just having that, that's been a game changer. >> I was so looking forward to interviewing you, Lena, thanks so much for coming. >> Thank you, this was a pleasure. >> It was really great to have you. >> Thank you so much. Thank you. >> You're really welcome. All right, keep it right there. This is Dave Villante for theCUBE. We'll be right back at AWS Re:inforce22 right after this short break.

>>And welcome to the DockerCon cube cover here on the main stage. So HIRA RA development manager at J Frogg. Welcome to the cube. You guys have been on many times, uh, with J Frogg on the cube, great product you guys are doing great. Congratulations on all the six. Thanks for coming on the cube. >>Thank you. Thank you for having >>Me. So I'm really interested in talking about the supply chain, uh, package management, supply chain, and software workflow, huge discussion. This is one of the hottest issues that's being solved on by, with, with in DevOps and DevSecOps in, in the planet. It's all over the, all over the news, a real challenge, open source, growing so fast and so successful with cloud scale and with automation, as you guys know, you gotta ha you gotta know what's trusted, so you gotta build trust into the, the product itself. So developers don't have to do all the rework. Everyone kind of knows this right now, and this is a key solve problem you guys are solving. So I gotta ask you, what is the package management issue? Why is it such an important topic when you're talking about security? >>Yeah. Uh, so if you look at, uh, look at how software is built today, about 80 to 90% of that is open source. And currently the way we, the way we pull those open source libraries, we just, we just have blind trust in, in repositories that are central, and we rely on whatever mechanism they have built to, to establish that trust, uh, with the developer who is building it. And from, from our experience, uh, we have learned that that is not sufficient, uh, that is not sufficient to tell us that that particular developer built that end product and, uh, whatever code that they build is actually coming out in the end product. So we need, we need something to bridge that gap. We need, we need a trustworthy mechanism there to bridge that gap. And there are, there are a few other, uh, elements to it. >>Um, all these center depositories are prone to, uh, single point of failures. And, you know, in, we have all experience what happens when one of those goes down and how it stops production and how it, how it stops just software, uh, development, right? And we, what we are working on is how do we build a system where we, we can actually have, uh, liquid software as a reality and just continue to build software, regardless of all these systems of being live all the time, uh, and also have a, an implicit, uh, way of mechanism to trust, uh, what is coming out of those systems? >>You know, we've talked with you guys in the past about the building blocks of software and what flows through the pipelines, all that stuff's part of what is automated these days and, and, and important. And what I gotta ask you because security these days is like, don't trust anything, you know, um, here it's, you're, you're trusting software to be in essence verified. I'm simplifying, obviously. So I gotta ask you what is being done to solve this problem, because states change, you know, you got data, you got software injections, and you got, we got containers and Kubernetes right here, helping all this is on the table now, but what is currently being done to solve the problem? Cause it's really hard. >>Yeah, it is. It is a really hard problem. And currently, right, when we develop software, we have a team, uh, which, which we work with and we trust whatever is coming out of the team. And we have, we have a, um, what do you call certified, uh, pro production mechanism to build that software and actually release it to our customers. And when it is done in house, it is easy because we are, we control all the pieces. Now what happens when, when we are doing this with open source, we don't have that chain. We need that chain, which is independent. We just independent of where the software was, you know, produced versus where it is going to be used. We need a way to have Providence of how it was built, which parts actually went in, uh, making, uh, making the end product. Uh, and, and what are the things that we see are, are, are, uh, continuing, uh, uh, continuing evidences that this software can be used. So if there is a vulnerability that is discovered now, that is discovered, and it is released in some database, and we need to do corrective action to say that this vulnerability associated with this version, and there is no, there's no automated mechanism. So we are working on an automated mechanism where, where you can run a command, which will tell you what has happened with this piece of, uh, software, this version of it, and whether it is production worthy or not. >>It's a great goal. I gotta say, but I'll tell you, I can guarantee there's gonna be a ton of skeptics on this security people. Oh, no, I don't. I doubt it's always a back door. Um, what's the relationship with Docker? How do you guys see this evolving? Obviously it's a super important mission. Um, it's not a trend that's gonna go away. Supply chain software is here to stay. Um, it's not gonna go away. And we saw this in hardware and everyone kind of knows kind of what happens when you see these vulnerabilities. Um, you gotta have trusted software, right? This is gonna be continuing what's the relationship with DockerCon? What are you guys doing with dock and here at DockerCon? >>So we, when we actually started working on this project, uh, both Docker and, uh, J frog had had similar ideas in mind of how, how do we make this, uh, this trust mechanism available to anyone, uh, who wants it, whether they're, whether they're in interacting with dock hub or, or regardless of that, right. And how do we actually make it a mechanism, uh, that just, uh, uh, that just provides this kind of, uh, this kind of trust, uh, without, without the developer having to do something. Uh, so what we worked with, uh, with Docker is actually integrating, um, integrating our solution so that anywhere there, uh, there is, uh, Docker being used currently, uh, people don't have to change those, uh, those behaviors or change those code, uh, those code lines, uh, right. Uh, because changing hand, uh, changing this a single line of code in hundreds of systems, hundreds of CI systems is gonna be really hard. Uh, and we wanted to build a seamless integration between Docker and the solution that we are building, uh, so that, so that you can continue to do Docker pro and dock push and, but get, uh, get all the benefits of the supply chain security solution that we have. >>Okay. So let's step back for a minute and let's discuss about the pro what is the project and where's the commercial J Frogg Docker intersect take that, break that apart, just step out the project for us. What's the intended goals. What is the project? Where is it? How do people get involved and how does that intersect with the commercial interest of JRO and Docker? >>Yeah. Yeah. My favorite topic to talk about. So the, the project is called Peria, uh, Peria is, uh, is an open source project. It is, it is an effort that started with JRO and, and Docker, but by no means limited to just JRO and dock contributing, we already have five companies contributing. Uh, we are actually building a working product, uh, which will demo during, uh, during our, uh, our talk. And there is more to come there's more to come. It is being built iteratively, and, and the solution is basically to provide a decentralized mechanism, uh, similar to similar to how, how you, uh, do things with GI, so that you have, you have the, uh, the packages that you are using available at your nearest peer. Uh, there is also going to be a multi load build verification mechanism, uh, and all of the information about the packages that you're going to use will be available on a Providence log. >>So you can always query that and find out what is the latest state of affairs, what ES were discovered and make, make quick decisions. And you don't have to react after the fact after it has been in the news for a while. Uh, so you can react to your customer's needs, um, uh, as quick as they happen. And we feel that the, our emphasis on open source is key here because, uh, given our experience, you know, 80 to 90% of software that is packaged, contains open source, and there is no way currently, which we, uh, or no engineering mechanisms currently that give us that, uh, that confidence that we, whatever we are building and whatever we are dependencies we are pulling is actually worthwhile putting it into production. >>I mean, you really, it's a great service. I mean, you think about like all that's coming out, open source, open source become very social, too. People are starting projects just to code and get, get in the, in the community and hang out, uh, and just get in the fray and just do stuff. And then you see venture capitals coming in funding those projects, it's a new economic system as well, not just code, so I can see this pipeline beautifully up for scale. How do people get involved with this project? Cause again, my, my questions all gonna be around integration, how frictionless it is. That's gonna be the challenge. You mentioned that, so I can see people getting involved. What's what's how do people join? What do they do? What can they do here at Docker con? >>Yeah. Uh, so we have a website, Percy, I P yr S I a.io, and you'll find all kinds of information there. Uh, we have a GI presence. Uh, we have community meetings that are open to public. We are all, we are all doing this under the, uh, under the umbrella limits foundation. We had a boots scrap project within Linux foundation. Uh, so people who have interest in, in all these areas can come in, just, just attend those meetings, uh, add, uh, you know, add comments or just attend our stand up. So we are running it like a, like a agile from, uh, process. We are doing stand up, we are doing retrospectives and we are, we are doing planning and, and we are, we are iteratively building this. So what you'll see at Dr. Conn is, is just a, a little bit of a teaser of what we have built so far and what you, what you can expect to, uh, see in, in future such events. >>So thanks for coming on the queue. We've got 30 seconds left, put a quick plug in for the swamp up, coming up. >>Yeah. Uh, so we, we will talk a lot more about Peria and our open source efforts and how we would like you all to collaborate. We'll be at swamp up, uh, in San Diego on May 26th, uh, May 24th to 26th. Uh, so hope to see you there, hope to discuss more about Peria and, and see what he will do with, uh, with this project. Thank you. >>All right. Thanks for coming on the back to the main stage. I'm John cube. Thanks for watching. >>Thank >>You.

>>Please >>welcome back to HPD discovered 2021. The cubes virtual coverage, continuous coverage of H P. S H. P. S. Annual customer event. My name is Dave Volonte and we're going to dive into the intersection of high performance computing data and AI with DR Eng limb go who is the senior vice president and CTO for AI Hewlett Packard enterprise Doctor go great to see you again. Welcome back to the cube. >>Hello Dave, Great to talk to you again. >>You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the day two keynotes here at discover and you talked about thriving in the age of insights and how to craft a data centric strategy. And you addressed you know some of the biggest problems I think organizations face with data that's You got a data is plentiful but insights they're harder to come by. And you really dug into some great examples in retail banking and medicine and health care and media. But stepping back a little bit with zoom out on discovered 21, what do you make of the events so far? And some of your big takeaways? >>Mm Well you started with the insightful question, Right? Yeah, data is everywhere then. But we like the insight. Right? That's also part of the reason why that's the main reason why you know Antonio on day one focused and talked about that. The fact that we are now in the age of insight, right? Uh and uh and and how to thrive thrive in that in this new age. What I then did on the day to kino following Antonio is to talk about the challenges that we need to overcome in order in order to thrive in this new asia. >>So maybe we could talk a little bit about some of the things that you took away in terms I'm specifically interested in some of the barriers to achieving insights when customers are drowning in data. What do you hear from customers? What we take away from some of the ones you talked about today? >>Oh, very pertinent question. Dave You know the two challenges I spoke about right now that we need to overcome in order to thrive in this new age. The first one is is the current challenge and that current challenge is uh you know stated is no barriers to insight. You know when we are awash with data. So that's a statement. Right? How to overcome those barriers. What are the barriers of these two insight when we are awash in data? Um I in the data keynote I spoke about three main things. Three main areas that received from customers. The first one, the first barrier is in many with many of our customers. A data is siloed. All right. You know, like in a big corporation you've got data siloed by sales, finance, engineering, manufacturing, and so on, uh supply chain and so on. And uh there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the that was the first barrier. We spoke about barriers to incite when we are washed with data. The second barrier is uh that we see amongst our customers is that uh data is raw and dispersed when they are stored and and uh and you know, it's tough to get tough to to get value out of them. Right? And I in that case I I used the example of uh you know the May 6 2010 event where the stock market dropped a trillion dollars in in tens of minutes. You know, we we all know those who are financially attuned with know about this uh incident, But this is not the only incident. There are many of them out there and for for that particular May six event, uh you know, it took a long time to get insight months. Yeah, before we for months we had no insight as to what happened, why it happened, right. Um, and and there were many other incidences like this and the regulators were looking for that one rule that could, that could mitigate many of these incidences. Um, one of our customers decided to take the hard road to go with the tough data right? Because data is rolling dispersed. So they went into all the different feeds of financial transaction information, took the took the tough took the tough road and analyze that data took a long time to assemble. And they discovered that there was quote stuffing right? That uh people were sending a lot of traits in and then cancelling them almost immediately. You have to manipulate the market. Um And why why why didn't we see it immediately? Well, the reason is the process reports that everybody sees the rule in there that says all trades, less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 103 100 100 shares trades uh to fly under the radar to do this manipulation. So here is here the second barrier right? Data could be raw and dispersed. Um Sometimes you just have to take the hard road and um and to get insight And this is 1 1 great example. And then the last barrier is uh is has to do with sometimes when you start a project to to get insight to get uh to get answers and insight. You you realize that all the datas around you but you don't you don't seem to find the right ones to get what you need. You don't you don't seem to get the right ones. Yeah. Um here we have three quick examples of customers. 111 was it was a great example right? Where uh they were trying to build a language translator, a machine language translator between two languages. Right? By not do that. They need to get hundreds of millions of word pairs, you know, of one language compared uh with a corresponding other hundreds of millions of them. They say, well I'm going to get all these word pairs. Someone creative thought of a willing source. And you thought it was the United Nations, you see. So sometimes you think you don't have the right data with you, but there might be another source. And the willing one that could give you that data Right? The 2nd 1 has to do with uh there was uh the uh sometimes you you may just have to generate that data, interesting one. We had an autonomous car customer that collects all these data from their cars, right? Massive amounts of data, loss of sensors, collect loss of data. And uh, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car uh in in um in fine weather and collected the car driving on this highway in rain and also in stone, but never had the opportunity to collect the car in hill because that's a rare occurrence. So instead of waiting for a time where the car can dr inhale, they build a simulation you by having the car collector in snow and simulated him. So, these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated the fact that data silo the Federated it various associated with data. That's tough to get that. They just took the hard road, right? And, and sometimes, thirdly, you just have to be creative to get the right data. You need, >>wow, I I'll tell you, I have about 100 questions based on what you just said. Uh, there's a great example, the flash crash. In fact, Michael Lewis wrote about this in his book The Flash Boys and essentially right. It was high frequency traders trying to front run the market and sending in small block trades trying to get on the front end it. So that's and they, and they chalked it up to a glitch like you said, for months. Nobody really knew what it was. So technology got us into this problem. I guess my question is, can technology help us get out of the problem? And that maybe is where AI fits in. >>Yes, yes. Uh, in fact, a lot of analytics, we went in to go back to the raw data that is highly dispersed from different sources, right, assemble them to see if you can find a material trend, right? You can see lots of trends, right? Like, uh, you know, we if if humans look at things right, we tend to see patterns in clouds, right? So sometimes you need to apply statistical analysis, um math to to be sure that what the model is seeing is is real. Right? And and that required work. That's one area. The second area is uh you know, when um uh there are times when you you just need to to go through that uh that tough approach to to find the answer. Now, the issue comes to mind now is is that humans put in the rules to decide what goes into a report that everybody sees. And in this case uh before the change in the rules. Right? But by the way, after the discovery, uh authorities change the rules and all all shares, all traits of different any sizes. It has to be reported. No. Yeah. Right. But the rule was applied uh you know, to say earlier that shares under 100 trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and and under for understandable reasons. I mean they probably didn't want that for various reasons not to put everything in there so that people could still read it uh in a reasonable amount of time. But uh we need to understand that rules were being put in by humans for the reports we read. And as such there are times you just need to go back to the raw data. >>I want to ask, >>it's gonna be tough. >>Yeah. So I want to ask a question about AI is obviously it's in your title and it's something you know a lot about but and I want to make a statement, you tell me if it's on point or off point. So it seems that most of the Ai going on in the enterprise is modeling data science applied to troves of data but but there's also a lot of ai going on in consumer whether it's you know, fingerprint technology or facial recognition or natural language processing will a two part question will the consumer market as has so often in the enterprise sort of inform us uh the first part and then will there be a shift from sort of modeling if you will to more you mentioned autonomous vehicles more ai influencing in real time. Especially with the edge you can help us understand that better. >>Yeah, it's a great question. Right. Uh there are three stages to just simplify, I mean, you know, it's probably more sophisticated than that but let's simplify three stages. All right. To to building an Ai system that ultimately can predict, make a prediction right or to to assist you in decision making, have an outcome. So you start with the data massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data and the machine uh starts to evolve a model based on all the data is seeing. It starts to evolve right to the point that using a test set of data that you have separately kept a site that you know the answer for. Then you test the model uh you know after you trained it with all that data to see whether it's prediction accuracy is high enough and once you are satisfied with it, you you then deploy the model to make the decision and that's the influence. Right? So a lot of times depend on what what we are focusing on. We we um in data science are we working hard on assembling the right data to feed the machine with, That's the data preparation organization work. And then after which you build your models, you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you you pick one model and the prediction isn't that robust, it is good but then it is not consistent right now. What you do is uh you try another model so sometimes it's just keep trying different models until you get the right kind. Yeah, that gives you a good robust decision making and prediction after which It is tested well Q eight. You would then take that model and deploy it at the edge. Yeah. And then at the edges is essentially just looking at new data, applying it to the model that you have trained and then that model will give you a prediction decision. Right? So uh it is these three stages. Yeah, but more and more uh your question reminds me that more and more people are thinking as the edge become more and more powerful. Can you also do learning at the edge? Right. That's the reason why we spoke about swarm learning the last time, learning at the edge as a swamp, right? Because maybe individually they may not have enough power to do so. But as a swamp they made >>is that learning from the edge? You're learning at the edge? In other words? >>Yes. >>Yeah, I understand the question. Yeah. >>That's a great question. That's a great question. Right? So uh the quick answer is learning at the edge, right? Uh and and also from the edge, but the main goal, right? The goal is to learn at the edge so that you don't have to move the data that the edge sees first back to the cloud or the core to do the learning because that would be the reason. One of the main reasons why you want to learn at the edge, right? Uh So so that you don't need to have to send all that data back and assemble it back from all the different Edge devices, assemble it back to the cloud side to to do the learning right. With someone you can learn it and keep the data at the edge and learn at that point. >>And then maybe only selectively send the autonomous vehicle example you gave us great because maybe there, you know, there may be only persisting, they're not persisting data that is inclement weather or when a deer runs across the front. And then maybe they they do that and then they send that smaller data set back and maybe that's where it's modelling done. But the rest can be done at the edges. It's a new world that's coming down. Let me ask you a question, is there a limit to what data should be collected and how it should be collected? >>That's a great question again, you know uh wow today, full of these uh insightful questions that actually touches on the second challenge. Right? How do we uh in order to thrive in this new age of insight? The second challenge is are you know the is our future challenge, right? What do we do for our future? And and in there is uh the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that I talk about what to collect right? When to organize it when you collect and where will your data be, you know, going forward that you are collecting from? So what, when and where for the what data for the what data to collect? That? That was the question you ask. Um it's it's a question that different industries have to ask themselves because it will vary, right? Um Let me give you the, you use the autonomous car example, let me use that. And We have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from the fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars, collecting data so they can train and eventually deploy commercial cars. Right? Um, so this data collection cars they collect as a fleet of them collect 10 petabytes a day and when it came to us uh building a storage system yeah, to store all of that data, they realized they don't want to afford to store all of it. Now here comes the dilemma, right? Should what should I after I spent so much effort building all these cars and sensors and collecting data, I've now decide what to delete. That's a dilemma right now in working with them on this process of trimming down what they collected. You know, I'm constantly reminded of the sixties and seventies, right? To remind myself 16 seventies we call a large part of our D. N. A junk DNA. Today we realize that a large part of that what we call john has function as valuable function. They are not jeans, but they regulate the function of jeans, you know? So, so what's jumped in the yesterday could be valuable today or what's junk today could be valuable tomorrow. Right? So, so there's this tension going on right between you decided not wanting to afford to store everything that you can get your hands on. But on the other hand, you you know, you worry you you you ignore the wrong ones, right? You can see this tension in our customers, right? And it depends on industry here. Right? In health care, they say I have no choice. I I want it. All right. One very insightful point brought up by one health care provider that really touched me was, you know, we are not we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But you also care for the people were not caring for. How do we find them? Mhm. Right. And that therefore they did not just need to collect data that is uh that they have with from their patients. They also need to reach out right to outside data so that they can figure out who they are not caring for. Right? So they want it all. So I tell us them. So what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and find out. Of course they also come back to us rightfully that, you know, we have to then work out a way to help them build that system, you know, so that health care, right? And and if you go to other industries like banking, they say they can't afford to keep them on, but they are regulated. Seems like healthcare, they are regulated as to uh privacy and such. Like so many examples different industries having different needs but different approaches to how what they collect. But there is this constant tension between um you perhaps deciding not wanting to fund all of that uh all that you can stall right on the other hand, you know, if you if you kind of don't want to afford it and decide not to store some uh if he does some become highly valuable in the future right? Don't worry. >>We can make some assumptions about the future, can't we? I mean, we know there's gonna be a lot more data than than we've ever seen before. We know that we know. Well notwithstanding supply constraints on things like nand, we know the prices of storage is gonna continue to decline. We also know and not a lot of people are really talking about this but the processing power but he says moore's law is dead. Okay, it's waning. But the processing power when you combine the Cpus and N. P. U. S. And Gpus and accelerators and and so forth actually is is increasing. And so when you think about these use cases at the edge, you're going to have much more processing power, you're going to have cheaper storage and it's going to be less expensive processing. And so as an ai practitioner, what can you do with that? >>So the amount of data that's gonna come in, it's gonna we exceed right? Our drop in storage costs are increasing computer power. Right? So what's the answer? Right? So so the the answer must be knowing that we don't and and even the drop in price and increase in bandwidth, it will overwhelm the increased five G will overwhelm five G. Right? Given amount 55 billion of them collecting. Right? So the answer must be that there might need to be a balance between you needing to bring all that data from the 55 billion devices data back to a central as a bunch of central. Cause because you may not be able to afford to do that firstly band with even with five G. M and and SD when you'll still be too expensive given the number of devices out there, Were you given storage costs dropping? You'll still be too expensive to try and store them all. So the answer must be to start at least to mitigate the problem to some leave both a lot of the data out there. Right? And only send back the pertinent ones as you said before. But then if you did that, then how are we gonna do machine learning at the core and the cloud side? If you don't have all the data, you want rich data to train with. Right? Some sometimes you wanna mix of the uh positive type data and the negative type data so you can train the machine in a more balanced way. So the answer must be eventually right. As we move forward with these huge number of devices out of the edge to do machine learning at the edge today, we don't have enough power. Right? The edge typically is characterized by a lower uh energy capability and therefore lower compute power. But soon, you know, even with lower energy they can do more with compute power, improving in energy efficiency, Right? Uh So learning at the edge today we do influence at the edge. So we data model deploy and you do in France at the age, that's what we do today. But more and more I believe given a massive amount of data at the edge, you, you have to have to start doing machine learning at the edge and, and if when you don't have enough power then you aggregate multiple devices, compute power into a swamp and learn as a swan. >>Oh, interesting. So now of course, if, if I were sitting and fly, fly on the wall in hp board meeting, I said okay. HB is as a leading provider of compute how do you take advantage of that? I mean we're going, we're, I know its future, but you must be thinking about that and participating in those markets. I know today you are, you have, you know, edge line and other products. But there's, it seems to me that it's, it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that >>opportunity for the customers? The world will have to have a balance right? Where today the default? Well, the more common mode is to collect the data from the edge and train at uh at some centralized location or a number of centralized location um going forward. Given the proliferation of the edge devices, we'll need a balance. We need both. We need capability at the cloud side. Right? And it has to be hybrid and then we need capability on the edge side. Yeah. That they want to build systems that that on one hand, uh is uh edge adapted, right? Meaning the environmentally adapted because the edge different. They are on a lot of times. On the outside. Uh They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery power. Right? Um, so you have to build systems that adapt to it. But at the same time they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run a rich set of applications. So yes. Um that's that's also the insightful for that Antonio announced in 2018 Uh the next four years from 2018, right $4 billion dollars invested to strengthen our edge portfolio. Edge product lines, Right. Edge solutions. >>I can doctor go, I could go on for hours with you. You're you're just such a great guest. Let's close. What are you most excited about in the future? Of of of it. Certainly H. P. E. But the industry in general. >>Yeah. I think the excitement is uh the customers, right? The diversity of customers and and the diversity in a way they have approached their different problems with data strategy. So the excitement is around data strategy, right? Just like you know uh you know, the the statement made was was so was profound, right? Um And Antonio said we are in the age of insight powered by data. That's the first line, right. Uh The line that comes after that is as such were becoming more and more data centric with data, the currency. Now the next step is even more profound. That is um You know, we are going as far as saying that you know um data should not be treated as cost anymore. No. Right. But instead as an investment in a new asset class called data with value on our balance sheet, this is a this is a step change right? In thinking that is going to change the way we look at data, the way we value it. So that's a statement that this is the exciting thing because because for for me, a city of Ai right uh machine is only as intelligent as the data you feed it with data is a source of the machine learning to be intelligent. So, so that's that's why when when people start to value data, right? And and and say that it is an investment when we collect it, it is very positive for AI because an AI system gets intelligent, get more intelligence because it has a huge amounts of data and the diversity of data. So it would be great if the community values values data. Well, >>you certainly see it in the valuations of many companies these days. Um and I think increasingly you see it on the income statement, you know, data products and people monetizing data services and maybe eventually you'll see it in the in the balance. You know, Doug Laney, when he was a gardener group wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? Dr >>yeah. Question is is the process and methods evaluation right. But I believe we'll get there, we need to get started and then we'll get there. Believe >>doctor goes on >>pleasure. And yeah. And then the Yeah, I will well benefit greatly from it. >>Oh yeah, no doubt people will better understand how to align you know, some of these technology investments, Doctor goes great to see you again. Thanks so much for coming back in the cube. It's been a real pleasure. >>Yes. A system. It's only as smart as the data you feed it with. >>Excellent. We'll leave it there, thank you for spending some time with us and keep it right there for more great interviews from HP discover 21 this is Dave Volonte for the cube. The leader in enterprise tech coverage right back

>>Welcome back to HPD discovered 2021 the cubes virtual coverage, continuous coverage of H P. S H. P. S. Annual customer event. My name is Dave Volonte and we're going to dive into the intersection of high performance computing data and AI with DR Eng limb go who is the senior vice president and CTO for AI at Hewlett Packard enterprise Doctor go great to see you again. Welcome back to the cube. >>Hello Dave, Great to talk to you again. >>You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the day two keynotes here at discover you talked about thriving in the age of insights and how to craft a data centric strategy and you addressed you know some of the biggest problems I think organizations face with data that's You got a data is plentiful but insights they're harder to come by. And you really dug into some great examples in retail banking and medicine and health care and media. But stepping back a little bit with zoom out on discovered 21, what do you make of the events so far? And some of your big takeaways? >>Mm Well you started with the insightful question, right? Yeah. Data is everywhere then. But we like the insight. Right? That's also part of the reason why that's the main reason why you know Antonio on day one focused and talked about that. The fact that we are now in the age of insight. Right? Uh and and uh and and how to thrive thrive in that in this new age. What I then did on the day to kino following Antonio is to talk about the challenges that we need to overcome in order in order to thrive in this new age. >>So maybe we could talk a little bit about some of the things that you took away in terms I'm specifically interested in some of the barriers to achieving insights when you know customers are drowning in data. What do you hear from customers? What we take away from some of the ones you talked about today? >>Oh, very pertinent question. Dave you know the two challenges I spoke about right now that we need to overcome in order to thrive in this new age. The first one is is the current challenge and that current challenge is uh you know stated is you know, barriers to insight, you know when we are awash with data. So that's a statement right? How to overcome those barriers. What are the barriers of these two insight when we are awash in data? Um I in the data keynote I spoke about three main things. Three main areas that received from customers. The first one, the first barrier is in many with many of our customers. A data is siloed. All right. You know, like in a big corporation you've got data siloed by sales, finance, engineering, manufacturing, and so on, uh supply chain and so on. And uh, there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the that was the first barrier we spoke about barriers to incite when we are washed with data. The second barrier is uh, that we see amongst our customers is that uh data is raw and dispersed when they are stored and and uh and you know, it's tough to get tough to to get value out of them. Right? And I in that case I I used the example of uh you know the May 6 2010 event where the stock market dropped a trillion dollars in in tens of ministerial. We we all know those who are financially attuned with know about this uh incident But this is not the only incident. There are many of them out there and for for that particular May six event uh you know, it took a long time to get insight months. Yeah before we for months we had no insight as to what happened, why it happened, right. Um and and there were many other incidences like this. And the regulators were looking for that one rule that could, that could mitigate many of these incidences. Um one of our customers decided to take the hard road go with the tough data right? Because data is rolling dispersed. So they went into all the different feeds of financial transaction information. Uh took the took the tough uh took the tough road and analyze that data took a long time to assemble and they discovered that there was court stuffing right? That uh people were sending a lot of traits in and then cancelling them almost immediately. You have to manipulate the market. Um And why why why didn't we see it immediately? Well the reason is the process reports that everybody sees uh rule in there that says all trades. Less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 103 100 100 shares trades uh to fly under the radar to do this manipulation. So here is here the second barrier right? Data could be raw and dispersed. Um Sometimes you just have to take the hard road and um and to get insight And this is 1 1 great example. And then the last barrier is uh is has to do with sometimes when you start a project to to get insight to get uh to get answers and insight. You you realize that all the datas around you but you don't you don't seem to find the right ones To get what you need. You don't you don't seem to get the right ones. Yeah. Um here we have three quick examples of customers. 111 was it was a great example right? Where uh they were trying to build a language translator, a machine language translator between two languages. Right? But not do that. They need to get hundreds of millions of word pairs, you know, of one language compared uh with the corresponding other hundreds of millions of them. They say we are going to get all these word pairs. Someone creative thought of a willing source and a huge, so it was a United Nations you see. So sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data right. The second one has to do with uh there was uh the uh sometimes you you may just have to generate that data, interesting one. We had an autonomous car customer that collects all these data from their cars, right, massive amounts of data, loss of senses, collect loss of data. And uh you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car uh in in um in fine weather and collected the car driving on this highway in rain and also in stone, but never had the opportunity to collect the car in hale because that's a rare occurrence. So instead of waiting for a time where the car can dr inhale, they build a simulation you by having the car collector in snow and simulated him. So these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated the fact that data is silo Federated, it various associated with data. That's tough to get that. They just took the hard road, right? And sometimes, thirdly, you just have to be creative to get the right data you need, >>wow, I tell you, I have about 100 questions based on what you just said. Uh, there's a great example, the flash crash. In fact, Michael Lewis wrote about this in his book, The Flash Boys and essentially right. It was high frequency traders trying to front run the market and sending in small block trades trying to get on the front end it. So that's and they, and they chalked it up to a glitch like you said, for months, nobody really knew what it was. So technology got us into this problem. I guess my question is, can technology help us get out of the problem? And that maybe is where AI fits in. >>Yes, yes. Uh, in fact, a lot of analytics, we went in, uh, to go back to the raw data that is highly dispersed from different sources, right, assemble them to see if you can find a material trend, right? You can see lots of trends right? Like, uh, you know, we, if if humans look at things right, we tend to see patterns in clouds, right? So sometimes you need to apply statistical analysis, um math to be sure that what the model is seeing is is real. Right? And and that required work. That's one area. The second area is uh you know, when um uh there are times when you you just need to to go through that uh that tough approach to to find the answer. Now, the issue comes to mind now is is that humans put in the rules to decide what goes into a report that everybody sees in this case uh before the change in the rules. Right? But by the way, after the discovery, the authorities change the rules and all all shares, all traits of different any sizes. It has to be reported. No. Yeah. Right. But the rule was applied uh you know, to say earlier that shares under 100 trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and and under for understandable reasons. I mean they probably didn't want that for various reasons not to put everything in there so that people could still read it uh in a reasonable amount of time. But uh we need to understand that rules were being put in by humans for the reports we read. And as such, there are times you just need to go back to the raw data. >>I want to ask, >>albeit that it's gonna be tough. >>Yeah. So I want to ask a question about AI is obviously it's in your title and it's something you know a lot about but and I want to make a statement, you tell me if it's on point or off point. So it seems that most of the Ai going on in the enterprise is modeling data science applied to troves of data >>but >>but there's also a lot of ai going on in consumer whether it's you know, fingerprint technology or facial recognition or natural language processing. Will a two part question will the consumer market has so often in the enterprise sort of inform us uh the first part and then will there be a shift from sort of modeling if you will to more you mentioned autonomous vehicles more ai influencing in real time. Especially with the edge. She can help us understand that better. >>Yeah, it's a great question. Right. Uh there are three stages to just simplify, I mean, you know, it's probably more sophisticated than that but let's simplify three stages. All right. To to building an Ai system that ultimately can predict, make a prediction right or to to assist you in decision making, have an outcome. So you start with the data massive amounts data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data and the machine uh starts to evolve a model based on all the data is seeing. It starts to evolve right to the point that using a test set of data that you have separately campus site that you know the answer for. Then you test the model uh you know after you trained it with all that data to see whether it's prediction accuracy is high enough and once you are satisfied with it, you you then deploy the model to make the decision and that's the influence. Right? So a lot of times depend on what what we are focusing on. We we um in data science are we working hard on assembling the right data to feed the machine with, That's the data preparation organization work. And then after which you build your models, you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you you pick one model and the prediction isn't that robust, it is good but then it is not consistent right now what you do is uh you try another model so sometimes it's just keep trying different models until you get the right kind. Yeah, that gives you a good robust decision making and prediction after which It is tested well Q eight. You would then take that model and deploy it at the edge. Yeah. And then at the edges is essentially just looking at new data, applying it to the model, you're you're trained and then that model will give you a prediction decision. Right? So uh it is these three stages. Yeah, but more and more uh you know, your question reminds me that more and more people are thinking as the edge become more and more powerful. Can you also do learning at the edge? Right. That's the reason why we spoke about swarm learning the last time, learning at the edge as a swamp, right? Because maybe individually they may not have enough power to do so. But as a swampy me, >>is that learning from the edge or learning at the edge? In other words? Yes. Yeah. Question Yeah. >>That's a great question. That's a great question. Right? So uh the quick answer is learning at the edge, right? Uh and also from the edge, but the main goal, right? The goal is to learn at the edge so that you don't have to move the data that the Edge sees first back to the cloud or the core to do the learning because that would be the reason. One of the main reasons why you want to learn at the edge, right? Uh So so that you don't need to have to send all that data back and assemble it back from all the different edge devices, assemble it back to the cloud side to to do the learning right? With swampland. You can learn it and keep the data at the edge and learn at that point. >>And then maybe only selectively send the autonomous vehicle example you gave us. Great because maybe there, you know, there may be only persisting, they're not persisting data that is inclement weather or when a deer runs across the front and then maybe they they do that and then they send that smaller data set back and maybe that's where it's modelling done. But the rest can be done at the edges. It's a new world that's coming down. Let me ask you a question, is there a limit to what data should be collected and how it should be collected? >>That's a great question again. You know uh wow today, full of these uh insightful questions that actually touches on the second challenge. Right? How do we uh in order to thrive in this new age of inside? The second challenge is are you know the is our future challenge, right? What do we do for our future? And and in there is uh the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that I talk about what to collect right? When to organize it when you collect and then where will your data be, you know going forward that you are collecting from? So what, when and where for the what data for the what data to collect? That? That was the question you ask. Um it's it's a question that different industries have to ask themselves because it will vary, right? Um let me give you the you use the autonomous car example, let me use that. And you have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from the fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars collecting data so they can train and eventually deploy commercial cars, right? Um so this data collection cars they collect as a fleet of them collect temporal bikes a day. And when it came to us building a storage system to store all of that data, they realized they don't want to afford to store all of it. Now, here comes the dilemma, right? What should I after I spent so much effort building all these cars and sensors and collecting data, I've now decide what to delete. That's a dilemma right now in working with them on this process of trimming down what they collected. You know, I'm constantly reminded of the sixties and seventies, right? To remind myself 60 and seventies, we call a large part of our D. N. A junk DNA. Today. We realize that a large part of that what we call john has function as valuable function. They are not jeans, but they regulate the function of jeans, you know, So, so what's jump in the yesterday could be valuable today or what's junk today could be valuable tomorrow, Right? So, so there's this tension going on right between you decided not wanting to afford to store everything that you can get your hands on. But on the other hand, you you know, you worry you you you ignore the wrong ones, right? You can see this tension in our customers, right? And it depends on industry here, right? In health care, they say I have no choice. I I want it. All right. One very insightful point brought up by one health care provider that really touched me was, you know, we are not we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But you also care for the people were not caring for. How do we find them? Mhm. Right. And that therefore, they did not just need to collect data. That is that they have with from their patients. They also need to reach out right to outside data so that they can figure out who they are not caring for, right? So they want it all. So I tell us them, so what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and find that. Of course they also come back to us rightfully that you know, we have to then work out a way to help them build that system, you know? So that's health care, right? And and if you go to other industries like banking, they say they can't afford to keep them off, but they are regulated, seems like healthcare, they are regulated as to uh privacy and such. Like so many examples different industries having different needs, but different approaches to how what they collect. But there is this constant tension between um you perhaps deciding not wanting to fund all of that uh all that you can store, right? But on the other hand, you know, if you if you kind of don't want to afford it and decide not to store some uh if he does some become highly valuable in the future, right? Yeah. >>We can make some assumptions about the future, can't we? I mean, we know there's gonna be a lot more data than than we've ever seen before. We know that we know well notwithstanding supply constraints on things like nand. We know the prices of storage is going to continue to decline. We also know, and not a lot of people are really talking about this but the processing power but he says moore's law is dead okay. It's waning. But the processing power when you combine the Cpus and NP US and GPUS and accelerators and and so forth actually is is increasing. And so when you think about these use cases at the edge, you're going to have much more processing power, you're gonna have cheaper storage and it's going to be less expensive processing And so as an ai practitioner, what can you do with that? >>Yeah, it's highly again, another insightful questions that we touched on our keynote and that that goes up to the why I do the where? Right, When will your data be? Right. We have one estimate that says that by next year there will be 55 billion connected devices out there. Right. 55 billion. Right. What's the population of the world? Of the other? Of 10 billion? But this thing is 55 billion. Right? Uh and many of them, most of them can collect data. So what do you what do you do? Right. Um So the amount of data that's gonna come in, it's gonna weigh exceed right? Our drop in storage costs are increasing computer power. Right? So what's the answer? Right. So, so the the answer must be knowing that we don't and and even the drop in price and increase in bandwidth, it will overwhelm the increased five G will overwhelm five G. Right? Given amount 55 billion of them collecting. Right? So, the answer must be that there might need to be a balance between you needing to bring all that data from the 55 billion devices of data back to a central as a bunch of central Cause because you may not be able to afford to do that firstly band with even with five G. M and and SD when you'll still be too expensive given the number of devices out there. Were you given storage cause dropping will still be too expensive to try and store them all. So the answer must be to start at least to mitigate the problem to some leave both a lot of the data out there. Right? And only send back the pertinent ones as you said before. But then if you did that, then how are we gonna do machine learning at the core and the cloud side? If you don't have all the data you want rich data to train with. Right? Some sometimes you want a mix of the uh positive type data and the negative type data so you can train the machine in a more balanced way. So the answer must be eventually right. As we move forward with these huge number of devices out of the edge to do machine learning at the edge. Today, we don't have enough power. Right? The edge typically is characterized by a lower uh, energy capability and therefore lower compute power. But soon, you know, even with lower energy, they can do more with compute power improving in energy efficiency, Right? Uh, so learning at the edge today, we do influence at the edge. So we data model deploy and you do influence at the age, that's what we do today. But more and more, I believe, given a massive amount of data at the edge, you you have to have to start doing machine learning at the edge. And and if when you don't have enough power, then you aggregate multiple devices, compute power into a swamp and learn as a swan, >>interesting. So now, of course, if I were sitting and fly on the wall in HP board meeting, I said, okay, HP is as a leading provider of compute, how do you take advantage of that? I mean, we're going, I know it's future, but you must be thinking about that and participating in those markets. I know today you are you have, you know, edge line and other products. But there's it seems to me that it's it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that >>opportunity for your customers? Uh the world will have to have a balance right? Where today the default, Well, the more common mode is to collect the data from the edge and train at uh at some centralized location or a number of centralized location um going forward. Given the proliferation of the edge devices, we'll need a balance. We need both. We need capability at the cloud side. Right. And it has to be hybrid. And then we need capability on the edge side. Yeah. That they want to build systems that that on one hand, uh is uh edge adapted, right? Meaning the environmentally adapted because the edge different they are on a lot of times on the outside. Uh They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery powered. Right? Um so you have to build systems that adapt to it, but at the same time they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run rich a set of applications. So yes. Um that's that's also the insightful for that Antonio announced in 2018, Uh the next four years from 2018, right, $4 billion dollars invested to strengthen our edge portfolio, edge product lines, right Edge solutions. >>I get a doctor go. I could go on for hours with you. You're you're just such a great guest. Let's close what are you most excited about in the future of of of it? Certainly H. P. E. But the industry in general. >>Yeah I think the excitement is uh the customers right? The diversity of customers and and the diversity in a way they have approached their different problems with data strategy. So the excitement is around data strategy right? Just like you know uh you know the the statement made was was so was profound. Right? Um And Antonio said we are in the age of insight powered by data. That's the first line right? The line that comes after that is as such were becoming more and more data centric with data the currency. Now the next step is even more profound. That is um you know we are going as far as saying that you know um data should not be treated as cost anymore. No right. But instead as an investment in a new asset class called data with value on our balance sheet, this is a this is a step change right in thinking that is going to change the way we look at data the way we value it. So that's a statement that this is the exciting thing because because for for me a city of AI right uh machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. So so that's that's why when when people start to value data right? And and and say that it is an investment when we collect it. It is very positive for ai because an Ai system gets intelligent, more intelligence because it has a huge amounts of data and the diversity of data. So it'd be great if the community values values data. Well >>you certainly see it in the valuations of many companies these days. Um and I think increasingly you see it on the income statement, you know data products and people monetizing data services and maybe eventually you'll see it in the in the balance. You know Doug Laney when he was a gardener group wrote a book about this and a lot of people are thinking about it. That's a big change isn't it? Dr >>yeah. Question is is the process and methods evaluation. Right. But uh I believe we'll get there, we need to get started then we'll get their belief >>doctor goes on and >>pleasure. And yeah and then the yeah I will will will will benefit greatly from it. >>Oh yeah, no doubt people will better understand how to align you know, some of these technology investments, Doctor goes great to see you again. Thanks so much for coming back in the cube. It's been a real pleasure. >>Yes. A system. It's only as smart as the data you feed it with. >>Excellent. We'll leave it there. Thank you for spending some time with us and keep it right there for more great interviews from HP discover 21. This is dave a lot for the cube. The leader in enterprise tech coverage right back.

(upbeat music) >> Welcome back to HPE Discover 2021, theCube's virtual coverage, continuous coverage of HPE's annual customer event. My name is Dave Vellante and we're going to dive into the intersection of high-performance computing, data and AI with Dr. Eng Lim Goh who's a Senior Vice President and CTO for AI at Hewlett Packard Enterprise. Dr. Goh, great to see you again. Welcome back to theCube. >> Hey, hello, Dave. Great to talk to you again. >> You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the Day 2 keynotes here at Discover. And you talked about thriving in the age of insights and how to craft a data-centric strategy and you addressed some of the biggest problems I think organizations face with data. And that's, you got to look, data is plentiful, but insights, they're harder to come by and you really dug into some great examples in retail, banking, and medicine and healthcare and media. But stepping back a little bit we'll zoom out on Discover '21, you know, what do you make of the events so far and some of your big takeaways? >> Hmm, well, you started with the insightful question. Data is everywhere then but we lack the insight. That's also part of the reason why that's a main reason why, Antonio on Day 1 focused and talked about that, the fact that we are in the now in the age of insight and how to thrive in this new age. What I then did on the Day 2 keynote following Antonio is to talk about the challenges that we need to overcome in order to thrive in this new age. >> So maybe we could talk a little bit about some of the things that you took away in terms of, I'm specifically interested in some of the barriers to achieving insights when you know customers are drowning in data. What do you hear from customers? What were your takeaway from some of the ones you talked about today? >> Very pertinent question, Dave. You know, the two challenges I spoke about how to, that we need to overcome in order to thrive in this new age, the first one is the current challenge. And that current challenge is, you know state of this, you know, barriers to insight, when we are awash with data. So that's a statement. How to overcome those barriers. One of the barriers to insight when we are awash in data, in the Day 2 keynote, I spoke about three main things, three main areas that receive from customers. The first one, the first barrier is with many of our customers, data is siloed. You know, like in a big corporation, you've got data siloed by sales, finance, engineering, manufacturing, and so on supply chain and so on. And there's a major effort ongoing in many corporations to build a Federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the first barrier we spoke about, you know, barriers to insight when we are awash with data. The second barrier is that we see amongst our customers is that data is raw and disperse when they are stored. And it's tough to get to value out of them. In that case I use the example of the May 6, 2010 event where the stock market dropped a trillion dollars in tens of minutes. We all know those who are financially attuned with, know about this incident. But that this is not the only incident. There are many of them out there. And for that particular May 6, event, you know it took a long time to get insight, months, yeah, before we, for months we had no insight as to what happened, why it happened. And there were many other incidences like this and the regulators were looking for that one rule that could mitigate many of these incidences. One of our customers decided to take the hard road to go with the tough data. Because data is raw and dispersed. So they went into all the different feeds of financial transaction information, took the tough, you know, took a tough road and analyze that data took a long time to assemble. And he discovered that there was quote stuffing. That people were sending a lot of trades in and then canceling them almost immediately. You have to manipulate the market. And why didn't we see it immediately? Well, the reason is the process reports that everybody sees had the rule in there that says all trades less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 100 shares trades to fly under the radar to do this manipulation. So here is, here the second barrier. Data could be raw and disperse. Sometimes it's just have to take the hard road and to get insight. And this is one great example. And then the last barrier has to do with sometimes when you start a project to get insight, to get answers and insight, you realize that all the data's around you, but you don't seem to find the right ones to get what you need. You don't seem to get the right ones, yeah. Here we have three quick examples of customers. One was a great example where they were trying to build a language translator a machine language translator between two languages. But in order to do that they need to get hundreds of millions of word pairs of one language compare with the corresponding other hundreds of millions of them. They say, "Where I'm going to get all these word pairs?" Someone creative thought of a willing source and huge source, it was a United Nations. You see, so sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data. The second one has to do with, there was the, sometimes you may just have to generate that data. Interesting one. We had an autonomous car customer that collects all these data from their cars. Massive amounts of data, lots of sensors, collect lots of data. And, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car in fine weather and collected the car driving on this highway in rain and also in snow. But never had the opportunity to collect the car in hail because that's a rare occurrence. So instead of waiting for a time where the car can drive in hail, they build a simulation by having the car collected in snow and simulated hail. So these are some of the examples where we have customers working to overcome barriers. You have barriers that is associated with the fact, that data silo, if federated barriers associated with data that's tough to get at. They just took the hard road. And sometimes thirdly, you just have to be creative to get the right data you need. >> Wow, I tell you, I have about 100 questions based on what you just said. And as a great example, the flash crash in fact Michael Lewis wrote about this in his book, the "Flash Boys" and essentially. It was high frequency traders trying to front run the market and sending in small block trades trying to get sort of front ended. So that's, and they chalked it up to a glitch. Like you said, for months, nobody really knew what it was. So technology got us into this problem. Can I guess my question is can technology help us get get out of the problem? And that maybe is where AI fits in. >> Yes. Yes. In fact, a lot of analytics work went in to go back to the raw data that is highly dispersed from different sources, assemble them to see if you can find a material trend. You can see lots of trends. Like, no, we, if humans at things we tend to see patterns in clouds. So sometimes you need to apply statistical analysis, math to be sure that what the model is seeing is real. And that required work. That's one area. The second area is, you know, when this, there are times when you just need to go through that tough approach to find the answer. Now, the issue comes to mind now is that humans put in the rules to decide what goes into a report that everybody sees. And in this case before the change in the rules. By the way, after the discovery, the authorities changed the rules and all shares all trades of different, any sizes it has to be reported. Not, yeah. But the rule was applied to to say earlier that shares under 100, trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and for understandable reasons. I mean, they probably didn't, wanted for various reasons not to put everything in there so that people could still read it in a reasonable amount of time. But we need to understand that rules were being put in by humans for the reports we read. And as such there are times we just need to go back to the raw data. >> I want to ask you-- Or be it that it's going to be tough there. >> Yeah, so I want to ask you a question about AI as obviously it's in your title and it's something you know a lot about and I'm going to make a statement. You tell me if it's on point or off point. Seems that most of the AI going on in the enterprise is modeling data science applied to troves of data. But there's also a lot of AI going on in consumer, whether it's fingerprint technology or facial recognition or natural language processing. Will, to two-part question, will the consumer market, let's say as it has so often in the enterprise sort of inform us is sort of first part. And then will there be a shift from sort of modeling, if you will, to more, you mentioned autonomous vehicles more AI inferencing in real-time, especially with the Edge. I think you can help us understand that better. >> Yeah, this is a great question. There are three stages to just simplify, I mean, you know, it's probably more sophisticated than that, but let's just simplify there're three stages to building an AI system that ultimately can predict, make a prediction. Or to assist you in decision-making, have an outcome. So you start with the data, massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data. And the machine starts to evolve a model based on all the data is seeing it starts to evolve. To a point that using a test set of data that you have separately kept a site that you know the answer for. Then you test the model, you know after you're trained it with all that data to see whether his prediction accuracy is high enough. And once you are satisfied with it, you then deploy the model to make the decision and that's the inference. So a lot of times depending on what we are focusing on. We in data science are we working hard on assembling the right data to feed the machine with? That's the data preparation organization work. And then after which you build your models you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you pick one model and a prediction isn't that a robust, it is good, but then it is not consistent. Now what you do is you try another model. So sometimes you just keep trying different models until you get the right kind, yeah, that gives you a good robust decision-making and prediction. Now, after which, if it's tested well, Q8 you will then take that model and deploy it at the Edge, yeah. And then at the Edge is essentially just looking at new data applying it to the model that you have trained and then that model will give you a prediction or a decision. So it is these three stages, yeah. But more and more, your question reminds me that more and more people are thinking as the Edge become more and more powerful, can you also do learning at the Edge? That's the reason why we spoke about swarm learning the last time, learning at the Edge as a swarm. Because maybe individually they may not have enough power to do so, but as a swarm, they may. >> Is that learning from the Edge or learning at the Edge. In other words, is it-- >> Yes. >> Yeah, you don't understand my question, yeah. >> That's a great question. That's a great question. So answer is learning at the Edge, and also from the Edge, but the main goal, the goal is to learn at the Edge so that you don't have to move the data that Edge sees first back to the Cloud or the call to do the learning. Because that would be the reason, one of the main reasons why you want to learn at the Edge. So that you don't need to have to send all that data back and assemble it back from all the different Edge devices assemble it back to the Cloud side to do the learning. With swarm learning, you can learn it and keep the data at the Edge and learn at that point, yeah. >> And then maybe only selectively send the autonomous vehicle example you gave is great 'cause maybe they're, you know, there may be only persisting. They're not persisting data that is an inclement weather, or when a deer runs across the front and then maybe they do that and then they send that smaller data set back and maybe that's where it's modeling done but the rest can be done at the Edge. It's a new world that's coming to, let me ask you a question. Is there a limit to what data should be collected and how it should be collected? >> That's a great question again, yeah, well, today full of these insightful questions that actually touches on the second challenge. How do we, to in order to thrive in this new age of insight. The second challenge is our future challenge. What do we do for our future? And in there is the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that, I talk about what to collect, and when to organize it when you collect, and then where will your data be going forward that you are collecting from? So what, when, and where. For the what data, for what data to collect that was the question you asked. It's a question that different industries have to ask themselves because it will vary. Let me give you the, you use the autonomous car example. Let me use that and you have this customer collecting massive amounts of data. You know, we talking about 10 petabytes a day from a fleet of their cars and these are not production autonomous cars. These are training autonomous cars, collecting data so they can train and eventually deploy a commercial cars. Also these data collection cars, they collect 10 as a fleet of them collect 10 petabytes a day. And then when it came to us, building a storage system to store all of that data they realize they don't want to afford to store all of it. Now here comes the dilemma. What should I, after I spent so much effort building all this cars and sensors and collecting data, I've now decide what to delete. That's a dilemma. Now in working with them on this process of trimming down what they collected. I'm constantly reminded of the 60s and 70s. To remind myself 60s and 70s, we call a large part of our DNA, junk DNA. Today we realized that a large part of that, what we call junk has function has valuable function. They are not genes but they regulate the function of genes. So what's junk in yesterday could be valuable today, or what's junk today could be valuable tomorrow. So there's this tension going on between you deciding not wanting to afford to store everything that you can get your hands on. But on the other hand, you know you worry, you ignore the wrong ones. You can see this tension in our customers. And then it depends on industry here. In healthcare they say, I have no choice. I want it all, why? One very insightful point brought up by one healthcare provider that really touched me was you know, we are not, we don't only care. Of course we care a lot. We care a lot about the people we are caring for. But we also care for the people we are not caring for. How do we find them? And therefore, they did not just need to collect data that they have with, from their patients they also need to reach out to outside data so that they can figure out who they are not caring for. So they want it all. So I asked them, "So what do you do with funding if you want it all?" They say they have no choice but they'll figure out a way to fund it and perhaps monetization of what they have now is the way to come around and fund that. Of course, they also come back to us, rightfully that you know, we have to then work out a way to to help them build a system. So that healthcare. And if you go to other industries like banking, they say they can afford to keep them all. But they are regulated same like healthcare. They are regulated as to privacy and such like. So many examples, different industries having different needs but different approaches to how, what they collect. But there is this constant tension between you perhaps deciding not wanting to fund all of that, all that you can store. But on the other hand you know, if you kind of don't want to afford it and decide not to store some, maybe those some become highly valuable in the future. You worry. >> Well, we can make some assumptions about the future, can't we? I mean we know there's going to be a lot more data than we've ever seen before, we know that. We know, well not withstanding supply constraints and things like NAND. We know the price of storage is going to continue to decline. We also know and not a lot of people are really talking about this but the processing power, everybody says, Moore's Law is dead. Okay, it's waning but the processing power when you combine the CPUs and NPUs, and GPUs and accelerators and so forth, actually is increasing. And so when you think about these use cases at the Edge you're going to have much more processing power. You're going to have cheaper storage and it's going to be less expensive processing. And so as an AI practitioner, what can you do with that? >> Yeah, it's a highly, again another insightful question that we touched on, on our keynote and that goes up to the why, I'll do the where. Where will your data be? We have one estimate that says that by next year, there will be 55 billion connected devices out there. 55 billion. What's the population of the world? Well, off the order of 10 billion, but this thing is 55 billion. And many of them, most of them can collect data. So what do you do? So the amount of data that's going to come in is going to way exceed our drop in storage costs our increasing compute power. So what's the answer? The answer must be knowing that we don't and even a drop in price and increase in bandwidth, it will overwhelm the 5G, it'll will overwhelm 5G, given the amount of 55 billion of them collecting. So the answer must be that there needs to be a balance between you needing to bring all that data from the 55 billion devices of the data back out to a central, as a bunch of central cost because you may not be able to afford to do that. Firstly bandwidth, even with 5G and as the, when you still be too expensive given the number of devices out there. You know given storage costs dropping it'll still be too expensive to try and install them all. So the answer must be to start at least to mitigate the problem to some leave most a lot of the data out there. And only send back the pertinent ones, as you said before. But then if you did that then, how are we going to do machine learning at the core and the Cloud side, if you don't have all the data you want rich data to train with. Sometimes you want to a mix of the positive type data, and the negative type data. So you can train the machine in a more balanced way. So the answer must be you eventually, as we move forward with these huge number of devices are at the Edge to do machine learning at the Edge. Today we don't even have power. The Edge typically is characterized by a lower energy capability and therefore, lower compute power. But soon, you know, even with low energy, they can do more with compute power, improving in energy efficiency. So learning at the Edge today we do inference at the Edge. So we data, model, deploy and you do inference at age. That's what we do today. But more and more, I believe given a massive amount of data at the Edge you have to have to start doing machine learning at the Edge. And if when you don't have enough power then you aggregate multiple devices' compute power into a swarm and learn as a swarm. >> Oh, interesting, so now of course, if I were sitting in a flyer flying the wall on HPE Board meeting I said, "Okay, HPE is a leading provider of compute." How do you take advantage that? I mean, we're going, I know it's future but you must be thinking about that and participating in those markets. I know today you are, you have, you know, Edge line and other products, but there's, it seems to me that it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that opportunity for your customers? >> The wall will have to have a balance. Where today the default, well, the more common mode is to collect the data from the Edge and train at some centralized location or number of centralized location. Going forward, given the proliferation of the Edge devices, we'll need a balance, we need both. We need capability at the Cloud side. And it has to be hybrid. And then we need capability on the Edge side. Yeah that we need to build systems that on one hand is Edge-adapted. Meaning they environmentally-adapted because the Edge differently are on it. A lot of times on the outside, they need to be packaging-adapted and also power-adapted. Because typically many of these devices are battery-powered. So you have to build systems that adapts to it. But at the same time, they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run a rich set of applications. So yes, that's also the insightful for that. Antonio announced in 2018 for the next four years from 2018, $4 billion invested to strengthen our Edge portfolio our Edge product lines, Edge solutions. >> Dr. Goh, I could go on for hours with you. You're just such a great guest. Let's close. What are you most excited about in the future of certainly HPE, but the industry in general? >> Yeah, I think the excitement is the customers. The diversity of customers and the diversity in the way they have approached their different problems with data strategy. So the excitement is around data strategy. Just like, you know, the statement made for us was so, was profound. And Antonio said we are in the age of insight powered by data. That's the first line. The line that comes after that is as such we are becoming more and more data-centric with data the currency. Now the next step is even more profound. That is, you know, we are going as far as saying that data should not be treated as cost anymore, no. But instead, as an investment in a new asset class called data with value on our balance sheet. This is a step change in thinking that is going to change the way we look at data, the way we value it. So that's a statement. So this is the exciting thing, because for me a CTO of AI, a machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. So that's why when the people start to value data and say that it is an investment when we collect it it is very positive for AI because an AI system gets intelligent, get more intelligence because it has huge amounts of data and a diversity of data. So it'd be great if the community values data. >> Well, are you certainly see it in the valuations of many companies these days? And I think increasingly you see it on the income statement, you know data products and people monetizing data services, and yeah, maybe eventually you'll see it in the balance sheet, I know. Doug Laney when he was at Gartner Group wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? Dr. Goh. >> Yeah, yeah, yeah. Your question is the process and methods in valuation. But I believe we'll get there. We need to get started and then we'll get there, I believe, yeah. >> Dr. Goh it's always my pleasure. >> And then the AI will benefit greatly from it. >> Oh yeah, no doubt. People will better understand how to align some of these technology investments. Dr. Goh, great to see you again. Thanks so much for coming back in theCube. It's been a real pleasure. >> Yes, a system is only as smart as the data you feed it with. (both chuckling) >> Well, excellent, we'll leave it there. Thank you for spending some time with us so keep it right there for more great interviews from HPE Discover '21. This is Dave Vellante for theCube, the leader in enterprise tech coverage. We'll be right back (upbeat music)

(upbeat music) >> Welcome back to HPE Discover 2021, theCUBE's virtual coverage, continuous coverage of HPE's Annual Customer Event. My name is Dave Vellante, and we're going to dive into the intersection of high-performance computing, data and AI with Doctor Eng Lim Goh, who's a Senior Vice President and CTO for AI at Hewlett Packard Enterprise. Doctor Goh, great to see you again. Welcome back to theCUBE. >> Hello, Dave, great to talk to you again. >> You might remember last year we talked a lot about Swarm intelligence and how AI is evolving. Of course, you hosted the Day 2 Keynotes here at Discover. And you talked about thriving in the age of insights, and how to craft a data-centric strategy. And you addressed some of the biggest problems, I think organizations face with data. That's, you've got a, data is plentiful, but insights, they're harder to come by. >> Yeah. >> And you really dug into some great examples in retail, banking, in medicine, healthcare and media. But stepping back a little bit we zoomed out on Discover '21. What do you make of the events so far and some of your big takeaways? >> Hmm, well, we started with the insightful question, right, yeah? Data is everywhere then, but we lack the insight. That's also part of the reason why, that's a main reason why Antonio on day one focused and talked about the fact that we are in the now in the age of insight, right? And how to try thrive in that age, in this new age? What I then did on a Day 2 Keynote following Antonio is to talk about the challenges that we need to overcome in order to thrive in this new age. >> So, maybe we could talk a little bit about some of the things that you took away in terms of, I'm specifically interested in some of the barriers to achieving insights. You know customers are drowning in data. What do you hear from customers? What were your takeaway from some of the ones you talked about today? >> Oh, very pertinent question, Dave. You know the two challenges I spoke about, that we need to overcome in order to thrive in this new age. The first one is the current challenge. And that current challenge is, you know, stated is now barriers to insight, when we are awash with data. So that's a statement on how do you overcome those barriers? What are the barriers to insight when we are awash in data? In the Day 2 Keynote, I spoke about three main things. Three main areas that we receive from customers. The first one, the first barrier is in many, with many of our customers, data is siloed, all right. You know, like in a big corporation, you've got data siloed by sales, finance, engineering, manufacturing and so on supply chain and so on. And there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above, they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the first barrier we spoke about, you know? Barriers to insight when we are awash with data. The second barrier is that we see amongst our customers is that data is raw and disperse when they are stored. And you know, it's tough to get at, to tough to get a value out of them, right? And in that case, I use the example of, you know, the May 6, 2010 event where the stock market dropped a trillion dollars in terms of minutes. We all know those who are financially attuned with know about this incident but that this is not the only incident. There are many of them out there. And for that particular May 6 event, you know, it took a long time to get insight. Months, yeah, before we, for months we had no insight as to what happened. Why it happened? Right, and there were many other incidences like this and the regulators were looking for that one rule that could mitigate many of these incidences. One of our customers decided to take the hard road they go with the tough data, right? Because data is raw and dispersed. So they went into all the different feeds of financial transaction information, took the tough, you know, took a tough road. And analyze that data took a long time to assemble. And they discovered that there was caught stuffing, right? That people were sending a lot of trades in and then canceling them almost immediately. You have to manipulate the market. And why didn't we see it immediately? Well, the reason is the process reports that everybody sees, the rule in there that says, all trades less than a hundred shares don't need to report in there. And so what people did was sending a lot of less than a hundred shares trades to fly under the radar to do this manipulation. So here is the second barrier, right? Data could be raw and dispersed. Sometimes it's just have to take the hard road and to get insight. And this is one great example. And then the last barrier has to do with sometimes when you start a project to get insight, to get answers and insight, you realize that all the data's around you, but you don't seem to find the right ones to get what you need. You don't seem to get the right ones, yeah? Here we have three quick examples of customers. One was a great example, right? Where they were trying to build a language translator or machine language translator between two languages, right? By not do that, they need to get hundreds of millions of word pairs. You know of one language compare with the corresponding other. Hundreds of millions of them. They say, well, I'm going to get all these word pairs. Someone creative thought of a willing source and a huge, it was a United Nations. You see? So sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data, right? The second one has to do with, there was the sometimes you may just have to generate that data. Interesting one, we had an autonomous car customer that collects all these data from their their cars, right? Massive amounts of data, lots of sensors, collect lots of data. And, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car in fine weather and collected the car driving on this highway in rain and also in snow. But never had the opportunity to collect the car in hill because that's a rare occurrence. So instead of waiting for a time where the car can drive in hill, they build a simulation by having the car collected in snow and simulated him. So these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated. In fact, that data silo, they federated it. Virus associated with data, that's tough to get at. They just took the hard road, right? And sometimes thirdly, you just have to be creative to get the right data you need. >> Wow! I tell you, I have about a hundred questions based on what you just said, you know? (Dave chuckles) And as a great example, the Flash Crash. In fact, Michael Lewis, wrote about this in his book, the Flash Boys. And essentially, right, it was high frequency traders trying to front run the market and sending into small block trades (Dave chuckles) trying to get sort of front ended. So that's, and they chalked it up to a glitch. Like you said, for months, nobody really knew what it was. So technology got us into this problem. (Dave chuckles) I guess my question is can technology help us get out of the problem? And that maybe is where AI fits in? >> Yes, yes. In fact, a lot of analytics work went in to go back to the raw data that is highly dispersed from different sources, right? Assembled them to see if you can find a material trend, right? You can see lots of trends, right? Like, no, we, if humans look at things that we tend to see patterns in Clouds, right? So sometimes you need to apply statistical analysis math to be sure that what the model is seeing is real, right? And that required, well, that's one area. The second area is you know, when this, there are times when you just need to go through that tough approach to find the answer. Now, the issue comes to mind now is that humans put in the rules to decide what goes into a report that everybody sees. Now, in this case, before the change in the rules, right? But by the way, after the discovery, the authorities changed the rules and all shares, all trades of different any sizes it has to be reported. >> Right. >> Right, yeah? But the rule was applied, you know, I say earlier that shares under a hundred, trades under a hundred shares need not be reported. So, sometimes you just have to understand that reports were decided by humans and for understandable reasons. I mean, they probably didn't wanted a various reasons not to put everything in there. So that people could still read it in a reasonable amount of time. But we need to understand that rules were being put in by humans for the reports we read. And as such, there are times we just need to go back to the raw data. >> I want to ask you... >> Oh, it could be, that it's going to be tough, yeah. >> Yeah, I want to ask you a question about AI as obviously it's in your title and it's something you know a lot about but. And I'm going to make a statement, you tell me if it's on point or off point. So seems that most of the AI going on in the enterprise is modeling data science applied to, you know, troves of data. But there's also a lot of AI going on in consumer. Whether it's, you know, fingerprint technology or facial recognition or natural language processing. Well, two part question will the consumer market, as it has so often in the enterprise sort of inform us is sort of first part. And then, there'll be a shift from sort of modeling if you will to more, you mentioned the autonomous vehicles, more AI inferencing in real time, especially with the Edge. Could you help us understand that better? >> Yeah, this is a great question, right? There are three stages to just simplify. I mean, you know, it's probably more sophisticated than that. But let's just simplify that three stages, right? To building an AI system that ultimately can predict, make a prediction, right? Or to assist you in decision-making. I have an outcome. So you start with the data, massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data, and the machine starts to evolve a model based on all the data it's seeing. It starts to evolve, right? To a point that using a test set of data that you have separately kept aside that you know the answer for. Then you test the model, you know? After you've trained it with all that data to see whether its prediction accuracy is high enough. And once you are satisfied with it, you then deploy the model to make the decision. And that's the inference, right? So a lot of times, depending on what we are focusing on, we in data science are, are we working hard on assembling the right data to feed the machine with? That's the data preparation organization work. And then after which you build your models you have to pick the right models for the decisions and prediction you need to make. You pick the right models. And then you start feeding the data with it. Sometimes you pick one model and a prediction isn't that robust. It is good, but then it is not consistent, right? Now what you do is you try another model. So sometimes it gets keep trying different models until you get the right kind, yeah? That gives you a good robust decision-making and prediction. Now, after which, if it's tested well, QA, you will then take that model and deploy it at the Edge. Yeah, and then at the Edge is essentially just looking at new data, applying it to the model that you have trained. And then that model will give you a prediction or a decision, right? So it is these three stages, yeah. But more and more, your question reminds me that more and more people are thinking as the Edge become more and more powerful. Can you also do learning at the Edge? >> Right. >> That's the reason why we spoke about Swarm Learning the last time. Learning at the Edge as a Swarm, right? Because maybe individually, they may not have enough power to do so. But as a Swarm, they may. >> Is that learning from the Edge or learning at the Edge? In other words, is that... >> Yes. >> Yeah. You do understand my question. >> Yes. >> Yeah. (Dave chuckles) >> That's a great question. That's a great question, right? So the quick answer is learning at the Edge, right? And also from the Edge, but the main goal, right? The goal is to learn at the Edge so that you don't have to move the data that Edge sees first back to the Cloud or the Call to do the learning. Because that would be the reason, one of the main reasons why you want to learn at the Edge. Right? So that you don't need to have to send all that data back and assemble it back from all the different Edge devices. Assemble it back to the Cloud Site to do the learning, right? Some on you can learn it and keep the data at the Edge and learn at that point, yeah. >> And then maybe only selectively send. >> Yeah. >> The autonomous vehicle, example you gave is great. 'Cause maybe they're, you know, there may be only persisting. They're not persisting data that is an inclement weather, or when a deer runs across the front. And then maybe they do that and then they send that smaller data setback and maybe that's where it's modeling done but the rest can be done at the Edge. It's a new world that's coming through. Let me ask you a question. Is there a limit to what data should be collected and how it should be collected? >> That's a great question again, yeah. Well, today full of these insightful questions. (Dr. Eng chuckles) That actually touches on the the second challenge, right? How do we, in order to thrive in this new age of insight? The second challenge is our future challenge, right? What do we do for our future? And in there is the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that, I talked about what to collect, right? When to organize it when you collect? And then where will your data be going forward that you are collecting from? So what, when, and where? For what data to collect? That was the question you asked, it's a question that different industries have to ask themselves because it will vary, right? Let me give you the, you use the autonomous car example. Let me use that. And we do have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from a fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars, collecting data so they can train and eventually deploy commercial cars, right? Also this data collection cars, they collect 10, as a fleet of them collect 10 petabytes a day. And then when they came to us, building a storage system you know, to store all of that data, they realized they don't want to afford to store all of it. Now here comes the dilemma, right? What should I, after I spent so much effort building all this cars and sensors and collecting data, I've now decide what to delete. That's a dilemma, right? Now in working with them on this process of trimming down what they collected, you know, I'm constantly reminded of the 60s and 70s, right? To remind myself 60s and 70s, we called a large part of our DNA, junk DNA. >> Yeah. (Dave chuckles) >> Ah! Today, we realized that a large part of that what we call junk has function as valuable function. They are not genes but they regulate the function of genes. You know? So what's junk in yesterday could be valuable today. Or what's junk today could be valuable tomorrow, right? So, there's this tension going on, right? Between you deciding not wanting to afford to store everything that you can get your hands on. But on the other hand, you worry, you ignore the wrong ones, right? You can see this tension in our customers, right? And then it depends on industry here, right? In healthcare they say, I have no choice. I want it all, right? Oh, one very insightful point brought up by one healthcare provider that really touched me was you know, we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But who also care for the people we are not caring for? How do we find them? >> Uh-huh. >> Right, and that definitely, they did not just need to collect data that they have with from their patients. They also need to reach out, right? To outside data so that they can figure out who they are not caring for, right? So they want it all. So I asked them, so what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and fund that. Of course, they also come back to us rightfully, that you know we have to then work out a way to help them build a system, you know? So that's healthcare, right? And if you go to other industries like banking, they say they can afford to keep them all. >> Yeah. >> But they are regulated, seemed like healthcare, they are regulated as to privacy and such like. So many examples different industries having different needs but different approaches to what they collect. But there is this constant tension between you perhaps deciding not wanting to fund all of that, all that you can install, right? But on the other hand, you know if you kind of don't want to afford it and decide not to start some. Maybe those some become highly valuable in the future, right? (Dr. Eng chuckles) You worry. >> Well, we can make some assumptions about the future. Can't we? I mean, we know there's going to be a lot more data than we've ever seen before. We know that. We know, well, not withstanding supply constraints and things like NAND. We know the prices of storage is going to continue to decline. We also know and not a lot of people are really talking about this, but the processing power, but the says, Moore's law is dead. Okay, it's waning, but the processing power when you combine the CPUs and NPUs, and GPUs and accelerators and so forth actually is increasing. And so when you think about these use cases at the Edge you're going to have much more processing power. You're going to have cheaper storage and it's going to be less expensive processing. And so as an AI practitioner, what can you do with that? >> Yeah, it's a highly, again, another insightful question that we touched on our Keynote. And that goes up to the why, uh, to the where? Where will your data be? Right? We have one estimate that says that by next year there will be 55 billion connected devices out there, right? 55 billion, right? What's the population of the world? Well, of the other 10 billion? But this thing is 55 billion. (Dave chuckles) Right? And many of them, most of them can collect data. So what do you do? Right? So the amount of data that's going to come in, it's going to way exceed, right? Drop in storage costs are increasing compute power. >> Right. >> Right. So what's the answer, right? So the answer must be knowing that we don't, and even a drop in price and increase in bandwidth, it will overwhelm the, 5G, it will overwhelm 5G, right? Given the amount of 55 billion of them collecting. So the answer must be that there needs to be a balance between you needing to bring all of that data from the 55 billion devices of the data back to a central, as a bunch of central cost. Because you may not be able to afford to do that. Firstly bandwidth, even with 5G and as the, when you'll still be too expensive given the number of devices out there. You know given storage costs dropping is still be too expensive to try and install them all. So the answer must be to start, at least to mitigate from to, some leave most a lot of the data out there, right? And only send back the pertinent ones, as you said before. But then if you did that then how are we going to do machine learning at the Core and the Cloud Site, if you don't have all the data? You want rich data to train with, right? Sometimes you want to mix up the positive type data and the negative type data. So you can train the machine in a more balanced way. So the answer must be eventually, right? As we move forward with these huge number of devices all at the Edge to do machine learning at the Edge. Today we don't even have power, right? The Edge typically is characterized by a lower energy capability and therefore lower compute power. But soon, you know? Even with low energy, they can do more with compute power improving in energy efficiency, right? So learning at the Edge, today we do inference at the Edge. So we data, model, deploy and you do inference there is. That's what we do today. But more and more, I believe given a massive amount of data at the Edge, you have to start doing machine learning at the Edge. And when you don't have enough power then you aggregate multiple devices, compute power into a Swarm and learn as a Swarm, yeah. >> Oh, interesting. So now of course, if I were sitting and fly on the wall and the HPE board meeting I said, okay, HPE is a leading provider of compute. How do you take advantage of that? I mean, we're going, I know it's future but you must be thinking about that and participating in those markets. I know today you are, you have, you know, Edge line and other products. But there's, it seems to me that it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that opportunity for the customers? >> Hmm, the wall will have to have a balance, right? Where today the default, well, the more common mode is to collect the data from the Edge and train at some centralized location or number of centralized location. Going forward, given the proliferation of the Edge devices, we'll need a balance, we need both. We need capability at the Cloud Site, right? And it has to be hybrid. And then we need capability on the Edge side that we need to build systems that on one hand is an Edge adapter, right? Meaning they environmentally adapted because the Edge differently are on it, a lot of times on the outside. They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery powered. Right? So you have to build systems that adapts to it. But at the same time, they must not be custom. That's my belief. It must be using standard processes and standard operating system so that they can run a rich set of applications. So yes, that's also the insight for that Antonio announced in 2018. For the next four years from 2018, right? $4 billion invested to strengthen our Edge portfolio. >> Uh-huh. >> Edge product lines. >> Right. >> Uh-huh, Edge solutions. >> I could, Doctor Goh, I could go on for hours with you. You're just such a great guest. Let's close. What are you most excited about in the future of, certainly HPE, but the industry in general? >> Yeah, I think the excitement is the customers, right? The diversity of customers and the diversity in the way they have approached different problems of data strategy. So the excitement is around data strategy, right? Just like, you know, the statement made for us was so was profound, right? And Antonio said, we are in the age of insight powered by data. That's the first line, right? The line that comes after that is as such we are becoming more and more data centric with data that currency. Now the next step is even more profound. That is, you know, we are going as far as saying that, you know, data should not be treated as cost anymore. No, right? But instead as an investment in a new asset class called data with value on our balance sheet. This is a step change, right? Right, in thinking that is going to change the way we look at data, the way we value it. So that's a statement. (Dr. Eng chuckles) This is the exciting thing, because for me a CTO of AI, right? A machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. Right? (Dr. Eng chuckles) So, that's why when the people start to value data, right? And say that it is an investment when we collect it it is very positive for AI. Because an AI system gets intelligent, get more intelligence because it has huge amounts of data and a diversity of data. >> Yeah. >> So it'd be great, if the community values data. >> Well, you certainly see it in the valuations of many companies these days. And I think increasingly you see it on the income statement. You know data products and people monetizing data services. And yeah, maybe eventually you'll see it in the balance sheet. I know Doug Laney, when he was at Gartner Group, wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? >> Yeah, yeah. >> Dr. Goh... (Dave chuckles) >> The question is the process and methods in valuation. Right? >> Yeah, right. >> But I believe we will get there. We need to get started. And then we'll get there. I believe, yeah. >> Doctor Goh, it's always my pleasure. >> And then the AI will benefit greatly from it. >> Oh, yeah, no doubt. People will better understand how to align, you know some of these technology investments. Dr. Goh, great to see you again. Thanks so much for coming back in theCUBE. It's been a real pleasure. >> Yes, a system is only as smart as the data you feed it with. (Dave chuckles) (Dr. Eng laughs) >> Excellent. We'll leave it there. Thank you for spending some time with us and keep it right there for more great interviews from HPE Discover 21. This is Dave Vellante for theCUBE, the leader in Enterprise Tech Coverage. We'll be right back. (upbeat music)

>>Mhm. Yes. Hello. Welcome back to the cubes coverage of dr khan 2021 virtual. I'm john for your host of the cube. We're messing my fair principal technologist at AWS amazon Web services messman. Thank you for coming on the cube, appreciate it. Um >>Thank you. Thank you for having me. >>Great to see you love this amazon integration with doctor want to get into that in a second. Um Been great to see the amazon cloud native integration working well. E. C. S very popular. Every interview I've done at reinvent uh every year it gets better and better more adoption every year. Um Tell us what's going on with amazon E. C. S because you have Pcs anywhere and now that's being available. >>Yeah that's fine, that's correct, join and uh yeah so customers has been appreciating the value and the simplicity of VCS for many years now. I mean we we launched GCS back in 2014 and we have seen great adoption of the product and customers has always been appreciating. Uh the fact that it was easy to operate and easy to use. Uh This is a journey with the CS anywhere that started a few years ago actually. And we started this journey uh listening to customers that had particular requirements. Um I'd like to talk about, you know, the the law of the land and the law um uh of the physic where customers wanted to go all in into uh into the cloud, but they did have this exception that they need to uh deal with with the application that could not move to the cloud. So as I said, this journey started three years ago when we launched outpost. Um and outpost is our managed infrastructure that customers can deploy in their own data centers. And we supported Pcs on day one on outpost. Um having that said, there are lots of customers that came to us and said we love outputs but there are certain applications and certain requirements, uh such as compliance or the fact simply that we have like assets that we need to reuse in our data center uh that we want to use and before we move into into the cloud. So they were asking us, we love the simplicity of Vcs but we have to use gears that we have in our data center. That is when we started thinking about Pcs anywhere. So basically the idea of VCS anywhere is that you can use e c s E C as part of that, you know, and love um uh appreciated the simplicity of using Pcs but using your customer managed infrastructure as the data plane, basically what you could do is you can define your application within the Ec. S country plane and deploy those applications on customer own um infrastructure. What that means from a very practical perspective is that you can deploy this application on your managed infrastructure ranging from uh raspberry pis this is the demo that we show the invent when we pronounce um e c s anywhere all the way up to bare metal server, we don't really care about the infrastructure underneath. As long as it supported, the OS is supported. Um we're fine with that. >>Okay, so let's take this to the next level and actually the big theme at dr Connors developer experience, you know, that's kind of want to talk about that and obviously developer productivity and innovation have to go hand in hand. You don't want to stunt the innovation equation, which is cloud, native and scale. Right. So how does the developer experience improve with amazon ECs and anywhere now that I'm on, on premises or in the cloud? Can you take me through? What's the improvements around pcs and the developer? >>Yeah I would argue that the the what you see as anywhere solved is more for operational aspect and the requirements that more that are more akin to the operation team that that they need to meet. Uh We're working very hard to um to improve the developing experience on top of the CS beyond what we're doing with the CS anywhere. So um I'd like to step back a little bit and maybe tell a little bit of a story of why we're working on those things. So um the customer as I said before, continue to appreciate the simplicity and the easier views of E. C. S. However what we learn um over the years is that as we added more features to E. C. S, we ended up uh leveraging more easy. Um AWS services um example uh would be a load balancer integration or secret manager or Fc. Or um other things like service discovery that uses underneath other AWS products like um clubman for around 53. And what happened is that the end user experience, the developer experience became a little bit more complicated because now customers opportunity easy of use of these fully managed services. However they were responsible for time and watering all uh together in the application definition. So what we're working on to simplify this experience is we're working on tools that kind of abstract these um this verbal city that you get with pcs. Um uh An example is a confirmation template that a developer we need to use uh to deploy an application leveraging all of these features. Could then could end up being uh many hundreds of transformation lines um in the in the in the definition of the service. So we're working on new tools and new capabilities to make this experience better. Uh Some of them are C d k uh the copilot cli, dws, copilot cli those are all instruments and technologies and tools that we're building to abstract that um uh verbosity that I was alluding to and this is where actually also the doctor composed integration with the CS falls in. >>Yeah, I'm just gonna ask you that the doctor piece because actually it's dr khan all the developers love containers, they love what they do. Um This is a native, you know, mindset of shifting left with security. How is the relationship with the Docker container ecosystem going with you guys? Can you take him in to explain for the folks here watching this event and participating in the community, explain the relationship with Docker container specifically. >>Yeah, absolutely. Uh so basically we started working with dR many, many years ago, um uh Pcs was based on on DR technology when we launch it. Uh and it's still using uh DR technology and last year we started to collaborate with dR more closely um when DR releases the doctor composed specification um as an open source projects. So basically doctor is trying to use the doctor composed specification to create uh infrastructure product gnostic, uh way to deploy Docker application um uh using those specification in multiple infrastructure as part of these journey, we work with dr to support pcs as a back end um for um for the specification, basically what this means from a very practical perspective, is that you can take a doctor composed an existing doctor composed file. Um and doctor says that there are 650,000 doctor composed files spread across the top and all um uh lose control uh system um over the world. And basically you can take those doctor composed file and uh composed up and deploy transparently um into E. C. S Target on AWS. So basically if we go back to what I was alluding to before, the fact that the developer would need to author many 100 line of confirmation template to be able to take their application and deploy it into the cloud. What they need to do now is um offering a new file, a um a file uh with a very clear and easy to use dr composed syntax composed up and deploy automatically on AWS. Um and using Pcs Fargate um and many other AWS services in the back end. >>And what's the expectation in your mind as you guys look at the container service to anywhere model the on premise and without post, what does he what's the vision? Because that's again, another question mark for me, it's like, okay, I get it totally makes sense. Um, but containers are showing the mainstream enterprises, not the hyper skills. You guys always been kind of the forward thinkers, but you know, main street enterprise, I call it. They're picking up adoption of containers in a massive way. They're looking at cloud native specifically as the place for modern application development period. That's happening. What's the story? Say it again? Because I want to make sure I get this right e C s anywhere if I want to get on premises hybrid, What's it mean for me? >>Uh, this goes back to what I was saying at the beginning. So there are there are there when we have been discussing here are mostly to or token of things. Right. So the fact that we enable these big enterprises to meet their requirements and meet their um their um checkboxes sometimes to be able to deploy outside of AWS when there is a need to do that. This could be for edge use cases or for um using years that exist in the data center. So this is where e c s anywhere is basically trying, this is what uh pcs anywhere is trying to address. There is another orthogonal discussion which is developer experience, uh and that development experience is being addressed by these additional tools. Um what I like to say is that uh the confirmation is becoming a little bit like assembler in a sense, right? It's becoming very low level, super powerful, but very low level and we want to abstract and bring the experience to the next level and make it simple for developers to leverage the simplicity of some of these tools including Docker compose um and and and being able to deploy into the cloud um and getting all the benefits of the cloud scalability, electricity and security. >>I love the assembler analogy because you think about it. A lot of the innovation has been kind of like low level foundational and if you start to see all the open source activity and the customers, the tooling does matter. And I think that's where the ease of use comes in. So the simplicity totally makes sense. Um can you give an example of some simplicity piece? Because I think, you know, you guys, you know, look at looking at ec. S as the cornerstone for simplicity. I get that. Can you give an example to walk us through a day in the life of of an example >>uh in an example of simplicity? Yeah, supposedly in action. Yeah. Well, one of the examples that I usually do and there is this uh, notion of being served less and I think that there is a little bit of a, of an obsession around surveillance and trying to talk about surveillance for so many things. When I talk about the C. S, I like to use another moniker that is version less. So to me, simplicity also means that I do not have to um update my service. Right? So the way E C. S works is that engineering in the service team keeps producing and keeps delivering new features for PCS overnight for customers to wake up in the morning and consuming those features without having to deal with upgrades and updates. I think that this is a very key, um, very key example of simplicity when it comes to e C s that is very hard to find um in other, um, solutions whether there are on prime or in the cloud. >>That's a great example in one of the big complaints I hear just anecdotally around the industry is, you know, the speed of the minds of business, want the apps to move faster and the iteration with some craft obviously with security and making sure things buttoned up, but things get pulled back. It's almost slowed down because the speed of the innovation is happening faster than the compliance of some sort of old governance model or code reviews. I want to approve everything. So there's a balance between making sure what's approved, whether security or some pipeline procedures and what not. >>So that I could have. I cannot agree more with you. Yeah, no, it's absolutely true because I think that we see these very interesting um, uh, economy, I would say between startups moving super fast and enterprises try to move fast but forced to move at their own speed. So when we when we deliver services based on, for example, open source software uh, that customers need to um, look after in terms of upgrade to latest release. What we usually see is start up asking us can you move faster? There is a new version of that software, can you enable us to deploy that version? And then on the other hand of the spectrum, there are these big enterprises trying to move faster but not so much that are asking us can use lower. Can you slow down a little bit? Right, because I cannot keep that pigs. So it's a very it's a very interesting um, um, a very interesting time to be alive. >>You know, one of the, one of the things that pop up into these conversations when you talk, when I talk to VP of engineering of companies and then enterprises that the operational efficiency, you got developer productivity and you've got innovation right, you've got the three kind of things going on there knobs and they all have to turn up. People want more efficiency of the operations, they want more developed productivity and more innovation. What's interesting is you start seeing, okay, it's not that easy. There's also a team formation and I know Andy Jassy kinda referred to this in his keynote at Reinvent last year around thinking differently around your organizational but you know, that could be applied to technologists too. So I'd love to get your thoughts while you're here. I know you blog about this and you tweet about this but this is kind of like okay if these things are all going to be knobs, we turned up innovation efficiency, operationally and develop productivity. What's the makeup of the team? Because some are saying, you have an SRE embedded, you've got the platform engineering, you've got version lists, you got survival is all these things are going on all goodness. But does that mean that the teams have to change? What's your thoughts on that you want to get your perspective? >>Yeah, no, absolutely. I think that there was a joke going around that um as soon as you see a job like VP of devoPS, I mean that is not going to work, right? Because these things are needs to be like embedded into each team, right? There shouldn't be a DEVOPS team or anything, it would be just a way of working. And I totally agree with you that these knobs needs to go insane, right? And you cannot just push too hard on innovation which are not having um other folks um to uh to be able to, you know, keep that pace um with you. And we're trying to health customers with multiple uh tools and services to try to um have not only developers and making developer experience uh better but also helping people that are building these underneath platforms. Like for example, prod on AWS protein is a good example of this, where we're focusing on helping these um teams that are trying to build platforms because they are not looking themselves as being a giant or very fast. But they're they're they're measured on being secure, being compliant and being, you know, within a guardrail uh that an enterprise um regulated enterprise needs to have. So we need to have all of these people um both organizationally as well as with providing tools and technologies that have them in their specific areas um to succeed. >>Yeah. And what's interesting about all this is that you know I think we're also having conversations and and again you're starting to see things more clearly here at dr khan we saw some things that coop con which the joke there was not joke but the observation was it's less about kubernetes which is now becoming boring, lee reliable to more about cloud native applications under the covers with program ability. So as all this is going on there truly is a flip of the script. You can actually re engineer and re factor everything, not just re platform your applications in I. T. At once. Right now there's a window whether it's security or whatever. Now that the containers and and the doctor ecosystem and the container ecosystem and the The kubernetes, you've got KS and you got six far gay and all the stuff of goodness. Companies can actually do this right now. They can actually change everything. This is a unique time. This window might close are certainly changed if you're not on it now, it's the same argument of the folks who got caught in the pandemic and weren't in the cloud got flat footed. So you're seeing that example of if you weren't in the cloud up during the pandemic before the pandemic, you were probably losing during the pandemic, the ones that one where the already guys are in the cloud. Now the same thing is true with cloud native. You're not getting into it now, you're probably gonna be on the wrong side of history. What's your reaction to that? >>Yeah, No, I I I agree totally. I I like to think about this. I usually uh talk about this if I can stay back step back a little bit and I think that in this industry and I have gray areas and I have seen lots of things, I think that there has been too big Democratisation event in 90 that happened and occurred in the last 30 years. So the first one was from, you know from when um the PC technology has been introduced, distributed computing from the mainframe area and that was the first Democratisation step. Right? So everyone had access to um uh computers so they could do things if you if you fast forward to these days. Um uh what happened is that on top of that computer, whatever that became a server or whatever, there is a state a very complex stack of technologies uh that allow you to deployment and develop and deploy your application. Right. But that stack of technology and the complexity of that stack of technology is daunting in some way. Right? So it is in a bit access and democratic access to technology. So to me this is what cloud enabled, Right? So the next step of democratisation was the introduction of services that allow you to bypass that stack, which we call undifferentiated heavy lifting because you know, um you don't get paid for managing, I don't know any M. R. Server or whatever, you get paid for extracting values through application logic from that big stack. So I totally agree with you that we're in a unique position to enable everyone um with what we're building uh to innovate a lot faster and in a more secure way. >>Yeah. And what comes out, I totally agree. And I think that's a great historical view and I think let's bring this down to the present today and then bring this as the as the bridge to the future. If you're a developer you could. And by the way, no matter whether you're programming infrastructure or just writing software or even just calling a PS and rolling your own, composing your services, it's programmable and it's just all accessible. So I think that that's going to change the again back to the three knobs, developer productivity or just people productivity, operational efficiency, which is scale and then innovation, which is the business logic where I think machine learning starts to come in, right? So if you can get the container thing going, you start tapping into that control plane. It's not so much just the data control plane. It's like a software control plane. >>Yeah, no, absolutely. The fact that you can, I mean as I said, I have great hair. So I've seen a lot of things and back in the days, I mean the, I mean the whole notion of being able to call an api and get 10 servers for example or today, 10 containers. It would be like, you know, almost a joke, right? So we spent a lot of time racking and um, and doing so much manual stuff that was so ever prone because we usually talk about velocity and agility, but we, we rarely talk about, you know, the difficulties and the problems that doing things manually introduced in the process, the way that you can get wrong. >>You know, you know, it reminds me of this industry and I was like finally get off my lawn in the old days. I walk to school with no shoes on in the snow. We had to build our own colonel and our own graphics libraries and then now they have all these tools. It's like, you're just an old, you know, coder, but joking aside, you know that experience, you're bringing up appointments for the younger generation who have never loaded a Linux operating system before or had done anything like that level. It's not so much old versus young, it's more of a systems thinking, he said distributed computing. If you look at all the action, it's essentially distributed computing with new software paradigm and it's a system architecture. It's not so much software engineering, software developer, you know, this that it's just basically all engineering at this point, all software. >>It is, it is very much indeed. It's uh, it's whole software, there is no other um, there is no other way to call it. It's um, I mean we go back to talk about, you know, infrastructure as code and everything is now uh corridor software in in in a way. It's, yeah. >>This is great to have you on. Congratulations. A CS anywhere being available. It's great stuff. Um, and great to see you and, and great to have this conversation. Um, amazon web services obviously, uh, the world has has gone super cloud. Uh, now you have distributed computing with edge iot exploding beautifully, which means a lot of new opportunities. So thanks for coming on. >>Thank you very much for having me. It was a pleasure. Okay, cube >>Coverage of Dr Khan 2021 virtual. This is the Cube. I'm John for your host. Thanks for watching.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 >>sponsored by >>Intel and AWS. Yeah, Welcome back to the cubes. Ongoing coverage of aws reinvent virtual the Cuba has gone virtual to. We're gonna talk about machine intelligence, cloud and transformation in healthcare. An industry that is rapidly evolving and reinventing itself to provide better quality care faster and more accurate diagnoses. And this has to be done at lower cost. And with me to talk about This is Dr Taha. Awesome. Who? Who is the director of machine learning at Amazon Web services? Doctor, good to see you again. Thanks for coming on. >>Thank you so much. Good to see Dave. >>Yeah, last time we talked, I think it was a couple of years ago. We remember we were talking about Amazon. Comprehend medical. And, of course, you've been so called so called raising the bar, so to speak, Over the past 24 months, you made some announcements today, including Amazon Health Lake, which we're gonna talk about. Tell us about it. >>Well, we're really excited about eso our customers. Amazon Half Lake, a new hip eligible service for health care providers health insurance companies and pharmaceutical companies to securely store, transform Aquarian, analyze health data in the cloud at petabytes scale, a Amazon health lake uses machine learning models trained to automatically understand context and extract meaningful data from medical data from raw, disparate information such as medications, procedures, Um, and diagnosis. Um Therefore, revolutionizing a process that was traditionally manual Arab prone and highly costly requires a lot of expertise on teams within these organizations. What healthcare Catholic does is it tags and indexes every piece of information on then structure in an open standard. The fire standard, or that's the fast healthcare interoperability resource, is in order to provide a complete view 360 degree view of each patient in a consistent way so you'll be able to curry and share that data securely. It also integrates with other machine learning services and a lot of services that AWS offers, such as Amazon Quicksight or Amazon sage maker. In order to visualize and understand the relationships in the data identify trends, Andi also make predictions. The other great benefit is since the Amazon health lake automatically structures all the health care organizations data into open standard. The fire industry format. The information now can be easily and securely shared between systems. Health systems onda with third party applications. So eso providers, health care providers will will enjoy the ability to collaborate more effectively with each other but also allowing patients and federal access to their medical information. >>I think now, so one of things that people are gonna ask is Okay, wait a minute. Hip eligible Is that like cable ready or HD ready? And but people need to understand that it's a shared responsibility. But you can't come out of the box and be HIPPA compliant there a number of things and processes, uh, that that your customer has to do. Maybe you could explain that a little >>bit. Absolutely. I mean, in practice a little bit. This is a very, very important thing, and and it's something that we really fully baked into the service and how we built Also the service, especially dealing with health care information. First off, AWS, as you know, is vigilant about customers, privacy and security. It is job zero for us. Your data and Health Lake is secure, compliant, and auditable data version is enabled to protect um, the data against any accident collision, for example, and per fire sophistication. If you are to delete one piece of data, it will be version it will be on Lee. Hidden from analysis is a result not believed from the service. So your dad is always encrypted on by using your own customer. Manage key in a keys in a single tenant. Architectures is another added benefit to provide the additional level of protection when the data is access and search for example, every time inquiry a value, for example, someone's glucose level if the data is encrypted and decrypted and and and and so on and so forth. So, additionally, this system in a single tenant architectures so that that way the data, uh, the key. The same key is not shared across multiple customers. So you're saying full ownership and control of your data along with the ability to encrypt, protect move, deleted in alignment with organization, security and policies. Now a little bit about the hip eligibility. It's a term that AWS uses eso for customers storing protected health information or P h. I A. DBS by its business associate agreement on also Business Associate amendment require customers to encrypt data addressed in transit when they're using area services. There are over 100 services today. They're hip eligible, including the Amazon. Health like this is very important, especially for, uh enabling discovered entities and their business associates subject to HIPAA regulations, and is be able to kind of and this shared model between what a the best protection and services and how it can process and store and managed ph I. But there's additional level of compliance is required on the on the customer side, um, about you know, anywhere from physical security thio how each application can be built, which is no different than how you manage it. For example, today in your own that data center, what not? But this is why many cats, growing number of health care providers, um, players as well as I, because professionals are using AWS utility based cloud services today to process, store and transmit pH. I. >>So tell us more about who was gonna benefit from this new capability, what types of organizations and would be some of the outcomes for for for patients, >>absolutely every healthcare provider today, or a payer like a health insurance company or a life. Science companies such as Pharma Company is just trying to solve the problem of organizing instruction their data. Because if you do, you make better sense of this information from better patient support decisions. Design better clinical trials, operate more efficiently, understand population health trends on be able them to share that that security. It's really all starts with making sense of that of that data. And those are the ultimate customers that we're trying to empower with the Amazon Amazon Health Lake. Um, >>well, And of course, there's downstream benefits for the patient. Absolutely. That's ultimately what we're trying to get to. I mean, absolutely. I mean, I set up front. I mean, it's it's everybody you know, feels the pain of high health care costs. A lot of times you're trying to get to see a doctor, and it it takes a long time now, especially with with covitz so and much of this, oftentimes it's even hard to get access to your own data s. So I think you're really trying to attack that problem. Aren't >>you absolutely give you a couple of examples like I mean, today, the most widely used clinical models, uh, in practice to predict. Let's say someone's disease risk lack personalization. Um, it's you and I can be lumped in the same in the same bucket, for example, based on a few attributes that common, UM, data elements or data points, which is problematic also because the resulting models produce are imprecise. However, if you look at an individual's medical records, for example, you know a diabetic type two diabetic patients there, if you look at the entire history and from all this information coming to them, whether it's doctor knows medication dosages, which line of treatment the second line treatment, uh, continuous monitoring of glucose and that sort of thing is over hundreds. You know, there are hundreds of thousands of data points in their entire medical history, but none of this is used today. At the point of care on. You want all this information to be organized, aggregated, structured in a way that you will be able to build even better models easily queried this information, aan den observed the health of the individual in comparison with the rest of the population because at that point you'll be able to make those personalized decisions and then also for patient engagement with the health lake ability to now emit data back on dshea air securely the a p i s that conform to the fire standard. So third party applications can be built also, um, Thio provide the access whether that's for analytics or digital health application, for example, a patient accident, that information all that is very, very, very important. Because ultimately you wanna, um, get at better care of these these populations better. In Roma, clinical trials reduce duplicative tests and waste and health care systems. All that comes when you have your entire information available in a way that structured and normalize on be able to Korean and analyze andan the seamless integration between the health lake and the arrest of the services like Amazon sage maker. You can really start to understand relationships and meaning of the information, build better, better decision support models and predictive models at the individual on a population level. >>Yeah, right. You talked about all this data that's not not really used on. It's because it's not accessible. I presume it's not in in one place that somebody can analyze its not standardized. It's not normalized. Uh, is that >>right, that is the biggest. That is the biggest challenge for every healthcare provider, pair or life science organization today. If you look at this data, it's difficult to work with. Medical health. Data is really different that I siloed spread out across multiple systems, and it's sort of not incompatible formats. If you look at the last decade, I mean, one of the greatest things is we witnessed a great transformation healthcare towards digitization of the record. But your data is scattered across many of these systems anywhere from found your family history, the clinical observation, diagnosis and treatment. When you see the vast majority of that data is contained in unstructured medical records like Dr Notes P. D efs of insurance, um, of laboratory reports or insurance claims and forms with the With With Covad, we've seen in quite a bit of uptake of digital sort of, um uh, delivery of care such as telemedicine and recorded audios and videos, X rays and images, uh, the large propagation of digital health, APS and and digital assistances and on and wearables and as well as these sort of monitors like glucose, monitor or not, data come in all shapes and form and form and start across all these things. It's a huge heavy lift for any health care organization to be able to aggregate normalized stored securely on. Then also be able to kind of analyze this information and structure in a way that zizi to scale. Um uh, with regards, Thio, the kind of problems that you're going after. >>Well, Dr Cox, who We have to leave it there. Thank you so much. I have been saying for years in the Cube. When is it? That machine's gonna be able to make it make better diagnoses than doctors. Maybe that's the wrong question. Maybe it's machines helping doctors make faster and more accurate diagnoses and lowering our costs. Thanks so much for coming. >>Thank you very much. Appreciate it. Thank you. >>Thank you for watching everybody keep it right there. This is Dave Volonte. We'll be back with more coverage of aws reinvent 2020. You virtual right after this short break

>> Male's Voice: From around the globe, its theCUBE. With digital coverage of a special announcement, brought to you by Nutanix. (soft music) >> Hi, I'm Stu Miniman. And welcome to this special announcement for Nutanix, about some new product releases in the public cloud. To help us kick this off for the Asia Pacific and Japan region. Happy to welcome to the program Jordan Reizes, who's the vice president of marketing, for APJ and Nutanix. Jordan, help us introduce it. Thanks Stu. So today we're really pleased to announce Nutanix Clusters, availability in Asia Pacific and Japan, at the same time as the rest of the world. And we think this technology is really important to our geographically dispersed customers, all across the region, in terms of helping them, On-Ramp to the cloud. So, we're really excited about this launch today. And Stu, I can't wait to see the rest of the program. And make sure you stay tuned at the end, for our interview with our CTO, Justin Hurst. Who's going to be answering a bunch of questions that are really specific to the APJ region. >> All right, thank you so much Jordan, for helping us kick this off. We're now going to cut over to my interview with Monica and Tarkan, with the news. >> Hi, I'm Stu Miniman. And I want to welcome you to this special event that we are doing with Nutanix. Of course, in 2020 many things have changed. And that has changed some of the priorities, for many companies out there. Acceleration of cloud adoption, absolutely have been there. I've talked to many companies that were dipping their toe, or thinking about, where they were going to cloud. And of course it's rapidly moved to accelerate to be able to leverage work from home, remote contact centers, and the like. So, we have to think about how we can accelerate what's happening, and make sure that our workforce, and our customers are all taken care of. So, one of the front seats of this, is of course, companies working to help modernize customers out there. And, Nutanix is part of that discussion. So, I want to welcome to join us for this special discussion of cloud and Nutanix. I have two of our CUBE alumnus. First of all, we have Monica Kumar. She's the senior vice president of product, with Nutanix. And Tarkan Maner, who's a relative newcomer. Second time on theCUBE, in his new role many time guests. Previously, Tarkan is the chief commercial officer with Nutanix. Monica and Tarkan, thank you so much for joining us. >> Thank you so much. So happy to be back on theCUBE. >> Yeah, thank you. >> All right. So, Tarkan as I was teeing up, we know that, IT staffs in general, CIO specifically, and companies overall, are under a lot of pressure in general. But in 2020, there are new pressures on them. So, why don't you explain to us, the special cloud announcement. Tell us, what's Nutanix launching, and why it's so important today. >> So, Stu first of all, thank you. And glad to be here with Monica. And basically you and I, spend some time with a few customers in the past few weeks and months. I'll tell you, the things in our industry are changing at a pace that we never seen before. Especially with this pandemic backdrop, as we're going through. And obviously, all the economic challenges that creates beyond the obviously, health challenges and across the world, all the pain it creates. But also it creates some opportunities for our customers and partners to deliver solutions to our enterprise customers, and commercial customers, and in a public sector customers, in multiple industries. From healthcare, obviously very importantly, to manufacturing, to supply chains, and to all the other industries, including financial services and public sector again. So in that context, Monica knows as well as she's our leader. You know, our strategy, we're putting lots of effort in this new multi-class strategy as a company. As you know, is too well, Nutanix wrote the book, in digital infrastructures with its own private, (mumbles) infrastructure story. Now they're taking that next level, via our data center solutions, via DevOps solutions, and end user computer solutions. Now, the multicloud fashion, working with partners like AWS. So, in this launch, we have our new, hybrid cloud infrastructure, Nutanix Clusters product now available in the AWS. We are super excited. We have more than 20 tech firms, and customers, and partners at sealable executive level support in this big launch. Timing is usually important, because of this pandemic backdrop. And the goal is obviously to help our customers save money, focus what's important for them, save money for them, and making sure they streamlined their IT operation. So it's a huge launch for us. And we're super excited about it. >> Yeah. And the one thing I would add too, what Tarkan said too is, look, we talk to a lot of customers, and obviously cloud is the constant, in terms of enabling innovation. But I think more with COVID, what's on top of mind is also how do we use cloud for innovation? But really be intelligent about cost optimization. So with this new announcement, what we are excited about is we're bringing, making really a hybrid cloud reality, across public and private cloud. But also making sure customers, get the cost efficiency they need, when they're deploying the solution. So we are super excited to bring true hybrid cloud offering with AWS to the market today. >> Well, I can tell you Nutanix cluster is absolutely one of the exciting technologies I've enjoyed, watching and getting ready for. And of course, a partnership with the largest public cloud player out there AWS, is really important. When I think about Nutanix from the earliest days, the word that we always used for the HI Space and Nutanix specifically, was simplicity. Anybody in the tech space know that, true simplicity is really hard to do. When I think about cloud, when I think about multicloud, simplicity is not the first thing that I think of. So, Tarkan has helped us connect, how is Nutanix going to extend the simplicity that it's done, for so long now in the data center, into places like AWS with this solution? >> So, Stu you're spot on. Look, Monica and I spend a lot of time with our customers. One thing about Nutanix executive team, you're very customer-driven. And I'm not just saying this to make a point. We really spent tons of time with them because our solutions are basically so critical for them to run their businesses. So, just recently I was with a senior executive, C level executive of an airline. Right before that, Monica and I spent actually with one of the largest banks in the world in France, in Paris. Right before the pandemic, we were actually traveling. Talking to, not all the CIO, the chief operating officer on one of these huge banks. And the biggest issue was, how these companies are trying to basically adjust their plans, business plans. I'm not talking about tech plans, IT plans, the business plans around this backdrop with the economic stress. And obviously, now pandemic is in a big way. One of the CIOs told me, he was an airline executive. "Look Tarkan, in the next four months, my business might be half of what it is today. And I need to do more with less, in so many different ways, while I'm cutting costs." So it's a tough time. So, in that context is to... Your actually right. Multicloud is in a difficult proposition, but it's critical, for these companies to manage their cost structures across multiple operating models. Cloud to us, is not a destination, it's a means to an ends. It is an operating model. At the end of the day, the differentiation is still the software. The unique software that we provide from digital infrastructures, to deliver, end to end discreet data center solutions, DevOps solutions for developers, as well as for end user computing individuals, to making sure to take advantage of, these VDI decibels service topic capability. So in that context, what we are providing now to this CIOs who are going through, this difficult time is, a platform, in which they can move their workloads from cloud to cloud, based on their needs, with freedom of choice. Look, one of these big banks that Monica and I visited in France, huge global bank. They have a workloads on AWS, they have workload on Azure, they have workloads on Google, workloads on (indistinct), the local XP, they have workloads in Germany. They have workloads providers in Asia, in Taiwan, and other locations. On top of that, they're also using Nutanix on-prem as well as Nutanix cloud, our own cloud services for VR. And then, this is not just in this nation. This is an operating model. So the biggest request from them is, look, can you guys make this cost effective? Can we use, all these operating models and move our data, and applications from cloud to cloud? In simple terms, can we get, some kind of a flexibility with commits as well as we pay credits they paid for so far? And, those are things we're working on. And I'm sure Monica is going to get a little bit more into detail, as we talk to this. You are super excited, to start this journey with AWS, with this launch, but you're not going to stop there. Our goal is, we just kind of discussed with Monica earlier, provide freedom of choice across multiple clouds, both on-prem and off-prem, for our customers to cut costs, and to focus on what's important for them. >> Yeah, and I would just add, to sum it up, we are really simplifying the multicloud complexity for our customers. And I can go into more detail, but that's really the gist of it. Is what Nutanix is doing with this announcement, and more coming up in the future. >> Well, Monica, when I think about customers, and how do they decide, what stays in their data center, what goes into the public cloud? It's really their application portfolio. I need to look at my workloads, I need to look at my skillset. So, when I look at the cluster solution, what are some of the key use cases? What workloads are going to be the first ones that you expect, or you're having customers use with it today? >> Sure. And as we talk to customers too, this clearly few key use cases that they've been trying to, build a hybrid strategy around. The first few ones are bursting into cloud, right? In case of, a demand of sudden demand, how do I burst and scale my, let's say a VDI environment. or database environment into the cloud? So that's clearly one that many of our customers want to be able to do simply, and without having to incur this extreme complexity of managing these environments. Number two, it's about DR, and we saw with COVID, right? Business continuity became a big deal for many organizations. They weren't prepared for it. So the ability to actually spin up your applications and data in the cloud seamlessly, in case of a disaster, that's another big use case. The third one, of which many customers talk about is, can I lift and shift my applications as is, into the cloud? Without having to rewrite a single line of code, or without having to rewrite all of it, right? That's another one. And last but not least, the one that we're also hearing a lot about is, how do I extend my current applications by using cloud native services, that's available on public cloud? So those are four, there's many more, of course. But in terms of workloads, I mentioned two examples, right? VDI, which is Virtual Desktop Infrastructure, and is a computing, and also databases. More and more of our customers, don't want to invest in again having, on-premises data center assets sitting there idly. And, wait for when the capacity surges, the demand for capacity surges, they want to be able to do that in the cloud. So I'd say those are the few use cases and workloads. One thing I want to go back to what Tarkan was talking about, really their three key reasons, why the current hybrid cloud solutions, haven't really panned out for customers. Number one, it's having a unified management environment across public and private cloud. There's a few solutions out there, but none of them have proved to be simple enough, to actually put into real execution. You know, with Nutanix, the one thing you can do is literally build a hybrid cloud within, under an hour. Under an hour, you can spin up Nutanix Clusters, which you have on-premises, the same exact cluster in Amazon, under one hour. There you go. And you have the same exact management plan, that we offer on-prem, that now can manage your AWS Nutanix Clusters. It's that easy, right? And then, you can easily move your data and applications across, if you choose to. You want to move and burst into public cloud? Do it. You want to keep some stuff on-prem? Do it. If you're going to develop in the cloud, do it. Want to keep production on-prem, do it. Single management plan, seamless mobility. And the third point is about cost. Simplicity of managing the costs, making sure you know, how you're going to incur costs. How about, if you can hibernate your AWS cluster when you're not using it? We allow the... We have the capability now in our software to do that. How about knowing, where to place which workload. Which workload goes into public cloud, which stays on-premises. We have an amazing tool called beam, that gives the customers that ability to assess, which is the right cloud for the right workload. So I can go on and on about this. You know, we've talked to so many customers, but this is in a nutshell. You know, the use cases and workloads that we are delivering to customers right out the gate. >> Well, Monica, I'd love to hear a little bit about the customers that have had early access to this. What customer stories can you share? Understand of course? You're probably going to need to anonymize. But, I'd like to understand, how they've been leveraging clusters, the value that they're getting from it. >> Absolutely. We've been working with a number of customers. And I'll give you a few examples. There's a customer in Australia, I'll start with that. And they basically run a big event that happens every five years for them. And that they have to scale something to 24 million people. Now imagine, if they have to keep capacity on site, anticipating the needs for five years in a row, well, they can't do that. And the big event is going to happen next year for them. So they are getting ready with now clusters, to really expand the VDI environments into the cloud, in a big way with AWS. So from Nutanix on-prem to AWS, and expand VDI and burst into the cloud. So that's one example. That's obviously when you have an event-driven capacity bursting into the cloud. Another customer, who is in the insurance business. For them, DR is of course very important. I mean, DR is important for every industry in every business. But for them, they realize that they need to be able to, transparently run the applications in the case of a disaster on the cloud. So they've been using non Nutanix Clusters with AWS to do that. Another customer is looking at lifting and shifting some of the database applications into, AWS with Nutanix, for example. And then we have yet another customer who's looking at retiring, their a part of the data center estate, and moving that completely to AWS, with Nutanix as a backbone, Nutanix Clusters as a backbone. I mean, and we have tons of examples of customers who during COVID, for example, were able to burst capacity, and spin up hundreds and thousands of remote employees, using clusters into AWS cloud. Using Citrix also by the way, as the desktop provider. So again, I can go on, we have tons of customers. There's obviously a big demand for the solution. Because now it's so easy to use. We have customers, really surprised going, "Wait, I now have built a whole hybrid card within an hour. And I was able to scale from, six nodes, to 60 nodes, just like that, on AWS cloud from on-prem six nodes, to 16 in AWS cloud. Our customers are really, really pleasantly surprised with the ease of use, and how quickly they can scale, using clusters in AWS. >> Yeah. Tarkan I have to imagine that, this is a real change for the conversation you have with customers. I mean, Nutanix has been partner with AWS for a number of years. I remember the first time that I saw Nutanix, at the reinvent show. But, cloud is definitely front and center, in a lot of your customer's conversations. So, with your partners, with your customers, has to be just a whole different aspect, to the conversations that you can have. >> Actually Stu, as you heard from Monica too. As I mentioned earlier, this is not just a destination for the customers, right? I know you using these buzzwords, at the end of day, there's an open end model. If it's an open end model they want to take advantage of, to cut costs and do more with less. So in that context, as you heard, even in this conversation, there is many pinpoint in this. Like again, being able to move the workloads from location to location, cost optimize those things, provide a streamlined operations. Again, as Monica suggested, making the apps, and the data relating those apps mobile, and obviously provide built-in networking capabilities. All those capabilities make it easier for them to cut costs. So we're hearing constantly, from the enterprises is small and large, private sector and public sector, nothing different. Clearly they have options. They want to have the freedom of choice. Some of these workloads are going to run on-prem, some of them off prem. And off prem is going to have, tons of different radiations. So in that context, as I mentioned earlier, we have our own cloud as well. We provide 20 plus skews to 17,000 customers around the world. It's a $2 billion software business run rate is as you know. And, a lot of those questions on-prem customers now, also coming to our own cloud services. With cloud partners, we have our own cloud services, with our own billing, payments, logistics, and service capabilities. With a credit card, you can actually, you can do DR. (mumbles) a service to Nutanix itself. But some of these customers also want to go be able to go to AWS, or Azure, or to a local service provider. Sometimes it's US companies, we think US only. But think about this, this is a global phenomenon. I have customers in India. We have customers in Australia as Monica talked about. In China, in Japan, in Germany. And some of these enterprise customers, public sector customers, they want to DR, Disaster Recovery as a service to a local service provider, within the country. Because of the new data governance, laws and security concerns, they don't want the data and us, to go outside of the boundaries of the country. In some cases, in the same continent, if you're in Switzerland, not even forget about the country, the same city. So we want to make sure, we give capabilities for customers, use the cloud as an operating model the way they want. And as part of this, just you know Stu, you're not alone in this, we can not do this alone. We have, tremendous level of partner support as you're going to see in the new announcements. From HP as one of our key partners, Lenovo, AMD, Intel, Fujitsu, Citrix for end user computing. You're partnering with Palo Alto networks for security, Azure partners, as you know we support (indistinct). We have partners like Red Hat, whose in tons of work in the Linux front. We partnered with IBM, we partner with Dell. So, the ecosystem makes it so much easier for our customers, especially with this pandemic backdrop. And I think what you're going to see from Nutanix, more partners, more customer proof points, to help the customers innovate the cut costs, in this difficult backdrop. Especially for the next 24 months, I think what you're going to see is, tremendous so to speak adoption, of this multicloud approach that you're focusing on right now. >> Yeah, and let me add, I know our partner list is long. So Tarkan also, we have the global size, of course. The WebPros, and HCL, and TCS, and Capgemini, and Zensar, you name it all. We're working with all of them to bring clusters based solutions to market. And, for the entire Nutanix stack, also partners like Equinix and Yoda. So it's a long list of partnerships. The one thing I did want to bring up Stu, which I forgot to mention earlier, and Tarkan reminded me is a superior architecture. So why is it that Nutanix can deliver this now to customers, right? I mean, our customers have been trying to build hybrid cloud for a little while now, and work across multiple clouds. And, we know it's been complex. The reason why we are able to deliver this in the way we are, is because of our architecture. The way we've architected clusters with AWS is, it's built in native network integration. And what that means is, if your customer and end user who's a practitioner, you can literally see the Nutanix VMs, in the same space as Amazon VMs. So for a customer, it's in the exact same space, it's really easy to then use other AWS services. And we bypass any, complex and latency issues with networking, because we are exactly part of AWS VPC for the customer. And also, the customers can use by the way, the Amazon credits, with the way we've architected this. And we allow for bringing your own license, by the way. That's the other true part about simplicity is, same license that our customers use on-premises today for Nutanix, can be brought exactly the same way to AWS, if they choose to. And now of course, we do also offer other licensing models that are cloud only. But I want to point out that DVIOL is something that we are very proud of. It's truly enabling, bring your own license to AWS cloud in this case. >> Well, it's interesting, Monica. Of course, one of the things everybody's watched of Nutanix over the last few years is that move, from an appliance primarily to a software model. And, as an industry as a whole, it's much more moving to the cloud model for pricing. And it sounds like, that's the primary model with some flexibility and options that you have, when you're talking about the cluster solution here, is that correct? >> Yeah, we also offer the pay as you go model of course, and cloud as popular. So, customers can decide they just want to pay for the amount they use, that's fine. Or they can bring their existing on-prem license, to AWS. Or we also have a commit model, where they commit for a certain capacity for the year, and they go with that. So we have two or three different kinds of models. Again, going with the freedom of choice for our customers. We offer them different models they can choose from. But to me, the best part is to bring your own license model. That's again, a true hybrid pricing model here. They can choose to use Nutanix where they want to. >> Yeah. Well, and Monica, I'm glad you brought up some of the architectural pieces here. 'Cause you talked about all the partners that you have out there. If I'm sitting in the partner world, I've been heard nothing over the last few years, but I've been inundated by all of the hybrid solutions. So, every public cloud provider, including AWS now, is talking about hybrid solutions. You've got virtualization players, infrastructure players, all talking out there. So, architecture you talked a bit about. Anything else, key differentiators that you want people to understand, as what sets Nutanix apart from the crowd, when it comes to hybrid cloud. >> Well, like I said, it's because of our architecture, you can build a hybrid cloud in under an hour. I mean, prove to me if you can do with other providers. And again, I don't mean that, having that ego. But really, I mean, honestly for our customers, it's all about how can we, speed up a customer's experience to cloud. So, building a cloud under an hour, being able to truly manage it with a single plan, being able to move apps and data, with one click in many cases. And last but not least, the license portability. All of that together. I think the way, (indistinct) I've talked about this as, we may not have been the first to market, but we believe they are the best to market in this space today. That's what I would say. >> Tarkan and I'd love to hear a little bit of the vision. So, with Monica kind of alluded to, anybody that kind of digs underneath the covers is, it's bare metal offerings from the cloud providers that are enabling this technology. There was a certain partnership that AWS had, that enabled this, and now you're taking advantage of it. What do you feel when you look at clusters going forward, give us a little bit what should we be looking for, when it comes to AWS and maybe even beyond. >> Thank you Stu. Actually, is spot on question. Most companies in the space, they follow these buzzwords, right? (indistinct) multicloud. And when you killed on, you and you find out, okay, you support two cloud services, and you actually own some kind of a marketplace. And you're one of the 19,000 services. We don't see this as a multicloud. Our view is, complete freedom of choice. So our vision includes a couple of our private clouds, government clouds success with our customers. We've got enterprise commercial and public sector customers. Also delivered to them choice, with Nutanix is own cloud as I mentioned earlier. With our own billing payment, we're just as capable starting with DR as a service, Disaster Recovery as a service. But take that to next level, the database as a service, with VDI based up as a service, and other services that we deliver. But on top of that also, as Monica talked about earlier, partnerships we have, with service providers, like Yoda in India, a lot going on with SoftBank in Japan, Brooklyn going on with OBH in France. And multiple countries that we are building this XSP (indistinct) telco relationships, give those international customers, choice within that own local region, in their own country, in some cases in their city, where they are, making sure the network latency is not an issue. Security, data governance, is not an issue. And obviously, third leg of this multilayer stool is, hyperscalers themselves like AWS. AWS has been a phenomenal partner, working with Doug (indistinct), Matt Garmin, the executive team under Andy Jassy and Jeff Bezos, biggest super partners. Obviously, that bare metal service capability, is huge differentiator. And with the typical AWS simplicity. And obviously, with Nutanix simplicity coming together. But given choice to our customers as we move forward obviously, our customer set a multicloud strategy. So I'm reading an amazing book called Silk Roads. It's an amazing book. I strongly suggest you all read it. It's all talking about partnerships. Throughout the history, those empires, those countries who have been successful, partnered well, connect the dots well. So that's what we're trying to learn from our own history. Connecting dots with the customers and partners as we talked about earlier. Working with companies that with Wipro. And we over deliver to the end user computer service called, best of a service door to desk. Database as a service, digital data services get that VA to other new services started in HCL and others. So all these things come together as a complete end to end strategy with our partners. So we want to make sure, as we move forward in upcoming weeks and months, you're going to see, these announcements coming up, one partner at a time. And obviously we are going to measure success, one customer at a time as we more forward with the strategy. >> All right. So Monica, you mentioned that if you were an existing Nutanix customer, you can spin up in the public cloud, in under an hour. I guess final question I have for you is, number one, if I'm not yet a Nutanix customer, is this something I could start in the public cloud. and leverage some capabilities? And, whether I'm an existing customer or a prospect, how do I get started with Nutanix Clusters? >> Absolutely. We are all about making it easy for our customers to get started. So in fact, I know seeing is believing. So if you go to nutanix.com today, you'll see we have a link there for something called a test drive. So we are giving our prospects, and customers the ability to go try this out. Either just take a tour, or even do a 30 day free trial today. So they can try it out. They can just get spun up in the cloud completely, and then connect to on-premises if they choose to. Or just, if they choose to stay in public cloud only with Nutanix, that's absolutely the customer choice. And I would say this is really, only the beginning for us as Tarkan was saying. I mean, I'm just really super excited about our future, and how we are going to enable customers, to use cloud for innovation going forward. In a really simple, manner that's cost efficient for our customers. >> All right. Well, Monica and Tarkan, thank you so much for sharing the updates. Congratulations to the team on bringing this solution out. And as you said, just the beginning. So, we look forward to, talking to you, your partners, and your customers going forward. >> Thank you so much. >> Thank you Stu. Thank you, Monica. >> Hi, and welcome back. We've just heard Nutanix's announcement about Nutanix Clusters on AWS, from Monica and Tarkan, And, to help understand some of the specific implications for the Asia Pacific and Japan region. Happy to welcome Justin Hurst, who is the CTO, for APJ with Nutanix. Justin, thanks for joining us. >> Well, thanks Stu. Thanks for having me. >> Absolutely. So, we know Justin of course, 2020, has had a lot of changes, for everyone globally. Heard some exciting news from your team. And, wondering if you can bring us inside the APJ region. And what will the impact specifically be for your customers in your region? >> Yeah, let's say, that's a great question. And, it has been a tremendously unusual year, of course, for everyone. We're all trying, to figure out how we can adapt. And how we can take this opportunity, to not only respond to the situation, but actually build our businesses in a way, that we can be more agile going forward. So, we're very excited about this announcement. And, the new capabilities it's going to bring to our customers in the region. >> Justin, one of the things we talk about is, right now, there's actually been an acceleration of how customers are looking to On-Ramp to the cloud. So when you look at the solution, what's the operational impact of Nutanix Clusters? And that acceleration to the cloud? >> Well, sure. And I think that, is really what we're trying to accomplish here, with this new technology is to take away a lot of the pain, in onboarding to the public cloud. For many customers I talk to, the cloud is aspirational at this point. They may be experimenting. They may have a few applications they've, spun up in the cloud or using a SaaS service. But really getting those core applications, into the public cloud, has been something they've struggled with. And so, by harmonizing the control plan and the data plan, between on-premises and the public cloud, we just completely remove that barrier, and allow that mobility, that's been, something people have really been looking forward to. >> All right, well, Justin, of course, the announcement being with AWS, is the global leader in public cloud. But we've seen the cluster solution, when has been discussed in earlier days, isn't necessarily only for AWS. So, what can you tell us about your customer's adoption with AWS, and maybe what we should look at down the road for clusters with other solutions? >> Yeah, for sure. Now of course, AWS is the global market leader, which is why we're so happy to have this launch event today of clusters on AWS. But with many of our customers, depending on their region, or their regulatory requirements, they may want to work as well, with other providers. And so when we built the Nutanix cluster solution, we were careful not to lock in, to any specific provider. Which gives us options going forward, to meet our customer demands, wherever they might be. >> All right. Well, when we look at cloud, of course, the implications are one of the things we need to think about. We've seen a number of hybrid solutions out there, that haven't necessarily been the most economical. So, what are the financial considerations, when we look at this solution? >> Yeah, definitely. I think when we look at using the public cloud, it's important not to bring along, the same operational mindset, as traditional on-premise infrastructure. And that's the power of the cloud, is the elasticity. And the ability to burst workloads, to grow and to shrink as needed. And so, to really help contain those costs, we've built in this amazing ability, to hibernate workloads. So that customers can run them, when they need them. Whether it's a seasonal business, whether it's something in education, where students are coming and going, for different terms. We've built this functionality, that allows you to take traditional applications that would normally run on-premises 24/7. And give them that elasticity of the public cloud, really combining the best of both worlds. And then, building tooling and automation around that. So it's not just guesswork. We can actually tell you, when to spin up a workload, or where to place a workload, to get the best financial impact. >> All right, Justin, final question for you is, this has been the works on Nutanix working on the cluster solution world for a bit now. What's exciting you, that you're going to be able to bring this to your customers? >> Yeah. There's a lot of new capabilities, that get unlocked by this new technology. I think about a customer I was talking to recently, that's expanding their business geographically. And, what they didn't want to do, was invest capital in building up a new data center, in a new region. Because here in APJ, the region is geographically vast, and connectivity can vary tremendously. And so for this company, to be able to spin up, a new data center effectively, in any AWS region around the world, really enables them to bring the data and the applications, to where they're expanding their business, without that capital outlay. And so, that's just one capability, that we're really excited about. And we think we'll have a big impact, in how people do business. And keeping those applications and data, close to where they're doing that business. >> All right. Well, Justin, thank you so much for giving us a look inside the APJ region. And congratulations to you and the team, on the Nutanix Clusters announcement. >> Thanks so much for having me Stu. >> All right. And thank you for watching I'm Stu Miniman. Thank you for watching theCUBE. (soft music)

>> From around the globe, it's theCUBE. With digital coverage of a special announcement, brought to you by Nutanix. >> Hi, I'm Stu Miniman. And I want to welcome you to this special event that we are doing with Nutanix. Of course, in 2020 many things have changed and that has changed some of the priorities for many companies out there, acceleration of cloud adoption, absolutely has been there. I've talked to many companies that were dipping their toe or thinking about where they were going to the cloud and of course it's rapidly moved to accelerate to be able to leverage work from home, remote contact centers and the like. So we have to think about how we can accelerate what's happening and make sure that our workforce and our customers are all taken care of. So at one of the front seats of this is of course companies working to help modernize customers out there and Nutanix is part of that discussion. So I want to welcome to join us for this special discussion of cloud and Nutanix, I've two of our CUBE alumnis. First of all, we have Monica Kumar, she's the Senior vice President of Product with Nutanix and Tarkan Maner, who's a relative newcomer, second time on theCUBE in his new role, many-time guest previously. Tarkan is the Chief Commercial Officer with Nutanix. Monica and Tarkan, thank you so much for joining us. >> Thank you so much. So happy to be back on theCUBE. >> Yeah, Thank you. >> All right, so Tarkan as I was teeing up, we know that IT staffs in general, CIO specifically, and companies overall, are under a lot of pressure in general, but in 2020, there are new pressures on them. So why don't you explain to us the special cloud announcement, tell us what's Nutanix's launching and why it's so important today. >> So first of all, thank you. Glad to be here with Monica. Basically, you and I spent some time with a few customers in the past few weeks and months. I'll tell you the things in our industry are changing at a pace that we've never seen before, especially with this pandemic backdrop as we're going through. And obviously all the economic challenges that creates beyond the obviously health challenges and across the globe, all the pain it creates, but also create some opportunities for our customers and partners to deliver solutions to our enterprise customers and commercial customers and public sector customers in multiple industries. From healthcare, obviously very importantly, to manufacturing, to supply chains and to all the other industries, including financial services and public sector again. So in that context and Monica knows this well as she's our leader in our strategy, we're putting lots of effort in this new multi-cloud strategy as a company. As you know Stu well, Nutanix wrote the book in digital infrastructures with its own hyperconverged infrastructure story. Now they're taking that next level via our data center solutions, via DevOps solutions and end user computer solutions now in multi-cloud fashion, working with partners like AWS. So in this launch, we have our new hybrid cloud infrastructure, Nutanix Clusters product now available on AWS. We are super excited. We have more than 20 tech firms and customers and partners at senior executive level support in this big launch. Timing is usually important because of this pandemic backdrop. And the goal is obviously to help our customers save money, focus on what's important for them, save money for them and making sure they streamline their IT operations. So it's a huge launch for us and we're super excited about it. >> Yeah, and the one thing I would add to what Tarkan said Stu is, look, we talked to a lot of customers and obviously cloud is the constant in terms of enabling innovation. But I think more with COVID, what's on top of mind is also how do we use cloud for innovation, but really be intelligent about cost optimization. So with this new announcement, what we're excited about is we're making really a hybrid cloud a reality across public and private cloud, but also making sure customers get the cost efficiency they need when they're deploying the solution. So we are super excited to bring true hybrid cloud offering with AWS to the market today. Well, I can tell you Nutanix Clusters is absolutely one of the exciting technologies I've enjoyed watching and getting ready for. And of course, a partnership with the largest public cloud player out there, AWS, is really important. When I think about Nutanix from the earliest days, the word that we always used for the HCI space in Nutanix specifically, was simplicity. Anybody in the tech space know that true simplicity is really hard to do. When I think about cloud, when I think about multi-cloud, simplicity's not the first thing that I think of. So Tarkan, help us connect, how is Nutanix going to extend the simplicity that it's done for so long now in the data center into places like AWS with this solution? >> So, Stu, you're right on, spot on. Look, Monica and I spend a lot of time with our customers. One thing about an Nutanix executive team we're very customer driven, and I'm not just saying this to make a point. We really spent tons of time with them because our solutions are basically so critical for them to run their businesses. So just recently, I was with a senior executive of an airline right before that Monica and I spent time with one of the largest banks in the world in France, in Paris, right before pandemic, we were actually traveling, talking to not only the CIO, the Chief Operating Officer on one of these huge banks, and the biggest issue was how these companies are trying to basically adjust their plans, business plans. I'm not talking about tech plans, IT plans, the business plans around this backdrop that the economic stress and obviously now pandemic is in a big way. One of the CIOs told me, it was an airline executive, "Look, Tarkan, in the next 12 months, my business might be half of what it is today. And I need to do more with less in so many different ways, while I'm cutting cost." So it's a tough time. So in that context is to, you're actually right, multi-cloud is a difficult proposition, but it's critical for these companies to manage their cost structures across multiple operating models. Cloud to us is not a destination. It's a means to an end. It is an operating model. At the end of the day, the differentiation is through the software. The unique software that we provide from digital infrastructures to deliver end to end discreet data center solutions, DevOps solutions for developers, as well as for end user computing individuals, to make you sure to take advantage of these VDI desktop-as-a-service capability. So in that context, what we're providing now, to these CIOs who are going through this difficult time is a platform in which they can move their workloads from cloud to cloud based on their needs, the freedom of choice. Look, one of these big banks that Monica and I visited in France, huge global bank, they have a workloads on AWS, they have workloads on Azure, they have workloads on Google, they have workloads on Trans Telecom, the local SP, they have workloads in Germany, they have workloads on cloud service providers in Asia, in Taiwan and other locations, On top of that, they're also using Nutanix on-prem as well as Nutanix cloud, our own cloud services for DR. And for them, this is not just a destination, this is an operating model. So the biggest request from them is, "Look, can you guys make this cost effective? Can we use all these operating models and move our data and applications from cloud to cloud?" In simple terms, can we get some flexibility with commits as well as with the credits they paid for so far? And those are the things we're working on, and I'm sure Monica is going to get a little bit more into detail as we talk though this. We're super excited to start this journey with AWS with this launch, but we're not going to stop there. Our goal is, we just discussed it with Monica earlier, provide freedom of choice across multiple clouds both on-prem and off-prem for our customers to cut costs and to focus on what's important for them. >> Yeah, and I would just add to sum it up, we are really simplifying the multi-cloud complexity for our customers. And I can go into more details but that's really the gist of it. Is what Nutanix is doing with this announcement and more coming up in the future. >> Well, Monica, when I think about customers and how do they decide what stays in their data center, what goes into the public cloud, it's really their application portfolio. I need to look at my workloads, I need to look at my skillset. So when I look at the Cluster solution, what are some of the key use cases? What workloads are going to be the first ones that you expect or you're having customers use with it today? >> Sure, and as we talk to customer too, there's clearly few key use cases that they've been trying to build a hybrid strategy around. The first few ones are bursting into cloud. In case of sudden demand, how do I burst and scale my, let's say, VDI environment or database environment into the cloud? So that's clearly one that many of our customers want to be able to do simply and without having to incur this extreme complexity of managing these environments. Number two, it's about DR. And we saw it with COVID, business continuity became a big deal for many organizations. They weren't prepared for it. So the ability to actually spin up your applications and data in the cloud seamlessly in case of a disaster, that's another big use case. The third one, which many customers talk about is can I lift and shift my applications as is into the cloud without having to rewrite a single line of code or without having to rewrite all of it? That's another one. And last but not least, the one that we're also hearing a lot about is how do I extend my current applications by using cloud native services that are available on public cloud? So those are four, there's many more, of course, but in terms of workloads, I mentioned two examples, VDI, which is virtual desktop infrastructure, end user computing and also databases. More and more of our customers don't want to invest, in again, having on premises data center assets, sitting there idly and wait for when the capacity surges, the demand for capacity surges, they want to be able to do that in the cloud. So I'd say those are the few use cases and workloads. One thing I want to go back to, what Tarkan was talking about, really there are three key reasons why the current hybrid cloud solutions haven't really panned out for customers. Number one, it's having a unified management environment across public and private cloud. There's a few solutions out there, but none of them have proved to be simple enough to actually put into real execution. With Nutanix, the one thing you can do is literally build a hybrid cloud within under an hour. Under an hour, you can spin up Nutanix Clusters which you have on premises, the same exact Cluster in Amazon. Under one hour. There you go. And you have the same exact management plane that we offer on-prem that now can manage your AWS Nutanix Clusters. It's that easy, right? And then you can easily move your data and applications across, if you choose to. You want to move and burst into cloud, public cloud? Do it. You want to keep some stuff on-prem? Do it. If you want to develop in the cloud, do it. Want to keep production on-prem, do it. Single management plane, seamless mobility. And the third point is about cost. Simplicity of managing the costs making sure you know how are you going to incur costs? How about if you can hibernate your AWS cluster when you're not using it? We have the capability now in our software to do that. How about knowing where to place, which workload, which workload goes into public node, which stays on-premises. We have an amazing tool called Beam that gives the customers that ability to assess which is the right cloud for the right workload. So I can go on and on about this, we've talked to so many customers, but this is in a nutshell, the use cases and workloads that we are delivering to customers right out the gate. >> Well, Monica, I'd love to hear a little bit about the customers that have had an early access to this. What customer stories can you share? Understand, of course, you're probably going to need to anonymize, but I'd like to understand how they've been leveraging Clusters, the value that they're getting from it. >> Absolutely. We've been working with a number of customers. And I'll give you a few examples. There's a customer in Australia. I'll start with that. And they basically run a big event that happens every five years for them. And that they have to scale something to 24 million people. Now imagine if they have to keep capacity on site, anticipating the needs for five years in a row. Well, they can't do that. And the big event is going to happen next year for them. So they're getting ready with our Clusters to really expand the VDI environments into the cloud in a big way with AWS. So from Nutanix on-prem to AWS and expand VDI and burst into the cloud. So that's one example. That's obviously when you have an event driven capacity bursting into the cloud. Another customer who is in the insurance business. For them DR Is of course very important. I mean, DR is important for every industry and every business, but for them they realize that they need to be able to transparently run their applications in the case of a disaster on the cloud. So they've been using Nutanix Clusters with AWS to do that. Another customer is looking at lifting and shifting some of their database applications into AWS with Nutanix, for example. And then we have yet another customer who's looking at retiring a part of the data center estate and moving that completely to AWS with Nutanix as a backbone, Nutanix Clusters as the backbone. I mean, and we have tons of examples of customers who during COVID, for example, were able to burst capacity and spin up remote, hundreds and thousands of remote employees using Clusters into AWS cloud, using Citrix also by the way, as the desktop provider. So again, I can go on, we have tons of customers. There's obviously a big demand for this solution because now it's so easy to use. We have customers really surprised going, "Wait, I have built a whole hybrid cloud within an hour? And I was able to scale from six nodes to 16 nodes just like that on AWS cloud from on prem six nodes to 16 and AWS cloud? Our customers are really, really pleasantly surprised with the ease of use and how quickly they can scale using Clusters in AWS. >> Yeah, Tarkan, I have to imagine that this is a real change for the conversations that you have with customers. I mean, Nutanix has been partnering with AWS for a number of years. I remember the first time that I saw Nutanix at the re:Invent show, but cloud is definitely front and center in a lot of your customer's conversations. So with your partners, with your customers, has to be just a whole different aspect to the conversations that you can have. >> Absolutely, Stu. As you heard from Monica too, as I mentioned earlier, this is not just a destination for the customers. I know you using these buzzwords, at the end of day, it's an operating model. It's an operating model they want to take advantage of to cut costs and do more with less. So in that context, as you heard even in this conversation, there isn't any pain point in this. Like, again, being able to move the workloads from location to location, cost-optimize those things, provide a streamlined operations, again, as Monica suggested, making the apps and the data related to those apps mobile, and obviously provide built-in networking capabilities, all those capabilities make it easier for them to cut costs. So what we're hearing constantly from the enterprises is, small and large, private sector and public sector, nothing different, clearly they have options, they want to have the freedom of choice, some of these workloads are going to run on-prem, some of them off-prem and off-prem is going to have tons of different variations. So in that context, as I mentioned earlier, we have our own cloud as well. We provide 20 plus SKUs to 17,000 customers around the world. There's a $2 billion software business run rate as you know and a lot of those customers, on-prem customers, now are also coming to our own cloud services with cloud partners we have our own cloud services with our own billing, payments, logistics, and service capabilities, fit a credit card, you can do DR it's actually come with this service to Nutanix itself. But some of these customers also want to be able to go to AWS or Azure or to a local service provider. Sometimes as US companies we think US only, but think about this, this is a global phenomenon. I have customers in India. We have customers in Australia as Monica talked about. In China, in Japan, in Germany. And some of these enterprise customers, public sector customers, they want a DR, Disaster Recovery as a service to a local service provider within the country. Because of the new data governance laws and security concerns, they don't want the data and apps to go outside of the boundaries of the country, in some cases in the same town. If you're in Switzerland, forget about the country, the same city. So we want to make sure we give capabilities to customers, use the cloud as an operating model the way they want. And as part of this, Stu, we're not alone on this. We can not do this alone. We have tremendous level of partner support as you're going to see the announcements from HP as one of our key partners, Lenovo, AMD, Intel, Fujitsu, Citrix for end user computing, we're partnering with Palo Alto Networks for security, a slew of partners, as you know we support VMware ESXi. We have partners like Red Hat who's done tons of work in the Linux front, we partnered with IBM, we partnered with Dell. So the ecosystem makes it so much easier for our customers, especially in this pandemic backdrop. And I think what you're going to see from Nutanix, more partners, more customer proof points to help the customers at end of the day to cut costs in this typical backdrop. Especially for the next 24 months, I think what you're going to see is tremendous, so to speak, adoption of this multi-cloud approach that we're focusing on right now. >> Yeah. And let me add, I know a partner list is long. So, Tarkan also we have the global size, of course, the Wipro and HCL and TCS and Capgemini and Zensar, you name it all. We're working with all of them to bring Clusters based solutions to market. And for the entire Nutanix stack, also partners like Equinix and Yotta. So it's a long list of partnerships. The one thing I did want to bring up Stu which I forgot to mention earlier and Tarkan reminded me, is our superior architecture. So why is it that Nutanix can deliver this now to customers? I mean, our customers have been trying to build hybrid cloud for a little while now and work across multiple clouds and we know it's been complex. The reason why we are able to deliver this in the way we are, is because of our architecture. The way we've architected Clusters with AWS it's a built-in native network integration. And what that means is if your customer and end user who's a practitioner, you can literally see the Nutanix VMs in the same space as Amazon VMs. So for a customer, it's in the exact same space, it's really easy to then use other AWS services and we bypass any complex and latency issues with networking because we're exactly part of AWS VPC for the customer. And also, the customers can use by the way, their Amazon credits with the way we've architected this. We allow for bringing your own license, by the way, that's the other true part about, simplicity is same license that our customers use on-premises today for Nutanix can be brought exactly the same way to AWS, if they choose to. And, of course, we do also offer other licensing models that are cloud only, but I want to point out that BYOL is, is something that we're very proud of. It's truly enabling bring your own license to AWS cloud in this case. >> Well, it's interesting, Monica. Of course, one of the things everybody's watched of Nutanix over the last few years is that move from an appliance primarily to a software model and as an industry as a whole, it's much more moving to the cloud model for pricing. And it sounds like that's the primary model with some flexibility and options that you have when you're talking about the Clusters solution here, is that correct? >> Yeah, we also offer the pay as you go model of course, on cloud it's popular. So customers can decide they just want to pay for the amount they use, that's fine, or they can bring their existing on-prem license to AWS, or we also have a commit model where they commit for a certain capacity for the year and they go with that. So we have two or three different kinds of models. Again, going with the freedom of choice for our customers, we offer them different models they can choose from. But to me, the best part is to bring own license model. That's again, a true hybrid pricing model here. They can choose to use Nutanix where they want to. >> Yeah, well, and, and Monica, I'm glad you brought up some of the architectural pieces here. Because you talked about all the partners that you have out there, if I'm sitting in the partner world, I've been heard nothing over the last few years, but I've been inundated by all the hybrid solutions. So every public cloud provider, including AWS now, is talking about hybrid solutions. You've got virtualization players, infrastructure players, all talking out there. So architecture, you talked a bit about, anything else, key differentiators that you want people to understand as what sets Nutanix apart from the crowd when it comes to hybrid cloud? >> Well, like I said, it's because of our architecture, you can build a hybrid cloud in under an hour. I mean, prove to me if you can do with other providers. And again, I don't mean that, having that ego, but really, honestly for our customers, it's all about how can we speed up a customer's experience to cloud. So building a cloud under an hour, being able to truly manage it with a single plane, being able to move apps and data with one click in many cases and last but not least the license portability, all of that together, I think the way, Dheeraj our CEO sums it and Tarkan have talked about this is, we may not have been the first to market, but we believe we're the best to market in this space today. That's what I would say. >> Now, Tarkan, I'd love to hear a little bit of the vision. So as Monica alluded to, anybody that digs underneath the covers it's bare metal offerings from the cloud providers that are enabling this technology. There was a certain partnership that AWS had that enabled this and now you're taking advantage of it. When you look at Clusters going forward, give us a little bit, what should we be looking for when it comes to AWS and maybe even beyond? >> Thank you, Stu, actually spot on question. Most companies in this space, they follow these buzzwords like, "Oh, multi-cloud." And when you drill-down and you find out, okay, you support two cloud services and you actually own some kind of a marketplace and you're one of the 19,000 services, you don't see this as a multi-cloud. Our view is complete freedom of choice. So our vision includes a couple of our private clouds, government cloud success with our customers, with enterprise, commercial and public sector customers also delivered to them choice with Nutanix's own cloud, as I mentioned earlier, with our own billing payment, logistics capabilities starting with DR as a service, disaster recovery as a service. But take that next level, the database as a service, VDI, desktop as a service and other services that we deliver. But on top of that, also as Monica talked about earlier, partnerships we have with service providers like Yotta in India, work going on with SoftBank in Japan, work going on with OVH in France and multiple countries that we're building this XSP service provider- customer relationships, give those international customers choice within their own local region in their own country, in some cases, even in their city where they are making sure the network latency is not an issue, security, data governance is not an issue. And obviously, third leg of this multi legged stool is hyperscalers themselves, like AWS. AWS has been a phenomenal partner working with Doug Hume, Matt Garmin, the executive team under Andy Jassy and Jeff Bezos they're just super partners, obviously that bare metal service capability is huge differentiator and typical AWS simplicity, and obviously data simplicity coming together, but giving choice to our customers has we move forward, obviously our customers have a multi-cloud strategy. So I'm reading an amazing book called "Silk Roads." It's an amazing book. I strongly suggest you all read it. It's all talking about partnerships. Throughout history, those empires, those countries who've been successful, partnered well, connect dots well. So that's what we're trying to learn from our own history, connecting the dots with the customers and partners as we talked about earlier, working with companies like Wipro and we all deliver an end user computing service called desktop-as-a-service virtual desk, database as a service, digital data services we have, few other new services started in HCL and others. So all these things come up together as a complete end to end strategy with our partners. So we want to make sure as we move forward, in upcoming weeks and months, your going to see these announcements coming up one partner at a time and obviously we're going to measure success one customer at a time as we move forward with this strategy. >> All right, so Monica, you mentioned that if you were an existing Nutanix customer, you can spin up in the public cloud in under an hour, I guess final the question I have for you is number one, if I'm not yet a Nutanix customer, is this something I could start in the public cloud and leverage some capabilities and whether I'm an existing customer or a prospect, how do I get started with Nutanix Clusters? >> Absolutely, we're all about making it easy for our customers to get started. So in fact, I know seeing is believing, so if you go to nutanix.com today, you'll see we have a link there for something called a test drive. So we are giving our prospects and customers the ability to go try this out, either just take a tour or even do a 30 day free trial today. So they can try it out, they can just get spun up in the cloud completely and then connect on premises if they choose to, or if they just sustain public cloud only with Nutanix, that's absolutely the customer choice. And I would say, this is really only the beginning for us as Tarkan saying. Our future, I mean, I'm just really super excited about our feature and how we're going to enable customers to use cloud for innovation going forward in a really simple manner that's cost efficient for our customers. >> All right. Well, Monica and Tarkan, thank you so much for sharing the updates. Congratulations to the team on bringing this solution out. And as you said, just the beginning so we look forward to talking to you, your partners and your customers going forward. >> Thank you so much. >> Thank you, Stu, thank you, Monica. >> All right, for Tarkan and Monica, I'm Stu Miniman with theCUBE. Thank you as always for watching this special Nutanix announcement. (upbeat music)

>> From around the globe, it's theCUBE. With digital coverage, have a special announcement, brought to you by Nutanix. >> Hi, I'm Stu Miniman. And I want to welcome you to this special event that we are doing with Nutanix. Of course, in 2020 many things have changed and that has changed some of the priorities for many companies out there, acceleration of cloud adoption, absolutely have been there. I've talked to many companies that were dipping their toe or thinking about where they were going to the cloud and of course it's rapidly moved to accelerate to be able to leverage work from home, remote contact centers and the like. So we have to think about how we can accelerate what's happening and make sure that our workforce and our customers are all taken care of. So at one of the front seats of this is of course companies working to help modernize customers out there and Nutanix is part of that discussion. So I want to welcome to join us for this special discussion of cloud and Nutanix, I've two of our CUBE alumnis. First of all, we have Monica Kumar, she's the Senior vice President of Product with Nutanix and Tarkan Maner, who's a relative newcomer, second time on theCUBE in his new role, many-time guest previously. Tarkan is the Chief Commercial Officer with Nutanix. Monica and Tarkan, thank you so much for joining us. >> Thank you so much. So happy to be back on theCUBE. >> Yeah, Thank you. >> All right, so Tarkan as I was teeing up, we know that IT staffs in general, CIO specifically, and companies overall, are under a lot of pressure in general, but in 2020, there are new pressures on them. So why don't you explain to us the special cloud announcement, tell us what's Nutanix's launching and why it's so important today. >> So first of all, thank you. Glad to be here with Monica. Basically, you and I spent some time with a few customers in the past few weeks and months. I'll tell you the things in our industry are changing at a pace that we've never seen before, especially with this pandemic backdrop as we're going through. And obviously all the economic challenges that creates beyond the obviously health challenges and across the globe, all the pain it creates, but also create some opportunities for our customers and partners to deliver solutions to our enterprise customers and infomercial customers and public sector customers in multiple industries. From healthcare, obviously very importantly, to manufacturing, to supply chains and to all the other industries, including financial services and public sector again. So in that context and Monica knows this well as she's our leader in our strategy, we're putting lots of effort in this new multi-cloud strategy as a company. As you know too well, Nutanix wrote the book in digital infrastructures with its own hybrid infrastructure story. Now they're taking that next level via our data center solutions, via DevOps solutions and end user computer solutions now in multi-cloud fashion, working with partners like AWS. So in this launch, we have our new multi-cloud infrastructure, clusters product now available on AWS. We are super excited. We have more than 20 tech firms and customers and partners at senior executive level support in this big launch. Timing is usually important because of this pandemic backdrop. And the goal is obviously to help our customers save money, focus on what's important for them, save money for them and making sure they streamline their IT operations. So it's a huge launch for us and we're super excited about it. >> Yeah, and the one thing I would add to what Tarkan said too is, look, we talked to a lot of customers and obviously cloud is the constant in terms of enabling innovation. But I think more with COVID, what's on top of mind is also how do we use cloud for innovation, but really be intelligent about cost optimization. So with this new announcement, what we're excited about is we're making really a hybrid cloud a reality across public and private cloud, but also making sure customers get the cost efficiency they need when they're deploying the solution. So we are super excited to bring true hybrid cloud offering with AWS to the market today. >> Well, I can tell you Nutanix cluster is absolutely one of the exciting technologies I've enjoyed watching and getting ready for. And of course, a partnership with the largest public cloud player out there, AWS, is really important. When I think about Nutanix from the earliest days, the word that we always used for the HI space in Nutanix specifically, was simplicity. Anybody in the tech space know that true simplicity is really hard to do. When I think about cloud, when I think about multi-cloud, simplicity's not the first thing that I think of. So Tarkan, help us connect, how is Nutanix going to extend the simplicity that it's done for so long now in the data center into places like AWS with this solution? >> So, Stu, you're right on, spot on. Look, Monica and I spend a lot of time with our customers. One thing about an Nutanix executive team we're very customer driven, and I'm not just saying this to make a point. We really spent tons of time with them because our solutions are basically so critical for them to run their businesses. So just recently, I was with a senior executives of an airline right before that Monica and I spent actually with one of the largest banks in the world in France, in Paris, right before pandemic, we were actually traveling, talking to not only the CIO, the Chief Operating Officer on one of these huge banks, and the biggest issue was how these companies are trying to basically adjust their plans, business plans. I'm not talking about tech plans, IT plans, the business plans around this backdrop that the economic stress and obviously now pandemic is in a big way. One of the CIOs told me, it was an airline executive, "Look, Tarkan, in the next 12 months, my business might be half of what it is today. And I need to do more with less in so many different ways, while I'm cutting cost." So it's a tough time. So in that context is to, you're actually right, multi-cloud is a difficult proposition, but it's critical for these companies to manage their cost structures across multiple operating models. Cloud to us is not a destination. It's a means to an end. It is an operating model. At the end of the day, the differentiation is to the software. The unique software that we provide from digital infrastructures to deliver end to end discreet data center solutions, DevOps solutions for developers, as well as for end user computing individuals, to make you sure to take advantage of these EDI disability service topic capability. So in that context, what we're providing now, to these CIOs who are going through this difficult time is a platform in which they can move their workloads from cloud to cloud based on their needs, the freedom of choice. Look, one of these big banks that Monica and I visited in France, huge global bank, they have a workloads on AWS, they have workloads on Azure, they have workloads on Google, they have workloads on (mumbles), the local XP, they have workloads in Germany, they have workloads on cloud service providers in Asia, in Taiwan and other locations, On top of that, they're also using Nutanix on Prem as well as Nutanix cloud, our own cloud services for BR. And for them, this is not just a destination, this is an operating model. So the biggest request from them is, "Look, can you guys make this cost effective? Can we use all these operating models and move our data and applications from cloud to cloud?" In simple terms, can we get some flexibility with commits as well as with the credits they paid for so far? And those are the things we're working on, and I'm sure Monica is going to get a little bit more into detail as we talk though this. We're super excited to start this journey with AWS with this launch, but we're not going to stop there. Our goal is, we just discussed it with Monica earlier, provide freedom of choice across multiple clouds both on Prem and off Prem for our customers to cut costs and to focus on what's important for them. >> Yeah, and I would just add to sum it up, we are really simplifying the multi-cloud complexity for our customers,. And I can go into more details but that's really the gist of it. Is what Nutanix is doing with this announcement and more coming up in the future. >> Well, Monica, when I think about customers and how do they decide what stays in their data center, what goes into the public cloud, it's really their application portfolio. I need to look at my workloads, I need to look at my skillset. So when I look at the cluster solution, what are some of the key use cases? What workloads are going to be the first ones that you expect or you're having customers use with it today? >> Sure, and as we talk to customer too, there's clearly few key use cases that they've been trying to build a hybrid strategy around. The first few ones are bursting into cloud. In case of sudden demand, how do I burst and scale my let's say a VDI environment or database environment into the cloud? So that's clearly one that many of our customers want to be able to do simply and without having to incur this extreme complexity of managing these environments. Number two, it's about DR. And we saw it with COVID, business continuity became a big deal for many organizations. They weren't prepared for it. So the ability to actually spin up your applications and data in the cloud seamlessly in case of a disaster, that's another big use case. The third one, which many customers talk about is can I lift and shift my applications as is into the cloud without having to rewrite a single line of code or without having to rewrite all of it? That's another one. And last but not least, the one that we're also hearing a lot about is how do I extend my current applications by using cloud native services that's available on public cloud? So those are four, there's many more, of course, but in terms of workloads, I mentioned two examples, VDI, which is virtual desktop infrastructure, and there's a computing and also databases. More and more of our customers don't want to invest, in again, having on premises data center assets, sitting there idlely and wait for when the capacity surges, the demand for capacity surges, they want to be able to do that in the cloud. So I'd say those are the few use cases and workloads. One thing I want to go back to, what Tarkan was talking about, really there're three key reasons why the current hybrid cloud solutions haven't really panned out for customers. Number one, it's having a unified management environment across public and private cloud. There's a few solutions out there, but none of them have proved to be simple enough to actually put into real execution. With Nutanix, the one thing you can do is literally build a hybrid cloud within under an hour. Under an hour, you can spin up new data clusters which you have on premises, the same exact cluster in Amazon. Under one hour. There you go. And you have the same exact management plan that we offer on Prem that now can manage your AWS Nutanix clusters. It's that easy, right? And then you can easily move your data and applications across, if you choose to. You want to move and burst into cloud, public cloud? Do it. You want to keep some stuff on prem? Do it. If you want to develop in the cloud, do it. Want to keep production on prem, do it. Single management plan, seamless mobility. And the third point is about cost. Simplicity of managing the costs making sure you know how are you going to incur costs? How about if you can hibernate your AWS cluster when you're not using it? We have the capability now in our software to do that. How about knowing where to place, which workload, which workload goes into public node, which stays on premises. We have an amazing tool called beam that gives the customers that ability to assess which is the right cloud for the right workload. So I can go on and on about this, we've talked to so many customers, but this is in a nutshell, the use cases and workloads that we are delivering to customers right out the gate. >> Well, Monica, I'd love to hear a little bit about the customers that have had an early access to this. What customer stories can you share? Understand, of course, you're probably going to need to anonymize, but I'd like to understand how they've been leveraging clusters, the value that they're getting from it. >> Absolutely. We've been working with a number of customers. And I'll give you a few examples. There's a customer in Australia. I'll start with that. And they basically run a big event that happens every five years for them. And that they have to scale something to 24 million people. Now imagine if they have to keep capacity on site, anticipating the needs for five years in a row. Well, they can't do that. And the big event is going to happen next year for them. So they're getting ready with our clusters to really expand the VDI environments into the cloud in a big way with AWS. So from Nutanix on prem to AWS and expand VDI and burst into the cloud. So that's one example. That's obviously when you have an event driven capacity bursting into the cloud. Another customer who is in the insurance business. For them DR Is of course very important. I mean, DR is important for every industry and every business, but for them they realize that they need to be able to transparently run their applications in the case of a disaster on the cloud. So they've been using Nutanix clusters with AWS to do that. Another customer is looking at lifting and shifting some of their database applications into AWS with Nutanix, for example. And then we have yet another customer who's looking at retiring a part of the data center estate and moving that completely to AWS with Nutanix as a backbone, Nutanix clusters as the backbone. I mean, and we have tons of examples of customers who during COVID, for example, were able to burst capacity and spin up remote, hundreds and thousands of remote employees using clusters into AWS cloud, using Citrix also by the way, as the desktop provider. So again, I can go on, we have tons of customers. There's obviously a big demand for this solution because now it's so easy to use. We have customers really surprised going, "Wait, I have built a whole hybrid cloud within an hour? And I was able to scale from six nodes to 16 nodes just like that on AWS cloud from on prem six nodes to 16 and AWS cloud? Our customers are really, really pleasantly surprised with the ease of use and how quickly they can scale using clusters in AWS. >> Yeah, Tarkan, I have to imagine that this is a real change for the conversations that you have with customers. I mean, Nutanix has been partnering with AWS for a number of years. I remember the first time that I saw Nutanics at the re:Invent show, but cloud is definitely front and center in a lot of your customer's conversations. So with your partners, with your customers, has to be just a whole different aspect to the conversations that you can have. >> Absolutely, Stu. As you heard from Monica too, as I mentioned earlier, this is not just a destination for the customers. I know you using these buzzwords, at the end of day, it's an operating model. It's an operating model they want to take advantage of to cut costs and do more with less. So in that context, as you heard even in this conversation, there's any pain point in this. Like, again, being able to move the workloads from location to location, cost-optimize those things, provide a streamlined operations, again, as Monica suggested, making the apps and the data related to those apps mobile, and obviously provide built-in networking capabilities, all those capabilities make it easier for them to cut costs. So what we're hearing constantly from the enterprises is, small and large, private sector and public sector, nothing different, clearly they have options, they want to have the freedom of choice, some of these workloads are going to run on prem, some of them off prem and off prem is going to have tons of different reactions. So in that context, as I mentioned earlier, we have our own cloud as well. We provide 20 plus skells to 17,000 customers around the world. There's a $2 billion software business run rate as you know and a lot of those customers, prem customers, now are also coming to our own cloud services with cloud partners we have our own cloud services with our own billing, payments, logistics, and service capabilities, fit a credit card, you can do DR it's actually come with this service to Nutanix itself. But some of these customers also want to go be able to go to AWS or Azure or to a local service provider. Sometimes as US companies we think US only, but think about this, this is a global phenomenon. I have customers in India. We have customers in Australia as Monica talked about. In China, in Japan, in Germany. And some of these enterprise customers, public sector customers, they want a DR, Disaster Recovery as a service to a local service provider within the country. Because of the new data governance laws and security concerns, they don't want the data and us to go outside of the boundaries of the country, in some cases in the same town. If you're in Switzerland, forget about the country, the same city. So we want to make sure we give capabilities to customers, use the cloud as an operating model the way they want. And as part of this, Stu, we're not alone on this. We can not do this alone. We have tremendous level of partner support as you're going to see the announcements from HP as one of our key partners, Lenovo, AMD, Intel, Fujitsu, Citrix for end user computing, we're partnering with Palo Alto Networks for security, a slew partners, as you know we support VMware is excited, We have partners like Red Hat who's done tons of work in the Linux front, we partnered with IBM, we partnered with Dell. So the ecosystem makes it so much easier for our customers, especially in this pandemic backdrop. And I think what you're going to see from Nutanix, more partners, more customer proof points to help the customers at of the day to cut costs in this typical backdrop. Especially for the next 24 months, I think what you're going to see is tremendous, so to speak, adoption of this multi-cloud approach that we're focusing on right now. >> Yeah. And let me add, I know a partner list is long. So Tarkan also, we have the global size, of course, the WebPros and FCL and TCS and Capgemini and Zinsser, you name it all. We're working with all of them to bring clusters based solutions to market. And for the entire Nutanix stack, also partners like Equinix and Yoda. So it's a long list of partnerships. The one thing I did want to bring up still, which I forgot to mention earlier and Tarkan reminded me, is our superior architecture. So why is it that Nutanix can deliver this now to customers? I mean, our customers have been trying to build hybrid cloud for a little while now and work across multiple clouds and we know it's been complex. The reason why we are able to deliver this in the way we are, is because of our architecture. The way we've architected clusters with AWS it's built-in native network integration. And what that means is if your customer and end user who's a practitioner, you can literally see the Nutanix VMs in the same space as Amazon VMs. So for a customer, it's in the exact same space, it's really easy to then use other AWS services and we bypass any complex and latency issues with networking because we're exactly part of AWS VPC for the customer. And also, the customers can use by the way, their Amazon credits with the way we've architected this. We allow for bringing your own license, by the way, that's the other true part about, simplicity is same license that our customers use on premises today for Nutanix can be brought exactly the same way to AWS, if they choose to. And, of course, we do also offer other licensing models that are cloud only, but I want to point out that (indistinct) is, is something that we're very proud of. It's truly enabling bring your own license to AWS cloud in this case. >> Well, it's interesting, Monica. Of course, one of the things everybody's watched of Nutanix over the last few years is that move from an appliance primarily to a software model and as an industry as a whole, it's much more moving to the cloud model for pricing. And it sounds like that's the primary model with some flexibility and options that you have when you're talking about the cluster solution here, is that correct? >> Yeah, we also offer the pay as you go model of course, on cloud it's popular. So customers can decide they just want to pay for the amount they use, that's fine, or they can bring their existing on prem license to AWS, or we also have a commit model where they commit for a certain capacity for the year and they go with that. So we have two or three different kinds of models. Again, going with the freedom of choice for our customers, we offer them different models they can choose from. But to me, the best part is to bring own license model. That's again, a true hybrid pricing model here. They can choose to use Nutanix where they want to. >> Yeah, well, and, and Monica, I'm glad you brought up some of the architectural pieces here. 'Cause you talked about all the partners that you have out there, if I'm sitting in the partner world, I've been heard nothing over the last few years, but I've been inundated by all the hybrid solutions. So every public cloud provider, including AWS now, is talking about hybrid solutions. You've got virtualization players, infrastructure players, all talking out there. So architecture, you talked a bit about, anything else, key differentiators that you want people to understand as what sets Nutanix apart from the crowd when it comes to hybrid cloud? >> Well, like I said, it's because of our architecture, you can build a hybrid cloud in under an hour. I mean, prove to me if you can do with other providers. And again, I don't mean that, having that ego, but really, honestly for our customers, it's all about how can we speed up a customer's experience to cloud. So building a cloud under an hour, being able to truly manage it with a single plane, being able to move apps and data with one click in many cases and last but not least the license portability, all of that together, I think the way, Durage RCO sums it and Tarkan have talked about this is, we may not have been the first to market, but we believe we're the best to market in this space today. That's what I would say. >> Now, Tarkan, I'd love to hear a little bit of the vision. So as Monica alluded to, anybody that digs underneath the covers it's bare metal offerings from the cloud providers that are enabling this technology. There was a certain partnership that AWS had that enabled this and now you're taking advantage of it. When you look at clusters going forward, give us a little bit, what should we be looking for when it comes to AWS and maybe even beyond? >> Thank you, Tsu, actually is spot on question. Most companies in this space, they follow these buzzwords like, "Oh, multi-cloud." And when you (indistinct) down and you find out, Okay, you support two cloud services and you actually own some kind of a marketplace and you're one of the 19,000 services, you don't see this as a multi-cloud. Our view is complete freedom of choice. So our vision includes a couple of our private clouds, government cloud success with our customers, with enterprise, commercial and public sector customers also delivered to them choice with Nutanix's own cloud, as I mentioned earlier, with our own billing payment, we'll just escapable these started with DR as a service, disaster recovery as a service. But take that next level, the database as a service, VDI, desktop as a service and other services that we deliver. But on top of that, also as Monica talked about earlier, partnerships we have with service providers like Yoda in India, work going on with SoftBank in Japan, work going on with OVH in France and multiple countries that we're building this XSP service provider- customer relationships, give those international customers choice within their own local region in their own country, in some cases, even in their city where they are making sure the network latency is not an issue, security, data governance is not an issue. And obviously, third leg of this multi legged stool is hyperscalers themselves, like AWS. AWS has been a phenomenal partner working with Hume, Matt Garmin, the executive team under Andy Jassy and Jeff Bezos they're just super partners, obviously that bare metal service capability is huge differentiator and typical AWS simplicity, and obviously data simplicity coming together, but giving choice to our customers has we move forward, obviously our customers have a multi-cloud strategy. So I'm reading an amazing book called "Silk Roads." It's an amazing book. I strongly suggest you all read it. It's all talking about partnerships. Throughout history, those empires, those countries who've been successful, partnered well, connect dots well. So that's what we're trying to learn from our own history, connecting the dots with the customers and partners as we talked about earlier, working with companies like WebPro and we all deliver an end user company service called database service go to desk, database as a service, digital data services with MBA, few other new services started in HCL and others. So all these things come up together as a complete end to end strategy with our partners. So we want to make sure as we move forward, in upcoming weeks and months, your going to see these announcements coming up one partner at a time and obviously we're going to measure success one customer at a time as we move forward with this strategy. >> All right, so Monica, you mentioned that if you were an existing Nutanix customer, you can spin up in the public cloud in under an hour, I guess final the question I have for you is number one, if I'm not yet a Nutanix customer, is this something I could start in the public cloud and leverage some capabilities and whether I'm an existing customer or a prospect, how do I get started with Nutanix clusters? >> Absolutely, we're all about making it easy for our customers to get started. So in fact, I know seeing is believing, so if you go to nutanix.com today, you'll see we have a link there for something called a test drive. So we are giving our prospects and customers the ability to go try this out, either just take a tour or even do a 30 day free trial today. So they can try it out, they can just get spun up in the cloud completely and then connect on premises if they choose to, or if they just sustain public cloud only with Nutanix, that's absolutely the customer choice. And I would say, this is really only the beginning for us as Tarkan saying. Our future, I mean, I'm just really super excited about our feature and how we're going to enable customers to use cloud for innovation going forward in a really simple manner that's cost efficient for our customers. >> All right. Well, Monica and Tarkan, thank you so much for sharing the updates. Congratulations to the team on bringing this solution out. And as you said, just the beginning so we look forward to talking to you, your partners and your customers going forward. >> Thank you so much. >> Thank you, Stu, thank you, Monica. >> All right, for Tarkan and Monica, I'm Stu Miniman with theCUBE. Thank you as always for watching this special Nutanix announcement. (upbeat music)