>> Narrator: From around the globe, it's theCUBE with digital coverage of Dell Technologies World Digital Experience brought to you by Dell Technologies. >> Hi welcome back everybody, Jeff Frick here with theCUBE coming to you from our Palo Alto studios, with our ongoing coverage of the Dell Technology World 2020, the digital experience, we can't be together this year, but we can still get together this way. And we're excited for our very next segment, really talking about one of the big leverage points that the Dell VMware relationship can result in, so we're excited. Joining us our next guest is Guy Bartram, he is the Director of Product Marketing for Cloud Director, for VMware. Guy great to see you, where are you coming in from? >> Thanks for having me on Jeff. >> Where are you coming in from today? (Guy chuckles) >> So this yeah, this London for me, this is from London. >> Excellent, great to see you. >> In the UK. >> And also joining us, Doug Lieberman, he is the Global Solutions Director for Dell Technology, Doug, great to see you, where are you coming in from today? >> Well, thanks for having me, I'm calling in from just outside of Philadelphia, Pennsylvania in the United States. >> Excellent, love Philly's lived there for a couple of years and man, there's some terrific food in that part of the world, I tell yah. So let's get into--- >> You say--- >> Are you Pat's or Geno's. >> Actually I'll eat either one but I think I prefer Pat. >> Okay buddy, I used to get one of each and eat half and half and piss people off that were the purest, but that's a difference--- >> That's the right way to do it. (Jeff and Guy laughs) >> Right, so let's get into it, you know, before we turned on the cameras, you guys were talking about this exciting announcement that you've been working on for a really long time. So before we get kind of into the depths and the importance, why don't we just go ahead and tell us, what is the big announcement that we're sharing today? Go to you Guy. >> And so VMware and Dell really have worked together and we both have partner programs that are focused on service providers, Cloud Service providers, and systems integrators and strategic outsourcers. And what we've done is work together to build a solution that is really targeted towards them in the cloud arena, so taking our cloud capabilities and solutions and optimizing it for cloud providers and doing that through what we call, leveraging our Dell Technologies Cloud Platform and putting VMware Cloud Director on top of that. >> So that's pretty amazing, and really, to you Guy, what does that enable Cloud Service providers to do that they couldn't do so well before? >> It brings a whole lot of benefits to a Cloud Service provider, I mean, for cloud providers, historically they've had to have infrastructure services that've been, you know, quite heavy for them to build, taken a long time to get the market, and really had a high burn and operational costs and this solution VMware Cloud Director on Dell Technologies Cloud Platform is going to bring them the multitenancy aspects of cloud director and all of the speed and efficiencies in application and infrastructure delivery to enable them to address the common need now around hybrid cloud management and hybrid cloud operations. >> And you talked about before, I'm sorry, go ahead, Doug. >> No, I was saying, you know, I think that the big key piece is that, there're special requirements that cloud providers really need from their infrastructure, from their cloud, that makes it special to their business model, and what this aims to do, is to provide those capabilities in a easily consumable and rapid implementation format so that they can get to revenue faster and they can get to higher level services faster. >> It's funny, you talked about getting to revenue faster, back in the day I worked at Intel and Craig Barrett was famous for TTM. TTM, everyone used to think it was time to market bringing a new product to market, and he said, no, no, no, it's time to money, right, how fast can you get operational, so that you can basically get this thing to start generating revenue, I always think of that when you look at seven 37 sitting at a gate, you know, how do you get it operational? So Doug, what were some of those special challenges that they have in their market and how are you helping them solve them? >> So it's a great question, Jeff, as we work with service providers all over the world, they've given us a consistent message, that the days of the value in their service being, how they build the underlying cloud and how they do that orchestration automation are really behind us, right, they're expecting today, an end to end capability delivered as sort of an appliance for that underlying infrastructure for the cloud components, so that they can focus on the higher level services and the things that provide more value and more margin for them, and so, you know, the as a service offerings that run on top of the underlying cloud. And so what this joint solution does is really provide a validated design so that they can redirect their engineering resources from figuring out how to make that base cloud work in a service provider format, with multitenancy, chargeback, showback, portals, et cetera, and get that up and running faster and not have to worry about how to automate all that themselves, so they can focus their engineering efforts on those higher level services that provide greater value to their bottom line, to be honest, >> Great, that's great, and Guy, I want to go back to you, you know, the Cloud Service providers probably don't get as much of publicity as you know, we hear all the time about the big public Cloud Providers, you know, the big three or four or however you want to count them and we hear a lot about data centers and staff migrating between those two, we don't hear a lot of conversation in kind of the hybrid or the multicloud discussion about the role of the smaller Cloud Service providers. So I wonder if you can share a little bit about how they play in the market, you know why this is a really important segment for everyone's, you know, kind of architecture and ability to deliver applications. >> That's great common, I mean, one of the things we tend to call on our partners internally is the fall of mega cloud, that you know you really haven't heard of, there's 4,000 partners in our partner program and all of them are providing very valuable cloud services. They provide cloud services they've in all areas of cloud, so this could be into Azure, Google, AWS or in their own data centers, and many of them have come from infrastructure rich environments or what we call asset heavy environments and delivering services in these environments. The recent kind of drive to cloud adoption and digital transformation has meant that there's been a growing demand for Cloud Service providers to deliver valuable managed services and professional services to help customer do that digital transformation and really help the customer identify, where their customer's workloads, would be best apt and running. And, you know, cloud providers specialize in delivering these services like Doug was saying, they're looking at that higher value and they brought a lot of skills and capability in those areas. >> That's great, 'cause it's really good to keep in mind they pay a really important role in this whole thing. And Doug I want to go back to you in terms of working together with VMware in the solution space, right, so it's one thing to talk about a relationship between two companies, it's one thing to see Michael Dell and Pat Gelsinger on stage together, it's a whole nother deal to get together and put in the investment in these joint solutions. So I wonder if you could share a little bit more color on not only today's announcement, but what this really means for you guys going forward and more importantly, your customers, and ultimately your customer's customers. >> Absolutely, so Dell and VMware are both committed to really driving the success of our Cloud Provider partners all over the world, and to do that, we recognize that there's an additional level of capabilities that we need to bring together and jointly do that. And so we agreed to work together to go build a series of capabilities that are really targeted at going beyond just the basic HCI market and the basic cloud market and extending that for capabilities that are targeted specifically and built specifically for our service providers. And so this solution that we're announcing today is the first step on a journey, but we both committed to and made investments in, continuing that and adding more and more capabilities as we move forward and really addressing that very specific market. And working with our Cloud Service provider partners to figure out what is the next step, what do they need from us, at the end of the day, we're looking to jointly help them be more successful and accelerate their time to market and their go to market capabilities. >> Right, that's great, and Guy back to you, you actually had some numbers, some IDC numbers that you can share in terms of some of the real measurable benefits of this. >> That's right Jeff, yeah, we have, IDC did a recent analysis for us with about 12 partners interviewed across the globe, and some of the results that came back were pretty astounding actually, this pay-for is available on our VCE product page on vmware.com. But just as kind of summarize, you know, we talk about getting to revenue faster, they found that on average service providers were able to onboard customers, i.e migrate them, into their cloud environment around 72% faster, 57% faster delivery of new services and we all know that, you know, portfolio and construction of services takes a long time, but you get business units to buy in to give it support services, so 57% faster delivery of services is incredible. And then, you know, obviously getting to revenue 32% more revenue from VCD services than without VCD and 51% overall more growth with VCD from things like more efficient operations, which are also marked at like 31%. So, you know, significant advantages to having Cloud Director bringing those economies of scale, bringing that capability to migrate from a customer premise into service providers cloud, and then obviously be able to utilize multiple larger clouds across multiple regions. >> That's great, and Doug, I wonder if you could share, are there some specific applications that are driving this more than others, is there any particular kind of subset of the solutions that you can highlight where you're getting the most demand and where you see kind of the both short term opportunity as well as mid and longterm opportunity? >> A great question, I think it really evolves around a couple of different aspects. So one is from a pure security standpoint and things like data sovereignty, we're seeing an increased demand for the service providers that are our partners, as in the ecosystem of cloud, there will always be a role for the hyperscaler clouds as well as the role of these independent Cloud Service providers that are at the next tier down, both for the data sovereignty issues, things like GDPR, but as well as kind of that personal feel, that personal touch and specialty in applications, some of the specific areas we're seeing are things like business process management capabilities, database as a service, VDI as a service, but even more critically things like cyber recovery and backup as a service we're seeing, especially in the current situation that we're in, really an uptick in the cyber attacks and the ransomware, et cetera, and so solutions such as our cyber recovery are critical in those capabilities and those higher level services tied into and integrated with an overall service provider framework are key. And so in the area that we're really seeing uptake are really the business critical mission functions that enterprises are looking to run in a trusted partner's data center, and that's what we're seeing, where we're a lot of traction for this Dell Technologies Cloud Platform, combining VCD and VCF together to give you all those features and enterprise reliability. >> Right, and I didn't ask you Guy kind of the partnership question about having the opportunity to put your capability, you know, on the Dell Cloud Platform, opens up a whole new set of field resources, a whole new set of technical resources, you know, a whole different resources, not that VMware's short on resources by any stretch of the imagination, but it's certainly an additive, you know, kind of one plus one makes three opportunity. >> Yeah, I mean, it's great to be doing this and we've actually already been doing this on a couple of other initiatives, so from my perspective, I, you know, I manage Cloud Director Portfolio and we've already integrated Dell, Data Domain Dell, Avamar backup solutions, Data Protection Suite, into VCD as self service and we've already put in quite a bit of work, working together with Dell on that, as we go forward we're going to be putting more work into supporting VCD on the Dell Technologies Cloud Platform and integrating more services from Dell and from other vendors into the solution as well. So all we want to really provide is the capability for service provider to have the easy to consume hardware model, easy to consume subscription software model, with our program, and then the extensibility of services over and above just the infrastructure layer. So looking at things like object storage, and as Doug said, data protection, migration services, container cluster services, there's a myriad of services that VCD provides today out the box, and then there's the a whole extensibility framework, which we use when we work with partners, like we've done with Dell to deliver things like data protection. >> Yeah, I want to go back to you Doug, in terms of kind of a higher level, this whole transition to as a service, you've been in the business for a long time, you've been in the solutions a long time, but, you know, switching everything to as a service, as often as we can, and as frequently as we can, and as broadly across portfolio is really a terrific response to what the customers now, are looking for. So I'm wondering if you share some color on, you know, this philosophy of trying to get to, as a service, as much as you can, across the broadest solution set as you can. >> Yeah and if you look over the last decade, and decade and a half, there has been this increasing trend to moving to as a service offerings and the public clouds really drove a large part of that, than in tier two service providers around the globe. The key piece especially in the current business model, then going forward is how do you optimize, your CapEx versus OPEX and how do you really leverage the IT infrastructure to the maximum extent possible, based upon current business conditions, and that means the ability to grow and train and the ability to only consume what you need. In the past, when we had traditional data centers, you basically built for the worst case, and so the worst case was you had, an accounting run that happened at the end of the month that required a lot of processing power, then you built to that and that's what you use, and for the rest of the month, it really mostly idle. The cloud model really gives you the ability to A, improve their, or only use what you need and consume when you want to use it, but also adds in really shifting the responsibility for the management and the operations into someone, people who are experts in that area, so that again, you as a business can focus on your mission critical aspects of what you do whether that's developing a drug, building cars, making pizza, whatever it is, really as a service model enables your business to drive their core competency and not have to worry about the IT infrastructure that other people can do more efficiently and with better value than you could do it internally. And all that drive to that as a service model with the additional financial models that really aligned to the business paradigm that really companies are looking for. >> As you're saying that I'm thinking, wow, remember those days when our worst case scenario, was running a big batch load at the end of the month or the end of the quarter, and that would be re-missed, right, we are 2020, we're spread out all over the country and the world on both sides of the Atlantics. If I didn't say something about, you know, kind of the COVID impacts in terms of this accelerate, 'cause we hear it all the time in social media, right, who's driving your digital transformation, is it the CEO, the CIO, of COVID, and we've moved from this kind of light switch moment and then merged to, hey, this is an ongoing thing, and you know, kind of the new normal, is the new normal. And it's really shifted, a lot of people are talking about, you know, kind of shifts in the cloud infrastructure, the direction of the traffic, right, from going now from East to West and it's North to South, 'cause it's going to everybody's home. I wonder, I'll go back to you Guy, in terms of, the response that you've heard from some of your customers, in a response to, you know, kind of A, let's put a stop gap in early March that was interesting, and critical, and done, but now, kind of looking forward as to, you know, kind of a redistribution of workloads and architecture and users and I think Doug talked about security. How are you seeing any kind of ongoing effects and how is this impacting, you know, kind of you go to market and what you guys are bringing to market. >> Yeah, we're definitely seeing a lot of change in the way that service providers are trying to address this now. At the start of COVID, it was really a struggle, I think, for everyone to get the resources that they required to keep customers up from running, a lot of people started re-examining their disaster recovery contingency planning, and realizing that actually, what has happened in the last couple of years is, you know, workloads have exploded, a lot of patient workloads have completely gone through the roof and container workloads have grown drastically, and what's happened is the contingency plans behind all this stuff haven't changed and they just simply can't keep up the dynamic nature of the way we're doing business. Quite simply put technology is outpacing our weight, our ability to deal with that, so, you know, service providers need to provide a platform solution that enables them to be able to orchestrate at scale and enables them to orchestrate securely at scale, and really that means they've got to move away from this is hardware analog and move into virtual resourcing, cloud resource pooling elasticity, and particularly hypothesy. I know VMware we talk a lot about hybrid solutions and multicloud, but it's a reality when you look at where customers are today in their cloud journey, most of them have a footprint in their premise, have a footprint in a cloud provider premise and have multiple footprints in public cloud environments, so they need to have that consistent security model across that, they need to have data contingency and backup solutions, and someone needs to be in that to manage that, and that's where the service providers come in. They need to move away from the kind of infrastructure day to day operations that they were doing before and scale it out to now application protection and application development environments. >> Right, so Doug, I'm going to give you the last word as we wrap up this segment, you know, it's easy for us and pundits and people to write about multicloud and hybrid cloud and all these concepts, you guys actually have to make it work on the ground with real customers and real workloads. So I wonder if you could just kind of, you know, share your perspective, you've been working on this Dell Cloud Platform, you know, kind of how you see this evolving over time, and again, kind of what gets you up in the morning as you look forward as to what this journey is going to be over the next six months, one year, two year, three years down the road. >> Brought a lot of functionality capabilities to the world, right, the ability to consume things as you need them, the ability to really rely on a combined set of clouds and multicloud, and if you look at any enterprise that by any estimate, any company of any size, it's probably got 12, 15 clouds that contain their multicloud between using hyperscalers, tier two service providers, as well as cloud based services like Salesforce.com or Office 365, and you combine all those together and what that provides is a lot of flexibility, a lot of functionality, but also an extreme amount of complexity. And that complexity is really where Dell Technologies Cloud and Dell Technologies Cloud Platform is looking to help and to reduce that complexity, 'cause ultimately a successful enterprise is going to leverage the best from multiple clouds across multiple different implementations in order to provide the end to end IT experience that they need for both their external facing and internal IT operations. And with Dell Technologies Cloud Platform and working with our service providers, what we aim to do is to simplify the implementation of those multiple clouds and how they work together and make it as seamless as possible to shift workloads where they need to be, see your entire virtual enterprise IT environment, no matter where it's running, and to really optimize on your business to understand how you're using cloud, where you're using cloud, and how those clouds work together. And so the integration of all the different features with VMware and Dell bring together that end to end capability to significantly simplify the multicloud experience, and then ultimately our service provider partners, can help you on that journey to provide that management and orchestration across those different clouds and the data transformation, the digital transformation necessary in order to drive success. >> That's great, well, thank you Doug, for putting a nice big bow on it, and congratulations to you both for getting this release out, I know there's a lot of hard work and effort behind it, so it's always kind of good to finally get to expose it to the real world, so thanks for taking a few minutes with us. >> Great, thank you for having us. >> Absolutely. >> Yeah thanks Jeff, thank you. >> All right, he's Guy, he's Doug, I'm Jeff, you're watching theCUBE's continuous coverage of Dell Technologies World 2020, the digital experience. Thanks for watching, we'll see you next time. (soft upbeat music)

>> Narrator: From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. >> Hi, I'm Stuart Miniman and welcome to this CUBE Conversation. I'm in our Boston area studio and one of the things we always love to do is talk to startups and really find out they're usually on the leading edge of helping customers, new technologies, conquering challenges. And to that point, we have the co-founder and CEO of env0, that is, Ohad Maislish and we brought along with him he's got two of his investors, one of his advisors. So sitting next to Maish, we have Ed Sim, who's the founder and managing partner of Boldstart Ventures and sitting next to him is Guy Podjarny, who is the founder of Snyk. So now, you know is the acronym for Snyk and if you didn't know that, I know I'd heard about the company a couple years before that and my understanding is, Guy your the ones that connected Ohad with Ed who was the first investor. So Guy let's talk to Ohad in a second, but how the conversation started? And what what piqued your interest about what is now env0? >> Yeah, I think it started with people. I mean, I think fundamentally when you think about technology and think about startups, it needs to be an interesting market, it needs to be a good idea, but it really, first and foremost is about the people. So I've I've known Ohad from actually some work that he's done at Snyk earlier on, and was really impressed with his sharpness, his technical chops, and a lot of times the bias for feedback. And then when he presented the idea to me around kind of making Infrastructure as Code easy, and I don't want to sort of steal his thunder, talking about it and about kind of engaging with developers for it, a thought that literally resonated with me, I think, we'll probably dig into it some more. But in we live in a world in which more and more activities, more and more decisions, and really more effort is rolled on to developers. So, there's a constant need for great solutions that make on one hand make it easy for developers to embrace these solutions, on the other hand, still kind of allow the right kind of governance and controls. And I felt like Infrastructure as Code was like a great space for that, where we asked developers to do more, there's a ton of value in developers doing more around controlling these Infrastructure decisions, but it's just too hard today. So, anyways, I kind of liked the skills, I liked the idea. And I pulled in Ed, who I felt was kind of natural to kind of help introduce these experiences with other startups that share a similar philosophy to kind of help make this happen. >> Awesome, thank you Guys. So Ohad, let's let's throw it to you. Give us a little bit about your background, your team, Infrastructure as Code is not a new term. So I guess would love you to kind of weave into it. You know why now? Is it becoming more real in why your solution is positioned to help the enterprise? >> Awesome, first of all, thank you for having me. It's really exciting and again thank you for the opportunity. Regarding your question, so my background is technical. I was maybe still am a geek started University at a young age at the age of 14 in Palo Alto High School. And started my career in non technical roles very early. I have now like 21 years of experience, this is my second startup and third company, as I mentioned, my previous company is services company, provided services for Snyk and we became friends and later on partners, investors, and so on. And, we we've seen huge shift, we call the Infrastructure as Code the third data center revolution. We look at the first one being virtualization about 20 years ago led by VMware and then ZenSourcer. The second obviously, is the public cloud when companies started clicking buttons in order to get those compute resources but now nobody is clicking those buttons anymore. And instead writing, maintaining and executing that code, that Infrastructure as Code and as the Guy mentioned, it made it much more relevant for developers to influence the Infrastructure decisions and not just the app decisions. With that many challenges and opportunities around Infrastructure as Code management and automation, and that's where we focus. >> All right, so Ed I'm sure like me, you've seen a number of companies, try to climb this mountain and fall down and crash so I feel like five years ago, I would talk to a company and they say, oh, we're going to help, really help the enterprise enable developers for networking for storage, for security or anything like that. And it was like, oh, okay, good luck with that. And they just kind of crashed and burned or got acquired or did something like that. So, I feel like from our viewpoint we've seen for a long time that growth of developers and how important that is, but that gap between the enterprise and the developers feels like we're getting there. So, it gets similar what I asked Ohad why now, why this group, why the investment from you? >> Yeah, so I'll echo Guy's comment about the people. So, first and foremost, I was fortunate enough to invest in Guy back in his prior company before he started Snyk and then invested in Snyk. And there are lots of elements of env0 that remind me of Snyk the idea, for example, that developers are doing more, and that security is no longer a separate piece of developing, it's now embedded kind of in what developers and teams are doing. And I felt like the opportunity was still there for Infrastructure as Code. How do you make developers more productive, but provide that control plan or governance that's centralized so that environments can easily be reproduced. And the thing that got me so excited, was the idea that Ohad was going to tie kind of cloud costs from a proactive basis versus a reactive basis. Meaning that once we know that your environments are up and running, you could actually automatically tag it and tie the environment to the actual application. And to me, tying the business piece to the development piece was a huge, huge opportunity that hasn't been tapped yet. And so there are lots of elements of both Snyk and env0 and we're super excited to be invested in both. >> Alright, so Ohad maybe just step back for a second, give us some of the speeds and feeds we read your blog post 3.3 million dollars of the early investment, how many people you have, what is the stage of the product customer acquisition and the like? >> Sure, so we just launched our public beta and announced the funding couple of months ago led by Boldstart and another VC in Israel named Grove, and then angel investors Guy is the greatest investor among those and so we have some others as well. And now we have like 10 employees nine in Israel, one in New York City, I'm relocating after this all pandemic thing will get better. I'm moving to the Bay Area as soon as possible. That's more or less the status. And as I've mentioned, we just launched our public beta. So we have our first few design partners and early like private beta customers now starting to grow more. >> Yeah, and how would you characterize, what is the relationship between what you're doing in the public clouds. We understand, in the early days, it was like, Oh, well, cloud is going to be easy, it's going to just be enable it, it has been a wonderful tool set for developers. But simple is definitely not, I think anyone would describe the current state of environments. So, help it help us give it a little bit of what you're seeing there. And how you deal with like some very large players in ecosystem. >> Our customers are the same as the cloud vendors customers. The cloud vendors provide great value with the technical aspect with Infrastructure. But once you want to manage your organization, you want to empower your developers, you want to shift left some decisions, APM, did shift left for a performance, Snyk is doing great shift left for security. I believe that we are doing similar things to the cost. And you in the cloud vendors are in charge of you being able to do some technical orchestration. But when do you need to tear down those resources? When do you understand that there is a problematic resource or environment and what exactly made it? What is the association, how you can prevent from (mumbles) deployments from even happening at first. So all of those management information and insight ties back to your business logic and processes that's where we fit. >> I think there's actually a lot of analogy if I can chime in, on maybe an ownership aspect that happens in cloud. So we talk about the cloud and oftentimes cloud is interpreted as the technical aspect of it. So the fact that it allows you to do a bunch of things in the clouds and sort of renting someone else's hardware, and then automating a lot of it. But what cloud also does and that definitely represents what we're doing security and I think applies here, is that it moves a lot of things that used to be IT responsibility being a part of the application. So a lot of decisions, including ones really security, and including ones related cost around anywhere from provisioning of servers to, network access, to when you burst out, and to the balancing of business value to the cost involved or the risk involved. Those are no longer done by a central IT organizations, but rather, they're being done by developers day in and day out. And so I think that's really where the analogy really works with cloud is, it's not so much, like clearly there's an aspect of that that is the the technical piece of tracking how much does it cost in the on demand surrounding of cloud, but there's a lot of the ownership change, or the fact that the decisions that impact that are done by developers, and they're not yet well equipped to have the insights, to have the tools, to make the right decisions with a press of button. >> Thank you Guy and absolutely, 'cause cloud is just one of the platforms you're living on, you know well from Snyk that integration between what's happening in the platform, where open source fits into it, the various parts of the organization that are there. So, you've got some good background, I'm sure, helps you're an advisor to Ohad there to helps pull through a little bit of some of those challenges. Yeah, I mean, Ed I'd love to hear just in general your viewpoint on how startups are doing at monetizing things in the era of... You've got the massive players like Amazon and Microsoft out there. >> Look, the enterprise pain is higher than ever right now, every fortune 500 is a tech company right now and they need engineers, and they're hiring engineers. In fact, many of the largest fortune 500 have more engineers than some of the tech companies. And developer productivity is number one, front and center. And if you talk to CIOs, we just hosted a panel with the CIO of Guardian Life and the CTO of Priceline. They're all looking at how do I kind of automate my tool chain? How do I get things done faster? How do I do things more scalable? And then how do I coordinate processes amongst teams. As Guy hit upon and Ohad as well, not just security, there's product design being embedded with developers as product management being embedded with developers. There's finance now, FinOps. If you're going to spend more and more in the cloud, how do you actually control that proactively before things happen versus after or months after that happens? So I think this is going to be a huge, huge opportunity on the FinOps side. And, the final thing I would say is that winning the hearts and minds of developers to win the enterprise is a tried and trued model, and I think it's going to be even more important as we move forward in the next few years, to be honest with you. >> All right, so Ohad you know I think Ed talked about those hearts and minds of developers absolutely critical. When you look at the tooling landscape out there, the challenge of course, is there's so many tools out there, that there's platform battles, there's developers that find certain things that they love, and then there's, oh, wait, can I have a general purpose solution that can help. You talk about this being the third wave, how does this kind of tie into or potentially replace some of the last generation of automation tools. How do you see yourself getting into the accounts and growing your developer base? >> I think, I have a very simple answer, because, now enterprises have two options. Either they go with productivity self-service, or they go with governance, but they cannot have both. So if it's the smaller or they have less risks, so they go with the productivity and they take those risks, take the extra costs, take that potential damage that can happen. But more we see the case of I cannot allow myself this mess, so I have to block this velocity. I have to block those developers, they cannot just orchestrate cloud resources as they wish they have to open tickets, they have to go through some manual process of approval or we see more and more developers that understand there is a challenge they built in-house env0 of self-service combined with governance solution, and they always struggle doing it well, because it's not their core business. So once you see the opportunity of a more and more customers doing a lot of investment in in-house solution that do the same thing, probably a good idea to do it, as a separate product. And also the fact that we have the visibility of different customers, we can be very early but for later on adds pattern recognition, and notice what makes sense, what is problematic and give those insights and more business logic back to the customers which is impossible for them to do if they're only isolated on their cases. So as providing the same great solution to different companies, allowing them self-service combined with governance, and then additionally, add those and Smart Insights later on. >> Yeah, I think what I love about what he said is that I don't think he even sort of said finance or cost at any time of those. So really, like you said, governance and I think you can swap governance or you can swap the kind of the entity that's doing the governance for security for all of those. And that sounds awfully familiar for Snyk, which really kind of begs the answer to be the same, it's the reason that env0 approach is promising and that it would win against competition is that it tends to be that the competition or the people that are around are focused on the governance piece, they're they're focused on just sort of the entity that is the controlling entity. I like to say that it's actually not about shift left, it's about if you want to choose a direction, it's going to be the sort of the top to bottom. So it's more about, like this governance entities, whether security or finance, they need to shift from a controlling mindset that is top down that is like this dictatorship of sort of telling you what you should and shouldn't do to more of a bottom up element and allowing the teams the people in the trenches people actually make decisions to make correct decisions, and in this case, correct decisions from a financial perspective. And then alongside that, the governing entity, they need to switch to being a supportive entity an enabling entity and I think that transition will happen across many aspects of sort of software development and definitely anything that requires that type of governance from from outside of the development process today that is to change. >> Yeah, to chime in and add to Guys point, development is so important, it touches every aspect of an organization. So I always think about it as almost a collaborative workflow layer versus being reliant on kind of one control entity. Great developers always want to move fast. But, how do you kind of build that collaborative workflow and I think that Ohad in env0 is providing that for the environment and finance. Guys doing it for security. And there's lots of other opportunities out there, like privacy as well. And I wouldn't be surprised if finance folks start getting embedded with development at some point just like security is, or design is, product management is as well, because that is probably one of the highest costs around right now for many companies, and they're all trying to figure out how to stop the bleeding much earlier. >> Yeah, it's been lots of discussion, of course, we kind of go beyond DevOps, I think FinOps is in there. Ohad you have a favorite term that you've had from your advisors yet, how you categorize what you're doing. Any final words on kind of that organizational dynamic which we know so often it's the technology can be the easy part, it's getting everybody in the org, pulling in the same direction. >> Yeah, I think I'm looking at maybe a physical metaphor, or just an example, if you just enter a developer's room, you might see a screen TV there with some APM Datadog, New Relic Metrics, developers care about performance. They know very early if they did something wrong. And now they see more and more in those dashboards, in the developers rooms, things like Snyk to make sure you're not putting any bad open source package, which has security or ability. What we believe is that now they don't have the right tools, the right product that they can be part of the responsibility, of course, and that's like somebody else's problem. In other rooms, you have those TVs, those screens that show what is the cost, and maybe only later on in the waterfall kind of way you try to isolate and root cause analysis on what went wrong, but there is no good reason why those graphs of the past should be in the same rooms next to the APM and the Snyks and to prevent those as early as possible, maybe to change the discussion and build more trust between the developers that now seem not to care about the cost because they used not to care like 10 years ago when we used to have is called Apex-Cloud. The VMware or even EC2 Instances with the predicted pricing, that's all school. Now you have auto scaling Kubernetes, you have Lambda those kind of things you pay per usage. So the possibility for engineers to know how much their code is about to cost to the organization is very challenging now. If we tie from the developer up to, the financial operations, we will provide better service, and just better business value for our customer. >> Awesome, so final question I have for you, and Ohad I'm going to have you go last on this one is you kind of painted the picture of where things are going to go. So give us what success look like, Ed, start with you, give us out 12 to 24 months as to env0 in this wave as what should we be looking for? >> Success to me would be that every large enterprise has this on their budget line item as a must have. And the market is still early and evolving right now, but I have no doubt in my mind, it's going to happen. And as you hear about many large enterprises saying that we were in the second inning of cloud migration now we're in the fourth. That is what success will be and I know it's going to happen faster than we all thought. >> I'll take the developer angle to it, I think success is really when developers are delighted, or sort of they feel they're building better software by using env0 and by factoring this aspect of quality into their daily activities. And I think a lot of that comes down to ease of use. Like, I kind of encourage folks to sort of try out the env0 and see the cost calculation, it's all about making it easy. So what excites me is really around that type of success where it's so easy that it's embedded into their sort of daily activities, and that they're happy it's not a forced thing. It's something they've accepted and like having as part of their software development process. >> I fully agree with both Ed in Guy, but I want to add on on a personal note, that one of the reasons we started env0 is because we saw developers quitting jobs at some places. And the reason for that was that they didn't give them self-service, they didn't empower those developers, they were blocked by DevOps, they needed to open tickets, to do trivial things. And this frustration is just a bigger motivation for us to solve. So we want to reduce this frustration. We want developers to be happy and productive, and do what they need to do, and not getting blocked by others. So that's, I think, another way to look at it, to make sure that those developers are really making good use out of their time and going back home at the end of the day, and feeling that they did what they were paid for, not for waiting for others to locate some cloud resources for them. >> All right, well, Ohad want to wish you the best, absolutely. Some of the early things that we've seen sometimes they're the tools that help, we've been talking gosh I remember 15, 20 years about breaking down the silos between various parts of the organization, some of the tools give you different viewpoints into what you're doing, help have some of the connection and hopefully some empathy as to what the various pieces are there. You really highlighted there's nothing worse than I'm not being appreciated for the work I'm doing, or they don't understand the challenges that I'm going through. So, congratulations on env0. We look forward to following going forward and definitely hope being part your customers in the future. Thanks so much. >> Thank you, thank you very much. >> All right, and Guy really appreciate your perspectives on this thank you for joining us. >> Thanks for having them. >> All right, be sure to check out theCUBE.net where you can find all of the events we're doing online these days, of course, where there's a huge back catalog of what we have in the thousands of interviews that we've done. I'm Stuart Miniman, and thank you for watching theCUBE. (upbeat music)

(upbeat music) >> Hello and welcome to the Cube Studios in Palo Alto California, for another Cube conversation. Where we go in-depth with thought leaders driving innovation across the tech industry. I'm your host Peter Burris. Every Enterprise is saddled with the challenge of how to get more value out of their data. While at the same time trying to find new ways of associating value with product or value with service and to work with the different technology suppliers to create an optimal relationship for how they can move their business forward within a data-driven world. It's a tall order but 2020 is going to feature an enormous amount of progress and how enterprises think about how to handle the people, process and technology of improving their overall stance towards getting value out of their data. So to have that conversation today, we're joined by a Guy Churchward, who's the CEO of Datera. Guy welcome back to the cube. >> Thank You Peter, I appreciate it. >> So before we go any further give us a quick update what's going on with Datera? >> We're doing pretty well. I mean this year's we're just going to close it off. So we're in Q4 right at the end of it. You mentioned data-driven, you know I mean that was obviously one of my key excitements, years ago we kind of moved from a hardware resiliency or Hardware-driven to software resiliency, Software defined and I do think that we've hit that data-defined, data-driven infrastructure right now. I've been in the CEO role now just about a year. I've been on the board since August of a year and change ago and part of it is we had a little bit of an impedance mismatch of message, technology and basically I go to market. So the team quite brilliantly produced this data services platform to do data driven architectures. >> Mmmh. >> But customers don't wake up every morning and go, I need to go buy a data-driven, how do I buy one? And so when I came in I realized that you know what they had was an exceptional solution but the market isn't ready yet for that thought process, and what they were really buying still was SDS, software defined storage. >> So it almost in a connect way. so I'm going to buy an SDS and connect it to something and get a little bit of flexibility over here but still worry about the lock in every where else. >> Yeah, exactly and in fact even on the SDS side. What they weren't looking for is bring your own server storage. What they were looking for was automation and they were looking to basically break out and have more data mobility and data freedom. And so that was good and then the second one was our technology really sells directly to enterprises, directly to large scale organizations and it's very difficult as a start-up, small company to basically be able to punch straight into a global account, you know. Because they'll sit back and say, well you know would you trust your family jewels to a company that's got 40 employees in Silicon Valley. >> Right. >> And so what you really have is this and get the message right and then make sure you have to flow through to the customer credibility right and we were fortunate to land a very strategic relationship with HP. And so that was our focus point. Right. So we basically got on board with HP, got into their complete program, started selling very closely to them of which their sales team has been marvelous and then we're just finishing that that year. The good news is and you know I'll give you a spoiler I care about Billings, you know I mean we actually move from an appliance business to a software business exclusively, and so we basically sell term agreement. So if you think about it from a bookings perspective, that's important but basically how much you bill out is more important. From a Billings perspective I think we're going to run roughly 350% up year-over-year. >> Ooh. >> Yeah which is kind of good. Right I mean in other words it was a bit of a pat on the back that seems very happy with that and then even from new account acquisitions if I count the amount of accounts that we bought in this year and to date, entirely since 2013 we've only had one customer churn, so all the customers are coming with us but if I count this year, if I look at 16 17 and 18 we've actually bought more customers on board in 19 than all three pulled together. So we're actually finishing a very very strong year. >> Congratulations. Now if we think about going into 2020 you're closing this quarter, but every startup has to have a notion of what's going to happen next and what role you're going to play. And what happens next. So if I look back I I see the enterprise starting to assert themselves in the cloud businesses. That's having an effect on on everybody. But it really becomes concrete you know, the rubber really meets the road at the level of data. So as you start to grow you're talking more customers, as you talk to more customers and they expressed what they need out of this new cloud oriented world, what kinds of problems are they bringing to the table as far as you're concerned? >> Yeah, I mean they initially come to us so what I would say is every account that we've run we've replaced traditional arrays storage arrays and every account we've run, we've actually competed against SDS vendors and whether that's something like Dells, VxFlex or even vSAN, VMware's vSAN and which are probably the two most well-known ones. A lot of cases I mean we actually have 100% win rate against that in these competitive situations, but interestingly most customers now are putting dual source in place. So in fact the reason that we've ridden pretty quickly and we've run lots of deals, isn't because we're going in and saying VxFlex is failing or vSAN is failing, but they want something extra, they want automation, they want desegregation, they want scale >> They want second source. In many respects of sales is, it's succeeding but you have to push a little bit harder and that is ease most easily done by bringing in another platform with crucial functionality... >> Yeah >> ...and a second source. >> And I think you're on the money there Peter because if I look at second source in the traditional array business, no CIO worth their soul is a single source vendor so they they will have Dell and they'll have HP or they'll have HP and they'll have Pure, doesn't matter and and even on HCI you'll see the HCI vendors, Nutanix is doing very well, so is Dell. So therefore they'll have that from second source if its critical. So if an environment is critical they always have a second source and so even now when you look into software-defined, this market in 2019 was very much like the, let's get the second source in place. And that shows you where we are on the maturity curve because people is basically moving on this en mass. Now that's 2019 you're asking about 20, 21, 22 moving forward. The reason that the traditional arrays weren't working for them is whether it's flexibility or it's basically management costs or maintenance, but it's data freedom. It's what they're really looking for. You know, what is a data center? Is it on-premise, is it cloud? It's definitely cloud but the question is is it on-premise cloud? Is it hybrid cloud, is it public cloud? And then you mention edge. You know we actually find customers who are looking and are saying look, the most important thing for us is being data-driven and what data-driven basically articulates is we get data in, we analyze it, we make decisions on it and we win and lose against our competition as fast as we can be accurate on that data set. And a lot of the decisions are getting made at the edge. So a lot of people are looking at saying my data center is actually at the edge, it's not in the center in the cloud, right. >> Well in many respects, it's for the first time a data center actually is what it says it is, right. Because the data center used to be where the hardware was and now increasingly enterprises are realizing that the services and the capabilities have to be where the data is. >> Yeah. >> Where the data is being produced, where the data is being utilized and certainly where the data, where decisions are being made about what to keep what not to keep, how much of it etc, and that that does start to drive forward an increased recognition that at some point in time we are going to talk more about the services that these platforms, or these devices or these software-defined environments provide. Have I got that right? >> Yeah, yeah you have and even if you look at that, you know ... what the AI/ML, you know I mean if I if I kind of step back and I look at what a customer's trying to do which is to utilize as much data as possible, in a way that they have data freedom that allows them to make decisions and that's really where AI and machine learning comes in. Right you know everybody employs that. I recently bought a camera, shockingly inside the camera it's got ML functionality into it, it's got AI built into it, my new photo editing software on my iPad is actually an ML-based system. They don't do it because it's a buzz word, they do it because basically they can get a much higher level of accuracy and then use data for enrichment, right. And then in the ML track, the classic route was I'm going to create a data lake, right. So I got my data lake and I've got everything in it then I'm going to analyze off the back of it. But everybody was analyzing once it's in the data lake. And what they've realized is to compete, they actually have to analyze much quicker. >> Right. >> And that's at the edge, and that's in real-time and that string based. And so that's really where people are sort of saying I can't ... I'm not going to have any long pole in my technology tent. I'm not going to have anything slow me down, I have to beat my competition and as part of that they need complete fluidity on their data. So I don't care whether it's at the edge or it's in the center or in the cloud, I need instant access to it for enrichment purposes and to make fast and accurate decisions. So they don't want data silos. You know, so any product out there that basically says me me me me give me my data and therefore I'm going to encrypt in such a ways you can't read it and it's not available to anybody else. They are just trying to eradicate that. And and we've sort of moved. It's a weird way of putting it but we've moved from hardware-defined to software-defined and I think we've moved into this data-defined era. But at the same time, it's the most stupid thing for me to say, because we've never not been in a data-defined era. But it's the way in which people think with their architecture as they sign up a data center now or a cloud and they're not saying, hey so about the hardware, it's based on that or it's the software. It's always going to be about the data. The access to the data, however before you get excited. (laughs) The thing that I kind of look at I say so what has fundamentally changed? And it's the fact that we always used to have to make a decision. You know, I ran a security analytics business and when you do things like log management, it's about collecting as much as data so in other words accuracy beats speed. And then security event management is speed beats accuracy. Because you can't ask questions of the same data. But technology is caught up now. So we've actually moved from the do you want accuracy? Or do you want speed? It's like "or arena". So people were building architectures in this "or" world, you know. Do you want software-defined? If you want software-defined you can't have Enterprisilities. Why not? Well, if you want an enterprise application, I mean remember the age-old adage. You should never buy a version 1.0 of an app. >> Right. But what happens is they want they want this ... people are turning around saying I need an enterprise application, I want full data access to the back of it, I actually need it to be fluid, I need it Software-defined, I don't know where it's going to be based and I don't want to do forklift upgrades. I want and and and and and. Not or, so what we've actually moved to is a software-defined era you know, and a data-defined architecture in an "and arena". And where customers are truly winning and where they're going to beat their competition, is where they don't settle and say oh I remember back two years ago, this happened and therefore we should learn from that, and we shouldn't do that. They're actually just breaking through and saying I'm going to fire the application up I want it up and running within 30 days, I want it to be an enterprise application, I need it to be flexible, I needed to have a hype of scale and then I'm going to break it down and by the way I'm not going to pay contractually to an organization to build all that infrastructure. And that's really why soup to nuts, as we move forward not only they sort of building an infrastructure is data-defined infrastructure, they don't want lock-in. They want optionality and that means they want term licenses which is sure, they don't want these proprietary silos and they need data flexibility on the back of it. And those are the progressive customers, and by the way I've not had to convince a single customer to move to software-defined or data-defined. Every client knows they're going there, the question on the journey is, how fast they want to get. >> Right, when? >> Yeah. >> So if so look every single every single enterprise, every single business person takes a look at what are regarded as the most valuable assets and then they hire people to take care of those assets, to get value out of those assets, to maintain those assets, and when we move from a hardware world where the most valuable asset is hardware that leads to one organization, one set of processes, one set of activities. Move into a software world to get the same thing. But we agree with you, we think that we are moving to a world that is data first, where data is increasingly going to be the primary citizen and as a consequence we're seeing firms reinstitutionalize how work is done, redefine the type of people they have, alter their sourcing arrangements, I mean there's an enormous amount of change happening because data is now becoming the primary citizen. So how is Datera going to help accelerate that in 2020? >> Yeah I mean and again that's part of data access. And then also part of data scale. Back probably six seven eight years ago. EMC we were even I remember Steve Manley is a good buddy of mine, we went on stage and we talked about bringing sexy back to back up. We were trying to move away from backup admins just being backup admins to backup admins actually morphing their job into being AI/ML. You know, I remember a big client of mine, and it wasn't in the EMC days, it was before that were basically saying they have to educate their IT staff, they want to bring them up as they move forward. In other words, you can't ... what you don't want is you don't want your team, because it all comes down to people. You don't want them stuck in an area to say we can't innovate forward because we can't get you away from this product, right. So one of our customers at Datera is a SaaS vendor. And their challenge is they had traditional array business even though it was in a SaaS model, it was basically hardware in the background and they would buy instances and they found that their HR cost, their headcount cost was scaling, >> With the hardware. >> Exactly, and and they were looking at and going, what does that do to my business? It does one or two things, either one is it means that cost I mean do I bear that I don't make profitability and I can't drive my business or do I lay that on my customers and then the cost goes up and therefore I'm actually not a cloud scale. And I can't hire all the people I need to hire into it. So they really needed to move to a point of saying how do I get to hyper scale? How do I drive the automation that allows me to basically take staff and do what they need to do. And so our thing isn't removing staff, it's actually taking the work that you have and the people and put them in a way they really matter. So in other words if you think about the old days of I'm going to mess this up but, I talked to somebody recently about what IT stands for. And they said IT should stand for information technology, right. I mean that's really what it is. But, but you know for the last 20 years it stood for infrastructure technology? >> Yeah. >> And that's frustrating, because in essence we got way too many people managing a lot of crap. And what they really should be doing is focusing on what makes the business happen. >> Yeah. >> And for instance I like to run a business by money in and money out, everybody else does and then you look at it and you say well, how do I get more money coming in? By being smarter and quicker than somebody else. How do I do that? By data analytics. Where do I want to put my work? Well I want to put it into the ML/AI and I want more analysts to work on it. I want my IT staff to do that. Let's move them into that. I don't want them you know rooms and reams of people trying to make it you know manage arrays that don't function the way they should or... >> One more percent out of that array of productivity. >> Yeah, abnormally trying to scale HCI solutions to a hyper scale that actually is impossible for them to do it. >> Right. >> You know and and that was the thing that really what Mark, who was the founder of Datera and the team really did is they looked at it from a cloud perspective and said it's got to be easier than this. There must be a way of doing low lights-out automation on storage. And that's why I was saying when I took over, I kind of did the company an injustice by calling it an SDS Tier 1 vendor. But in reality that was what customers could assume. And we're basically a data services platform that allows them to scale and then if you hop forward you go how do you open up the platform? How do you become data movement? How do you handle multi-cloud? How do you make sure that they don't have this issue? And the policies that they put in place and the way in which they've innovated, it allows that open and flexible choice. So for me, one is you get the scale, two you don't have forklift upgrade three is you don't have human capital cost on every decision you make, and it actually fits in in a very fluid way. And so even though customers move to us and buy us as a second source for SDS, once they've got the power of this thing they realize actually now they've got a data service platform and they start then layering in other policies and other systems and what we've seen is then a good uptick of us being seen as a strategic part of their data movement infrastructure. >> You expand. >> Exactly. >> Guy Churchward, CEO of Datera, thanks again for being on the Cube. >> My pleasure. Thank you Peter. >> And thank you for joining us for another CUBEConversation. I'm Peter Burris, see you next time. (upbeat music)

>> Narrator: Live from Las Vegas, it's theCUBE. Covering UiPath Forward Americas, 2019. Brought to you by UiPath. >> Welcome back everyone to theCUBE's live coverage of UiPath Forward here at the Bellagio in Las Vegas, Nevada. I'm your host, Rebecca Knight, co-hosting alongside of Dave Vellante. We're joined by Cathy Tornbohm, she is the distinguished VP Analyst at Gartner. Thank you so much for coming on theCUBE. >> Very welcome, nice to be here. >> And Guy Kirkwood, he is the Chief Evangelist at UiPath. Thank you so much. >> Thanks Rebecca. >> So, we're hearing so much of these mantras, these catchphrases of UiPath. "automation first", "a robot for every person", "we're re-booting work", these are the theme's that Guy was touting up on the main stage, Cathy. Beyond that, I'd like to hear from you a little bit about what you're seeing in the RPA space at the moment. What are the trends and the themes that you think are most salient? >> I think the most fascinating thing about RPA right now is that it's really highlighting the problems the organizations have. All their accidents of history are really being brought up by RPA. And then you've got these digital darlings that they're trying to compete with, the Greenfield site kind of people. And some of those don't have beautiful back offices, but let's not go there for a minute. So, it, RPA is an opportunity for companies to link their digital dreams with their existing legacy nightmares. >> And those legacy nightmares include all of the things that Guy was talking about today: the drudgery, the dreariness, those mundane tasks that take up so much of our time. >> Absolutely, and really, if you think about it, in organizations, typically less than 15% of the applications that they're using have got some sort of application programming interface. So if you don't have a way of linking them, you end up with this long turn of applications that aren't linked together, with people literally being swivel-chair integration between the applications. >> Well, why can't you just string a bunch of API's together and automate that way? >> Well, in fact, there's a guy called Ian Barkin who works for Symphony, one of their organizations, it was set up to create automations for organizations. So one of the services businesses since been acquired by Sykes. And he describes it as process sediment, and it builds up in businesses in the same way that sedimentary rock builds up over millions of years. And digging through that, so that you can actually become more efficient is very difficult to do. So doing it on API level means you got to join up all those things individually. Whereas, using RPA, if system 'A' has a user interface, and system 'B' has a user interface, you can just use RPA. >> So, Cathy, you've been following process automation as a category for a number of years. Why RPA, why is it so hot, and why now? We've heard that it's the number one software category... >> Cathy: Fastest growing, yeah. Fastest growing, from Gartner. We've seen spending data that confirms that. Why now? (sighing) >> It's the digital competition that companies are facing, and the recognition that they cannot continue to be quite as bad at some of the things that they are bad at. So it's really that business transformation story back again, business process re-engineering, the same story that we had with BPO like ten years ago, but now, with robots instead. >> Yeah, it's interesting, I was at a, we had a show last weekend, it was the CEO of Suze, Suze... How do ya say it? Anyway, Suze, she said to me, "Well, you know, digital transformation's really about business transformation." And you kind of said the same thing. I mean, thoughts on that? >> I mean, you look at the start of the outsourcing market, the BPA market, twenty years ago. The very first deals were actually IT outsourcing deals that then transformed the business using IT as the enabler. So the first deal that I got involved with ever, in the outsourcing market, was Perot Systems with a British and Asian company. And we were putting in business process re-engineering consultants who actually transformed the business using IT as the enabler for that. There is no difference now, in fact one of the, one of the partners here, one of our original customers, actually put together a plan where we did the implementation, you know, soup to nuts, so that we could find out how we fit in to that whole transformation piece. And our team put together a whole package on all the learnings that we got out of that. And I had to laugh, because they're exactly the same things that every transformation program has had for the last thirty years. >> You know, if you look at kind of the history of certain segments, and I wonder if, Cathy, if you see RPA as one of them, like if you could've figured out who was implementing ERP the best, you didn't know SAP was going to become the leader, but if you could've figured out who was adopting ERP, you could've made a lot of money in the stock market, 'cause those companies had a huge productivity boost. Kind of same thing with Big Data, nobody really made any money in Big Data, so-called 'Big Data', a dupe. But the guys who applied it probably did pretty well. Do you see RPA as similar where the practitioners are going to actually be the ones that add more value to the industry than the new, the newly minted billionaires? >> It's almost the opposite. So the more RPA a company needs, it means the worse they did at managing their ERP in the first place. >> So they're kind of a mess? >> Yeah, yes. That need to be cleaned up, yeah. >> Yes, if you've got a hundred and twenty four ERP's that don't talk to each other, and you want to close your books in any kind of reasonable time frame, you're going to be a massive adopter of RPA, which basically means the more rubbish you are and activity, the more opportunity there is to automate more of it. >> So, what are the metrics that matter when you talk to your clients? >> Well, what I try and encourage clients to do is to really focus on business outcomes. So, much as Guy probably doesn't want me to say this, I don't really care how many 'scripts', aka robots, you've built, or how many run times you've deployed. What I care about is the business impact that you've managed to achieve. So, whatever KPI's are important to you, so are you managing to collect more revenue? Are you managing to make your customers happier because you're managing to decrease average handle times? or increase right first time activities. So anything that you're doing that actually improves the good old business metrics, is just going to be fantastic. So those are the sort of metrics that, really, companies should be focusing on. Not how many scripts they've built, that's absolutely pointless. >> I mean, are they focusing on that? I mean, when you... >> Yeah, lots of people are. >> Yeah? >> Yeah. >> In terms of ROI, we hear from customers that it has had them more accurate, they're more efficient, they're cost saving on human hours of the mundane tasks. But, when you were up on the main stage talking about how we're rebooting work, we're changing this moment, is it sparking the creativity, the imagination, the time spent on strategy in the more higher-level things? Is that, I mean that seems like that's the goal of return on investment. >> It is, within those organizations that are the most mature. So, what we're seeing, is the bifurcation, really, of the market between those organizations that are just starting and scaling up what they can, internal senses of excellence. Those organizations that are using the partners behind us. Those organizations that are using external parties to help them develop that. So Delight, for instance, they are sort of a managed service business. And instead of using people, they're using automation. So, Delight, by accident, has a BPA business in Spain, but then they'll turn that into an automation-heavy business and then providing that managed service. And then, the smartest customers, including SNBC, who we heard from yesterday, are actually turning their back office cost operations into a front office of revenue generator. Now, that is radically different from what we've seen prior. >> So Cathy, I got to ask you, when I was on a plane out here, somebody texted me a picture of the latest hype cycle. And they said, they knew I was going to UiPath, they said, "RPA has entered the trough of disillusionment." I said, "Oh, awesome, Gartner's, Cathy's coming on, and I can ask her about that." Well, what's your take on that? >> I think as Guy says, some people have already sailed through the trough, they've already gone through the challenges, or some of the challenges, and they've already found these fantastic productive things. I mean, we're estimating that people will save close to a million dollars for a large company, and just not having to do re-work of getting it wrong first time with re-keying that data. So, where there's some fantastic savings available, that you know, some of the ones have gone through the trough and done that, a lot of the other ones, they kind of, they don't understand the limitations of RPA and all those other partner tools that they need to put with it. So, don't understand it, can't handle unstructured data by itself. It needs a sister tool, so, what Gartner's talking about right now is this concept of hyper automation where you look across all the different activities that you would need to, sort of replace a person. So the people that are heading into the trough as sort of this second wave of adopters that Guy talked about, that will really struggle because they didn't understand the limitations in the first place. >> Well then, you know the, sometimes, things like the Magic Quadrant, and the trough of disillusionment, they're somewhat misunderstood sometimes, people, you know they see 'em, Gartner's very clever with the way it works things, but, so how should we think about that hype cycle? It's actually, in a way it's progress, isn't it? For an industry where they start... Entering that trough. >> Its, what Gartner says, is all industries have to go through that type of growing pains. And I think that we're seeing that, UiPath's expanded massively, and that's always a challenge for companies as they grow very rapidly. And as companies try and, as they say, take these wrong metrics. So I think things like UiPath buying ProcessGold is fantastic, it's a really, really good move for them. And I expect to see a lot of other process mining companies acquired, brought in to the RPA fold, because, there's four reasons why companies are going to go into this disillusionment, right? These are the main challenges with companies trying to use RPA properly. One is, they don't know what the processes are. So ProcessGold will give you a really good indication, they don't know about the microscopic level, and they don't know about the macro level. So things like digital twins will be something else that we would expect to see very closely partnered with companies like UiPath. And they don't know how to orchestrate their resources. So, other companies, like Innate, that can help you figure out how to do that will become... So its kind of like we're sort of breaking down a lot of what happened in other software categories and re-building them all up, in the way that the business can actually adopt them, hence, the AI Fabric sort of idea. So they don't know the processes, politics, people will lie to you about what they do all day, so they can sabotage your process, and there's a lot of silos within organizations that hate each other and throw things over the wall. So that all needs streamlining, and the more you can do across silos, the more successful any automation project would be. Then you've got, when you take a person out of a process, you take their eyes, their ears, the mouth, the nose. How are you going to replace that when you're trying to take them out, because you've got the keyboard fingers thing with the RPA tool? You need all these other activities replaced, replicated, supported. And then you've got the economics of production, so actually making sure that the scripts that you've built are actually worthwhile and are going to be cost-effective. It's something that we're studying at the moment. So you've got all these, all these different barriers, from all these different angles that are really going to push this thing into the trough for a little bit. And that's why it's great that RPA companies are looking at ways to mitigate that for their customers. >> Now, remember we said, as the understandings. So RPA is really good at dealing with structured data. Rule-spaced activities, deterministic things. That's why in regulatory, highly regulated environments, it's very effective, and the regulators love this sort of stuff. Because it's deterministic. When you look at AI, then we look at it in four ways. So you've got process understanding, which is the ProcessGold acquisition, you look at conversational understanding, 'cause ultimately robots are going to be controlled by voice. So you have to understand, the system has to understand that, let's say you're sitting in a bank, and the robot doesn't understand something, you say, "Okay, robot, stick that in the Well's account." It has to understand that Well's, in this case, means Well's Fargo. It does not mean a hole in the ground, water at the bottom, or a town in Somerset, in the UK, 'cause they're well's. So getting those ontologies correct is so important. So, that's conversational understanding. Document understanding. Because, as Cathy said, companies are still wading around in paper. So, understanding what those different documents are and how to action them is going to be really important. And finally, you're looking at visual understanding. So understanding and viewing things on the screen exactly the same way that humans do. So it's getting that combination right. >> So for RPA to live up to the hype, and there's a lot of hype, and it's a good thing, it's fun to track. It's got to go presumably beyond cleaning up the crime scene, if you will, to this new vision that you and Guy just laid out. What is the distance between, I dunno, sometimes I say 'paving the cow path', which gives you a nice hit, but as you say, it's 'cause companies... Ya know, they're messed up, to this vision of this, actually the guy from Pepsi today talked about it, this fabric of automation across the organization. How big of a gap is that? >> It's very different by every different company on the planet, really, in terms of their accidents of history, what their IT application landscape looks like, and what their business landscape looks like. And when you try and put the two things together, that's where you find the opportunities for any type of automation. >> Well come on, that's such an 'it depends' answer. (laughing) At the macro, will... In your expert opinion, will RPA live up to the hype? So many trends haven't, enterprise data warehousing, Big Data, Doob, all that stuff. You think RPA has the potential to crack through that. >> You mentioned a very good point. I think the most successful companies are the ones that actually will take the person that's managing the data and analytics of how their process is performing, and doing that with their automation strategy. And there are very few companies that've actually worked that out. They've still got totally two walls and they just meet up here at the CEO. So, unless companies actually take a more active business outcomes approach, and look at their end-to-end processes of order to cash and source to pay, these problems will carry on for some time. >> Well that's a great point, I mean, so it's data, it's machine intelligence, I guess Cloud for scale, you guys made a SAS announcement today, it's "automation first", to use your buzz word. >> Cathy: You need it all to come together. >> And it's really developing those best practices in your role as Chief Evangelist in helping understand what the most successful companies do, and then making sure that's implemented. >> Well that's why I spend more of my time listening than I do talking. Because the very nature of being a Chief Evangelist is the best job and the worst job title in the world. It's the best job because I spend my entire time talking to people like Cathy who know about what's happening within the market, and then feeding it back into our organization so we can make the right bets, so we can make the right acquisitions, but develop the right things. The bad thing about the job, is that I keep getting an inordinate number of people on LinkedIn saying, "So pleased that Jesus has entered your life." And I'm not that type of evangelist. (laughing) >> It's in the title. >> You know there's always this age-old debate in the industry of best of breed versus kind of a sweet approach. You see in SAP, for instance, acquired an RPA company, In Four talks about it. And then you get the specialist, UiPath. How do you see that shaking out, as the industry gets kind of more consolidated, how do you see a company like UiPath thriving, continuing to thrive? >> Gartner's going to predict coming in our new prediction series, but... Roughly 20 to 30% of enterprise adoption of AI, machine learning activities for process-based activities, will go through the RPA market. So, and with the IBPMS market, sort of combined together, that process management, because RPA has managed, cleverly, to capture the imagination of the business person. So, actually, there's a lot of IT departments that are talking to us about, how do we, how do we enshrine this activity, foreshadow IT, that's happening in the business, and make it successful, put governance plans in place so it will actually be successful in the way that it's actually now dealing with its own crime scene... (laughing) (mumbling) Its own rubbish, in a much better way. And I think that responsibility of business to understand how it can automate things and how it can manage things will really help a lot. So, I think the RPA players are well-placed to either be acquired into that bigger set of the established, large... Software providers, all to kind of keep blazing a trail for independence of the business. I'm not so sure about this idea that everybody should be programming their own scripts, I think that's a challenge. And I think the new interfaces will help mitigate some of the problems that we've seen with that approach, that hasn't been, haven't been very well done historically, so that's another area that will probably be a bit trough of disillusionment, but, actually, well-managed RPA projects have actually got a really good chance of delivering back very interesting benefits for businesses. >> Yeah, as a discreet innovation category, it does kind of feel that way, and often times, those markets are winner take most, the winner makes a ton of dough, number two makes a little bit of money, number three kind of breaks even, and everybody else gets consolidated or goes out of business, so, you guys go big or go home. That's kind of... Your posture. >> Tomorrow morning I'm doing, I'm doing my predictions for next year, and one of them is that the challenger RPA vendors, and indeed the service organizations that are small, are going to continue to consolidate and get acquired next year. So that's the 2020 prediction for us. >> Great. Well, Guy and Cathy, thank you both so much for coming on theCUBE. It was a great conversation. >> Oh, good, thank you. >> Thank you very much, indeed. Thanks Rebecca. >> Dave: Thanks you guys. >> I'm Rebecca Knight for Dave Vellante, stay tuned for more of theCUBES live coverage of UiPath. (techno music)

>> Reporter: From New York City, it's theCUBE, covering New Relic FutureStack 2019, brought to you by New Relic. >> I'm Stu Miniman, we're here in New York City right next door to Grand Central Station, at the Grand Hyatt. This first year of theCUBE, attending New Relic's Futurestack, the seventh year of the show, and happy to welcome to the program, Guy Fighel, who's the vice president and general manager of New Relic AI of course, CEO was up on stage this morning announcing New Relic AI, it's in beta, Lew said expect early 2020 for to come out, so thank you so much for joining us. >> Thank you for being here. >> All right, so Guy, you came to New Relic by way of the acquisition of signifAI. And that ends in AI of course, even though we pronounce it signify, so help us understand is this a repackaging, rebranding you know, New Relic-izing the product that was through the acquisition, tell us how we've gotten here. >> Yes, sure, so New Relic AI is a whole new set of capabilities, it's a suite of capabilities that we are launching today in beta that pretty much augments the site reliability engineers with AI and ML capabilities. It runs on top of the New Relic One platform, which is the first observability platform that is connected, open, and programmable so you have all of the existing information and data that you already have inside New Relic. And we've incorporated a lot of the technologies and the techniques that we have developed as part of signifAI with existing capabilities that New Relic already had, and pretty much integrated all of that into single user experience and single type of capabilities across the stack. >> All right so, Guy, AI is a really broad category you know, you got your AI and ML and cognitive and you know all these things, what was kind of the core IP of SignifAI when they came in. >> Sure, so we really focused on correlating and reducing the noise of all of your different alerts and incidents but not just that, we've actually built a recommendation engine on top of that, to provide you much faster context to get into potential root cause of all of your different information focused on events. And now we're combining that with all the time series data that New Relic as a platform has to offer, so you're getting a much broader capabilities for understanding. >> Yeah, you know, definitely there's that promise of AI as we know that humans alone or my traditional tooling just can't keep up, you know, talk about all the different sources of data, the volume of data. I just saw Lew talking about the amount of the millions of items being ingested into the New Relic database, and the billions of items that are being read basically per second. So, help us understand. You say we love, we talk about our videos or extracting the signal from the noise, so, did I hear it was like 80, 85% your early customers are helping to reduce that noise Bring us in a little bit more. >> True, yes, so definitely early results shows us over 80% noise reduction for some of the customers and it is important to understand this is automatic relations, so this is truly based on the engines with no human interaction. Now, we actually have even greater results when some user input is driven into the system and that raises the capabilities as well. In terms of the number of events, yes, we are dealing with huge amount of events and information in the platform and I think it's, all around, not replacing the humans, but actually augmenting the site reliability engineers, so you talked about how systems, you know, there is a great promise for those capabilities. We believe that applied intelligence is a much better term, because it gives really enabling the augmentation for the site reliability engineers. We don't believe that site reliability engineers needs to go away or can even be replaced anytime soon. We definitely think that we can help them understand better and faster, what is the type of problems that they see in their production environments, and then help them resolve that much faster and better. >> Yeah, absolutely, we're huge supporters of really, the best solutions are when you have the people plus machines, there are certain things the machines are going to do on their own, but it's the marrying, so help us understand who's going to be using New Relic AI how is it going to change their day-to-day life and maybe even kind of organizationally, what the impact will be. >> Sure, so if you're a site reliability engineer, or a DevOps themed depending on, how you want to call yourself and, you know, there's a big debate in the industry, whether it's DevOps or site reliability engineers. Pretty much anyone who is responsible for Op time in the digital production environments you're a relevant user, If you carry the pager, if you're on call, you're a relevant user, so you're going to be interacting with the system to be able to actually see what are the problems with potential recommendations and then, you can infuse the system with your own logic. Whether it's based on the logic, we also provide very easy user experience we'd like thumbs up, thumbs down, different types of feedbacks as part of the workflow and I think the most important piece is that we're connecting to users where they are. Meaning, we don't believe we need to change the workflows so, if you're a user and you're already using with a specific internet management providers and you've already connected some of the additional monitoring tools to those providers, we now offer you a streamline of syncing to those instant management platforms and then, in reaching them with all of the information that we already have on the platform. >> So Guy, we've talked about AI but, let's talk a little bit about AI Ops. So, you know I've talked to the number of the vendors I actually went to an AI ops conference earlier this year and some of the talk track was, APM is the old way, AI ops is going to replace what you were doing before Let's take all your scattered tools and consolidate them down. some of the messaging reminds me of what I heard this morning, the New Relic One platform is going to replace a number of tools, pull everything together. Help us kind of, you know, square that circle of APM and AI ops and where you see New Relic compared to some of those competitors out there today. >> Sure, so APM is application performance monitoring. it's all about monitor and have that visibility to your application layer, it has nothing to do with AI ops it has nothing to do with replacing the tools. We believe that everyone should have visibility into their application, and that's, a lot of that messaging came through Lew's key note this morning, and opening it up to any type of open source instrumentation so we can bring it to the platform whether you want to drop an agent, whether you want to use any other open source SDK, we allow you to do that. Pretty much opening up the platform and giving you the option. AI ops is a term coined by Gardner actually, and it is pretty much applying some automation, AI capabilities, ML capabilities, statistical analysis capabilities on huge amount of data that you have in a centralized place. It has nothing to do with the monitoring, per se, so, I definitely think that the industry's going into a new space, where there is a consolidation obviously with different vendors. I believe that New Relic is giving customers the choice to make, whether they want to go and continue using their old tools, and that's okay, and we are an open platform so we will sync up with their data as part of New Relic AI we'll be able to bring in the new data whether by, again inter-connecting with their incident management platform or through a rest API or native integrations or if customer choose to do that, they can just send us all of the data directly and then, we apply the AI ops capabilities on top of the existing platforms. So, it's really opening up for the choice of the customer. >> All right it's been less than a year since the acquisition of SignifAI we know that some of the things when you do an acquisition it's an area of investment, you're going to get more resources, more people but, you've mentioned customers a couple of times, maybe give us a little bit of insight as to how the customer conversations have changed now working for New Relic, as opposed to being a customer understanding that piece of the New Relic ecosystem. >> Oh absolutely, I think, you know, as you transition from a small start up into a company like New Relic you get much more exposure to enterprise customer, your scaling capabilities are much better so we're in serious conversations with a lot of the enterprises customers that have a lot of interest in what we do. A lot of it is part of the branding recognition and all of the great capabilities that New Relic has already, and then marinade that with all of the capabilities that we're bringing or that we brought into New Relic as a young start-up with all of the latest technologies and a lot of the AI capabilities which are truly innovative ones, so definitely see a lot of traction from the enterprise customers, the more sophisticated ones as well. >> All right, so the solution announced today is in beta give us a little bit of a look forward as to what we should expect to see and what feedback you're hoping to get from customers along the way and how they might get engaged if they want to. >> Yeah so definitely we are in beta today. We've engaged with customers prior to the beta, so, we already got a lot of feedback and great feedback and we make some tweaks to the product based on that. We're actually announcing AGI of a small feature today which is enhanced incident context, which provides you active detection for time series data all the way to your slack channels but the overall solution is currently in beta and as we are progressing, within every month we're going to get more and more customers engaging with the platform, and then we're going to release a much more advanced capabilities even than what we have today in GA coming early next year. >> All right great, last thing, big mention and push about observability this morning, help us understand where AI fits into the broader discussion of observability. >> So again, as I mentioned before the observability will allow you to see all of your data in a centralized place. So, it's combining matrix, events, logs and traces in a specific place that now algorithms and different techniques such as AI and ML based algorithms really, really be successful in gathering, understanding, because you have all of that different information for the human brain, it's very hard to actually go and crawl and kind of ingest all of that vast amount of different data points for machines, they're very good at that. They're starving for broad amount of data and so having that capability, building on top of a true observability platform is what makes the AI and ML so successful and drive value to customers in really understanding what the data means. >> All right well, Guy thank you so much for sharing best of luck on the journey towards GA for the the full New Relic AI in the future. We look forward to, launching it. >> Thank you so much. >> All right and once more here, walking through at the New Relic Futurestack 2019, here in New York City. I'm Stu Miniman and thanks for watching theCUBE. (upbeat music)

>> From our studios in the heart of Silicon Valley. Holloway Alto, California. It is a cube conversation. >> He will come back and ready Geoffrey here with the Cuban Interpol about those details for acute conversation. We've got a really great guess. He's been on many, many times. We're always excited. Have them on to a bunch of different companies a lot of years and really a great perspective. So we're excited. Guy. Church word. The CEO of Da Terra. Back >> in the politest. EEO guy. Great to see you. >> Thank you, Jeff. Appreciate it. >> Absolutely. So I think last time you were here, I was looking it up. Actually, Was November of twenty eighteen. You were >> kind of just getting started on your day. Terror of the adventure. Give us kind of the update. >> Yeah, I was gonna say last time we had Mark in whose CEO when found a cofounder of Data and I was edging in. So I was executive chairman at the time, you know? And obviously I found the technology. I was looking for an organization that had some forward thinking on storage. Andi, we started to get very close with a large strategic and actually We re announced it on the go to market, I think in February with HP, and I thought that myself and Mark kind of sat down, did a pinky swear and said, OK, maybe it's time for me to step in and take the CEO role just to make sure that we had that sort of marriage of innovation and then some of the operations stuff they could bring inside the business. >> So you've been at this for a >> while, but in the industry for a long time. What was it that you saw? Um, that really wanted you to get deeper in with date. Eriks. Obviously, I'm sure you have tons of opportunities coming your way. You know, to kind of move from the board seat into the CEO position. >> Yeah. Yeah, a bad bet. Maybe stupidity or being drunk. It, to be honest, it was. You know, the first thing is, I was looking for this technology that basically spanned forward, and I had this gut hunch that organizations were looking for data freedom. There's why did the Data Analytics job before that? I did security analytics, and, you know, we were looking at that when we were you know, back when we talk to things like I'm seeing Del and so from appear technology standpoint, I wanted to be in that space, but in the last few months, because you know, jobs are all about learning and then adjusting and learning and adjusting and learning. Adjusting on what I saw is a great bunch of guys, good technology. But we were sort of flapping around on DH had an idea that we were an Advanced data services platform. It's to do with multi, you know, multi cloud. And in essence, I've kind of come to this fundamental kind of understanding because I've been on both sides, which is date era is a bunch of cloud people trying to solve storage needs for what the cloud needs. But they have the experience. They walked that mile. You know, when people say you've gotta learn by walking in their shoes, right? Right on DH there, Done that versus where? Bean. In the past, where we were a ray specialists pushing towards the future that we didn't quite understand, you know, and and there is a fundamental philosopher philosophical difference between the two. Andi weirdly, my analogy or my R har moment came with the Tessler piece. And I know that, you know, you've pinned me a few times on Twitter over this, right? I'm not a tesler. Bigger to the extent of, you know, and probably am now, I should have a test a T shirt on, But I always thought it was an electric car and all they've done is electrified a car and there was on DH, You know, I've resisted it for years and bean know exactly an advocate, but I ended up buying one because I just I felt from a technology standpoint, their platform that they were the right thing. And once I started to really understand what they were about, I saw these severe differences. And, you know, we've chatted a little bit about this Onda again. It's part of the analogy of what's happening in the storage industry, but what's happening in the industry in in a global position. But if you compare contrast something like Tesler, too, maybe Volkswagon and it might be a bad example. But you know, Audi there first trance into electric vehicles was the Audi A three, and I could imagine that they were traditional car people pushing their car forward sort is a combustion engine will if I change that and put some salt powertrain in place, which is the equivalent of a you know, a system to basically drive the wheels and then a bunch of batteries Job done or good, right? Right. And I assume the test it was the same. But I had a weird experience, which is, once you get it into autopilot, you can actually set the navigation direction, and then it will indicate it'll it'Ll hint to you went to change lanes. And so, for instance, I'm driving to the office and I'm going along eight eighty and I want to go toe Wanna one? It says, You know you need to pull across. They hit the indicator will change lanes and they'LL do some of the stuff and that's all well and good. But I was up going to a board meeting on two eighty, going off for the Rosewood. You know, Sandra El Santo and I was listening to a book one of these, you know, audiobooks, and I wasn't really paying much attention. I'm in the outside lane, obviously hitting the speed limit gnome or but I wasn't paying attention. And all of a sudden the car basically indicates form A changes lanes, slows down, change lane again and then takes a junction, slows down, comes up to a junction, and you start to realize that actually Tesla's know about electrified vehicles. It's actually about the telemetry and the analytics and then feeding that back into the system. And I always thought Tesler might be collecting how faster cars going when they break. You know the usual thing. Everybody has this conversation. It's always over worked. But if you've sort of look at it and he said no, maybe they collect everything and then maybe what they're doing is they're collecting, hitting the indicator stalk. So when I'm coming out to a junction and I indicate, How long do I stay? Indicating before I break? And then I changed lanes and then I basically slow down and I go into the junction. And then what they do is they take that live information and crowdsource it, pull it back into the system, and then when they're absolutely bulletproof, that junction, then is exactly as a human would normally do this. They then let the car take over So the difference between the two junctions is one they totally understood, the other one there still learning from right when you look at it and you go done. So they're basically an edge telemetry at a micro level organization, you know, And that is a massive difference between what Tesla's doing and a lot of the other car manufacturers are doing. They're catching up, which is really why I believe that they're going to be a head for a long time. >> It's really interesting. I was >> Elektronik wholesale for ten years before come back to school. Can't got in the tech industry. And so really distribution was king from the manufacturer point of view. Always. They just like ship their products for ages, right? These distribution to break bulk thes distribution, educate the customer these distribution just to get this stuff out. But they never knew how people actually operate their products. Whether that be a car, a washing machine. Ah, cassette player, whatever. So what? What What fascinates me about thes connected devices is what, what a fundamentally different set of data. Now manufacturers have people have in how people actually use the product. But even more importantly, that as you said, they could take that data and make adjustments on the fly because since so much of its software now, we talked again before we turned on some of your software upgrades that you've gotten in the Tesla over the last six months, which we're all driven by customers. But they had a platform in place that enabled them to update functionality and to basically repurpose hardware elements for a new function, which is which is, you know, so in sync with Dev ops and kind of this dev up culture in this continuous this continuous upgrade, this continuous innovation with actual data from real people operating the products that they should come to the market. >> Andi, think once you step back. And that was really why was keen to sit down and talk. And it's not specifically around software defined storage, which is the data. A piece in our example is yes, I am the Tessler because we can do all of the analytics and all of the telemetry versus of standard array. If you scratch that away and you say let's have a look at our whole lives are macro lives. Another example was my wife and I. We've got friends of ours always banging on about these sleep by number beds and and so we went past the store wandered in, and the sales rep got us lying on a bed and he was doing there, you know, pumping the bed up to a size. It's just Well, you are sixty five, a US seventy or seventy five, and I kind of got bored of that. And I went here, Okay, I'm that and he goes, Okay, your wife's of fifty and you're a seventy five, Andi said. But let's kind of daft. And he goes, Well, here's and he shows them a map and it shows a thermal image of me lying on the bed. I'm a side sleeper back sleeper, and then what they do is they feed the information so that comes back off their edge, which is now Abed. And then what they do is they then analyzing continuously prove it to try and increase my bed sleeping patterns. So you look at it and you say what they're not doing is just manufacturing of mattress and throwing it out. What they've done is they said, we're going to treat each individual that lies on the mattress differently on, we're going to take feedback and we're going to make that experience even better. So that the same thing, which is this asset telemetry my crisis telemetry happens to be on the edge is identical to what they have, you know. And then I look at it and I go, Why don't I like the array systems? Will, because the majority of stuff is I'm a far system. My brain is inherently looking at the Dr types underneath and saying, As long as that works fine, everything that sits inside that OK, it'LL do its thing right, and that was built around the whole process and premise of an application has a single function. But now applications create data. That data has multiple functions, and as people start to use it in different ways, you need to feed that data on the way in which is processed differently. And so it all has the intelligence houses in home automation. I'm a junkie on anything that has a plug on it, and I've now got to a point where I have light switches or light fittings would have multiple bulbs on every bulb now is actually Khun B has telemetry around it, which I can adjust it dynamically based on the environment. Right? Right. And I wish it got wine. You know, I got the wine. Fridge is that's my biggest beef right now is you gotta wine, fridge. You can have Jules, you know, you have jewels climates, which means that you don't fan to one side of it and they overheat the bottom right. But it'LL break the grapes down. Would it be really cool if the cork actually had some way of figuring out what it needs to be fed? And then each of them could be individual, right? But our entire being, you know, if you think about it's not just technology or technologies driving it, but it's not the IT industry, but our entire lives. And now driven around exactly what you just described, which is manufacturers dropping something out into the wild to the edge and then having enough telemetry to be able to enhance that experience and then provide over the air, you know, enhancements, >> right? And the other thing, I think it's fascinating as it's looking up. We interviewed Derek Curtain >> from the architect council on. That's a group locally that just try work, too, along with municipalities and car manufacturers, tech companies. But >> he made a really interesting >> comment because there's the individual adjustment to you to know that you want to get off it at Page Milan or sandhill on DH. You've got a counter on your point of this is meeting the Rosewood. But >> then the other thing is, when you aggregate >> that now back up. You know, not that you're going to be sharing other people's data, but when he start to get usage patterns from a large population that you can again incorporate best practices into upgrades of the product and used a really good example of this was right after the one pedestrian got killed by the test of the lady with the bike that ran across the front of the street and it it it literally happened a week before. I think the conference so very hot topic at an autonomous vehicle conference and >> what he said, which is really important. You know, if if I get >> in an automobile accident and I'm going to learn something, the person I hits pride gonna learn something. The insurance adjusters going to take some notes and we're going to learn it's a bad intersection. I made a mistake, whatever, but when an autonomous vehicle gets in a Brack when it's connected, all that telemetry goes back up into the system to feed the system, to make improvements for the whole system. So every car learns every time one car has a problem every time one car gets into a sticky situation. So again, kind of this crowd sourced. Learning an optimization opportunity is fundamentally different than I'm just shipping stuff out, and I don't know what's going to happen to it, and maybe a couple pieces come back. So I think people that are not into this into the direct connection are so missing out on those you said this whole different level of data, this whole different level of engagement, a whole different level of product improvement and road map that's not a PR D. It's not an M R G. It's all about Get it out there, you know, get feedback from the usage and make those improvements on this >> guy finish improvements and micro analytics. I mean, even, you know, we talk back when you were adjusting how you deliver content for the Cube, you know, rather than a big blob, You really want to say, Well, I need more value for that. My clients need more value for that. So you've almost done that Mike segmentation by taking the information and then met attacking every single word in every single interview right to enrich the customer's experience, you know, And it kind of Then you Matt back and you say, We've got to the age now where the staff, the execs that we talked to over the other side, the table there, us they're living our lives. They've got the same kids as we've got the same ages we've got. They do the same person's we've got. They understand the same things and they get frustrated when things naturally don't work the way they should. Like I've got a home theater system and I've still got three remote controls. I can't get down. I've got a universal remote control, but it won't work because the components don't think so. What's happened is we've got to a world where everything's kind of interconnected and everything kind of learns and everything gets enriched when something doesn't it now stands out like a sore thumb and goes, That doesn't That is not the right way to do business on DH. Then you look that you say, translate that then into it and then into data centers. And there's these natural big red flag that says That's an old way of doing things. That's the old economy that doesn't enable me to go forward. I need to go forward. I need more agility. You know, I've got to get data freedom and then how do I solve that issue? And then what? Cos they're going to take me there because they're thinking the same ways as we are. This is why Tesler screamingly successful. This is why something like these beds are there. This is why things like Philips Hue systems are good and the list just goes on. And right now we're naturally inclined to work with products that enable us to enrich our lives and actually give feedback and then benefit us over the air. We don't like things that are too static now, and actually, there is this whole philosophy of cloud, which I think from an economic standpoint, is superb, you know? I mean, our product is Tier one enterprise storage in an SD s fashion for public private hybrid clouds. But we're seeing a lot of people doing bring backs. You know, out of the cloud is a whole thread of it right now, but I would actually say maybe it's not because the cloud philosophy is right, but it's the business model of the cloud guise of God. Because a lot of people have looked at cloud as they're setting. Forget, dump my stuff in the cloud. I get good economics. But what we're talking about now is data gets poked and prodded and moved and adjusted constantly. But the movement of the data is such that if you put in, the cloud is going to impinge you based on the business model. So that whole thing is going to mature as well, right? >> You're such a good position to because >> the, you know the growth of date is going. Bananas were just at at Arcee a couple weeks ago. In one of the conversation was about smart smart buildings, another zip zip devices on shades that tie back to the HBC, and if anybody's in the room or not, should be open should be closed. Where's the sun? But >> there was really interesting comment about >> you know, if you look at things from a software to find way you take what was an independent system that ran the elevator and independent system that ran the HBC and independent system that ran the locks? One that ran the fire alarm. But guess what? If the fire alarm goes off, baby, it would be convenient to unlock all the doors and baby. It would convenient automatically throw the elevator control system into fire mode, which is don't move. Maybe, you know so in reconnecting these things in new and imaginative ways, and then you tie it back to the I T side of the house. You know, it's it's it's it's getting a one plus one makes three effect. With all these previously silent systems that now can be, you know, connected. They can be software defined, you know, they can kind of take the operation till I would have never thought of that one hundred years. I thought that just again this fascinating twist of the Linz and how to get more value out of the existing systems by adding some intelligence and adding this back and forth telemetry. >> Yeah, and and and again, part of May is being the CEO of date era. I want advocates the right platform for people to use. But part of this is my visceral obsession of this market is moving through this software defined pattern. So it's going from being hardware resilient to software resilient to allow youto have flexibility across it. But things have to kind of interconnecting work, as you just described on SDF software to find storage as an example comes in different forms. HD is an example of it and clouds an example. I mean, everything is utterly software defined in Amazon. It so is the term gets misused, could be suffered to find you could say data centric data to find or you could say software resilient. But the whole point is what you've just described, which is open it up, allow data freedom, allow access to it and then make sure that your business is agile and whatever you do, Khun, take the feedback in a continuous loop on at lashing. Move forward as opposed to I've just got this sentence forget or lock mentality that allows me just to sort of look down the stack and say, I've got the silo. I'm owning that customer of owning the data and by the way, that's the job. It's going to doe, right? So this is just the whole concept of kind of people opening their eyes on DH. My encouragement on DI we can encourage anybody, whether customers or basically vendors, is to look around your life and figure out what enriches it from a technology standpoint. On odds on it will be something in the arena that we've just described, right? >> Do you think it's It's because I think software defined, maybe in its early days was >> just kind of an alternative thought to somebody doing it to flipping switches. But as you said in the early example, with the car, propulsion wasn't kind of a fundamentally different way to attack the problem. It was just applying a different way to execute action. What we're talking about now is a is a totally higher order of magnitude because now you've got analytics. You actually want to enable action based on the analytics based on the data for your card. Actually take action, not just a guy. Maybe you should you know, give given alert and notice that pops up on your phone. So, you know, >> maybe we need something different because it's not just redoing >> what we did a different way. It's actually elevating the whole interaction on a whole different kind of love. >> And this is this is kind of thank you for that. It was the profound kind of high got wasn't joining data and watching it. It was I got a demo off the cloud. You I the callback piece of what cloud? What data has. And I was watching a dashboard off a live data stream. You know of information that we were getting back from multiple customers and in each of the customers, it would make recommendations of, you know, how many gets on, how many times it would hear cash on DH. So it was actually coming back dynamically and recommending moving workloads across onto or flash systems. You, Khun, do things where once you've got this freedom on application, a data set isn't unknown. It's now basically in a template, and you say this is what priority has. And so you say it's got high priority. So whatever the best legacy you could give me. Give me right, You drop it onto a disk. And at the moment I've got hybrid. That's all I've got, but I decide to addle flash. So I put some all flash into the into the system. Now it becomes part of this fabric and its spots it and goes well on our second. That will disservice me better and then migrates the workload across onto it without you touching it, right? So, in other words, complete lights out so that the whole thing of this is what Mark and the team have done is looked at and said the only way forward is running this massively agile data center based on a swarm of servers that will basically be plugged together into something that would look like a fabric array. But but you can't. Then you've got to assume that you can now handle application life cycles across onto it. It'LL make recommendations like the bed thing. You know what I was saying? I was lying there and what I liked about it. So So I set my thing to fifty nine, and then it realizes I'm not sleeping very well. It's not suggested. Sixty sixty one sixty. Sleeping well, OK, that's it. And then that's good. We'LL do the same thing where an application will actually say, Here's my template. This is what it looks like. The top priority, by the way. I need the most expensive drives you've got, drops it onto it, and then it look at it and go. Actually, we could do just as good a job if there's on hybrid and then migrated across and optimize the workload, right? And so it's not again. Part of it is not. Data is the best STDs, and it is for Tier one for enterprise storage. It's the fact that the entire industry, no matter where you look at it, not just our industry but everybody is providing tech is doing is exactly the same thing, which is, and you kind of look it and you go. It's kind of edge asset micro telemetry, and then that feedback loop and then continuous adjustment allows you to be successful. That's what products are basically getting underpants. >> Just, you know, it's when he's traveling. Just No, we're almost out of time, but I just can't help it but >> say it, you know, because we used to make decisions >> based on samples of old data with samples. And it was old. And now, because of where we are on the technology lifecycle of drives and networks and CPS and GPS, we can now make decisions based on all the data now. And what a fundamentally different, different decision that's going to drive this too. And then to your point, it's like, What do you optimizing for? And you don't necessarily optimize for the same thing all the time that maybe low priority work, load optimized for cost and maybe a super high value workload optimized for speeding late in sea. And that might change >> over time when Anu workload comes in. So it's such a different way to look at the world >> and it is temporal, right? I mean, again, I know you're going kick me off now, but think about it right the old days and writing a car building a car is you thought, well, what's going to need to be in the car in three years time, put it in now, build manufacture, coming out and then with a Tesler i by the current December. Since December, I've now got pinned based authentication I've got century mode. I've got Dash Cam, They've got all free. I've got a pet mode into it now. My car's got more range. It's got high performance. This guy highest top speed, and I haven't even taken the current or it's all over the air And this is all about, continues optimization. They've done around the platform and you just go. That's the way this linked in. Recently, someone posted something said, You know, keep the eyes are dead. Well, the reason there saying that isn't because there's a stupid thing to do Q. B. Ours is because if you're not measuring your business and adjusting on a continuous basis, you're gonna be dead anyway. So our whole economy is moving this way. So you need an infrastructure architecture to support that. But where everybody's the same, we're all thinking the same. And it doesn't matter what industry or, you know, proclivity have this. This adjustment and this speed of adjustment is what you need. And like I said, I'm That's why I wanted to get to date era. That's what I'm excited about it and that is the are hard I had I kinda looked. It went Oh my God, I'm now working with cloud people who understand what they've walked in the shoes And I kind of got this way of sense of can Imagine what it had been like if you were ill on the first time You saw a hundred thousand cars worth of life data spilling in of what power you have right to adjust and to basically help your client base. And you can't do that if you are in fixed things, right? And so that's That's the world moving forward >> just in time for twenty twenty one will all have great insight in a few short months. We'LL all know >> everything Well, guy great Teo Great to >> sit down Love to keep keeping tabs on you on Twitter and social And thanks for stopping by. I >> appreciate it. All >> right. He's guy. I'm Jeff. You're watching the cube within a cube conversation Or Paulo? What? The studio's thanks for watching >> we'LL see you next time

(orchestral music playing) >> Hi. I'm Peter Burris. Welcome to another Cube Conversation. Brought to you by theCUBE from our beautiful studios in Palo Alto, California. Great conversation today. We're going to be speaking with Datera about some of the new trends and how we're going to utilize data within the business, with greater success, generating more value to superior customer objectives. To do that, we've got Marc Fleischmann, who's the CEO and Founder of Datera. Marc, welcome to theCUBE. >> Thank you. >> And Guy Churchward, who's the Executive Chairman of Datera. >> Yeah, thank you Peter. >> So guys, this is a great topic, great conversation, very very timely industry. One of the reasons is we've heard a lot about the Cloud-native stack. Now the Cloud-native stack is increasingly going to reach into the enterprise and not just demand that everything come back to the cloud, but bring the cloud more to the enterprise. Well one of the things that's still something of a challenge is and how do we bring data given it's native attributes into that model more successfully. Marc, what are the issues? So look, ultimately we believe it's all about data freedom, the capability to extract the value of data across the enterprise. However, as long as we continue to think about proprietary systems silos, where data is trapped, where it can't move freely across the enterprise, we're not going to be able to get there. So ultimately what it requires is changing our thinking of infrastructure from a hard for centric prospective to a service centric prospective. Ready applications drive the needs from the data, where it's an application centric perspective that automatically drives how data is actually consumed across the enterprise. >> But the, we've been thinking about that through software defined, ECI, and other, you know, hyperconversion infrastructure in other things. But at the end of the day, we really have to make sure that we're doing so in a way that marries to the realities of data. >> Absolutely. >> Talk to us a little bit about how Datera is providing that substrate that is native to data, but also native to the cloud. >> Absolutely. So I would describe Datera as Datera is to data what Kubernetes is to compute. What do I mean by that? First of all, it's all about data orchestration. We orchestrate the data just like Kubernetes would orchestrate compute. That's the foundation of our platform. Now if we don't deliver enterprise performance, so that we can actually, you know, replace existing storage, we wouldn't be able to actually broadly deploy. So we have enterprise performance as well. And lastly, to get away from a hard for centric model, we offer wide variety, wide choice, future ready choice of Harver. Those are the three key tenants that we actually see as getting to that vision. >> So Guy, you've been in this business a long time. You've looked at a lot of changes in technology, for rays where we were mainly focused on persisting data to now some of the new technologies, we were more focusing on delivering data to new classes of applications. From your perspective, how does this message Marc's bringing line up with customer needs? >> Yeah, I know, appreciate it. I mean that was one of the reasons that when I had the opportunity to work closely with Datera, I kind of jumped into it. You know, because part of this is, as Marc said, data freedom. Unlocking, in other words, unlocking from the boundaries of basically a physical location. I think, you know, we always aspire and believe that we want to move towards a cloud, a pure cloud model. But we're going to be in this transition for five, six, seven years where we have on premise a bit of hybrid and a bit of distributed and things like Intelligent Edge. So in other words, the whole concept is to say how do I utilize data no matter where it is into a fabric or a mesh. And I think that the industry that we all live in sort of, by accident, tries to own the data, you know. It doesn't matter whether you own it in a physical construct of a data center or we own it in a physical construct of a piece of hardware or a proprietary format. But in essence you have these data silos absolutely everywhere. And so for me to move to a cloud, you've got the simplicity you need. You've got the orchestration that you actually need. But you need this freedom outside of the bounds of a physical location or a piece of tent. >> I want to return back to the issues of performance >> Yeah. >> and the need for performance because the world that you just laid out guys, makes an enormous amount of sense to me and the Wikibon community. But it does mean that this data generated by that application in this location may have value to some other applications somewhere else that may have completely different performance action. >> Absolutely. >> So let's talk about that need for ensuring, that again, this notion of a native data approach to incorporating data into the cloud. How does the performance angle really work? >> I would argue where traditional self defined storage, SDS, fell short was exactly on the promise of performance. We saw that we contributed a significant part of the Linux data path itself. The way we architected the system, we delivered true, primary application performance. So that in combination with the ability to orchestrate data across the data center, across multiple data centers, and ultimately across the data center and the cloud gives you the best of both worlds. It gives you primary workloads, the ability to actually serve primary workloads across multiple protocols, but to serve them location dependent, wherever you like, because we orchestrate the data through those places. >> And- >> So- >> Oops! Go ahead. >> Sorry. It's the coffee. It's going to kick in. (Peter laughs) So I mean part of it is not just that, but it's also the life cycle. >> Ah, very true. Right, I mean and, you know, this is the thing that kind of attracts me is, and you mentioned, you know, what you learn with the amount of hair I don't have now and the gray beard I've got is, you know, there's one thing about this sort of data boundaries and things getting locked in. The other one is the speed of which people want to build an application. They need it to be have the enterprisilities, and then they'll take the application down. You know, if you kind of think when we started in the industry and it would last 20 years. And then 10 years. And then five years. And now you look at it saying somebody wants an enterprisility application up and running within two or three months, which is preposterous, but needs to be done. And then it might be down within a month. Because- >> Oh 15 years ago it took us two or three months to create the test data required for the application to follow up. >> Right, and how many people would ever used to tell you never use an application if it's a window zero. But we're talking about, in a window zero period, they're actually going to serve their communities, the most critical thing. Data is it for a company. If you're analytics don't run as fast as your company's competitive space, you're behind. So if you're going to analyze something that application that you bring up to analyze has to be critical to your business. And that's going to go up and it's going to go down. So in other words, it's going to go from test and dev, up into production, tier zero, then tier one, tier two, tier three, and then out into an archive in a period of time that normally a window zero would gestate. And so you need a platform that has that ultimate agility and again it can't be bound by anything. And this is something that, you know, Datera has as unique. This was why I like software defined and why I believe that this market's place is now for this space. Everything prior to SDS is basically what I call new legacy. You know, it doesn't matter whether it's a ray or it's hyperconversion, and they're great and they've got their place. But each one of them has this fixed boundary that allows you to flex but inside of its own control. Businesses aren't like that. They can't be done like that and applications can't be done like that now. So it's all multi-cloud, it's all going to be versed. >> Well let's build on that. So the Kubernetes describes, as you said, a cluster of compute. When you pull away the- It's really a network of compute. >> That's right. >> It's a network of compute resources that Kubernetes has visibility into so we can move resources >> That's right. >> Or move elements where they need to be to be optimally utilized. Let's build on that. So what where is Datera in this relationship between resources as it starts to build a an orchestrator, a manager, a network of data elements, and pull that into something that makes it easier for developers to do what they need to do, operators to do what they need to do, and the business to do what it needs to do? >> Yeah, so you can call Kubernetes the network of compute or a swarm of compute, right? So the power of Kubernetes is that it abstracts the infrastructure to a level where it gets delivered continuously to the application on demand. We do exactly the same thing for data, for the ability to store, manage, and ultimately life cycle data. So simply label based, like Kubernetes is, you specify the service level objectives for every individual application, and Kubernetes pretty much does all the rest of the job, completely independent of the hardware underneath. Again, we do that for data. You have certain access requirements, protocols, authentications, security. You have certain performance requirements. You have certain reliability requirements. You articulate them simply in similar SLO, service level objectives. Datera does all the actual implementation automatically across the data center. So now you get to a point where in the modern data center and the soft defined data center, I would argue we are the data foundation in those kinds of scenarios, we can co-orchestrate data along, since you said Kubernetes specifically with Kubernetes, with its compute. Obviously we work in other environments as well. We work equally well for Enver. We work for some other, a number of other cloud orchestration frameworks. But Kubernetes is a really good example here. >> So who's going to buy it? I mean cause going back to this issue of the orchestrator, the developers clearly need this because they want access to real data, but they typically don't think in terms of underlying data structures. If it's available that's all they care about. Data administrators, business people. Who do you find your customers today are really making that, not the initial contact, but actually driving the adoption of this new data fabric? >> So Marc, I mean I know you'll answer it more accurately than I will. But just from a higher level to step down, there seems to be two types of people inside of large companies. One is a project owner. So for instance, you know, I've been blessed with a job inside of BMW that I have to do, autonomic cars. And I'm tying together a very complicated pipeline that has to be extremely agile. So that's the type of person that would basically look to buy and move us forward. And the other one is an internal service provider to the enterprise. So in other words, instead of being a group that has a physical job, what I'm actually doing is I'm saying I'm now going to be a service provider, or a cloud provider, or a resource provider to an organization that now has complexity that's moving into and embracing the digital economy or digital transformation. So if those are the two types of person inside of an organization, I think if you get a tie kicker, you know, there are places that we struggle with, I think it would be fair to say, is there's always going to be a geek somewhere that wants to kick the latest, cool technology, so we get involved with that. And then by the time you go all the way through it, there's no project there. They just really enjoyed themselves and so have we. But in essence there's enough people now who recognize my business is going through this transformation, I need to get out of my technical debt, I'm throwing business into, you know, this economy. It's normally around machine learning applications, Kubernetes, things that are fast moving, you know. And they need that level of ility that they're used to getting through fixed bounded technology, you know. And so we're actually seeing that as a service provider, both external and internal. But internal, inside the enterprises, is something which we're very key on. >> And let me give you perhaps a few examples. We're looking at Fortune 2000 companies. A good example, for instance, would be one of the top airlines in the world that is replatforming from a more rigid siloed IT to really deliver all their applications to internal and external customers as a service. It would also be digital businesses where there currency really is speed, agility, and obviously data is their currency. So if you're looking here at one of the top travel fare aggregators, that's one of the customers, actually interestingly we are in their tier zero at Storch. That's quite an endorsement of the performance aspect. We are also in one of, I would say, the leading service providers outside of the typical crowd you think, those are one of the up and coming guys. So those are typical markets and customers we're looking at. Really Fortune 2000 companies that are replatforming to cloud, hybrid cloud, and digital service businesses. Digital businesses. >> But it is most people who are basically going from, they're transforming their data center into a metadata center. They're embracing the distribution and then cloud. But they're not going wholesale and just saying (claps hands) we're over. They have this practicality of first thing I need to do is to free up my data, make my data center agile, and then decide how I want to distribute it across. >> Marc Fleischmann. Guy Churchward. Datera. Thank you very much for being on theCUBE. >> Thank you very much Peter. >> A pleasure. Thank you. >> And once again, this is Peter Burris from our CUBE studios in Palo Alto, California. Thanks very much for participating in this CUBE conversation with Datera. (orchestral music plays)

(inspirational music) >> Hello, I'm Peter Burris of Wikibon. Welcome to another Cube Conversation. We're going to have a great time talking over the next few minutes about the role that performance in the data plain is going to play at making possible both the options provided by the Cloud, but at the same time in a way that actually allows us to actually run the applications at the speed and the scale the business requires. To do that, we've got Marc Fleishman, who's the CEO and founder of Datera, and Marc Churchward who's the Executive Chairman at Datera. Welcome back to theCube, guys. >> Thank you for having us, Peter. >> So, Marc, I want to start with what I started with. That at the end of the day we've got this enormous agility that we're provided with in the CloudStack, but you still have to run on real computers that have real constraints and everybody knows that there is no greater constraint than maintaining the state of data and moving data. So how does Datera address those issues? >> Part of data freedom obviously is not only about automation, the the promise of software-defined storage is seamless automation. But unfortunately in many cases with unimpressive performance. In our case, we've engineered the whole data path down to the physical devices, ourselves. The levels of performance we can deliver is millions of IOPS across the data center at less than two hundred microseconds latency. Most importantly, on standard servers, over standard protocols, so nothing fancy in terms of hardware required. That's the true promise of software-defined storage. >> Now you mentioned automation. That kind of performance has got to open up new classes of automation potential, so that the storage or the data resources are that much easier to envision, that much easier to apply, that much easier to exploit by the development community. Tell us a little bit about how automation plays into this. >> Absolutely, once you've made data delivery frictionless, and you've made data orchestration and data automation frictionless, you do unlock new classes of applications. What we're specifically seeing is folks who traditionally run an array of databases on very dedicated proprietary hardware, and then again they get the data trapped in those silos, and they have a real hard time to extract the value off that data, we see a lot of database farms coming on our unified platform across the data center, basically being able to really extract the value of the data across a range of applications. >> Now we've been in the last few years investing pretty heavily in storage area networks and arrays and those types of resources. Flash is changing that, but it sounds as though you guys are actually making it easier to bring servers into the mix of this. What's the real direction you see? Where's this resource going to be managed by and what's the opportunity? >> Ultimately the resource should be managed by the applications, it should be driven by the applications and managed by machine learning. So as we understand the requirements of the applications, every individual application, it should be managed by machine learning in terms of the physical resources on the servers. The server capabilities you put underneath it, and then obviously start rolling the server hardware, as technology improves as well over time. >> So it's really being driven by the server, that's where the market opportunity's coming from. >> That's right, yes. >> The last question I have here is, when we think about new technology, new classes of automation, new trends in the industry, people always immediately go, "Yeah, but new companies?" Where does Datera fit in its lifecycle as it works with customers and as it delivers value out? >> If you look at the market today, server-based storage is already larger than traditional array-based storage. It's growing at five X, year by year. Since we've been on theCUBE the last time, about two years ago, we are now looking at a 240% kegger every year, so the market has clearly come our way. This is the time for this kind of product. >> So the market's good, company's good, trends are good. As we think ultimately about where this ends up in a few years, what role will Datera play within the evolved computing industry? What do you see for it? >> Given that we have the broad data orchestration, enterprise performance and choice on hardware, we really do see ourselves as the data foundation for the software-defined data center. What I mean by that, again, just in an operational model, we are to data what Cooper Ladies is to compute, across a number of operating environments. So it's a really broad data foundation for everyone who wants to deliver ITS service. >> Guy, I have a very simple question for you, very complex answer. One of the places where this seems to be especially important, where the need is especially great, is in that world of analytics. Especially as we try to close the loop between the analytical systems and the operational systems. How does Datera and analytics come together, not just in the use of analytics to make Datera better, but Datera in making analytics applications run better. >> Yeah, and as you said, an easy question, complicated answer. In reality, what companies are trying to do is to run the analytics at the speed of which they're competing in their market space. Which means that it has to get a lot faster. Today's classic environment is an ETL with a data leak, so parking stale data and analyzing it, post-event and tomorrow, in an environment where people are using AI and ML is now in stream and it's in real time. So part of that is you actually have very very fast applications, both from a performance perspective, but also how long their lifecycle is. Because people are doing AB testing on the web, they're doing analytics on the fly, and it really is a kind of a different world. It's a different pace. When I started this business, or when I was in business early and I had hair, we used to look at organizations that had applications that were lasting 10 or 20 years. Now we're looking at enterprise applications that are up and down within a period of months, if not weeks. So managing that lifecycle and not having to invest in infrastructure to support something, that age-old adage of you don't buy an application if it's in 1.0 is gone. Because by the time you're into 1.1, that opportunity's disappeared as well. So, part of what I saw in the attraction with Datera is because it's absolutely software-defined, and all the resilience handles in the software not the hardware. There's not the infrastructure burden. It has much more agility to get up. It can provide tier zero, tier one. Again, you land and expand, so in test endeavor you have the same environment. By a matter of flipping a few switches, you can have tier-one-ilities and then you can drop down in that lifecycle. It doesn't matter whether it's on premise, whether it's a distributed environment or on Cloud. It's the same infrastructure, same architecture, so, back to what Marc said, you have data freedom. >> So we're trying to tie the physical realities of data, to the virtual realities of machine resources in IT, to the Cloud realities of the new wave of applications. >> That's exactly right. >> Marc Fleishman, CEO and founder of Datera, Guy Churchward, Executive Chairman of Datera, thanks very much for being on theCUBE. >> Thanks for having us Peter. >> Thank you Peter. >> And once again, this is Peter Burris, Wikibon, thanks for watching theCUBE. (inspirational music)

>> Live from Miami Beach, Florida, it's theCUBE. Covering UiPathForward Americas. Brought to you by UiPath. >> Welcome back to Miami everybody. You're watching theCUBE, the leader in live tech coverage. We go out to events, we extract the signal from the noise. A lot of noise here but the signal's all around automation and robotic process automation. I'm Dave Vellante, he's Stu Miniman, my co-host. Guy Kirkwood's here he's the UiPath chief evangelist otherwise known as the chief injector of Kool-Aid. Welcome. (guests chuckling) And Craig LeClair, the Vice President at Forrester. Covers this market, wrote the seminal document on this space. Knows it inside out. Craig, great to see you again. >> Yeah, nice to see you again. It's great to be back at theCUBE. >> So let's start with the analyst perspective. Take us back to when you first discovered RPA, why you got excited about it, and what Forrester Research is all about in that space. >> Yeah, it's been a very a interesting ride. Most of these companies, at least that are the higher value ones in the category they've been around for a long time. They've been around for over a decade, and no one ever heard of them three years ago. So I had covered at Forrester, business process management and some of the business rules engines, and I've always been in process. I just got this sense that there was a way that companies could make progress and digital transformation and overcome the technical debt that they had. A lot of the progress has been tepid in digital transformation because it takes tremendous amount of time and tons of consultants to modernize that core system that really runs the company. So along comes this RPA technology that allows you to build human equivalence that patch up the inefficiencies without touching. I came in on American Airlines and the system that cut my ticket was designed in 1960. It's the same Sabre reservation system. That's the big obstacle that a lot of companies have been struggling to really take advantage of AI in general. A lot of the more moonshot and more sophisticated promises haven't been realized. RPA is a very practical form of automation that companies can get a handle on right now, and move the dial for digital transformation. >> So Guy we heard a vision set forth by Daniel this morning. Basically a chicken in every pot, I call it, a robot for every person. Now what Craig was just saying about essentially cutting the line on technical debt, do you have clear evidence of that in your customer base? Maybe you could give some examples. >> What we're really seeing is that as organizations have to deal with the stresses, what Leslie Wilcox professor at LSE describes as the stresses within organizations and particularly in environments where the demographics are changing. What we're seeing is that organizations have to automate. So the best example of that is in Japan where the Japanese population peaked in 2010. It's now falling as a whole, plus all the baby boomers, people of Craig's and my age are now retiring. So we're now in a position where they measure levels of dangerous overwork as being more that 106 hours a week. That isn't 106 hour a week in total, that's 106 hours a week in addition to the 60 hours a week the Japanese people normally work. And there is a word in Japanese, which is (speaking in foreign language), which means to work oneself to death. So there really is no choice. So what we're seeing happening in Japan will be replicated in Western Europe and certainly in the US over the next few years. So what's driving that is the rise of the ecosystems of technologies of which RPA and AI are part, and that's really what we're seeing within the market. >> Craig, sometimes these big waves particularly in infrastructure, you kind of saw it with virtualization and some other wonky techs, like data reduction. They could be a one-time step function, and not an ongoing business value creator. Where does RPA fit in there? How can organizations make sure that this is a continuous business value generator as opposed to a one time hit? >> Good question. >> Well, I like the concept of RPA as a platform that can lead to more intelligence and more integration with AI components. It allows companies to build an automation center or a center of excellence focused on automation. But the next thing they're going to do after building some simple robots that are doing repetitive tasks, is they're going to say "Oh well wouldn't it be better "if my employee could have a textual chat with a chatbot "that then was interacting with the digital worker "that I built with the bot." Or they're going to say "You know what? I really want to use that machine learning algorithm "for my underwriting process, but I can use these bots "to go out and collect all the data from the core systems "and elsewhere and from the web and feed the algorithms "so that I could make a better decision." So again it goes back to that backing off the moonshot approach that we've been talking about that AI has been taking because of the tremendous amount of money spent by the major players to lay out the promise of AI has really been a little dysfunctional in getting organizations' eye off the ball in terms of what could be done with slightly more intelligent automation. So RPA will be a flash in the pan unless it starts to embed these more learning-capable AI modules. But I think it has a very good chance of doing that particularly now with so much investment coming into the category right. >> Craig, it's really interesting. When I heard you describe that it reminds me of the home automation. The Cortanas and Alexas and consumer side where you're seeing this. You've got the consumer side where you can build skills yourself, you know teenagers people can do that. One of the challenges always on the business side is how do you get the momentum when you don't have the consumer side. How do those interact? >> It's the technical debt issue and it's just like the mobile peak in 2011. Consumers in their hands had much better mobility right away than businesses. It took businesses five, they're still not there in building a great mobile environment. So these Alexa in our kitchen snooping on our conversation and to some extent Netflix that observes our behavior. That's a light form of AI. There is a learning from that behavior that's updating an algorithm autonomously in Netflix to understand what you want to watch. There's no one with a spreadsheet back there right. So this has given us in a sense a false sense of progress with all of AI. The reality is business is just getting started. Business is nowhere with AI. RPA is an initial foray on that path. We're in Miami so I'll call it a gateway drug. >> In fact there's also an element that the Siris, the Cortanas, the Alexas, are very poor at understanding specific ontologies that are required for industry, and that's where the limitation is right now. We're working with an organization called Humly, they're focused on those ontologies for specific industries. So if the robot doesn't understand something, then you could say to the robot Okay sit that in the Wells account, if you're in a bank, and it understands that Wells in that case means Wells Fargo it doesn't mean a hole in the ground with water at the bottom or a town in Somerset in the UK, 'cause they're all wells. So it's getting that understanding correct. >> I wonder if you guys could comment on this. Stu and I were at Splunk earlier this week and they were talking up NLP and we were saying one of the problems is that NLP is sometimes not that great. And they made a comment that I thought was very interesting. They said frankly a lot of the stuff that we're ingesting is text and it's actually pretty good. I would imagine the same is true for RPA. Is that what you see? >> You were talking about that on stage. With regards to the text analytics. >> Yes. So RPA doesn't handle unstructured content the way that NLP does. So NLP can handle voice, it can handle text. For the bots to work in RPA today you have to have a layer of analytics that understands those documents, understands those emails and creates a nice clean file that the bots can then work with. But what's happening is the text analytics layer is slowly merging with the RPA bots platforms so it's going to be viewed as one solution. But it's more about categories of use cases that deal with forms and documents and emails rather than natural language, which is where it's at. >> So known business processes really is the starting point. >> Known business-- >> One example we've got live is an insurance company in South Africa called Hollard, and they've used a combination of Microsoft Cognitive Toolkit, plus IBM Watson and it's orchestrated doing NLP and orchestrated by UiPath. So that's dealing with utterly unstructured data. That's the 1.5 million emails that that organization gets in a year. They've managed to automate 98% of that, so it never sees a human. And their reduction in cost is 91% cost in reduction per transaction. And that's done by one of our implementation partners, a company called LarcAI down there. It's superb. >> Yeah, so text analytics is hard. Last several years we have that sentiment out of it, but if I understand it correctly Craig, you're saying if you apply it to a known process it actually could have outcomes that can save money. >> Yes, absolutely yes. >> As Guy was just saying. >> I think it's moving from that rules-based activity to more experience-based activity as more of these technologies become merged. >> Will the technology in your view advance to the point, because the known processes. okay, there's probably a lot of work to be done there, but today there's so many unknown processes. It's like this messy, unpredictable thing. Will machine intelligence combined with robotic process automation get to the point, and if so when, that we can actually be more flexible and adapt to some of these unknown processes or is that just decades off? >> No, no, I think we talk at Forrester about the concept of convergence. Meaning the convergence of the physical world and the digital world. So essentially digital's getting embedded in everything physical that we have right. Think of IoT applications and so forth. But essentially that data coming from those physical devices is unstructured data that the machine learning algorithms are going to make sense of, and make decisions about. So we're very close to seeing that in factory environments. We're seeing that in self-driving cars. The fleet managers that are now understanding where things are based on the signals coming from them. So there's a lot of opportunity that's right here on the horizon. >> Craig, a lot of the technologies you mentioned, we may have had a lot of the technical issues sorted out, but it's the people interactions some things like autonomous vehicles, there's government policies going to be one of the biggest inhibitors out there. When you look at the RPA space, what should workers how do they prepare for this? How do companies, make sure that they can embrace this and be better for it? >> That's a really tough and thoughtful question. The RPA category really attacks what we call the cubicle population. And there are we're estimating four million cubicles will be emptied out in five years by RPA technology specifically. That's how we built the market forecast 'cause each one of the digital workers replacing a cubicle worker will cost $11,000 or what. That's how we built up the market forecast. They're going to be automation deficits. It's not all going to be relocating people. We think that there's going to be a lot of disruption in the outsource community first. So companies are going to look at contractors. They're going to look at the BPO contract. Then they're going to look at their internal staff. Our numbers are pretty clear. We think they're going to be four million automation deficits in five years due to RPA technology specifically. Now there will be better jobs for those that are remaining. But I think it's a big change management issue. When you first talk about robots to employees you can tell them that their jobs are going to get better, they're going to be more human. They're going to have a much more exhilarating experience. And their response to you is, What they're thinking is, "Damn robot's going to take my job." That's what they're thinking. So you have to walk them up the mountain and really understand what their career path is and move them into this motion of adaptive and continual learning and what we call constructive ambition. Which is another whole subject. But there are employees that have a higher level of curiosity and are more willing to adapt to get on the other side of the digital divide. Yep. >> You mentioned the market. You guys did a market forecast. I've seen, read stats, a little over a billion today. I don't know if that's consistent with your numbers? >> Yeah that's about right. >> Is this a 10X market? When does it get to 10 billion? Is it five, seven, 10 years? >> So we go out five years and have it be close to three billion. I think the numbers I presented on stage were 3.2 billion in five years. Now that's just software licenses and it's not the services community that surround that. >> You'd probably triple it if you add in services. >> I think two to three times service license ratio. There's always an issue at this point in emerging markets. Some of the valuations that are there, that market three billion has to be a bit bigger than that in eight or nine years to justify those valuations. That's always the fascinating capital structure questions we create with these sorts of things. >> So you describe this sort of one for one replacement. I'm presuming there's other potential use cases, or maybe not, that you forecast. Is that right? >> Oh no for the cubicles? >> Yes, it's not just cubicle replacement in that three billion right? It's other uplifts. >> No there are use cases that help in factory automation, in supply chain, in guys carrying around clipboards in warehouses. There are a tremendous number of use cases, but the primary focus are back office workers that tend to be in cubicles and contact center employees who are always in cubicles. >> And then we'll see if the non-obvious ones emerge. >> I think ultimately what's going to happen is the number of people doing back office corporate functions, so that's both finance and accounting procurement, HR type roles and indeed the industry specific roles. So claims processing insurance will diminish over time. But I think what we're going to see is an increase in the number of people doing customer experience, because it's the customer intimacy that is really going to differentiate organizations going forward. >> The market's moving very fast. Reading your report, it's like you were saying yesterday's features are now table steaks. Everybody's watching everybody else. You heard Daniel today saying, "Hey our competitors are watching. "We're open they're going to steal from us so be it." The rising tide lifts all boats. What do you advise clients in terms of where they should start, how they should get started? Obviously pick some quick wins. But what do you tell people? >> I always same pretty much the same advice you give almost on any emerging technology. Start with a good solution provider that you trust. Focus on a proof of concept, POC and a pilot. Start small and grow incrementally, and walk people up the mountain as you do that. That's the solution. I also have this report I call The Rule of Fives, that there are certain tasks that are perfect for RPA and they should meet these three rules of five. A relatively small number of decisions, relatively small number of applications involved, and a relatively small number of clicks in the click stream. 500 clicks, five apps, five decisions. Look for those in high volume that have high transaction volume and you'll hit RPA goal. You'll be able to offset 2 1/2 to four FTE's for one bot. And if you follow those rules, follow the proof of concept, good solution partner everyone's winning. >> You have practical advice to get started and actually get to an outcome. Anything you'd add to that? >> In most organizations what they're now doing, is picking one, two, or three different technologies to actually play with to start. And that's a really good way. So we recommend that organizations pick three, four, five processes and do a hackathon and very quickly they work out which organizations they want to work with. It's not necessarily just the technology and in a lot of cases UiPath isn't the right answer. But that is a very good way for them to realize what they want to do and the speed with which they'll want to do it. >> Great, well guys thanks for coming on theCUBE, sharing your knowledge. >> Thank you. >> Pleasure. >> Appreciate your time. >> Thanks very much indeed. >> Alright keep it right there everybody. Stu and I will be back from UiPathForward Americas. This is theCUBE. Be right back. (upbeat music)

>> Announcer: Live from the Computer History Museum, in Mountain View, California, it's theCUBE! Covering DevNet Create 2018, brought to you by Cisco. >> Hello and welcome back to theCUBE's exclusive live coverage here in Mountain View, California, the heart of Silicon Valley at the Computer History Museum for Cisco's DevNet Create. I'm here with Lauren Cooney, the analyst, for the Wikibon team and our next guest is I'm proud to have Guy Kawasaki here on theCUBE. Guy is, goes without mentioning, a legend in the industry. Currently, the chief evangelist for Canva author of Art of the Start, a real pioneer in entrepreneurship, tech entrepreneurship, tech evangelism. Guy, great to see you, thanks for joining us. >> Thank you. >> Among other things, you've done a lot of amazing things. Thanks for joining us. >> What better place to be. >> The tech culture now is so mainstream. You're seeing Facebook CEO draw in more audience than a Supreme Court justice. >> More people watched the Senate hearings yesterday-- >> He probably has more impact than a Supreme Court justice. >> He's running the world. The tech culture has really grown to be a mainstream...in the early days the computer industry when it was really the beginning of the revolution, the PC revolution, Macintosh and the PC, you were there. So much has happened. I mean, as you look back, I mean looked out at the young guns coming up, what's your view, what's your reaction to all this? You have these (mumbles) moments. >> What's your take on all this? >> I suppose many people would say, we never thought it would get to this point. It's turned destructive and negative and all that. But it's a short snapshot of time and, first of all, can we put the genie back in the bottle? No, so it doesn't really matter. But, all things considered, the democratization of computing, everybody has a computer, whether it's a phone or a computer. The democratization of the transfer of information, obviously some information may be faint, may be not what you like. But would we go back to a time where we send things by fax machines? Not at all, I mean all things considered, >> it's a great time to be alive. >> Democratization goes through these waves, democratization with the PC, democratization with the internet, democratization of web 2.0 and social media. The beginning of social media, about 15 years, maybe 10, whatever way you might want to mark it. And now democratization with data and AI is interesting. So you're having these waves of democratization. It's going to take some time to sort out. I mean, as you look at the tech trends, how do you make sense of it, or what do you get excited about? How do you surf that wave? (chuckling) If you're going to surf the wave, the big wave coming, which some say is block chain and cryptocurrency and decentralization. What's the wave that you're on, that's the question? >> To use a surfing analogy, if we're going to go down that rat hole, a good, experienced surfer knows where to sit, can look out and say, I'll take the fourth wave. And I'll sit in the right place, turn around at the right time, paddle at the right time, you know, all that. And then there's people like me. We sit in the same place, and every 15 minutes, the right wave comes along and catches us. Those are the two theories. >> I think if only predicting tech trends were as easy as predicting surfing. >> Interviewer: Timing's everything. >> Timing is everything, luck is a lot to do with it. We only learn about the Apples and the Googles and the Ciscos and the Facebooks and the Pinterests and the Instagrams. I think you think, well, there are these really smart people and they can predict the trend or cause a trend. I think it's more the game of big numbers where if you have enough surfers in the water, somebody's going to catch a wave. (chuckling) And then you can say, yeah, I knew he was the best surfer. >> But really, right place, right time. >> And you got to know what a wave looks like. >> Guy: Well, yeah. >> You got to be, like, okay, am I in a tide pool >> or am I on a boogie board. >> And to your point, you've got to be in the water. [John] Yeah, yeah. >> You can't be standing on the shore, saying I'm going to catch a wave. You have to be in the water, and if you're in the water, >> nine times out of ten you're going to get crushed. (chuckling) >> If you're not out in front of that next wave, you're driftwood. In surfing, people will jump and try to take your wave, this sounds like the tactic of the whole industry. >> Guy: Exactly, right, right. >> What waves do you see that are coming, in your mind. You've seen a lot of waves in your day. I mean, right now, what wave is exciting you right now. >> If you look at the waves, what's out there? >> What I learn about that is, you can only declare your intelligence and victory after the fact, right. I can tell you the internet of things is big. I can tell you that social media is big. I can tell you that computing is big. Problem is I could tell you that because I know it's big now. Can I tell you what's in the future, no. If I could...first of all I wouldn't tell you. (chuckling) So I think in a rare moment of humility it's the law of big numbers. Infinite monkeys typing at keyboards, somebody's going to come up with Beethoven. >> I want to ask you a question because I get asked this question a lot, Hey, John, you've been around a while. I want to catch that next big wave, I want to be in the next Google, I want to be rich on stock options. (Guy chuckling) I said, a lot of times the best companies where you take the most advantage of is when no one else wants to work there or no one yet knows it. We really can't say, Oh, I'm going to get rich on that company because by that time it's either too late and people are chasing the wrong thing. >> Guy: Absolutely. >> How do you give that same advice to someone? >> Listen, you're talking to a guy who quit Apple twice and turned down Steve once. So how smart could I be? (John chuckling) Now we can say Apple is the most valuable company in the world, you should have stayed there. Well, thank you very much, thanks for tell me now. I think it's really... I don't want to be too dramatic, but I could almost build a case that you should invest in or work for the most dumb-ass idea you heard of. Because at any given point-- >> Airbnb, we're going to rent out mattresses >> and give out cereal. >> Very good example, Airbnb. Let's face it, if somebody told you Airbnb, before there was Airbnb, you would say, So you're telling me that I'm going to rent a room from somebody I met on the internet, and I'm going to sleep in that person's house, hoping he's not a murderer or pedophile. On the flip side, you're saying, I'm going to rent out my room to someone who I hope is not a pedophile or an ax murderer. Or ebay...I'm going to buy this printer from 3000 miles away and I'm going to assume it works. Or I'm going to sell my good printer to someone 3000 miles away and assume that he's not going to say he never got it or that it didn't work and he wants a refund. So if you go down the line of all these ideas, you'd have to say at the time, nobody. Even take an extreme: Zappos. If you told me that women would buy shoes without trying them on, seeing them, smelling them, and touching them, I would tell you you're crazy. You'd buy a book that way. You'd buy a CD that way, you'd buy a DVD. Would you buy shoes, would you buy shoes without trying them on. >> I totally would. (laughing) Now I can say that. >> To Zappos's credit, some of the way it made that work is it offered shipping back for free. So there was really no risk. But I would have been a skeptic about Zappos. >> Well, it was one of those things for me, Zappos, where they shipped in one day so I could get them immediately, try them on and if they didn't work, I could ship them back and get a different size. It was no big deal, it was very low overhead. So that's one of the reasons that that worked. But I think when you mention all of these great things like Ebay and Airbnb, it's really part of the sharing economy with people really wanting to share the goodness of their goods with other people that need them. >> It's just really connecting those folks. >> Places like Oakland and San Francisco, where there are certain streets where you line up and you just get in the next car with a stranger, and you go to San Francisco with them. >> Lauren: Yeah. >> And it's not computerized or anything. It's just trust. >> I did that once and it was frightening. (laughs) You never know who the driver is going to be or how they're going to drive. >> But you did it. >> I did it. >> People do it every day. >> I know. >> I'm amazed. >> I did it once, but... (laughing) >> Let's ask you a question. What's the craziest idea that you've seen that worked and the craziest idea that didn't work. >> Let's start with the easy one. I had a company called garage.com, and we were a venture capitalist investment bank, so we got pitched all the time. One day, a guy comes in and says, I'm going to build... A dirigible hotel over San Francisco. So you stay in the dirigible. Another person said, We're going to build a geodesic dome over Los Angeles. And I can't remember if it was to keep the air pollution in or out. I'll just tell you one really great one. These people were from Seagate so they had Cray, they worked for Seagate. And they say, We have this patent-pending, curb-jumping, patent-pending whatever technology so that if you drop your laptop with your hard disk, the head won't crash into the hard disk and ruin the hard disk. And at the time, this was 15 years ago, that was a great idea, right. It wasn't solid state. Heads crashing into hard disks. >> Moving parts. >> Seagate, so this is a great idea. Every hard disk in every laptop should be like it. So we get in the car, we go to their office, and the receptionist says, Oh, they're running late because they're on the phone with IBM. IBM is really interested in using this technology for the IBM PC laptop. Keep us waiting, keep us waiting. And they get out, and, Yeah, IBM was really, they're so excited, they're ready to move. And I, like, we're really excited. And finally I said, Give me the jist, what is your technology, is it like some special chip that detects gravitational fall, it's too fast, it's got to be hitting the ground so it parks the head because it recognizes motion or whatever. And I swear to God, I swear to God, he brings out this piece of foam and he says this is military spec foam. So we take your hard disk, we put this foam thing around it, and we put it in the laptop. And I swear to God, I was having an out of body experience. >> You're telling me-- >> I drove all the way here-- >> That your proprietary technology is putting foam around the hard disk, and IBM is excited by this foam. So welcome to my life. >> So what are you up to now. Talk about your evangelism. I know you're a (mumbles) Mercedes. You have a bunch of things going on. You've been very prolific in social media. You were on the suggested user list from day one on Twitter. >> No, I wasn't. >> Oh, no, you weren't, that's right. But you have a zillion followers. >> That's why I have never forgiven Twitter for that. >> I thought they put you on. >> Guy: No. >> Okay, I stand corrected. >> You had to be an actress. >> Some tech people got on there, I know. >> Guy: Yeah. >> But I was not on. >> There you go. >> Measly 20,000 or so. But you got a million and a half followers active. You've really been prolific in a good way. (laughing) Engaging with communities. >> Yeah. >> What have you learned and how do you view this next generation of social because you're seeing the Facebooks, you're seeing LinkedIn. There's siloed platforms. Is there hope? What's your take on it, is it going to grow? >> I've come to the point where I always believe things are never as good or as bad as they seem. So I don't think it's as bad as people say. If these social media sites are selling my data, they're going to go broke selling my data. (laughs) I don't know how you could look at my data. First of all, I never look at ads, so go ahead, sell my data. I'm not going to look at the ad anyway. It doesn't matter. I think the ability to spread ideas, arguably good or bad, the ability to spread ideas with social media, all things considered, is better. It's going to be abused and all that. My father was a state senator in Honolulu, and we were into banner ads way before anybody else. Banner was literally a piece of cloth with his name on it that you staple to the side of a building, saying Vote for Duke Kawasaki. That was the nature of banner advertisement back then. Do I think that social media targeting and all that for sales is a good thing? Yes, I do. If you're a real estate broker, and you wanted to reach people who live in Silicon Valley, age 50 to 70, female or male or whatever, in such-and-such an income bracket, how else can you do it but Facebook? >> It's good and bad. >> That's why Facebook is so successful. >> The metadata is all about the clan and the culture, and I think putting ideas out there is a way to send your ideas into the ether, make it happen. So, that's key. Now, we're here at a developer conference, so one of the things that's also a big part of this community is the notion of how open source has become a tier one citizen, and it's really running the world. Which is also grounded in community as well. You have this ethos of community, ethos of software open. >> I believe in open source. I believe that the more intelligent people pounding on your stuff, the better it is. I'm an author, and what I do is, speaking in the sense of open source. So right now I'm about 80% done with my book. I put out a post on social media saying anybody that wants to review my book, test my book, send me your information. So I do this, I cut it off at about 280 people. I send them the Word document, the entire Word document of my book. Does that mean they can take it and publish it in China tomorrow, yes. But, from that, I get hundreds and hundreds of comments. >> John: Wisdom of the crowds, self-editing. >> Yeah, and they point out stuff that I never would have noticed because I'm too close to at this point. So is there a downside, yes. Is there piracy, yes. Arguably, would those pirates have bought the book anyway? No. >> Our content's all free. We're really big in China because they actually take it and translate it in the native language. >> Guy: Which you would never have done. >> With all the jargon, you can't hire a-- >> Guy: You would never have done that. >> Yeah, exactly. >> Guy, great to catch up with you. Thanks for coming on. What are you working on now, you mentioned the book, what's the book about? >> The book is called Wise Guy, and it's a compilation of the stories that have influenced my life. So it's not an auto-biography. It is not a memoir. Have you ever heard of the book Chicken Soup for the Soul? >> John: Yeah, yeah. >> You know, it's inspirational stories. This is miso soup for the soul. (laughing) So I'm working on that, TV evangelism with Canva is just going gangbusters. Brand ambassadors for Mercedes Benz. I'm on the board of directors of a company called Cheeze with a zee. It's an anti-social photo-sharing and vidoo-sharing app. And that's it. >> You've been an inspiration to many, great job of the year has been a big fan of your work. Thanks for coming on theCUBE. Really appreciate it. >> Thank you. >> Guy Kawasaki here inside theCUBE. We're at Devnet Create. This is Cisco's cloud developer conference. Different from their core Devnet Cisco Networking developer, and this is all about dev ops open source. And this is theCUBE bringing you all the action here in Mountain View, California. We'll be right back with more after this short break.

>> Announcer: Live from San Jose, it's theCUBE, presenting Big Data, Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. >> Welcome back to theCUBE. Our continuing coverage of our event, Big Data SV, continues, this is our first day. We are down the street from the Strata Data Conference. Come by, we're at this really cool venue, the Forager Tasting Room. We've got a cocktail party tonight. You're going to hear some insights there as well as tomorrow morning. I am Lisa Martin, joined by my co-host, George Gilbert, and we welcome back to theCUBE, for I think the 900 millionth time, the president and CEO of DataTorrent, Guy Churchward. Hey Guy, welcome back! >> Thank you, Lisa, I appreciate it. >> So you're one of our regular VIP's. Give us the update on DataTorrent. What's new, what's going on? >> We actually talked to you a couple of weeks ago. We did a big announcement which was around 3.10, so it's a new release that we have. In all small companies, and we're a small startup, in the big data and analytic space, there is a plethora of features that I can reel through. But it actually makes something a little bit more fundamental. So in the last year... In fact, I think we chatted with you maybe six months ago. We've been looking very carefully at how customers purchase and what they want and how they execute against technology, and it's very very different to what I expected when I came into the company about a year ago off the EMC role that I had. And so, although the features are there, there's a huge amount of underpinning around the experience that a customer would have around big data applications. I'm reminded of, I think it's Gartner that quoted that something like 80% of big data applications fail. And this is one of the things that we really wanted to look at. We have very large customers in production, and we did the analysis of what are we doing well with them, and why can't we do that en masse, and what are people really looking for? So that was really what the release was about. >> Let's elaborate on this a little bit. I want to drill into something where you said many projects, as we've all heard, have not succeeded. There's a huge amount of complexity. The terminology we use is, without tarring and feathering any one particular product, the open source community is kind of like, you're sort of harnessing a couple dozen animals and a zookeeper that works in triplicate... How does DataTorrent tackle that problem? >> Yeah, I mean, in fact I was desperately interested in writing a blog recently about using the word community after open source, because in some respects, there isn't a huge community around the open source movement. What we find is it's the du jour way in which we want to deliver technology, so I have a huge amount of developers that work on a thing called Apache Apex, which is a component in a solution, or in an architecture and in an outcome. And we love what we do, and we do the best we do, and it's better than anybody else's thing. But that's not an application, that's not an outcome. And what happens is, we kind of don't think about what else a customer has to put together, so then they have to go out to the zoo and pick loads of bits and pieces and then try to figure out how to stitch them all together in the best they can. And that takes an inordinately long time. And, in general, people who love this love tinkering with technologies, and their projects never get to production. And large enterprises are used to sitting down and saying, "I need a bulletproof application. "It has to be industrialized. "I need a full SLA on the back of it. "This thing has to have lights out technology. "And I need it quick." Because that was the other thing, as an aspect, is this market is moving so fast, and you look at things like digital economy or any other buzz term, but it really means that if you realize you need to do something, you're probably already too late. And therefore, you need it speedy, expedited. So the idea of being able to wait for 12 months, or two years for an application, also makes no sense. So the arch of this is basically deliver an outcome, don't try and change the way in which open source is currently developed, because they're in components, but embrace them. And so what we did is we sort of looked at it and said, "Well what do people really want to do?" And it's big data analytics, and I want to ingest a lot of information, I want to enrich it, I want to analyze it, and I want to take actions, and then I want to go park it. And so, we looked at it and said, "Okay, so the majority "of stuff we need is what we call a cache stack, "which is KAFKA, Apache Apex, Spark and Hadoop, "and then put complex compute on top." So you would have heard of terms like machine learning, and dimensional compute, so we have their modules. So we actually created an opinionated stack... Because otherwise you have a thousand to choose from and people get confused with choice. I equate it to going into a menu at a restaurant, there's two types of restaurants, you walk into one and you can turn pages and pages and pages and pages of stuff, and you think that's great, I got loads of choice, but the choice kind of confuses you. And also, there's only one chef at the back, and he can't cook everything well. So you know if he chooses the components and puts them together, you're probably not going to get the best meal. And then you go to restaurants that you know are really good, they generally give you one piece of paper and they say, "Here's your three entrees." And you know every single one of them. It's not a lot of choice, but at the end of the day, it's going to be a really good meal. >> So when you go into a customer... You're leading us to ask you the question which is, you're selling the prix fixe tasting menu, and you're putting all the ingredients together. What are some of those solutions and then, sort of, what happens to the platform underneath? >> Yeah, so what you don't want to do is to take these flexible, microdata services, which are open source projects, and hard glue them together to create an application that then has no flexibility. Because, again, one of the myths that I used to assume is applications would last us seven to 10 years. But what we're finding in this space is this movement towards consumerization of enterprise applications. In other words, I need an app and I need it tomorrow because I'm competitively disadvantaged, but it might be wrong, so I then need to adjust it really quick. It's this idea of continual developed, continual adjustment. But that flies in the face of all of this gluing and enterprise-ilities. And I want to base it on open source, and open source, by default, doesn't glue well together. And so what we did is we said okay, not only do you have to create an opinionated stack, and you do that because you want them all to scale into all industries, and they don't need a huge amount of choice, just pick best of breed. But you need to then put a sleeve around them so they all act as though they are a single application. And so we actually announced a thing calls Epoxy. It's a bit of a riff on gluing, but it's called DataTorrent Epoxy. So we have, it's like a microdata service bus, and you can then interchange the components. For instance, right now, Apache Apex is this string-based processing engine in that component. But if there's a better unit, we're quite happy to pull it out, chuck it away, and then put another one in. This isn't a ubiquitous snap-on toolset, because, again, the premise is use open source, get the innovation from there. It has to be bulletproof and enterprise-ility and move really fast. So those are the components I was working on. >> Guy, as CEO, I'm sure you speak with a lot of customers often. What are some of the buying patterns that you're seeing across industries, and what are some of the major business value that DataTorrent can help deliver to your customers? >> The buying patterns when we get involved, and I'm kind of breaking this down into a slightly different way, because we normally get involved when a project's in flight, one of the 80% that's failing, and in general, it's driven by a strategic business partner that has an agenda. And what you see is proprietary application vendors will say, "We can solve everything for you." So they put the tool in and realize it doesn't have the flexibility, it does have enterprise-ility, but it can't adjust fast. And then you get the other type who say, "Well we'll go to a distro or we'll go "to a general purpose practitioner, "and they'll build an application for us." And they'll take open source components, but they'll glue it together with proprietary mush, and then that doesn't then grow past. And then you get the other ones, which is, "Well if I actually am not guided by anybody, "I'll buy a bunch of developers, stick them in my company, "and I've got control on that." But they fiddle around a lot. So we arrive in and, in general, they're in this middle process of saying, "I'm at a competitive disadvantage, "I want to move forward and I want to move forward fast, "and we're working on one of those three channels." The types of outcomes, we just, and back to the expediency of this, we had a telco come to us recently, and it was just before the iPhone X launched, and they wanted to do AB testing on the launch on their platform. We got them up and running within three months. Subsequent from that launch, they then repurposed the platform and some of the components with some augmentation, and they've come out with three further applications. They've all gone into production. So the idea is then these fast cycles of microdata services being stitched together with the Epoxy resin type approach-- >> So faster time to value, lower TCO-- >> Exactly. >> Being able to get to meet their customers' needs faster-- >> Exactly, so it's outcome-based and time to value, and it's time to proof. Because this is, again, the thing that Gartner picked up on, is Hadoop's difficult, this market's complex and people kick the tires a lot. And I sort of joke with customers, "Hey if you want to "obsess about components rather than the outcome, "then your successor will probably come see us "once you're out and your group's failed." And I don't mean that in an obnoxious way. It's not just DataTorrent that solves this same thing, but this it the movement, right? Deal with open source, get enterprise-ilities, get us up and running within a quarter or two, and then let us have some use and agile repurposing. >> Following on that, just to understand going in with a solution to an economic buyer, but then having the platform be reusable, is it opinionated and focused on continuous processing applications, or does it also address both the continuous processing and batch processing? >> Yeah, it's a good answer. In general, and again Gatekeeper, you've got batch and you've got realtime and string, and so we deal with data in motion, which is string-based processing. A string-based processing engine can deal with batch as well, but a batch cannot deal with string. >> George: So you do both-- >> Yeah >> And the idea being that you can have one programming model for both. >> Exactly. >> It's just a window, batch is just a window. >> And the other thing is, a myth bust, is for the last maybe eight plus years, companies assume that the first thing you do in big data analytics is collect all the data, create a data lake, and so they go in there, they ingest the information, they put it into a data lake, and then they poke the data lake posthumously. But the data in the data lake is, by default, already old. So the latency of sticking it into a data lake and then sorting it, and then basically poking it, means that if anybody deals with the data that's in motion, you lose. Because I'm analyzing as it's happening and then you would be analyzing it after at rest, right? So now the architecture of choice is ingest the information, use high performance storage and compute, and then, in essence, ingest, normalize, enrich, analyze, and act on data in motion, in memory. And then when I've used it, then throw it off into a data lake because then I can basically do posthumous analytics and use that for enrichment later. >> You said something also interesting where the DataTorrent customers, the initial successful ones sort of tended to be larger organizations. Those are typically the ones with skillsets to, if anyone's going to be able to put pieces together, it's those guys. Have you not... Well, we always expected big data applications, or sort of adaptive applications, to go mainstream when they were either packaged apps to take all the analysis and embed it, or when you had end to end integrated products to make it simple. Where do you think, what's going to drive this mainstream? >> Yeah, it depends on how mainstream you want mainstream. It's kind of like saying how fast is a fast car. If you want a contractor that comes into IT to create a dashboard, go buy Tableau, and that's mainstream analytics, but it's not. It's mainstream dashboarding of data. The applications that we deal with, by default, the more complex data, they're going to be larger organizations. Don't misunderstand when I say, "We deal with these organizations." We don't have a professional services arm. We work very closely with people like HCL, and we do have a jumpstart team that helps people get there. But our job is teach someone, it's like a kid with a bike and the training wheels, our job is to teach them how to ride the bike, and kick the wheels off, and step away. Because what we don't want to do is to put a professional services drip feed into them and just keep sucking the money out. Our job is to get them there. Now, we've got one company who actually are going to go live next month, and it's a kid tracker, you know like a GPS one that you put on bags and with your kids, and it'll be realtime tracking for the school and also for the individuals. And they had absolutely zero Hadoop experience when we got involved with them. And so we've brought them up, we've helped them with the application, we've kicked the wheels off and now they're going to be sailing. I would say, in a year's time, they're going to be comfortable to just ignore us completely, and in the first year, there's still going to be some handholding and covering up a bruise as they fall off the bike every so often. But that's our job, it's IP, technology, all about outcomes and all about time to value. >> And from a differentiation standpoint, that ability to enable that self service and kick off the training wheels, is that one of the biggest differentiators that you find DataTorret has, versus the Tableau's and the other competitors on the market? >> I don't want to say there's no one doing what we're doing, because that will sound like we're doing something odd. But there's no one doing what we're doing. And it's almost like Tesla. Are they an electric car or are they a platform? They've spurred an industry on, and Uber did the same thing, and Lyft's done something and AirBNB has. And what we've noticed is customer's buying patterns are very specific now. Use open source, get up their enterprise-ilities, and have that level of agility. Nobody else is really doing that. The only people that will do that is your contract with someone like Hortonworks or a Cloudera, and actually pay them a lot of money to build the application for you. And our job is really saying, "No, instead of you paying "them on professional services, we'll give you the sleeve, "we'll make it a little bit more opinionated, "and we'll get you there really quickly, "and then we'll let you and set you free." And so that's one. We have a thing called the Application Factory. That's the snap on toolset where they can literally go to a GUI and say, "I'm in the financial market, "I want a fraud prevention application." And we literally then just self assemble the stack, they can pick it up, and then put their input and output in. And then, as we move forward, we'll have partners who are building the spoke applications in verticals, and they will put them up on our website, so the customers can come in and download them. Everything is subscription software. >> Fantastic, I wish we had more time, but thanks so much for finding some time today to come by theCUBE, tell us what's new, and we look forward to seeing you on the show again very soon. >> I appreciate it, thank you very much. >> We want to thank you for watching theCUBE. Again, Lisa Martin with my co-host George Gilbert, we're live at our event, Big Data SV, in downtown San Jose, down the street from the Strata Data Conference. Stick around, George and I will be back after a short break with our next guest. (light electronic jingle)

>> Hey welcome back everybody Jeff Frick here with theCUBE. We're at Node Summit 2015 in Downtown San Francisco Mission Bay Conference Center. About 800 people talking about nodes, Node JS. The crazy growth in this application development platform and we're excited to have our next guest to talk about security. Which I don't think we've talked about yet. He's Guy Podjarny, I'm sorry. >> Podjarny Correct. >> Welcome, he's a CEO of Snyk, not spelled like Snyk. (laughing) You'll see it on the lower third. >> It's amazing how often we that question. How do you pronounce Snyk? >> Well I know, obviously people that have never had this start up and tried to go through a URL search. >> Indeed. >> Just don't know what's it's all about. >> It's sort of Google dominance. It's short for so now you know. So now you know. >> Oh, so now you know. Okay perfect, super. First off welcome, great to see you. >> Thank you. Thanks for having me. >> You said this is your second year at the conference. Just kind of share your general impressions of what's going on here. >> Sure, well I think Node Summit is an awesome conference. I think this year's event is bigger, better organized. I don't know if it's bigger people wise but definitely feels that way. It sort of feels more structured. It's nice to see in the audience as well. Just an increased amount of larger organizations that are around and talking about their challenges and a little bit a lot earlier in the conference but a little bit of more experienced conversations. So conversations about hey, we've used node and we've encountered these issues versus we're about to use it. We're thinking of using it so definitely can see the enterprise adoption kind of growing up. That's my primary impression so far. >> Yeah and it's it in 'cause you're a start up but Microsoft is here, Google's here, Intel is here, IBM is here so a lot of the big players. Who've demonstrated in other open source communities that they have completely embraced open source as a method and way to get actually more than the software is getting closer to development community. >> Yeah, agreed and I think another adjacent trend that's happening is ServerList and ServerList has grown ridiculously, by massive amounts in these last while. And Node JS is sort of the de facto default language for ServerList. LAM just started with it and AWS and many of the other platforms only support it. I think that contribution also brings the giants a little bit more in here. The Cloud giants but also I think again just sort of boost the Node JS. As though the Node JS echo system needed a boost. They get another amplifier. Just raise enterprise awareness and general usage. >> Okay, so what's the Snyk all about? Gives us, some people aren't familiar with the company. >> Cool, so Snyk deals with open source security and specifically in Node JS, the world of MPMs. MPM is amazing and it allows us to build on the shoulders of giants and all the others in the community. But there are some inherent security risks with just pulling code off the internet and running it in your application. >> Jeff: Right, right. >> What we do at Snyk is we help you find known security flaws, known vulnerabilities in MPM packages, and do that in a natural fashion as part of your continuous development process, and then fix those efficiently and monitor for them over time. That's basically. >> That's your focus is really keeping track of all these other packages that people are using to their development. Precisely and we're helping you just use open source code and stay secure. The word node is our flag ship and it's where we started and build and now we support a bunch of other systems as well. >> It's interesting, Monica from Intel said that in some of their work they found that some of these applications. The actual developers only contributing 2% of the code 'cause they're pulling in all this other stuff. >> Precisely, I have this example I use in a bunch of my talks that shows ServerList example that has 19 lines of codes. Copies some file from URL and puts it on S3. That's 19 lines of codes which is awesome. Uses two packages which in turn use 19 packages which bring in 190,000 lines of code. >> Wow. >> That's a massive-- >> So what is that step function again? Start from the beginning. >> 19 to 190,000. >> It starts at two? >> 19 lines of code use two MPM packages. They use 19 packages because every package uses other packages as well, and combined those 19 packages bring in 190,000 lines of code. >> Wow, that's amazing. That's an extreme example but you see that pattern. You see this again and again that the majority of your code in your applications especially node is not first party it's third party code. >> Jeff: Right. >> And that means most of your security risks. Most of your vulnerabilities, they come from there so there is a lot of challenges around managing dependencies. I know it's called dependency help for a reason but specifically security is still not sufficiently taken care of. It's still overlooked and we need to make sure that it's not just addressed by security people. But it's addressed a part of the development process by developers. >> How do you keep up? Both with the number as the proliferation grows as well as the revisions and versions inside of any particular package? You kind of chasing a multi headed beast there. >> It's definitely tough. First of all the short answer is automation. Any scale solution has to start with automation. I've got a security research team in Israel that has a vulnerability pipeline that feeds in from activity in the open source world. Some developer opens an issue and gets helps that say SQL injection in some package and that disappears into the ether. So we try to surface those, get it to our security analysts, determine if it's a real vulnerability curated in our database, and then just build that database with your own research but a lot of it is around tapping into community. And then subsequently when you consume this if you want to be able to apply security correctly as you develop your applications Node JS or otherwise. It has to come to you. The security tool has to be a seamless integration with how you currently work. If you impose another step, another two steps, another three steps on the developers. They're just not going to use it. That's a lot of our emphasis is scale on the consumption and the tracking of the database and simplicity and ease of use on the developer on the user side. >> And do you help with just like flagging. Flagging is a problem or is there an alternative. I mean I would imagine with all these interdependencies, you find one rotten apple kind of have a huge impact. It's a huge scale of impact right. >> Absolutely so we do really what our moniker is that we don't find vulnerabilities, we fix them and our goal is to fix vulnerabilities. So we actually, first of all in the flow we have single click, open a fixed PR. We figure out what changes we need to do. What upgrades you need to make the vulnerability go away. Literally click a button to fix it. Put on one bat for everything and then what we also do. We build patches, sort of a little known fact is in the world of operation systems RedHat and Canonical. They build a lot of fixes or they back port a lot open source fixes, and they put them into their repository. You can just say on updates or upgrade and just get those fixes. You don't even know which vulnerabilities you're fixing. You're just getting the fixes so we build patches for our MPM packages as well to allow you to patch vulnerabilities you can not upgrade away. A lot of it is around fix. Make fix easy. >> Right and then the other part as you said is baking security in the development all the way through which we hear over and over and over. >> Build it in and bolt it in. >> The cast in method doesn't work anymore. You've got to have it throughout the application so you said you're speaking on a panel tomorrow. And I wondered if you can just highlight some of the topics for tomorrow for the folks that aren't going to be here and see the panel. When you look at ServerList security. Say that three times fast. What are some of the real special challenges that people need to be thinking about? >> Sure, so you know I actually have two talks tomorrow. One is a panel on Node JS security as a whole and that's sort of a broader panel. We have a few other colleagues in there and we talk about the evolution of Node JS security that includes the platform itself which is increasingly well handled by the foundation. Definitely some improvements there over the years and some of it is around best practices like the ones that was just discussed which is understanding known pitfalls and Node JS sort of security mistakes that you might do as well as handling the MPM echo system. The other talk that I have later in the day is around ServerList security. ServerList security is interesting because a lot of the promise of ServerList is function as a service is that a lot of the concerns. A lot of the earlier or lower levels get abstracted away from you. You don't need to manage servers. You don't need to manage operation systems and with those auto security concerns go away. Which in turns focuses the attackers and should focus you on the application. As attackers are not just going to give up because they can't hack the operating system that the pros are managing. They would look at the next low hanging fruit and that would be the application. Platform as a service and function as a service really increase the importance of dealing with application security as a whole. So my talk is a lot about that but also deals with other security concerns that you might of course any new methodology introduces its own concerns so talk a little bit about how to address those. ServerList like Node JS is an opportunity to build security into the culture and into our methodologies from the early day so trying to help us get that right. >> Alright, as you look forward, the next 12 months. I won't say more than 12 months, 6 months, 9 months, 12 months. What are some of your priorities at Snyk? What are you working on if we get together a year from now, what will we be talking about? I think, so two primary ones. One is continuing the emphasis on fix. Making fixing trivial in the Node JS environments as well as others. I think we've done well there but there is more work to be done. It needs to be as seamless as possible. The other aspect is indeed in this sort of past and fast world and platform and function as a service. Where increasingly there is this awareness as we work with different platforms to the blind spot that they have to open source libraries. They fix your NGX vulnerabilities but not your express vulnerabilities. I sometimes refer to MPM packages or open source packages as sprinkles of infrastructure that are just scattered through your application. And today, all of these Cloud platforms are blind to it so I expect us at Snyk to be helping past and fast users dealing with that security concerns efficiently. >> Alright, well I look forwards to the conversation. >> Thanks. >> Thanks for stopping by. >> Thank you. >> He's Guy Podjarny. He is from Snyk. The CEO of Snyk. I'm Jeff Frick, you're watching theCUBE. (uptempo techno music)

(upbeat electronic music) >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're having a CUBE Conversation in the Palo Alto studio, a little bit of a break from the crazy conference season, so we can have a little more intimate conversation without the madness of some of the shows. So we're really excited to have many-time CUBE alumni, Guy Churchward, on. He's the president and CEO of DataTorrent. Guy, great to see you. >> Thank you, Jeff, 'preciate it. >> So how have you been surviving the crazy conference season? >> It's been crazy. This is very unusual. It's just calm and quiet and relaxed, and there's not people buzzing around, so it's different. >> So you've been at DataTorrent for a while now, so give us kind of the quick update, where you guys are, how things are moving along for you. >> Yeah, I mean, I've kicked in about five months, so I think I'm just coming up to sort of five and a half, six months, so it's a enough time to get my feet wet, understand whether I made a massive mistake or whether it's exciting. I'm still-- >> Jeff: Still here, you're wearing the T-shirt. >> Yeah, I'm pleased to say I'm still very excited about it. It's a great opportunity, and the space is just hot, hot. >> So you guys are involved in streaming data and streaming analytics, and you know, we had Hadoop, was kind of the hot thing in big data, and really the focus has shifted now to streaming analytics. You guys are playing right in that space and have been for a while, but you're starting to make some changes and come at the problem from a slightly different twist. Give us an update on what you guys are up to. >> Yeah, I mean, so when I dropped into DataTorrent, obviously, it's real-time data analytics, based on stream processing or event processing. So the idea is to say instead of doing things like analytics, insight, and action on data at rest, you know, traditional way of doing things is sucking data into a data store and then poking it litigiously at sort of a real-time analytics basis. And what the company decided to do, and again, this is around the founders, is to say if you could take the insight and action piece and shift it left of the data store in memory and then literally garner the insight and action when an event happens, then that's obviously faster and it's quicker. And it was interesting, a client said to us recently that batch, or stream, or near real-time, or microbatch, is sort of like real-time for a person, 'cause a person can't think that fast. So the latency is a factor of that, but what we do is real-time for a computer. So the idea here is that you literally have sub-second latency and response and actions and insight. But anyway, they built a toolkit, and they built a development platform, and it's completely extensible, and we've got a dozen customers on board, and they're high production, and people are running a billion events per second, so it's very cool. But there wasn't this repeatable business, and I think the deeper I got into it, you also look at it and you say, "Well, Hadoop isn't the easiest thing to deploy." >> Jeff: Right, right, consistently. >> And, the company had this mantra, really, of going to solve total cost of ownership and time to value, so in other words, how fast can I get to an outcome and how cheap is it to run it. So can you create unique IP on top of opensource that allows you to basically get up and running quickly, it's got a good budget constraint from a scale-up perspective and scale-out, but at the same time, you don't need these genius developers to work on it because there's only a small portion of people who basically can deploy a Hadoop cluster in a massive scale in a reliable way. So we thought, well, the thing to do is to really bring it into the masses. But again, if you bring a toolkit down, you're really saying here's a toolkit and an opportunity, and then build the applications and see what you can do. What we figured is actually what you want to do is to say, no, let's just see if we can take Hadoop out of the picture and the complexity of it, and actually provide an end-to-end application. So we looked to each of the customers' current deployments and then figured out, can we actually industrialize that pipeline? In other words, take the opensource components, ruggedize them, scale them, make sure that they stay up, they're full torrents, 7x24, and then provide them as an application. So we're actually shifting our focus, I think, from just what are called the apex platform and the stream-based processing platform to an application factory and actually producing end-to-end applications. >> 'Cause it's so interesting to think of batch and batch in not real-time compared to real-time streaming, right? We used to take action on a sample of old data, and now, you've got the opportunity to actually take action on all of the now data. Pretty significant difference. >> Yeah, I mean, it kills me. I've got to say, since the last time we met, I literally wrote a blog series, and one of them was called Analytics, Real-Time Analytics versus Real-Time Analytics. And I had this hilarious situation where I was talking to a client, and I asked then, and I said, "Do you do real-time analytics?" They go, "Yeah." And I said, "Do you work on real-time data?" And they said, "Yeah." And I said, "What's your latency between an event happening "and you being able to take an action on the event?" And he said, "Well, 60 milliseconds." It's just amazing. I said, "Well, tell me what your architecture looks like." And he says, "Well I take Kafka into Apex as a stream. "I then import it in essence into Cassandra, "and then I allow my customers to poke the data." So I said, "Well, but that's not 60 milliseconds." And he goes, "No, no, it is." And I said, "What are you measuring?" He goes, "Well, the customer basically puts "an inquiry onto the data store." And so literally, what he's doing is a real-time query against a stale data that's sitting inside of a date lake. But he swore blind. >> But it's fast though, right? >> And that's the thing is he's looking, he say, "Hey, well, I can get a really quick response." Well, I can as well. I mean, I can look at Google World and I can look at my house, and I can find out that my house is not real-time. And that's really what it was. So you then say to yourself, well look, the whole security market is based around this technology. It's classic ETL, and it's basically get the data, suck it in, park it into a data store, and then poke at it. >> Jeff: Right >> But that means that that latency, by just the sheer fact that you're taking the data in and you're normalizing it and dropping it into a data store, your latency's already out there. And so one of the applications that we looked at is around fraud, and specifically payment fraud and credit card fraud. And everything out there in the market today is basically, it's detection because of the latency. If you kind of think about it, credit card swipe, the transaction's happened, they catch the first one, they look at it and say, "Well, that's a bit weird." If another one of these ones comes up, then we know we've got fraud. Well, of course, what happens is they suck the data in, it sits inside a data store, they poke the data a little bit later, and they figure out, actually, it is fraud. But the second action has happened. So they detected fraud, but they couldn't prevent it, so everything out there is payment fraud prevention, or payment fraud detection because it's basically got that latency. So what we've done is we said to ourself, "No, we actually can prevent it." Because if you can move the insight and actions to the left-hand side of the data store, and as the event is happening, you literally can grab that card swipe and say no, no, no, you don't do it anymore, you prevent it. So, it's literally taking that whole market from, in essence, detection to prevention. And this is, it's kind of fascinating because there's other angles to this. There's a marketplace inside the credit card site that talks about card not present, and there's a thing called OmniChannel, and OmniChannel's interesting, 'cause most retailers have gone out there and they've got their bricks and mortar infrastructure and architecture and data centers, and they've gone and acquired an online company. And so, now, they have these two different architectures, and if you imagine if you got to hop between the two, it kind of has gaps. And so, the fraudsters will exploit OmniChannel because there's multiple different architectures around, right? So if you think about it, there's one side of saying, hey, if we can prevent that, so taking in a huge amount of data, having it talk, having a life cycle around it, and literally being able to detect and then prevent fraud before the fraudsters can actually figure out what to do, that's fantastic, and then on the plus side, you could take that same pipeline and that same application, and you can actually provide it to the retailers and say, well, what you'd want to do is things like, again, I wrote another blog on it, loyalty brand. You know, on the retail side, is for instance, my wife, we shop like crazy, everybody does. I try not to, but let's say she's been on the Nordstrom site, and we've got a Nordstrom. So Nordstrom has a cookie on their system and they can figure what had been done. And she's surfing around, and she finds a dress she kind of likes, but she doesn't buy it because she doesn't want to spend the money. Now, I'm in Nordstrom's about four weeks later, and I'm literally buying a pair of socks. A card swipe, and what it does is because you've got this OmniChannel and you can connect the two, what they want to do is to be able to turn around and say, "Oh, Guy, before we run this credit card, "we noticed that your wife was looking at this dress. "We know her birthday's coming up. "And by the way, we've checked our store, "and we've got the color and the size "she wants it in, and if you want, "we'll put it on the credit card." >> Don't tell her that, she already bought too much. She won't want you to get that dress. Nah, it's a great, it's a really interesting example, right? >> But it is that, and if you kind of think about it, and this where, when they say every second counts, it's like every millisecond counts. And so it really is machine-to-machine, real-time, and that's what we're providing. >> Well, that's the interesting, you know, a couple things just jump into mind as you're talking. One is by going the application route, right, you're reducing the overhead for just pure talent that we keep hearing about. It's such a shortage in some of these big data applications, Hadoop, specifically. So now, you're delivering a bunch of that, that's already packaged to do a degree in an application, is that accurate? >> Yeah, I mean I kind of look at the engineering talent inside an organization is like a triangle. And at the very top, you have talented engineers that basically can hard code and that's really where our technology has sat traditionally. So, we go to a large organization. They have a hundred people dedicated to this sport. The challenge is then it means the small organizations who don't have it can't take advantage. And then you've got at the base end, you have technologies like Tableau, you know, as a GUI that you can use by an IT guy. And in the middle you've got this massive swath of engineering talent that literally isn't the, Yoda hardcode on the analytics stuff and really can't do the Hadoop cluster. But they want to basically get dangerous on this technology, and if you can take your, you know, the top talent, and you bring that in to that center and then provide it at a cost economics that makes sense, then you're away. And that's really what we've seen is. So our client base is going to go from the 1410, 1420, 1450s, into the 14,000s and you bring it down, and that's really, if you think about it, that's where Splunk kind of got their roots. Which is really, get an application, allow people to use it, execute against it and then build that base up. >> That's ironically that you bring up Splunk 'cause George Gilbert, one of our Wikibon analysts, loves to say that Splunk is the best imitation of Hadoop that was ever created. He thinks of it really as a Hadoop application as opposed to Splunk, because they're super successful. They found a great application. They've been doing a terrific job. But the other piece that you brought up that triggered my mind was really the machine-to-machine. And real-time is always an interesting topic. What is real time? I always think of real time means in time to do something about it. That can be a wide spectrum depending on what you're actually doing. And the machine-to-machine aspect is really important because they do operate at a completely different level of speed. And time is very different for a machine-to-machine operation interaction interface than trying to provide some insight to a human, so they can start to make a decision. >> Yeah, I mean, you know, it was, again, one of those moments through the last five months I was looking at it. There's a very popular technology in our space called Spark, Apache Spark. And it's successful and it's great in batch and it's got micro-batch and there's actually a thing called Spark Streaming, which is micro-batch. But in essence, it's about a second latency, and so you look at it and you go, but what's in a second? You know what I mean? I mean, surely that's good enough. And absolutely, it's good enough for some stuff. But if you were, I mean we joke about it with things like autonomous cars. If you have cruise control, adaptive cruise control, you don't want that run on batch because that second is the difference between you slamming into a truck or not. If you have DHL, they're doing delivery drops to you, and you're actually measuring weather patterns against it, and correlating where you're going to drive and how and high and where, there's no way that you're going to run on a batch process. And then batch is just so slow in comparison. We actually built an application and it's a demo up on our web. And it's a live app, and when I sat down with the engineering team, and I said, "Look, I need people to understand "what real real-time does and the benefits of it." And it's simply doing is shifting the analytics and actions from the right-hand side of where the data store is, to the left-hand side. So you take all of the latency of parting the data and then go find the data. And what we did is we said, look, well, I want to do this really fair and, when you were a kid, there used to be games like Snap, you know, where the cards that you would turn over and you'd go snap and it's mine. So we're just looking and say, "Okay, "why don't we do something like that?" It's like fishing, you know, tickling fish and who sees the first fish, you grab it, it's yours. So we created an application that basically creates random numbers at a very, very huge speed, and whichever process, we have three processes running, whichever one sees it the first time, puts their hands up and says, "I got that." And if somebody else says, "I've got that," but they see a timestamp on the other one, they can't claim it. One wins, and the other two lose. And I did it, and we optimized around, basically, the Apache Apex code, which is ours in stream mode, the Apache Apex, believe it or not, in a micro-batch mode, and Spark Streaming, as fast as they can, and we literally engineered the hell out of them to get them as fast as possible. And if you look at the results, it literally is, win every time for stream, and a loss every time for the other two. So from a speed perspective, now the reality is like I said, is if I'm showing a dashboard to you, by the time you blink, all three have gotten you the data. It's immaterial, and this isn't knocking on Spark. Our largest deployments all run on what we call, like a cask-type architecture, which is basically Kafka Apache, Spark. So we see this in Hadoop, and it's always in there. So it's kind of this cache thing. So we like it for what it is, but where customers come unbundled, is where they try and force-fit a technology into the wrong space. And so again, you mentioned Splunk, these sort of waves of innovation. We find every client sitting there, going, "I want to get inside quicker". The amount of meetings that we're all in, where you sit there and go, "If I'd only known that now "or before, then I would've made a decision." And, you know, in the good old days, we worked at-rest data. At-rest was really the kingdom of Splunk. If you think about it, we're now in the tail end of batch, which is really where Spark's done. So Splunk and Spark are kind of there, and now you're into this real-time. So again, it's running at a fair pace, but the learnings that we've had over the last few months is toolkits are great, platforms are great, but to bring this out into a mass adoption, you really need to make sure that you've provided hardened application. So we see ourselves now as, you know, real-time big data applications company, not just Apache. >> And when you look at the application space that you're going to attack, do you look at it kind of vertically, do you look at it functionally, kind of, you mentioned fraud as one of the earlier ones. How are you kind of organizing yourself around the application space? >> Yeah, and so, the best way for me to describe it, and I want to spin it in a better way than this, but I'll tell you exactly as we've done it, which is, I've looked at what the customers have currently got and we have deployments in about a dozen big customers and they're all different use cases, and then I've looked at it and said, "What you really want to do is you want to go "to a market that people have a current problem, "and also in a vertical where they're prepared "to pay for something and solving a problem "that if they give you money, they either "make money quickly or they save money quickly." So it's actually-- >> So simple. (laughs) >> But it would be much better if I said it in a pure way and I made some magical thing up, but in reality is I'm looking and going, "You got to go where the hardest problems are," And right now, a few things like card not present, you look at roaming abuse and you look at OmniChannel from payment fraud, everybody is looking for something. Now, the challenge is the market's noisy there, and so what happens is everybody's saying, "But I've got it." >> That's what strikes me about the fraud thing is you would think that that's a pretty sophisticated market place in which to compete. So you clearly have to have an advantage to even get a meeting, I would imagine. >> Yeah, and again, we've tested the market. The market's pretty hard on the back of it. We've got an application coming out shortly, and we're actually doing design partnerships with a couple of big banks. So but we don't want to be seen as just a fraud, now, just a fraud, just a fraud prevention company. (chuckles) I'll stay with a fraud, myself. But you kind of look and you say, look, they'll be a set of fraud applications because there's about half a dozen only to be done, retail, like I mentioned on things like the loyalty brand stuff. We have a number of companies that are using us for ad tech. So again, I can't mention the names. Actually, we've just published one, Publix, no, PubMatic is one of the ad tech organizations that's using our products. But we'll literally come out and harden that pipeline as well. So we're going to strut along but instead of just saying, "Hey, we've solved absolutely everything," what I want to do is to solve a problem for someone and then just move forward. You know, most of our customers have somewhere between three to five different applications that are running up and that are in production. So once the platform's in, you know, then they see the value of it. But we really want to make sure that we're closer to the end result and to an outcome, because that's the du jour way that customers want to buy things now. >> Well, and they always have, right? Like you said, they've got a burning issue. You either got to make money or save money. And if it's not a burning issue, it falls to the bottom of the pile, 'cause there's something that's burning that they need to fix quickly. >> And the other thing, Jeff, is if you, and again, it's dirty laundry, but if you think about it, I go to an account and the account's got a fraud solution, and it's all right but it's not doing what they want, but we come along up with a platform, say, "We can do absolutely anything." And then they go, "Well, I've got this really difficult "problem that no one's solved for me, "but I'm not even sure if I've got a budget for it. "Let's spend two year messing around with it. And that's no good, you know? From a small company, you really want that tractionable event, so my thing is just say, "No, what we want to do is I want to go "talk to John about John's problem," and say, "I can solve it better than the current one." And there is nothing in the market today, on the payment fraud side, that will provide prevention. It is all detection. So, there's a unique value. The question is whether we can get the noise out. >> All right, well, we look forward to watching the progress and we'll check again in five months or so. >> Thank you, Jeff, 'preciate it. >> Guy Churchward, he's from DataTorrent, President and CEO. Took over about five months ago and kind of changed the course a little bit. Exciting to watch, thanks for stopping by. >> Guy: Thank you >> All right, Jeff Frick, you're watching the theCUBE. See you next time. Thanks for watching. (upbeat electronic music)

(techno music) >> Announcer: Live, from Silicon Valley, it's "the Cube," covering Mobile World Congress 2017. Brought to you by Mintel. >> Okay, welcome back everyone. We're here live in Palo Alto, California, covering Mobile World Congress, which is later in Spain right now, in Barcelona, it's gettin' close to bedtime, or, if you're a night owl, you're out hittin' the town, because Barcelona stays out very late, or just finishing your dinner. Of course, we'll bring in all theCube coverage here. News analysis, commentary, and of course, reaction to all the big mega-trends. And our next two guests is Guy Churchward who is the President and CEO of Data Torrent, formerly of EMC. You probably recognize him from theCube, from the EMC world, the many times he's been on. Cube alumni. And Phu Hoang, who's the co-founder and Chief Strategy Officer of Data Torrent. Co-founder, one of the founders. Also one of the early, early Yahoo engineers. I think he was the fourth engineer at Yahoo. Going way back on the 90s. Built that to a large scale. And Yahoo is credited for the invention of Hadoop, and many other great big data things. And we all know Yahoo was data-full. Guys, welcome to theCube's special coverage. Great to see you. >> Thank you so much. So I'm psyched that you guys came in, because, two things. I want to talk about the new opportunity at Data Torrent, and get some stories around the large scales experience that you guys have dealing with data. 'Cause you're in the middle of where this is intersecting with Mobile World Congress. Right now, Mobile World Congress is on the collision course between cloud-ready, classic enterprise network architectures with consumer, all happening at the same time. And data, with internet of things, is that going to be at the center of all the action? So, (laughing) these are not devices. So, that's the core theme. So, Guy, I want to get your take on, what attracted you to Data Torrent? What was the appeal for the opportunity? >> You mean, why am I here, why have I just arrived? >> I've always data-obsessed. You know this. From the days of running the storage business on their data protection, before that I was doing data analytics and security forensics. And if you look at, as you said, whether it's big data, or cloud, and the immersion of IOT, one thing's for sure, for me. It was never about big data, as in a big blob of stuff. It was all about small data sprawl. And the world's just getting more diverse by the second, and you can see that by Mobile World, right? The challenge then you have is, companies, they need to analyze their business. In other words, data analytics. About 30 years ago, when I was working for BA Systems, I remember meeting a general of the army. And he said the next war will be one in the data center, not on the battlegrounds. And so you really understand-- >> He's right about that. >> Yeah. And you have to be very, very close. So in other words, companies have started to obsess about what I call the do loop. And that really means, when data is created, and then ingesting the data, and getting insight from the data, and then actioning on that. And it's that do loop. And what you want to do, is you want to squeeze that down into a sub-second. And if you can run your analytics at the pace of your business, then you're in good shape. If you can't, you lose. And that means from a security perspective, or you're not going to win the bids. In any shape or form. That's not a business-- >> John: So speed is critical. >> Yeah, and people say, speed and accuracy. Because what you don't want to do is to run really really fast and fall off a cliff. So you really need to make sure that speed is there and accuracy is there. In the good old days, when I was running security forensics, you could either do complex end processing, which was a very small amount of information coming in and then querying it like crazy, or things like log management, where you would store data at rest, and then look at it afterwards. But now with the paradigm of all the technology catching up, so whether that's the disk space that you get, and the storage and the processing, and things like Hadoop with the clustering, you now break that paradigm. Where you can collect all the information from a business and process it before you land the data, and then get the insight out of it, and then action. So that was my thing, of looking and saying, look, this whole thing's going to happen. In last year -- >> And at large scale, too. I mean, what you're talking about in the theoretical side makes a lot of sense, but also putting that into large scale, is even more challenging. >> Yeah, we had, when I was going through the processes, dating, you know, to see whether was a company that made sense, I chatted one of our investors. And they're also a customer. And I said, why did you choose Data Torrent? And they said, "We tested everything in production, we tested all the competitive products out there, and we broke everything except Data Torrent. And actually, we tested you in production up to a billion events per second, and you didn't break. And we believe that that quantity is something that you need as a stepping stone to move forward." >> And what use cases does that fit for? Just give me some anecdotal (snaps fingers) billion transactions. At that speed, what's some use cases that really take advantage of that? >> They were mastering in, what I would call, industrialization of IT. So in other words, once you get into things like turbines, wind generation, train parts. We're going to be very very soon, looking out of a window and seeing -- >> John: So is it flow data? Is it the speed of the flow? Is it the feed of all the calculations, or both? >> It's a bit of both. And what I'll do, is I'll give Phu a chance, otherwise, we'll end up chatting about it. >> John: Phu, come on, you're the star. (laughing) When you founded this company, you had a background at Yahoo, which you built from scratch, but that was a first-mover opportunity, Web 1.0, as they say. That evolved up and then, everyone used Yahoo Finance. Everyone used Yahoo Search as a directory early on. And then everything just got bigger and bigger and bigger, and then you had to build your own stuff with Hadoop. >> Yeah. >> So you lived it. The telcos don't have the same problem. They actually got backed into the data, from being in the voice business, and then the data business. The data came after the voice. So what's the motivation behind Data Torrent? Tell us a little bit more. >> It's exactly what you say, actually. Going through the 12 years at Yahoo, and really, we learned big data the hard way. Making mistakes month after month, about how to do this thing right. We didn't have the money, and then we found out that, actually, proprietary systems of the shelf system that we thought were available, really couldn't do their jobs. So we had to invent our own technology, to deal with the kind of data processing that we had. At some point, Yahoo had a billion users using Yahoo at any given point in time, right? And the amount of impressions, the amount of clicks, the amount of activity, that a billion users have, onto the system. And all of the log files that you have to process to understand what's going on. On the other side of that, we need to be able to understand all of those activities in order to sell to our advertisers. Slice and dice behaviors and users, and so on. We didn't have the technology to do that. The only thing we knew how to do was, to have these cheap racks of cheap servers, that we were using to serve webpages. And we turned to that to say, this is what we're going to need to do, to solve these big data problems. And so, the idea of, okay we need to take this big problem and divide it into smaller pieces, so that we can run on these cheap servers, sort of became the core tenant of how we do distributor processing that became Hadoop, at the end of the day, right? >> You had big data come in because you were, big data-full, as we say. You weren't building software to solve someone else's problem. You had your own problem, you had a lot of data. You were full with data. >> Exactly. >> Had to go on a data diet, to your point. (crosstalk) >> And no one to turn to. >> And no one to turn to. >> All right. So let's spin this around or Mobile World Congress. 'Cause the big theme is, obviously, we all know what device is. In fact, we just released here on theCube early this morning Peter Burris pre-announced our new research initiative called IOTP. Which stands for Internet Of Things And People. And so now you add the complexity of people devices, whether that's going to be some sort of watch, phones, anything around them. That adds to the industrial aspect of turbines and what not. Internet of Things is a new edge architecture. So the data tsunami coming, besides the challenges of telcos to provision these devices, are going to be very challenging. So the question I want to ask you guys is, how do you see this evolving, because you have certainly connectivity. Yeah, you know, low latency, small little data coming from the windmills or whatever. Versus big high-dense bandwidth, mobility. And then you got network core issues, right. So how does this going to look like? Where does the data piece fit in? Because all aspects of this have data. What's your thoughts on this, and architecture. Tell us about your impressions, and the conversations you've had. >> First of all, I think data will exist everywhere. On the fringe, in the middle, at the center. And there's going to be data analytics and processing in every path of that. The challenge will be to kind of figure out what part of processing do you put on the fringe, what part do you put at the center. And I think that's a fluid thing that is going to be constantly changing. Going back to the telcos. We've had numbers of conversationw with telcos. And, yes we're helping them right now with their current set of issues around capacity management and billing, all those things. But they are also looking to the next step in their business. They're making all this money from provisioning, but they know they sit on top of this massive amount of really valuable data, from their customers. Every cellphone is sending them all of this data. And so there's a huge opportunity for them to monetize, or really produce value, back to their customers. And that could come in form of offers, to customers. But now you're talking about massive analytics targeting. That is also real-time, because if you're sending an offer to someone at a particular location, if you do that slowly, or in batch, and you give them an offer 10 minutes later, they're no longer where they are. They're 10 minutes away, right? >> Well, first two questions to follow up on that. One, do they know they have a data advantage opportunity here? Do they know that data is potentially a competitive advantage? >> From our conversation, they absolutely do. They're just trying to figure out, so what do we do here? It's new to them. >> I want to get both your perspectives. Guy, I want you to weigh in on this one, 'cause this is another theme that's coming out of the reporting and analysis from Mobile World Congress. This has come also from the cloud side as well. Integration now, is more important than ever, because, for instance, they might have an Oracle there, there might be Oracle databases outside their network. That they might want to tap into. So tapping other people's data. Not just what they can get, the telcos. It's going to be important. So how do you guys see the integration aspect, how we, top of the first inning, national anthem going on. I mean, where are we in this integration? There's a pregame, or, what inning are we in on this? >> Yeah, we're definitely not on the home run on it. I think our friend, and your friend Steve Manly, I sat down with him, and I gave him a brief, you know, what we were doing, and he was blown away by the technology and the opportunity, but he was certainly saying, but the challenge is the diversity of the data types. And then where they're going to be. Autonomic cars. You know each manufacturer will tell the car behind it, what it just experienced, but the question is, when will a Tesla tell a Range Rover, or tell a BMW? So you have actually -- >> They're different platforms, just different stats, it's a nightmare. >> Right. So in other words, >> And trackability. And whether it's going to be open APIs, whether it's technologies like Kafka. But the integration of that, and making sure that you can do transformation and then normalize it and drive it forward. It's kind of interesting, you know. You mentioned the telco space, and do they understand it. In some respects, what Phu went through with Yahoo, in other words, you go to a webpage, you pull it up, it knows you because of a cookie and it figures out, and then sells advertising to you on that page. Now think about you as a location, and you're walking past a Starbucks, and they want to sell you a coffee for ten cents less than they would normally do. They need to know you're there then. And this is the thing, and this is why real-time is going to be so critical. And similarly, like you said, you look out the window and you see DHL, or UPS, or FedEx drones out the window. You not only have an insight issue. You also have a security issue, you have a compliance issue, you have a locational issue. >> I think you're onto something. And I think I actually had this talk today with Steve Manly EMC World last year, around time series data. So this is interesting. Everyone wants to store everything, but it actually might not be worth anything anymore. If the drone is delivering your package, or whatever realtime data is in realtime, it's really important right there in realtime, or near realtime. It might not be worth anything after. But yet a purchase at a store, at a time, might be worth knowing that as a record to pull in. You get what I'm saying? So there's a notion of data that's interesting. >> And I think, and again, Phu's the expert. I'm still running up onto it. It's just a pet hobby, an obsession of mine. But the market has this term ETL. In other words, Extract, Transform, Land. Or load. But in essence, it's always talked about in that (mumbles) batch. In other words, I get the data, transform it, drop it, and then I have a look at it. We're going upside-down. So the idea now is to actually extract, transform, insight, action, then landing. So in other words, get the value at the fresh data, before it's the data late. Because if you set the data late, by default, it's actually stale. And actually, then there's the fascination of saying, if you're delivering realtime data to a person, you can't think fast enough to actually make a live decision. So therefore, you've almost got any information that comes to you, has to tier out. So it comes to a process. You get that fresh use of it, and then it drops into a data lake. And so I think there's using both, but I think what will you see in the market, and, again, you've experienced the disk flash momentum that happened last year. You're going to see that from a data source from at-rest, advanced, to real-time data streams on our applications next year. So I think the issue is, the formative year, and back to your, you know, get it right, get the integration, but make sure your APIs are there, talking to the right technologies. I think everything's going to be exciting this year and new and fresh and people really want to do it. Next year is going to be the year where you're going to see an absolute changing of the guards. >> And then also the SLA requirements, they'll start to get into this when you start looking at integration. >> You're absolutely right. Actually, the SLA part is actually very very important here. Because, as you move analytics from this back world, where it has, you do it once a day, and if it dies, it's okay, you just do it again. To where it is now continuous, 24 by 7, giving you insight continuously about your business, your people, your services, and so on. Then all of a sudden, it has to have the same characteristics as your business. Which is, it's 24 by 7, it can never go down, it can never lose data. So, all of a sudden you're putting tremendous requirements on an analytics system, which has, all the way from the beginning of history 'til now, been a very relaxed batch thing, to all of a sudden being something that is enterprise-grade, 24 by 7. And I think that that's actually where it's going to be the toughest nut to crack. >> So tell about some of the things that you've learned. And pretend for a second, let's pretend that you, as a co-founder at Data Torrent, and Guy, and you are teamed up. You guys run this telco. Let's just make one up, Verizon. Or AT&T, or pick one. And you sit there saying, okay, you've got the keys to the kingdom. And you can do whatever you want (laughing). You can be Donald Trump, or you can be whoever you want. You can fire everybody, or you can pick it over and run it. What would you do? You know you've got IOT. So this is business model innovation opportunities. I want you to put the technical hat on, plus knowing what you know around the business model opportunities. What do you do? You know IOT's an opportunity. Amazon is going after that heavily. Do you bolt a cloud together? Do you go after Amazon? Do you co-op with Amazon? Do you co-integrate? Do you grab the IOT? Do you use the data? I mean, given where we are today, what's the best move if we were consulting with this. >> You know, I will be the last person to be talking about giving advice to a telco. But since we are, we own our own telco here, and then we're pretending, I would say the following. IOT is going to happen, right? Earlier, when I say a billion people, that's just human beings. Once you now talk about censoring, you can program how many times they can send you data per second, then the growth in volume is immense, right? I think there's a huge opportunity, as a telco, in terms of the data that they have available and the insight that they could have about what's going on. That is not easy. I don't think that, as a telco, in the current DNA of a telco, I can go ahead and do all that analytics and really open up my business to the data insight layer. I would partner, and find a way-- >> Well, we're consulting, we're going to sit around and say hey, what do we have? We have relationship with the consumer, big marketing budgets. We can talk to them directly, we have access to their device. >> But you'll bifurcate the business. We're in the boardroom here, this is nothing more than that. But I would look at it and say look, you've got a consumer business, the same as in IOT. There's really, for me, there's three parts of IOT. There is the bit that I love which, you can geek out, which is basically the consumer market, which, there's no money in for a large-scale tenant, right, enterprise. And then you have the industrialization of IOT, which is I've got a leaky pipe, and I want a hardened device, ruggedized, which is wifi, so, now as a telco, I could create a IOT cloud, that allows me to put these devices out there, and in fact, I use Arlo, the little cameras. And they've got one now, where I can basically float it with its own cellular signal. So it's its own cellphone. That's a great use of IOT for that. And then you step to the consumer side of, I've got a cellphone, and then what I'll do is literally, in essence, riff off what Yahoo did in the early days and say, I'm now the new browser. The person's the browser. So in other words, follow the location, follow where he is, and then basically do locational-based advertising. >> By the way, you have to license the patent from our earlier guest, he'll say will he leak, 'cause he's got th6e patent on personal firewall for personal server. He's built a mobile personal server. >> Yeah. >> But this is the opportunity around wireless. Why I love the confusion, but the opportunity around wireless right now is, you can get bandwidth at high capacity. You have millimeter wave four, that doesn't go through walls, but you have other diverse frequencies and spectrum for instance, you can blend it all together to have that little drip signal, if you will, going into the cloud from the leaky pipe. Or if you need turbine, full-fat pipe, you maybe go somewhere. So, I think this is an interesting opportunity. >> And they're going to end up watching the data centers as well. There's still the gamut of saying our customer is going to continue to support their own data centers, or are there going to be one to a hundred data centers out there? And then how does selling a manufacturer or a telco play into that, and do they want to be that guy or not? >> Guy, Phu, thanks for coming in. I want to give you guys a chance to put a plug in for Data Torrent. Thanks for sharing some great commentary on the industry. So, what's up with you guys? Give us the update. Are you hiring? You growing? What are you guys doing? Customers? What's the update? Technology, innovations? >> So we've got a release coming out tomorrow which is a momentum release. I can't talk too much about the numbers, but in essence, from a fact base, we have a thing called a patchy apex. So it's open sourced, so you can use our product for free. But that's growing like gangbusters. From a top-level project, that's actually the fastest-growing one, and it's only been out for seven months. We just broke through 50,000 users on it. From our product, we're doing very well on the back of it. So we actually have subscription for the production side. >> So revenue is a subscription model. >> Yeah, and we meet both sides. So in other words, for the engineer who writes it, you've got the open source. And then when you put it into production, from the operations side, you can then license our products to enable you to manage an easy-- >> So when it gets commercialized, you pay as you go, when you use it. >> And you don't have to, if you don't want to. You've got all the tools to do it. But, we focus for our products group of, time to value, total cost of ownership. We're trying to bring Hadoop and real scale, realtime streaming to the masses. So what's the technology innovation? What's the disruptive enabler for you guys? >> I think we talked about it, right? You've got two really competing trends going on here. On one side, data is getting more and more and more massive. So it's going to take longer and longer to process it. Yet at the other side, business wants to be able to get data, have insight, and take action sub-second. So how do you get both at the same time? That's really the magic of the technology. >> Thanks for coming in. Great to meet you, Phu. I'd love to talk about the old Yahoo days, a total throwback, Web 1.0, a great time in history, pre-bubble bursting. Greatness happening in the valley and all around the world, and I remember those days clearly. Guy, great to see you. Congratulations on your new CEO committee. And great to have you on theCube. This is theCube bringing the coverage, and commentary, and reaction of Mobile World Congress here, in California. As everyone goes to bed in Barcelona, we're just gettin' down to the end of our day here in the afternoon in California. Be right back with more after this short break. (techno music)