>> John Furrier: Hello, welcome back to theCUBE's coverage of Cloud Native Security Con 2023 North America the inaugural event. I'm John Furrier, host of theCUBE, along with Dave Alonte and Lisa Martin covering from the studio. But we have on location Emmy Eide, who is with Red Hat, director of Supply Chain Security. Emmy, great to have you on from location. Thanks for joining us. >> Emmy Eide: Yeah, thank you. >> So everyone wants to know this event is new, it's an aural event, cloud native con, coup con. Very successful. Was this event successful? They all want to know what's going on there. What's the vibe? What's the tracks like? Is it different? Why this event? Was it successful? What's different? >> Yeah, I've really enjoyed being here. The food is wonderful. There's also quite a few vendors here that are just some really cool emerging technologies coming out and a lot from open source, which is really cool to see as well. The talks are very interesting. It's really, they're very diverse in subject but still all security related which is really cool to see. And there's also a lot of different perspectives of how to approach security problems and the people behind them, which I love to see. And it's very nice to hear the different innovative ideas that we can go about doing security. >> We heard from some startups as well that they're very happy with the, with the decision to have a dedicated event. Red Hat is no stranger to open source. Obviously coup con, you guys are very successful there in cloud native con, Now the security con. Why do you think they did this? What's the vibe? What's the rationale? What's your take on this? And what's different from a topic standpoint? >> For non-security specific like events? Is that what you mean? >> What's different from coup con, cloud native con, and here at the cloud native security con? Obviously security's the focus. Is it just deeper dives? Is it more under the hood? Is it root problems or is this beyond Kubernetes? What's the focus, I guess. People want to know, you know, why the new event? >> I mean, there's a lot of focus on supply chain security, right? Like that's the hot topic in security right now. So that's been a huge focus. I can't speak to the differences of those other conferences. I haven't been able to attend them. But I will say that having a security specific conference, it really focuses on the open community and how technology is evolving, and how do you apply security. It's not just talking about tools which I think other conferences tend to focus on just the tools and you can really, I think, get lost in that as someone trying to learn about security or trying to even implement security, but they talk about what it takes to implement those tools, What's behind the people behind implementing those tools? >> Let's get into some of the key topics that we've identified and get your reaction. One, supply chain security, which I know you'll give a lot of commentary on 'cause that's your focus. Also we heard, like, Liz Rice talking about the extended Berkeley packet filtering. Okay, that's big. You know, your root kernel management, that's big. Developer productivity was kind of implied around removing the blockers of security, making it, you know, more aligned with developer first mentality. So that seems to be our takeaway. What's your reaction to those things? You see the same thing? >> I don't have a specific reaction to those things. >> Do you see the same thing happening on the ground there? Are they covering supply? >> Oh, yeah. >> Those three things are they the big focus? >> Yeah. Yeah, I think it's all of those things kind of like wrapped into one, right? But yeah, there's... I'm not sure how to answer your question. >> Well, let's jump into supply chain for instance. 'Cause that has come up a lot. >> Sure. >> What's the focus there on the supply chain security? Is it SBOMs? Is it the container security? What's the key conversations and topics being discussed around supply chain security? >> Well, I think there's a lot of laughter around SBOM right now because no one can really define it, specifically, and everyone's talking about it. So there's, there's a lot more than just the SBOM conversation. We're talking about like full end-to-end development process and that whole software supply chain that goes with it. So there's everything from infrastructure, security, all the way through to like signing transparency logs. Really the full gambit of supply chain, which is is really neat to see because it is such a broad topic. I think a lot of folks now are involved in supply chain security in some way. And so just kind of bringing that to the surface of what are the different people that are involved in this space, thinking about, what's on the top of their mind when it comes to supply chain security. >> How would you scope the order of magnitude of the uptick in supply chain attacks? Is it pretty heavy right now or is it, you know, people with the hair on fire or is it... What's the, give us the taste of the temperature in the room on the supply chain attacks? >> I think most of the folks who are involved in the space understand just that it's increasing. I mean, like, what is it? A 742% increase average annual year, year over year in supply chain attacks. So the amount of attacks increasing is a little daunting, right, for most of us. But it is what it is. So I think most of us right now are just trying to come together to say, "What are you doing that works? This is what I'm doing that works." And in all the different facets of that. 'cause I think we try to throw, we try to throw tools at a lot of problems and this problem is so big and broad reaching that we really are needing to share best practices as a community and as a security community. So this has been, this conference has been really great for that. >> Yeah, I've heard that a lot. You know, too many tools, not enough platform thinking, not enough architecture, needs some structure. Are you seeing any best practice around frameworks and structure around how to start getting in and and building out more of a better approach or posture? I mean, what's that, what's the, what's the state of the union for supply chain, how to handle that? >> Well, I talked about that a little bit in my my keynote that I gave, actually, which was about... And I've heard other other leaders talk about it too. And obviously it keyed my ear just because I'm so passionate about it, about partnership. So you know, empathetic security where the security team that's enforcing the policies, creating the policies, guidelines is working with the teams that are actually doing the production and the development, hand-in-hand, right? Like I can sit there and tell you, "Hey, you have all these problems and here's your security checklist or framework you need to follow." But that's not going to do them any good and it's going to create a ton of holes, right? So actually partnering with them helping them to understand the risks that are associated with their very specific need and use case, because every product has a different kind of quirk to it, right? Like how it's being developed. It might use a different tool and if I sit there and say, "Hey, you need to log on to this, you need to like make your tool work this platform over here and it's not compatible." I'm going to have to completely reframe how I'm doing productization. I need to know that as a security practitioner because me disrupting productization is not something that I should be doing. And I've heard a couple a couple of folks kind of talking about that, the people aspect behind how we implement these tools, the frameworks and the platforms, and how do we draw out risk, right? Like how do we talk about risk with these teams and really make them understand so it's part of their core culture in their understanding. So when they go back to their, when they go back and having to make decisions without me in the room they know they can make those business decisions with the risk as part of that decision. >> I love that empathetic angle because that's really going to, what needs to happen. It's not just, "Hey, that's your department, see you later." Or not even having a knowledge of the information. This idea of team construction, team management is a huge cultural shift. I'm sure the reaction was very positive. How do you explain that to an organization that's out there? Like how do you... what's the first three steps you got to take? Is there anything that you can share for advice people watch you saying, "Yeah we need to we need to change how our teams operate and interact with each other." >> Yeah, I think the first step is to take a good hard look at yourself. And if you are standing there on an ivory tower with a clipboard, you're probably doing it wrong. Check the box security is never going to be any way that works long term. It's going to take you a long time to implement any changes. At Red Hat, we did not look ourselves. You know, we've been doing a lot of great things in supply chain security for a while, but really taking that look and saying, "How can we be more empathetic leaders in the security space?" So we looked at that, then you say, "Okay, what is my my rate of change going to happen?" So if I need to make so many security changes explaining to these organizations, you're actually going to go faster. We improved our efficiency by 2000% just by doing that, just by creating this more empathetic. So why it seems like it's more hands-on, so it's going to be harder, it's easy to send out an email and say, "Hey, meet the security standard, right?" That might seem like the easy way 'cause you don't have time to engage. It's so much faster if you actually engage and share that message and have a a common understanding between the teams that like, "I'm here to deliver a product, so is the security team. The security team's here to deliver that same product and I want to help you do it in a trusted way." Right? >> Yeah. Dave Alonte, my co-host, was just on a session. We were talking together about security teams jumping on every team and putting a C on their jersey to be like the captain of the intramural team, and being involved, and it goes beyond just like the checklist, like you said, "Oh, I got the SBOM list of materials and I got a code scanning thing." That's not enough, is what we're hearing. >> No. >> Is there a framework or a methodology to go beyond that? You got the empathetic, that's really kind of team issue. You got to go beyond some of the tactical things. What's next beyond, you got the empathy and what's that framework structure when you say where you say anything there? >> So what do you do after you have the empathy, right? >> Yeah. >> I would say Salsa is a good place to start, the software levels. Supply chain levels for software artifacts. It's a mouthful. That's a really good maturity framework to start with. No matter what size organization you have, they're just going to be coming out here soon with version one. They release 0.1 a few months back. That's a really good place to give yourself a gut check of where you are in maturity and where you can go, what are best practices. And then there's the SSDF, which is the Secure Software Development framework. I think NIST wrote that one. But that is also a really, a really good framework and they map really well to each other, actually, When you work through Salsa, you're actually working through the SSDF requirements. >> Awesome. Well, great to have you on and great to get that that knowledge. I have to ask you like coup con, I remember when it started in Seattle, their first coup con events, right? Kind of small, similar to this one, but there's a lot of end user activities. Certainly the CNCF kind of was coming together like right after that. What's the end user activity like there this week? That seems to always been the driver of these events. It's a little bit organic. You got some of the key experts coming together, focus. Have you observed any end user activity in terms of contributions, participation? What's the story on the end user piece there? Is it heavy? Is it light? What's the... >> Um, yeah... It seems moderate. I guess somewhere in the middle. I would say largely heavy, but there's definitely participation. There is a lot of communing and networking happening between different organizations to partner together, which is important. But I haven't really paid attention much to like the Twitter side of this. >> Yeah, you've been busy doing the keynotes. How's Red Hat doing all this? You guys have been great positioned with the cloud native movement. Been following the Red Hat's moves since OpenStack days. Really good, good line of product, good open source, Mojo, of course. Good product mix, right, and relevant. Where's the security focus here? Obviously, you guys are clearly focused on security. How's the Red Hat story going on over there? >> There was yesterday a really good talk that explains that super well. It was given by a Red Hatter, connecting all of the open source projects we've been a part of and kind of explaining them. And obviously again, I'm keying in 'cause it's a supply chain kind of conversation, but I'd recommend that anyone who's going to go back and watch these on YouTube to check that one out just to see kind of how we're approaching the security space as well as how we contribute back to the community in that way. >> Awesome. Great to have you on. Final word, I'll give you the final word. What's the big buzz on supply chain? How would you peg the progress there? Feeling good about where things are? What's the current progress on supply chain security? >> I think that it has opened up a lot of doors for communication between security organizations that have tended to be closed. I'm in product security. Product securities, information securities tend to not speak externally about what we're doing. So you don't want to, you know, look bad or you don't want to expose any risk that we have, right? But it is, I think, necessary to open those lines of communication, to be able to start tackling this. It's a big problem throughout all of our industries, and if one supply chain is attacked and those products are used in someone else's supply chain, that can continue, right? So I think it's good. We have a lot of work to do as an industry and the advancements in technology is going to make that a little bit more complicated. But I'm excited for it. >> You can just throw AI at it. That's the big, everyone's doing AI. Just throw AI at it, it'll solve it. Isn't that the new thing? >> I do secure AI though. >> Super important. I love what you're doing there. Supply chain, open source needs, supply chain security. Open source needs this big time. It has to be there. Thank you for the work that you do. Really appreciate you coming on. Thank you. >> Yeah, thanks for having me. >> Yeah, good stuff. Supply chain, critical to open source growth. Open source is going to be the key to success in the future with automation and AI right around the corner. And that's important. This theCUBE covers from cloud native con, security con in North America, 2023. I'm John Furrier. Thanks for watching.

>> Live from Los Vegas. It's theCUBE covering Dell Technologies World 2019. Brought to you by Dell Technologies and it's ecosystem partners. >> Welcome back to Los Vegas everybody. You're watching theCUBE, the leader in live tech coverage. This is our wall-to-wall coverage. We're wrapping up day one. I'm Dave Alonte, my cohost here at this segment is Stu Miniman. Jon Siegal is here as the vice president of product marketing, cubulum from Dehli MC. Good to see you again Jon. >> Great to be back as always guys! >> And I love that you brought a customer, Suzan Pickett is here. >> That's what I do, by the way. You realize that, that's my new thing. >> Suzan is the VP and director of Converged Infrastructure at US Bank. >> Thank you for having me. >> Welcome, one of my banks. I got a lease with US Bank. You guys are great. >> Thank you. >> Great to have you guys. >> Let's start with a customer, if that's okay? >> Absolutely. >> Tell us about your role, you got CI in your title that's interesting. >> I do. >> That's a relatively new trend, explain that. >> Yeah absolutely, so I've been at the bank a couple years now and my teams focus on Converged and Hyperconverged Infrastructure, delivering solutions and infrastructure as a service for our business. >> You guys have been working together for a while if I understand it Jon, right? Talk a little bit about what's happening here at the show maybe give us a quick overview of what's happening in CI and HCI in your world. >> Absolutely, so a lot going on as you saw today in Dell Tech cloud announcement. HCI was a key pillar there. Really VxRail, in particular, was featured as the simple and fast foundation for the Dell Tech cloud both as the on-prem manage version as well as, as you heard, the Data Center as a service. So really exciting to see how HCI continues to evolve and it's use cases around cloud and infrastructure as a service, as well as platform as a service as well. So a lot of exciting announcements there. In addition to that, just this past week, by the way, we also, since you mentioned CI, Converged Infrastructure, we just announced that we re-upped our agreement with Cisco, a new multiyear agreement extension to continue to innovate with Cisco on the VxBlock, which, as you know, was the pioneer in this, Converged Infrastructure space and with all the recent integrations we've done now with VMware, VxBlock as well as HCI, is really built to be a on-prem foundation for the cloud. >> Yeah so, this goes back to 2009, when Cisco and VMware and EMC got together and created this concept of Converged Infrastructure. There were other competitors in the market, but you guys kind of lead that trend, and so when you go back to that years ago, that's our storage and networking and compute, they were different parts of the organization. I presume you guys went through a similar journey. You had to put all that together. Herd some calves. And what did that do for your business? What was your journey like to CI? >> I think we're still on that journey, but I think it's also evolving as we go more Agile and more DevOps, more software-defined, we're seeing a lot more blending of the teams as well so we're creating a lot of virtual teams that encompass not just infrastructure but security, developers, networking as well and really being able to deliver that infrastructure's service, platform as a service, end-to-end provisioning for our business lines. >> Suzan, I love that story because I remember talking to, when this started, you talk to the storage group and they'd say, "Oh my gosh, you're going to take away my job." I'm like, "You know that security thing that they've been yelling at you to fix for a while? You talk about the new business apps that we need to do. These are the types of jobs that we want you to do." I heard you talk about Agile and DevOps and all these things. Talk a little bit about, what are the pressures you're facing from the business and the relationship between your group that help you to meet those now. >> Sure, well the first thing we did was we created an infrastructure automation services team and people looked at us like we're a little crazy to do that and we pull those highly, highly motivated potentials from within the organization that we already had to focus on automation and get the foundation for infrastructure as a service and get that part right. Something as basic as provisioning a virtual machine would take 12 weeks or longer and through our journey with Kubernetes today, containers, vRealize Automation Suite, on both Converge and Hyperconverge, VxFlex. We're now reducing that down to about three days and we anticipate, with a lot of our sprints and iterations, that we're going to be getting that down to less than a day within the next quarter. >> So John Furry says that automation is the killer app for infrastructure, so are you guys, are you building essentially out on infrastructure as a service platform, where people used to call it private cloud. I don't know if you use that term still. I think it's still valid. >> We do, yeah. >> How's that going? What's been the business impact of that so-called private cloud? >> We had a Business Critical Application that would often take year release cycles, more than 12 weeks to get a server, primarily focusing on physical servers, and now what we're doing is we're partnering with them with not only the business, the application folks, the developers, the middleware teams, networks, security, but also all of the infrastructure teams to deliver that faster speed to market, and so now they're down to days now to provision. They actually gave us a stat the other day that said, "By using our automation with Kubernetes on Hyperconverge VxFlex, that they were able to have cost avoidance of hiring a bunch of people to build physical servers. So that in and of itself was a huge win, but the fact that we can repurpose and releverage that automation, those workflows, the orchestration models, means that we can continue this conversation with the next business line and the next business line and keep telling that story and it's a good one. >> Jon I'd live to hear from what Suzan was saying and there's so many of the modern things that they're doing. When you look at your customer base, how are they doing on that journey? We used to always ask, in the earlier days, it was like, alright how much was I just eliminating sub-silos but pretty much doing the same apps and same processes before or have I really gone through some transformation? >> I tell you what, we've seen quite a bit of transformation in our customer base because they had to. You look at now, as you see with US Banks, they're now transforming their organization to support DevOps, right? That's an entirely new realm for them to focus on. That means they need to make infrastructure easier and simpler so we're finding that is really, I think, that's the catalyst and that they're realizing that the way to do this is let's make infrastructure as simple as possible. Infrastructure service. Make that platform as a service available so our customers can spend less, wait, our IT department can spend less time on the speeds and fees, if you will, of maintaining infrastructure, more time innovating up the stack versus down the stack, right? >> Alright Suzan, I got to ask a question Jon probably doesn't want me to ask you. You're trying to simplify, 'cause you're doing all this stuff that's not really adding value to your business, you want to do stuff that's going to make you more competitive. Well why don't you just throw all this stuff in the cloud? >> Good question and I think that eventually we will have a multicloud strategy, but it is a bank and we don't want to be in the news for a data breach and that's the real answer but also because we want to, again, lay that foundation for an on-premise, solid infrastructure as a service with service catalogs at the business. We can then drive that product taxonomy and they know that they get a good, solid product from IT and then we extend that into the cloud so as much as we can do that, and maybe there will be some cloud native apps down the road that go 100% in the public cloud. I don't have a crystal ball. I suspect there will be, but again we want to do it right and we think this is the right foundation to lay for that. >> You want to have total control over, certainly, your mission critical apps, I'm presuming, right? Maybe put some stuff up. I'm sure you have plenty of stuff in the cloud. Well why Dell EMC? >> I think it goes back to our strategic partnership. It's always been that strong partnership, that enablement, and that continuous feedback loop. We need something, we go talk to our product teams. We get that back, we get it back from our product teams, so it's not always perfect, and there are competitors out there, but at the end of the day, when we look at the Dell Technologies family and that ecosystem and our ability to integrate, iterate, automate within that family, it just helps us stream like that and standardize. >> We've heard this morning from a lot of folks. Michael Dell talked about it. Jeff Clark talked about it. Companies want to consolidate the number of suppliers, certainly infrastructure suppliers, throwing sass forget it, so many apps now. Are you seeing that? Is there pressure to consolidate the number of suppliers, or do you still have, in certain cases, where you really want to go best of breed, so-called best of breed, for some niche app, or do you want to consolidate suppliers? >> So I always want to standardize because that's going to help our automation story, but I still want best of breed, and so that's one of the primary reasons that we're standardized on Dell Technologies today. VxFlex being one of them and Converged Infrastructure being another. There are use cases for multi-vendor strategy, but again, you would look at the right solution for the right job at the right time. >> Okay Jon, that was a totally loaded question, so can you be both a portfolio company like yours and still be best of breed and if so, how so? >> Well I think what we are, we certainly are a portfolio company in the way that, but I think we have leading infrastructure, leading solutions in each case. You take things like Hyperconverge and Converge, great example of that, and I think what we see at the US Bank is that that porfolio of solutions is what's actually enabling US Bank to essentially address all other challenges, right? Whether it's the IS, whether it's the crown jewel applications that Suzan's trying to support, whether it's the DevOps that they're trying to actually build out right now. We've got best of breed solutions for each of those as well within our portfolio. And also, I would say that we're really focused on, ultimately, a portfolio with a purpose meaning that we're taking our networking, for example, portfolio, you just talk to Drew Shulkin. Together with out HCI portfolio, and we're ensuring that they work really seamlessly together so that in the case of, for example, working with, say VxRail or VxRack, we're able to automate all the networking for a HCI environment or at least 98% of it. That's really, again, taking but that's because we're best of breed and porfolio at the same time. >> Yeah so, I'm throwing all kinds of loaded questions out here, and I want to understand this because as independent observers you get Company A says this, Company B says that, but the customer's ultimately the arbiter. How do you, maybe not define, but how do you look at best of breed, what is best of breed to you? >> I look at the technology that's going to make me look good and that's going to make my teams look good and that's not just day one, that's day two and I think that's where the differentiator is as well. We've always found that Dell Technologies is there to support us. Stuff breaks, right? Your car needs oil, your tires need rotating, and it's the same with equipment in the Data Center. How those companies react and they support and they have your back when that happens, I think is the key differentiator and we always found Dell Technologies to be there for us. >> So I'm hearing the breadth, the porfolio. We haven't talked about services but I know that's a key part of it. >> Well, Suzan I hear you talking about day two. CI helped simplify that day one and then, as it matured, it worked more on the day two, and HCI even more. When you talk about the cloud solutions from Dell EMC, that cloud operating model. When you think about public cloud, I don't think about what version I'm on, it takes care of that. When I hear some of the solutions from Dell, it's getting to that model. How are they doing along that that spectrum, I guess, from the, "Okay I need to do the RCM and manage when I do the updates" to "I don't even think about it anymore." >> Sure, I think it is still something that we all care about as much as we're told we shouldn't care about it, I care. I want to make sure that we're doing the right things at the right time. I think it's a journey. I think we've come a long way in the last few years and I think that every year it gets better and as we start extending to that multicloud, obviously that's going to drive some of that solutioning as well. I think we'll continue to see improvement in that area. >> What is something that you'd like to see Jon do to make your life better? (laughs) Besides cut prices, you can't say cut prices. >> Alright, cut prices. >> Every year you cut prices. >> Let's talk about that deal. I think just continuing to be there, continuing to represent, bringing forth the products, the products team, helping us be strategic and also be very tactical. While I have this one last opportunity 'cause I don't know where we are timewise. I just want to shout out to my team. Right, so it's not just the Dell Technologies team that's bringing all this to the table, it's my team and the organization and my peer teams as well. We just keep sharing, we keep collaborating, and we keep iterating. >> Yeah Jon, one of the things, talk about collaboration, my understanding is Suzan's part of one of the user groups here. You know, big community. >> Yes. >> We always talk about at these shows. Maybe you can share that. >> Yeah so Suzan is actually a new board member for our Converge user group which has been around for several years now and she just joined a few months ago. >> I did. >> And I think that we talk about collaboration and feedback. Suzan is representing not just her own team, she's representing teams around IT around the world. And I think she's a great example of providing feedback, not just at Dell EMC directly, but to other users as well, and best practices and tips and tricks. We have a user group tomorrow at three o'clock. I think couple big executives might be there as well, so it's going to be a lot of fun. So tomorrow at three o'clock. I think it's, at least, our sixth annual that we've had here. But the user group itself, I think exemplifies as much as you've been talking about 'cause that's evolved from being what used to just be about a user group just about blocks, VxBlocks, now it's about CI, it's about HCI, it's about VxBlock, it's about Dell Tech cloud. We have VMware on the panel as well as Dell EMC so I think you see the user group has evolved with our customers and with our portfolio. >> It's a community, it's a mechanism for people to say, "How did you do that" or "How should I do this" or "How do I get my team motivated" or "How do I collaborate with security?" These are tough questions and so I think just having that network of people that can come together and ask those questions and be transparent and be authentic, that's what it's about. >> Appearance, problem-solving, sharing ideas. >> Yeah. >> You've been a Converged Infrastructure client, customer for a number of years. >> I have. >> So you've seen pre-acquisition, how has the Dell EMC merger affected your perception of the company and your relationship with them? >> I think in the last year, or the previous year, we were all waiting to see where things fell and what was going to happen, and I think now it's found it's feet, right? We're starting to see some announcements in both the Converged and the VxFlex space, and it's really starting to come together and I think that story, the Dell Technologies family story is really starting to come together where maybe in the last 12, 18 months, there was a little bit of unknown there and so, we just kind of sitting back and waiting and curious but keep doing what we're doing using that best of breed, the best practices that we have on the floor. >> Alright awesome. Suzan, Jon, thanks so much for coming on theCUBE. It was a great segment. >> Thank you. >> I appreciate it. Alright, that's a wrap for day one. Dave Alonte, Stu Miniman, John Furry's over there. Lisa Martin, Rebecca Knight is here. This is day one, we got wall-to-wall coverage. Tomorrow, day two and day three. Check out siliconangle.com for all the news. Michael Dell's coming on tomorrow. We got Pat Kelsey going to be on tomorrow. Tom Sweet's coming on later on in the week. Awesome coverage, check out thecube.net. This is Dave Alonte, Stu Miniman. We'll see you tomorrow, thanks for watching.

(upbeat music) >> Live from Barcelona, Spain. It's theCUBE covering Cisco Live Europe, brought to you by Cisco and its ecosystem partners. >> Hello everyone welcome back to theCUBE's live coverage here in Barcelona, Spain for Cisco Live Europe 2019. I'm John Furrier with theCUBE, with Stu Miniman and Dave Alonte also here doing interviews. Our next guests, two guests from the DNA center platform, Cisco, the agent platform team, Prakash Rajamani, director of product management, Cisco and Ronnie Ray, vice president of product management, Cisco, the DNA center platform growing 70% of the use cases, software distractions, API automation. Congratulations. Great success. Thanks for joining us. >> Thanks John. >> Big Fan of the DNA center. You guys have made great progress. Take a step through us. The positioning, how things are rolling, what's some of the feedback? Where's the DNA center platform at right now for Cisco? >> Yup. >> So DNA center was launched about 80 months back and it's probably one of the products in Cisco that has completely started to transform how we do the selling motions. So this is one of the key drivers of Cisco moving into light sensing mode switch, more software like. Now as part of how we do management Typically and traditionally it has been very much a manual driven process there's some reporting but it is a lot of expert light capabilities that you need to have to do management of the infrastructure then it's kind of moving that access to where you can now do machine-lift management. Of course it doesn't solve all the use cases absolutely as you mentioned, more than 70% but there's a whole host of new capabilities that you have to put on top and that's where developers come in because this is a platform that's built for developers to be able to extend it's capabilities to really look at solving problems for our customers. >> I think you know, after listening to all the announcements in temp based networking, ACI anywhere, hyperflex anywhere, data at the center of the value, data centered as you guys say, it's clever but I think it highlights what you guys are doing because you're talking about programmability of the network as two worlds collide actually three worlds collide, Cloud, On Premises and Edge into one network, you have a network, the network is key it's getting bigger, to cross domains is a big theme here, these are hard problems that are being solved by Cisco more complex cause there's more moving parts but it still has to operate as one network. This is essentially highlights the success of the DNA platform, am I kind of getting it right or is that kind of in line with how you guys see it? >> Sure, I mean I think Cisco DNA centered I mean if you look at the evolution we started in the network domain. You're absolutely right we have kind of extended to the brand change, there's nine integrations that are happening with the data center integrations, happening with the cloud, so yeah absolutely looking at the fabric that we launched about 18 months back now extending and stretching to all of those domains and wherever users connect and wherever users go to and that's of Cisco data center but think about that as we kind of do that, yes there is a change that also required not just in the product but also in the IT process because earlier companies had silos of things and now those silos will be forced to work together and CI was one that our network folks that support us because really they want to see cross domain bring power to the organizations but we are the enabler of making that happen. >> No brainer. >> Prakash, I'd love for you to take us inside ya know, we love looking at the product management piece here because you've had a lot of constituencies. You've got the internal product teams that all I'm sure want to get in and mature and expand their used cases. You've got all your partners that are building the platform. You've got the customers asking for feedback You've got a - ya know, a lot of options to choose from which is a good thing but you've obviously got limited resources. So take us inside that, what you've learned over the last year and how you helped prioritize and move this product forward so fast over the last 18 months. >> So one of the main things we did when we started with Data Center is to start thinking and having the vision to get a data center platform. With that in mind, every feature, every capability that we built in the product was built API first before we built a UI around it. Right? That has helped us immensely in the last couple releases we've started delivering features as APIs even before it had a face to it, and I think that has helped us prioritize and make sure that we are able to meet the demands going demands of customer or partner we had a customer who was like "I need this feature now" and we were hands strapped, we had a big back log, we couldn't get things done but the fact that we were able to get the APIs we were able to work with the customer and say "Hey here you can wire these three APIs and you can get what you're looking for" and he was like "Wow, that's so simple and I'm on my own" he was happy, we are happy we are able to manage our back log better. So I think the main strategy for us that's working is going API first on a pragmatic basis. This is us moving completely software driven as Ronnie was highlighting earlier in that relevant process that is helping us get there and that's part of it >> Well, it's customers a lot I mean they get to roll their own if you will without having be customized, it's still standardized with the APIs >> That's right, right? I mean the benefit is as you start getting into the 30% used case where "Hey, what's coming out of the box is not meeting exactly what I do today" we provide very grander APIs to very business driven, simplified interned APIs. The grander APIs allows the customer who wants to say I want A, B and then D and E to move forward compared to intern based API who is using the pride in the simplicity in driving that formula. >> Yeah, Ronnie I'm wondering if we can up level for a second here cause feedback I've gotten over the last year. Ya know, a year ago we heard Cisco is moving heavily towards software. When I talked to a lot of the partners both technology partners and channel partners they said this had a ripple effect inside Cisco it's not so much okay here's the skews and here's the new boards and here's the products but I need to sell a solution and therefore that's platforms that I have to have and therefore everything needs to work together and I have to think API first and like it does significant changes to how Cisco is, the joke I used to have is Cisco is like 100 companies and some people were like "Well, maybe it's 100, maybe it's 200." But today it's now something like platform is a unifying place, is that what is your solution set part of that drive and is that something you're seeing more broadly inside Cisco? >> Certainly, I think you're absolutely right that is does have a unifying effect if I might put it that way. >> Yeah Right? Because there's so many different capabilities that existed in different tools that are coalescing on Cisco data central and which is becoming part of the platform which is now customizable by our entire development community but think how fast that happens in a now within the sales force, within Cisco as a company there is no more cross domain knowledge that'll be required because now it operates different parts it can tune different things, that also means that is supposed to change the business model because going into software and kind of bringing it together and is increasing Cisco is obviously ya know foyering into softer subscriptions, this is a key product that's kind of supporting that, so in many ways it's not just the technology, it's not just APIs but also as a business process that's changing Cisco just like it'll change customers. >> One of the things we're seeing is a lot of design thinking principles this year. Love the new positioning bridged to the future bridged to tomorrow, wherever it goes but it's clean. Connecting the worlds are connecting together through the network get that. What has been some of the challenges and opportunities you guys are seeing around simplicity? Love this API, exposing API allows for customization, I love the broader intent based templates are great but it's hard to make things simple. Can you just elaborate on how you guys are thinking about the product short, medium, long term in terms of continuing to work the back log, I'm sure the feature list is growing like crazy but you got a challenge to make it simpler. >> Absolutely >> How hard is it? What does it entail? Share some insight there. >> So lets take the question in two parts and Prakash can talk to the product simplicity because that is a certainly something that we've got to manage very very carefully but think about also when simple doesn't just mean usable product, it also means a product that can fit into the ecosystem and make the process simpler. So there's a lot of deeper understanding that we are developing through the learning as we work with customers and how do we embed how do we make customers life easier how do we make the process easier and then after goal is how do we make their operational expenses lower? Because we want them to go faster, we want them to go faster at a lower cost and so there's a certainly both learning and investment that's happening there and the product side Prakash. >> On the product side it's about how we used to build to how we are building right now the way we used to do was a new feature comes in it goes to the device layer first the device team builds it puts CLI around it ships it off, sends it to the management team and the management team says "Oh, I got to support this feature" They go, they wrap a UI around it to support the feature, ships. Now we have flipped it turn completely around we start with like what is a customer's work field? What do they need to do and how can we do it in the minimal steps? Once we identify that we push that down to saying "Here is what the user interface looks like here are the three steps that they need to do. That trickles down to saying what we need as an APA on the device layer to develop the feature so we've gone down from going a bottom up way to build a product to a top down, customer driven, used case driven way to build a product. That means we are addressing the customer head on from a simplicity perspective and that's basically what has made us successful in moving the ball forward on this one. >> What has been some of the customer feedback? Can you share some anecdotes around some of the early customers you started rolling this out and what are the ones receiving on the receiving end today saying? >> So when you see from a simplicity feedback perspective I have a large retail store rolling out like maybe 60 APs in a single store over night and they've gone from having that be done over three nights to one person spending 20 minutes putting all the APs up going to the tool and the tool recognizing everything that's come up and deployed. So it's a night and day transformation on how it used to be to how it is right now. So the simplicity >> Sounds like the old way was >> Sounds like you saved a night in a day >> Manually configure, go put a wireless ping to it >> Yep, the old way was yeah you go you plugged the AP, you come back you look at the tool, the AP is there >> Check the channel, stuff is there. >> Map it to the right controller, do all the mappings Now you don't have to do anything just plug the APs and upload preloaded to say these APs are going to the store. The tool takes care of the rest of the stuff that's how simple it is become >> It's almost like old way new way What why are we doing that? And it's good when they have consistent environments with policies there's definitely more expansion. I get that, what about other used cases? Wireless is one hot one, I could see that branch off it's deployments what are some of the popular used cases that you're seeing in the customer base I know you got a broad base but what are the ones what are the patterns that are emerging out of this? >> So let me start another then have Ronnie chime in on the used cases he's seen. Some of the ones that are probably very transformational is that on the policy based used case, we have companies turning around and creating small subdivisions within their organizations. We have a large government in Yasha who is deploying that, they have 20 divisions. Earlier to do that it's extremely complex. They have to go in, they have to understand what division, who is using on which device, which ports mapped to them, just planning that it says it's so huge. For the new policy different approach that we have going, they don't have to know about anything they just need to know Prakash works for division A, Ronnie works for division B assign me to respective divisions, as I come in my policy gets right over to the network. I deploy the network as is, as I speak that is basically the level of simplicity that has changed and that all ties back to doing your network from a policy perspective not a networking from a feature perspective. >> Got it, Ronnie any comments on used case on your end? >> Yeah absolutely so think about we've talked about assurance we launched segmentation that's doing very very well of course even with when all of the public acknowledgement that goes with it but an interesting used case that's come up which is in fact in the keynote this week at Cisco live is about IUT extensions. So Data seto owa is extending to the factory floor, the production equipment and transportation and these are tremendous neo opportunities that are both for companies to kind of look at IT and OT and how this comes together, again going back to the unification simplification theme that do many more things at the same time they try to make it in a rationally much more operable. >> Okay so lot of progress in 18 months give us the road map going forward. We're at the beginning of 2019 what you'll be looking for, can a high level show show us what we should expect to see down the road >> K so from a road map perspective it's in a think about that we've been very focused on getting the customer value. Now the lens is kind of shifting to how do we deal with large enterprise capabilities? So both the hardening of the system itself, how do we look at, for example multiple clusters opening up in diverse locations will give us geo diversity and support there from that perspective and high availability. So these are enterprise class features every large customer requires it and as they move from smaller deployments to full scale deployments that is something that the labs look to need >> Yeah, Prakash when I heard you talking about things I need to think a little bit differently. It's like okay I'm used to going into the deploy and it's going to take me three days wait how do I learn about the fact that I can do it now in a couple of hours? What kind of training or retraining or education is that part of what you're doing in your team or where does that happen? >> It's part of the education, part of the videos we double up and publish to customers so that they don't think about this as I'm going to approach my same 20 steps and think that I'm going do that through data center except that I'm going to do that through a user interface. The first thing that we tell them is like "You're going to do 20" You're going to do two. Right? So the immediate feedback is oh does it address everything I want to do? And so that's the 70% used case more would rather say yes it addresses only thing is we have simplified it, we have compressed it so you don't have to go and go through all these 20 steps but instead get it done in two, so the watts have helped some of the trainings that you have done has helped even talking to from a sales process the customer to know "Hey this is what I'm embracing" so when they come in they don't come in with I'm going to run my network the same way but no no I'm going to run it differently has helped us immensely to make the transition >> Well guys, congratulations on a great successful product, big fan I love that thing, I think it's going to be the future there's a lot more head room there that's cause we're looking at automations the devnet zone we're in is showing massive growth. The appetite for automation the appetite for configuration and scale and managing the complexity is a sweet spot I think that you guys had a nice formally hear looking forward to it. Final question for these guys Ronnie and Prakash are going to both answer it. Say something about DNA center platform that people should pay attention to that they might not hear in the mainstream chatter that's important that they should maybe want to kick the tires or understand it further, an area that they should know about that they might not hear about or they should know about what's the most important feature. Share some, share some insight. >> So again just looking at a little bit into the future of Cisco data center platform, right now we're kind of talking of APIs, there's capability that's coming in the future that will also deal with work flows and the work flows will be built on something which is machine built so there will be a lot of analytics in fact in a data center not only does automation but also extends data analytics so a lot of cool stuff that'll come there and again we'll talk about it more as we get to the next Cisco live. >> Prakash anything? >> I'm going to go a little more ground level people tend to talk about simplicity, talk about how we can do things way differently with data center and people tend to forget that we have not forgotten the network engineer who has been managing the network. We have APIs for you to do the same things you've done all along, create articles create re-lance, do some of the basic networking stuff so that it's not about this just as simple we also have the more detailed breakdown of the API so that you can still continue to know the nuts and the bolts and other things as well as much as the simple stuff so it's the >> It's an empowering all personas in the network from network engineer low level getting down and dirty to large scale automations, whatever the use case is you got the empowerment. >> Yep that's basically what I would like to >> That's awesome, well congratulations Again big fan, DNA center takeover here in the Devnet zone I'm John Furrier with Stu Miniman Cube coverage day two of three days stay with us for more after this short break. (electronic music plays)

>> From the SiliconANGLE media office in Boston, Massachusetts it's theCUBE. Now, here's your host Stu Miniman. >> Hi, I'm Stu Miniman and welcome to our Boston area studio. I'm happy to welcome back to the program a multi-time guest, Josh Rogers, who's the CEO of Syncsort. Josh, great to see ya. >> Great to see you. Thanks for having me. >> Alright so, Syncsort is a company that I would say is, you guys are deep in the data ocean. Data is at the center of everything. When Wikibon, when we did our predictions everything whether you're talking about cloud, whether you're talking about infrastructure, of course everything like IoT and Edge, it is at the center of it. I want you to help start off is there's this term, big iron, big data. Help explain to us what that is and what that means to both Syncsort and your customers. >> Sure yeah, so we like to talk about Syncsort as the leader in big iron to big data and it's a it's a positioning that we've chosen for the firm because we think it represents the value proposition that we bring to our customers but we also think it represents a collection of use cases that are really at the top of the agenda of CIOs today. And really we talk about it in two areas. The first is a recognition that large enterprises still run mission critical workloads on systems that they've built over the last 20, 30, 40 years. Those systems leverage mainframe computing, they leveraging IBM i or AS400 and they spent trillions of dollars building those systems and they still deliver core workloads that power their businesses. So mission number one is that these firms want to make sure that they optimize those environments. They run them as efficiently as possible. They can't go down. They've got the proper security kind of protocols around them and of course that situation's always changing as workloads grow and change on these environments. So first is how do I optimize the systems that while they may be mature, they are still mission critical. The second is a recognition that most of the critical data assets for our customers are created in these systems. These are the systems that execute the transactions and as a result have core information around the results of the firm, the firm's customers, et cetera. So second value proposition is how do I maximize the value of that data that gets produced in those systems which tends to be a focus on liberating it, making a copy of it and moving it into next generation analytic systems. And then you look at the technical requirements of that it turns out that it's hard. I'm taking data from systems that were created 50 years ago and I'm integrating it with systems that were created five years ago. And so we've got a special set of expertise and solutions that allow customers to both optimize these old systems and maximize the value data produced in those systems. >> You bring up some really good points. I've been talking the last couple of years to people about how do I really wrap my arms around my data and we're talking about a multi-cloud world and where we have pockets of information trapped. That's a challenge. So it's not just about my data center and Amazon. It's like oh wait, I've got all these SaaS deployments and I think it's probably, it's a blind spot that I had had as to sure, right, you've got companies that have let's call them legacy systems, ones that they've got a lot investment but these are mission critical, these are the ones that it is not easy to modernize them but if I can get access to the data and put this into these next generation systems it sounds like you kind of free that data and allow that to be leveraged much easier. >> That's right, that's right and we, what we try to do is focus on what are the next generation trends in data and how are they going to intersect with these older systems. And so that started as big data but it includes cloud and the multi-cloud. It includes real-time and IoT. It includes thing like Blockchain. We're really scanning the horizon for what are these kind of generational shifts in terms of how am I going to leverage data and how do we get really tight on the use cases that our customers are gonna need. So I'll integrate those new technologies with these old investments. >> Josh, I'd love to hear what you're seeing from customers. So we've talked to you at some of the big data shows. I know we've spoken to you at the Splunk shows. I felt like what we as an industry got bogged down in some of the tools for a couple of years. While Wikibon, we did the first market forecast on big data everybody was like oh, Hadoop Hadoop Hadoop and we're like well, Hadoop will catalyze a lot of things and companies will rod a lot of things but Hadoop itself will be a small piece of the market and we've started to see some consolidation in that market. So data and the value that I get out of the data is the important thing. So what are your customers focused on? How do they get from their traditional data warehouses to a more modern? What are the challenges that they're dealing with and where are you engaging with them? >> Right, sure. So I mean one of the challenges they do have is this explosion of kind of options. Am I doing things in Hadoop? What is Hadoop at this point? Which projects actually constitute Hadoop? So what repository I'm gonna use. Am I gonna use Hive? Am I gonna use something, am I gonna use MongoDB, Elastic? What are, what's the repository I'm targeting? Generally what we see is that each of those has, and a long list of additional repositories, has a role to play for the specific use case. And then how am I going to get the data there and integrate it and then get the data out and deliver insights? And that stack of technologies and tools is pretty intimidating. And so we see customers starting to coalesce around some market leaders in that space. The merger of Hortonworks and Cloudera I think was a very good thing for the industry. It just simplifies the life of the customer in terms of making decisions in confidence in that stack. It certainly simplifies our life as a partner of those firms and I think it will help accelerate maturity in that tech stack. And so I think we're starting to see pockets of maturation which I think will accelerate customers' investments in leveraging these next generation technologies. That then creates a big opportunity for us because now it's becoming real. Now I really have to get on a real-time basis my data out of my mainframe or my IBM i system into these next generation repositories and it turns out that's technically a challenge and so what we're seeing in our businesses real acceleration of our big data solutions against what I would say production-targeted workloads and projects, which is great. >> Alright, M&A, you got a always really active in this space. We've done ThinkSort for many years so we've watched some of the changes along the way. I believe you've got some news to share regarding M&A activity and there's also some recent stuff to tap in the last year. Maybe bring us up to speed. >> Sure so we've made two announcements. We made an announcement in the last few weeks and then one very recently that I'd like to share. The first is about two months ago we struck up a developmental relationship with IBM around their B2B collaboration portfolio and this product set really gives us exposure to integration styles between businesses. Historically we've been focused on integration within a business and so we really like the exposure to that. More importantly, it intersects with one of these next generational data themes around Blockchain and we believe there's a huge opportunity to help be a leader and how do you take Blockchain infrastructure and integrate it to these existing systems. So we're really excited to partner with IBM on that front. And IBM obviously is making huge investments there. >> Before we got, what's Syncsort's play there when it comes to Blockchain? We have definitely talked to IBM quite a bit about Blockchain, Hyperledger, everything going into there. So maybe give a little more color there. >> Sure, so look, we still think that production workloads on Blockchain are a few years out and we see a lot of pilot activity. So I think people are still trying to understand the specific use cases they're gonna deliver real value. But one thing is for certain, that as customers start to stand up production workloads on the Blockchain they're going to need to integrate what's happening in that new infrastructure with these traditional systems that are still managing the large majority of their transactions. And how do I add data to the Blockchain? How do I verify data on the Blockchain? How do I improve the quality of data on the Blockchain? How do I pull data off of the Blockchain? We think there's a really important role for us to play around understanding the specifics of those use cases, how they intersect with some of these legacy systems and how we provide tailored solutions that are best in class. And it's one of the reasons, it's one of the primary reasons we've struck up the relationship with IBM but also joined Hyperledger. So hopefully that gives you a little bit more context. >> That's great. >> The more recent announcement I want to make is that we've acquired a company called Eview and Eview is a terrific leader in the machine data integration space. They have a number of solutions that are complementary to what we've done with our iron string product and what we're trying to do there is support as many use cases as possible for people to maximize the value of that they can get out of machine data, particularly as it relates to older systems like mainframe and IBM i. And what this acquisition does is it allows us to take another step forward in terms of the value proposition that we offer our customers. One specific use case where Eview's been a leader that we're very excited about is integration with ServiceNow. And you can think of ServiceNow as kind of a next generation platform that we to date have not had integration with. This acquisition gives us that integration. It also gives us a set of technology and talent that we can put towards accelerating our overall big data plans. And so we're really excited about having the Evue team join the Syncsort family and what we can deliver for customers. >> Yeah great great. Absolutely, companies like ServiceNow and Workday, huge amounts of data there, are seeing a lot of it. Dave Alonte's been at the ServiceNow knowledge show with theCUBE for a number of years. Really interesting. Seems like this acquisition ties well in with I believe it was Vision that a year ago? >> Well so it ties in mostly with our iron string product. >> Okay. >> Now Vision contributed to the iron string product in that that gave us the expertise to deliver integration for IBM i log data into next generation analytic platforms like Splunk and Elastic. So we had built a product that was focused on delivering mainframe data in real-time to those platforms. Vision gave us both real-time capability and a huge franchise in the IBM i space. Eview builds on that and gives us additional capability in terms of delivering data to new repositories like ServiceNow. >> Great, maybe step back for a second. Give us kind of some of the speeds and feeds of Syncsort itself. Memento the company, you've been CEO for a while now. Tell us how we're doing. >> Yeah, we're doing well. We're having a record year. It's important to actually recognize that in September we celebrated our 50th anniversary. So I think we're a bit unusual in terms of our heritage. Having said that, we've never driven more innovation than we have over the last 12 months. We have tripled the size of the business over the last three years since I've been CEO. We've quadrupled the employee base. And we will continue to see I think rapid growth given the opportunity we set and we see in this big iron to big data space. >> Yeah, Josh, you talk about that. When I look at okay, a 50-year-old company. We talked about data quite a bit differently 50 years ago. What is the digital transformation today? What does that mean for Syncsort? What does that mean for your customers? Help put us in context. >> Yeah, I mean, it kind of goes back to this original positioning which is, the largest banks int he world, the largest telecommunications vendors in the world, healthcare, government, you pick the industry, they built a set of systems that they still run today over the last four or five decades. Those systems tend to produce the most important data of that enterprise, not the only data you want to analyze, but it tends to be that reference data that makes everything else, allows you to make sense of everything else. And as you think about how am I gonna analyze that data, how am I gonna maximize the value of that data there is a need to integrate the data and move it off of those platforms and into these next generation platforms. And if you look at the way a vSAN file was designed for computing requirements in 1970 it turns out it's really different than the way that you would design a file type JSON or a file for Impala. And so kind of knitting that together takes a lot of deep expertise on both sides of the equation and we uniquely have that expertise and are solving that. And what we've seen is as new technologies continue to come to market, which we refer to as the next wave, that our enterprise customer base of 7,000 customers needs a partner that can say how do I take advantage of that new technology trend in the context of the past 30, 40, 50 years of investment I've made in mission critical systems and how do I support the key integration use cases? And that's what we've determined where we can make a difference in the market is focusing on what are those use cases and how do we deliver differentiate solutions to solve 'em that help both our customers and these partners. >> Absolutely, it's always great to talk about some of the new stuff but you need to meet the customers where they are, get to that data where it is and help move it forward. Alright, Josh, why don't you give it the final words? Kind of broadly open. Big challenges, opportunities, what's exciting you as you look forward kind of the next six months? >> Yeah, so we'll continue to make investments in cloud, in data governance, in supporting real-time data streaming and in security. Those are the areas that we'll be focused on driving innovation and delivering additional capability to our customers. Some of that will come through taking technologies like Eview or like the B2B products and enhancing them for specific use cases where they intersect those things. It will also be additional investments from an acquisition perspective in those domains and you can count on Syncsort to continue to expand the value proposition that it is delivering to its customers both through new technology introductions but also through additional integration with these next generation platforms. So we're really excited I mean, we believe our strategy is working. It's led to record results in our 50th year and we think we've got many years to run with this strategy. >> Alright well Josh Rogers, CEO of Syncsort. Congratulations on the progress. New acquisition, deeper partnership with IBM and I look forward to tracking the updates. >> Thanks so much. Appreciate the opportunity. >> Alright, and thank you as always for joining. I'm Stu Miniman. Thanks for watching theCUBE. (upbeat electronic music)

(upbeat music) >> Narrator: Live from Las Vegas, its The Cube, covering Dell Technologies World 2018. Brought to you by Dell EMC and it's ecosystem partners. (soft electronic music) >> Welcome to The Cube's live coverage of Dell Technologies World 2018. I'm Stu Miniman And this is the second of three days of wall to wall coverage we have here at The Sands convention center and I am thrilled to welcome to the program, back to the program, Michael Dell, who is the CEO of Dell Technologies. Michael thank you so much for having us here and thanks for joining us on The Cube. >> Oh, great to be here. Thank you guys for all the great coverage. You always do a wonderful job getting into the technical details and kind of exploring everything in depth and we appreciate you and your team being here. >> Well thanks so much. You started off the keynote talking about the platform for the possible, said it was 34 years in the making. Now this is my 15th year at the show formally known as EMC World. I'd attended the Dell Show for a number of years, so tell us, start with what's really different now about the company's all together, it's renamed now, Dell Technologies World. Why is this the platform for the possible? >> I'm kind of amazed and inspired when I step back and look at what our customers are doing with our technology and we have hundreds of technical sessions here where we get in depth as we've always done at, historically, EMC World, but we're also taking a broader view and saying, "Hey, what's this really all about?" What's the impact on the world? This was one of the motivations for bringing together Dell and EMC and VMWare, and Pivotal and the whole family and it's working. So we're telling the story through the eyes of our customers and it is really an amazing time when you think about what's going on in the world. We have this incredible platform that's been built over the last 30 years, but now there are all these new enabling technologies that are going to take it much further and the domain of information technology is not the IT department anymore and we're seeing that in a big way, so it's a super exciting time and obviously we think we're a unique company across digital transformation, IT, workforce security and it's working. So it's all good, Stu. >> Michael, one of the great lines we liked in the keynote was today we'll have the most change that you've ever had in your life, but compared to what we'll see tomorrow, it's going to keep changing faster. When I look at the Dell Technologies family, I know a lot has changed. Pivotal just went through an IPO. I have to imagine the tax laws changing in the recent administration has impact. What's changed since the day one decision to purchase EMC, the largest merger in technology history to today, maybe give us a little bit of insight as to what's happening inside the family that's different. >> You know, there've been a lot of reports about the tax law. That actually was not much of a change. Kind of inconsequential change. It's very good for the broader industry growth and kind of broader economic growth and we're quite excited about that and so I see it as a net positive. You know, when we step back and go back a little bit in time here to 2009 when Joe and I first talked about this idea, 2008-2009. Wasn't the right time, financial crisis. We re-started it in 2014, announced it in 2015. Here we are four years after we had the last set of initial discussions and it's all come together very well. Look, I mean, the revenues are much stronger that we thought. Business is excellent. The demand is very strong. There's a portfolio effect. I think you're seeing increasing integration of the family of businesses, particularly with VMware and Dell EMC and Pivotal. And the relevance of what we are doing has never been greater and so we're able to have conversations with companies that are very different that we had before. At the same time this is occurring, the business leaders and the chief executives of companies are waking up to the power of technology, whether it's because of some new disruptor showing up or because they realize that they have to change and evolve. Used to be it was just us folks in the tech world that were in this fast changing world where everything was moving very quickly and we used to, when people wanted to come work for us, we'd say, "Hey, how do you like it when things change? "How are you dealing with ambiguity?" If they didn't like it, we'd say, "Yeah, you probably shouldn't come work here "because you won't be happy "if things are changing all the time." It's like that in every business now and, like you said, it's only going to get faster. >> Right. So, wondering if, you look at the portfolio, Michael. One of the things since the EMC acquisition and it's a pretty broad portfolio. There's some streamlining that I understand's happening. How do you balance the streamlining with the breadth of portfolio, make sure you're reaching the customers? >> There's absolutely some kind of simplification and optimization of the expansive set of capabilities we have. We also have some incredible platforms and so what you want to do is rally around the platforms and that's exactly what we're doing, so you'll see us not only create a very seamless and logical path for every customer, but rally around the winning platforms and you already detect that as a theme here at Dell Technologies World and it's going well. >> When you look at your overall portfolio, wonder if you could talk to some of the macroeconomic things happening, on margins that are happening. If Dave Alonte was here, are we talking a half of your business is client. You've got the ISG portfolio. That transformation of when Dell went private and now bringing EMC in, which allows you to change things. How do you look at that and what does Dell look like when you get to, say, the 2020, 2030? >> You know, right now it looks great and I think it'll look even better in 2020. What I see is we have positioned ourself as the essential infrastructure company and there's a massive infrastructure build-out and it's on the edge, it's a distributed core, and it's the cloud, and cloud is not just the public cloud and everybody's kind of figured that out now. We were saying it before it was cool, So if I think about the different businesses, you know Pivotal's doing great and we don't need to say too much about that because it just went public and we're in a bit of a quiet period, but the Pivotal business is a great business. VMware is doing fabulously well. Pat did a great job yesterday with the keynote and I think if you watch the keynote, you see, wow, Dell, Dell EMC, Pivotal, VMware, really, really working together at a very deep level. And then you go into our client business. Client business is growing really fast, but not as fast as our data center business. The data center business is growing even faster, so we're gaining share. You'll see it in the first quarter. We'll gain share in storage, we'll gain share in servers, we'll gain share in clients, and there's a portfolio effect where customers look across everything that we're doing and they say, "Yeah, I don't really want "to deal with 25 little companies. "I want to have a bigger relationship "with Dell Technologies." So bringing everything together, putting real effort behind these big platforms that we have, and look, we've got some big new initiatives. NSX, network virtualization. You know I'm a big believer in that and I think this is ultimately bigger than server virtualization and we're in an ideal position with our open networking and VMware NSX to drive that forward. >> Michael, both Allison and Jeff brought some great customer stories up on stage. One of the things sometimes you hear out there it's like, well, Dell, they're just an infrastructure company, and infrastructure, you know, I care about my data, I care about my applications. What's the role of infrastructure and maybe give us, what does infrastructure mean to you when we talk about those digital transformations that you're helping your customers through? >> Well you sort of go back to what's the plot here? And the plot is better outcomes, results, and success for a business. Well how do you do that? Well you do that with data, right? And people talk about clouds. Well what are clouds? The clouds are built on infrastructure. It's a bit like the internet. 20 years ago we'd say, hey we have the internet, we have the internet product strategy, Vice President of the internet, internet product division. Where's all that now? It's just everywhere. Cloud, AI, very, very similar. At the core of all this is the data and the computer science. You want to have artificial intelligence machine learning? Got to have data, so that's infrastructure. AI is eating software and software is eating hardware, but AI doesn't run on software. Software doesn't run on software. Software runs on hardware, so you got to put it all together, right? And that's exactly what we do. >> Alright, Michael what learnings have you had going through this? I know there was a lot of planning. We talked to Howard yesterday, talking about some of the cultures coming together, the big survey they did that like the top five things across everybody. It was like, not only were the top five things in agreement, but even the order was in agreement, but have to imagine that there were some things, bringing these large companies together. I might notice that in the keynote so far it's been all people that came from the Dell side that are up on stage. PowerMax Bob I know is from the EMC side, but mostly from the Dell side. What have you learned so far? How have some of those cultural pieces come together and how do you keep a quite large organization rallying and focused around what's an ever-changing and broad portfolio? >> You know, it's been a lot of fun, first of all to have so many unbelievably talented people join our company and that was a real delight because there's just a wealth of enormously talented people now in our company. Over-communicating, listening, getting to know them, understanding their point of view, and ultimately creating a shared vision and an aspiring vision for what we want to do in the future. And then, of course, when you're winning, everybody sees it and everybody's excited and they want to be part of it and they're engaged and it's working. So, certainly during the period before the integration and still today, we're in the business of technology and we've got products and services, but ultimately it's a people business right? And the talent comes in and walks out every single day, so you got to keep them engaged, excited, and fortunately we're doing that. And we're adding a lot more, so we need a few more thousand sales people, so if you're really talented and you know how to sell stuff, come join us at Dell Technologies, because we're hiring more sales people. >> Well Michael, I think you're going to get calls there. On a personal note, I've been watching on social media. Everybody's really, you give your time back. You spend time. I know something you really enjoy is speaking to people, understanding what's going on, getting into it, and for someone, Michael you created all of this and you've been there, just giving your time and getting involved is impressive. I've read like every book that Walter Isaacson's done. We're going to see a biography from him about you some time in the future or? >> Well look, I think if you're honored enough to have a biography by Walter Isaacson, that's pretty good. I'll leave that to him. He's a great one for sure. Look, I mean, I just think this is my job right? (laughs) Our job is to be with our customers, be with our people, learn, listen. That's how we become a better company and I wouldn't know what else to do if I wasn't doing that. >> Yeah. One of the things in your keynote you spoke about is helping customers, making it real. Like in Jeff's keynote, it was that the business and the IT are becoming one and the same. Maybe if you, do you have any good customer stories or how are you helping customers making it real? >> Yeah I think this topic of change management is really important because let's say you're a customer and you come to Dell Technologies World and you see this amazing, dizzying array of new things and you're like, "Wow, that sounds great but how do I do it?" And so, I'll give you one story. We met with a large, rather large company, and they had a situation where for any number of reasons, the IT environment was sort of put on hold for a couple of years. There were things going on around them that were beyond their control. They just really couldn't do anything, so the environment very quickly atrophied and they wanted very quickly to get up to speed and needed a lot of help and so we pulled in our professional services team. Make no mistake, we're not trying to replace Accenture or TCS or something, but a thin layer of architecture consulting to very quickly help them map out what the new architecture should look like and then go make it happen. And of course, we have lots of partners all over the world that also are engaged in helping that happen. But we're very aware that change management is a big topic for a lot of our customers and we're spending a lot of time on how do we make it easier, so make these more ready-made solutions for the fast track to the modern data center, like the VX Rack, VX Rail, V Block Solution. >> Yeah, we touched on it briefly, but that concept of change, when I talk to customers, one of the challenges they have is they learn about something, they get ramped on it. By the time they've rolled it out, there's something else that it's like, oh wait, maybe I should have waited. It used to be, oh geez, I should have started that project two years ago and now it feels like, wait, maybe I should wait another year because things are changing so fast, economics are changing. How do you work with customers and, internally, how does the team manage this just unprecedented rate of change? >> I think there's a pretty massive movement going on across organizations to be more agile and it kind of started in software development, some technical organizations, but now you're seeing it spread. We're certainly working as a company to do more and more of that and I think we're living in a very dynamic world. First we had the internet and all the things that that brought. Now we have the 5G and the block chain and autonomous computing and all kinds of new things that are being explored out there and so we have to be highly adaptable and flexible. I think companies that aren't able to do that are going to have a problem. We are in a way blessed that we grew up in a world where if we didn't do that, we'd have been out of business a long time ago. >> Michael, you mentioned crypto. We've talked to the VMware and Dell EMC teams that are starting to look at those technologies, do some of the underlying things, but you're a big investor. You've made some big things, everything from, I think about the radio frequencies in the sports arena. What do you think of this whole crypto, Bitcoin, all that. What's your take on that from a personal side? >> Well look, as a personal investor, I have almost none of my money in cryptocurrency, so I'll be clear about that. I'm a massive believer in distributed computing and block chain, but I don't have a lot of my money, or really, in anything to speak of in cryptocurrency, so maybe I'm missing out on the next great investment opportunity. Don't really know. I guess we'll find out, but big believer in distributed computing and block chain. >> Yeah I think you'll be doing okay either way, Michael. Want to give you the-- >> It's worked out pretty well so far, so I'm... >> (laughs) Want to give you the final world. There's so much here, over 14,000 people, lots of tracks. I've been talking to all my friends. It's a great nerd fest as I think some people have said, so always geeking out. Give us a final takeaway, what you hope people walk away, and what maybe they understand Dell Technologies a little better about than they might not have in the past? >> Well first, very grateful for our customers, for the trust they place in us. It's really gratifying to see how the Dell Technologies capabilities have resonated, and look, I think a lot of people are a bit surprised at all the capability we have across the company. That's really the purpose of this event is to bring it all together, explain the capabilities we have. We want them to engage in the hundreds of technical sessions that we have, but still come away with, I wish I could have gone to some more, right? And so we have all those online and for us this is also big ears. We're listening and we're learning. We're hearing from our customers and we're going to go take that back and bring the next set of innovations and we want to be the trusted partner for our customers. We think there's never been a better time to be doing what we're doing and there's a business investment cycle that's technology-led that's very powerful and there's no company on the planet that has the capabilities Dell Technologies has across all the four transformations. >> All right, well Michael Dell, thank you so much for joining us here. Really appreciate getting to talk with you and getting to cover this event. We have two more days full of live coverage here from Las Vegas. I'm Stu Miniman And you're watching The Cube. Thanks Michael. >> Michael: Great, thanks Stu. (soft electronic music)

>> Announcer: Live, from Midtown Manhattan, it's theCUBE, covering Big Data New York City 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsors. (upbeat techno music) >> Okay welcome back, we're live here in New York City. It's theCUBE's presentation of Big Data NYC, our fifth year doing this event in conjunction with Strata Data, formerly Strata Hadoop, formerly Strata Conference, formerly Hadoop World, we've been there from the beginning. Eight years covering Hadoop's ecosystem now Big Data. This is theCUBE, I'm John Furrier. Our next guest is Santhosh Mahendiran, who is the global head of technology analytics at Standard Chartered Bank. A practitioner in the field, here getting the data, checking out the scene, giving a presentation on your journey with Data at a bank, which is big financial obviously an adopter. Welcome to theCUBE. >> Thank you very much. >> So we always want to know what the practitioners are doing because at the end of the day there's a lot of vendors selling stuff here, so you got, everyone's got their story. End of the day you got to implement. >> That's right. >> And one of the themes is the data democratization which sounds warm and fuzzy, collaborating with data, this is all good stuff and you feel good and you move into the future, but at the end of the day it's got to have business value. >> That's right. >> And as you look at that, how do you look at the business value? Cause you want to be in the bleeding edge, you want to provide value and get that edge operationally. >> That's right. >> Where's the value in data democratization? How did you guys roll this out? Share your story. >> Okay, so let me start with the journey first before I come to the value part of it, right? So, data democratization is an outcome, but the journey has been something we started three years back. So what did we do, right? So we had some guiding principles to start our journey. The first was to say that we believed in the three S's, which is speed, scale, and it should be really, really flexible and super fast. So one of the challenges that we had was our historical data warehouses was entirely becoming redundant. And why was it? Because it was RDBMS centric, and it was extremely disparate. So we weren't able to scale up to meet the demands of managing huge chunks of data. So, the first step that we did was to re-pivot it to say that okay, let's embrace Hadoop. And what you mean by embracing is just not putting in the data lake, but we said that all our data will land into the data lake. And this journey started in 2015, so we have close to 80% of the Bank's data in the lake and it is end of day data right now and this data flows in on daily basis, and we have consumers who feed off that data. Now coming to your question about-- >> So the data lake's working? >> The data lake is working, up and running. >> People like it, you just got a good spot, batch 'em all you throw everything in the lake. >> So it is not real time, it is end of day. There is some data that is real-time, but the data lake is not entirely real-time, that I have to tell you. But one part is that the data lake is working. Second part to your question is how do I actually monetize it? Are you getting some value out of it? But I think that's where tools like Paxata has actually enabled us to accelerate this journey. So we call it data democratization. So the best part it's not about having the data. We want the business users to actually use the data. Typically, data has always been either delayed or denied in most of the cases to end-users and we have end-users waiting for the data but they don't get access to the data. It was done because primarily the size of the data was too huge and it wasn't flexible enough to be shared with. So how did tools like Paxata and the data lake help us? So what we did with data democratization is basically to say that "hey we'll get end-users to access the data first in a fast manner, in a self-service manner, and something that gives operational assurance to the data, so you don't hold the data and then say that you're going to get a subset of data to play with. We'll give you the entire set of data and we'll give you the right tools which you can play with. Most importantly, from an IT perspective, we'll be able to govern it. So that's the key about democratization. It's not about just giving them a tool, giving them all data and then say "go figure it out." It's about ensuring that "okay, you've got the tools, you've got the data, but we'll also govern it," so that you obviously have control over what they're doing. >> So now you govern it, they don't have to get involved in the governance, they just have access? >> No they don't need to. Yeah, they have access. So governance works both ways. We establish the boundaries. Look at it as a referee, and then say that "okay, there are guidelines that you don't," and within the datasets that key people have access to, you can further set rules. Now, coming back to specific use cases, I can talk about two specific cases which actually helped us to move the needle. The first is on stress testing, so being a financial institution, we typically have to report various numbers to our regulators, etc. The turnaround time was extremely huge. These kind of stress testing typically involve taking huge amount-- >> What were some of the turnaround times? >> Normally it was two to three weeks, some cases a month-- >> Wow. >> So we were able to narrow it down to days, but what we essentially did was as with any stress testing or reporting, it involved taking huge amounts of data, crunching them and then running some models and then showing the output, basically a number of transformations involved. Earlier, you first couldn't access the entire dataset, so that we solved-- >> So check, that was a good step one-- >> That was step one. >> But was there automation involved in that, the Paxata piece? >> Yeah, I wouldn't say it was fully automated end-to-end, but there was definitely automation given the fact that now you got Paxata to work off the data rather than someone extracting the data and then going off and figuring what needs to be done. The ability to work off the entire dataset was a big plus. So stress testing, bringing down the cycle time. The second one use case I can talk about is again anti-money laundering, and in our financial crime compliance space. We had processes that took time to report, given the clunkiness in the various handoffs that we needed to do. But again, empowering the users, giving the tool to them and then saying "hey, this"-- >> How about know your user, because we have to anti-money launder, you need to have to know your user base, that's all set their too? >> Yeah. So the good part is know the user, know your customer, KYCs all that part is set, but the key part is making sure the end-users are able to access the data much more earlier in the life cycle and are able to play with it. In the case of anti-money laundering, again first question of three weeks to four weeks was shortened down to question of days by giving tools like Paxata again in a structured manner and with which we're able to govern. >> You control this, so you knew what you were doing, but you let their tools do the job? >> Correct, so look at it this way. Typically, the data journey has always been IT-led. It has never been business-led. If you look at the generations of what happens is, you source the data which is IT-led, then you model the data which is IT-led, then you prepare then massage the data which is again IT-led and then you have tools on top of it which is again IT-led so the end-users get it only after the fourth stage. Now look at the generations within. All these life cycles apart from the fact that you source the data which is typically an IT issue, the rest need to be done by the actual business users and that's what we did. That's the progression of the generations in which we now we're in the third generation as I call it where our role is just to source the data and then say, "yeah we'll govern it in the matter and then preparation-- >> It's really an operating system and we were talking with Aaron with Elation's co-founder, we used the analogy of a car, how this show was like a car show engine show, what's in the engine and the technology and then it evolved every year, now it's like we're talking about the cars, now we're talking about driver experience-- >> That's right. >> At the end of the day, you just want to drive. You don't really care what's under the hood, you do but you don't, but there's those people who do care what's under the hood, so you can have best of both worlds. You've got the engines, you set up the infrastructure, but ultimately, you in the business side, you just want to drive, that's what's you're getting at? >> That's right. The time-to-market and speed to empower the users to play around with the data rather than IT trying to churn the data and confine access to data, that's a thing of the past. So we want more users to have faster access to data but at the same time govern it in a seamless manner. The word governance is still important because it's not about just give the data. >> And seamless is key. >> Seamless is key. >> Cause if you have democratization of data, you're implying that it is community-oriented, means that it's available, with access privileges all transparently or abstracted away from the users. >> Absolutely. >> So here's the question I want to ask you. There's been talk, I've been saying it for years going back to 2012 that an abstraction layer, a data layer will evolve and that'll be the real key. And then here in this show, I heard things like intelligent information fabric that is business, consumer-friendly. Okay, it's a mouthful, but intelligent information fabric in essence talks about an abstraction layer-- >> That's right. >> That doesn't really compromise anything but gives some enablement, creates some enabling value-- >> That's right. >> For software, how do you see that? >> As the word suggests, the earlier model was trying to build something for the end-users, but not which was end-user friendly, meaning to say, let me just give you a simple example. You had a data model that existed. Historically the way that we have approached using data is to say "hey, I've got a model and then let's fit that data into this model," without actually saying that "does this model actually serve the purpose?" You abstracted the model to a higher level. The whole point about intelligent data is about saying that, I'll give you a very simple analogy. Take zip code. Zipcode in US is very different from zipcode in India, it's very different from zipcode in Singapore. So if I had the ability for my data to come in, to say that "I know it's a zipcode, but this zipcode belongs to US, this zipcode belongs to Singapore, and this zipcode belongs to India," and more importantly, if I can further rev it up a notch, if I say that "this belongs to India, and this zipcode is valid." Look at where I'm going with intelligent sense. So that's what's up. If you look at the earlier model, you have to say that "yeah, this is a placeholder for zipcode." Now that makes sense, but what are you doing with it? >> Being a relational database model, it's just a field in a schema, you're taking it and abstracting it and creating value out of it. >> Precisely. So what I'm actually doing is accelerating the adoption, I'm making it more simpler for users to understand what the data is. So I don't need to as a user figure out "I got a zipcode, now is it a Singapore, India or what zipcode." >> So all this automation, Paxata's got a good system, we'll come back to the Paxata question in a second, I do want to drill down on that. But the big thing that I've been seeing at the show, and again Dave Alonte, my partner, co-CEO of Silicon Angle, we always talk about this all the time. He's more less bullish on Hadoop than I am. Although I love Hadoop, I think it's great but it's not the end-all, be-all. It's a great use case. We were critical early on and the thing we were critical on it was it was too much time being spent on the engine and how things are built, not on the business value. So there's like a lull period in the business where it was just too costly-- >> That's right. >> Total cost of ownership was a huge, huge problem. >> That's right. >> So now today, how did you deal with that and are you measuring the TCO or total cost of ownership cause at the end of the day, time to value, which is can you be up and running in 90 days with value and can you continue to do that, and then what's the overall cost to get there. Thoughts? >> So look I think TCO always underpins any technology investment. If someone said I'm doing a technology investment without thinking about TCO, I don't think he's a good technology leader, so TCO is obviously a driving factor. But TCO has multiple components. One is the TCO of the solution. The other aspect is TCO of what my value I'm going to get out of this system. So talking from an implementation perspective, what I look at as TCO is my whole ecosystem which is my hardware, software, so you spoke about Hadoop, you spoke about RDBMS, is Hadoop cheaper, etc? I don't want to get into that debate of cheaper or not but what I know is the ecosystem is becoming much, much more cheaper than before. And when I talk about ecosystem, I'm talking about RDBMS tools, I'm talking about Hadoop, I'm talking about BI tools, I'm talking about governance, I'm talking about this whole framework becoming cheaper. And it is also underpinned by the fact that hardware is also becoming cheaper. So the reality is all components in the whole ecosystem are becoming cheaper and given the fact that software is also becoming more open-sourced and people are open to using open-source software, I think the whole question of TCO becomes a much more pertinent question. Now coming to your point, do you measure it regularly? I think the honest answer is I don't think we are doing a good job of measuring it that well, but we do have that as one of the criteria for us to actually measure the success of our project. The way that we do is our implementation cost, at the time of writing out our PETs, we call it PETs, which is the Project Execution Document, we talk about cost. We say that "what's the implementation cost?" What are the business cases that are going to be an outcome of this? I'll give you an example of our anti-money laundering. I told you we reduced our cycle time from few weeks to a few days, and that in turn means the number of people involved in this whole process, you're reducing the overheads and the operational folks involved in it. That itself tells you how much we're able to save. So definitely, TCO is there and to say that-- >> And you are mindful of, it's what you look at, it's key. TCO is on your radar 100% you evaluate that into your deals? >> Yes, we do. >> So Paxata, what's so great about Paxata? Obviously you've had success with them. You're a customer, what's the deal. Was it the tech, was it the automation, the team? What was the key thing that got you engaged with them or specifically why Paxata? >> Look, I think the key to partnership there cannot be one ingredient that makes a partnership successful, I think there are multiple ingredients that make a partnership successful. We were one of the earliest adopters of Paxata. Given that we're a bank and we have multiple different systems and we have lot of manual processing involved, we saw Paxata as a good fit to govern these processes and ensure at the same time, users don't lose their experience. The good thing about Paxata that we like was obviously the simplicity and the look and feel of the tool. That's number one. Simplicity was a big point. The second one is about scale. The scale, the fact that it can take in millions of roles, it's not about just working off a sample of data. It can work on the entire dataset. That's very key for us. The third is to leverage our ecosystem, so it's not about saying "okay you give me this data, let me go figure out what to do and then," so Paxata works off the data lake. The fact that it can leverage the lake that we built, the fact that it's a simple and self-preparation tool which doesn't require a lot of time to bootstrap, so end-use people like you-- >> So it makes it usable. >> It's extremely user-friendly and usable in a very short period of time. >> And that helped with the journey? >> That really helped with the journey. >> Santosh, thanks so much for sharing. Santosh Mahendiran, who is the Global Tech Lead at the Analytics of the Bank at Standard Chartered Bank. Again, financial services, always a great early adopter, and you get success under your belt, congratulations. Data democratization is huge and again, it's an ecosystem, you got all that anti-money laundering to figure out, you got to get those reports out, lot of heavylifting? >> That's right, >> So thanks so much for sharing your story. >> Thank you very much. >> We'll give you more coverage after this short break, I'm John Furrier, stay tuned. More live coverage in New York City, its theCube.

>> Man: Live from Washington DC it's the Cube. Covering .conf2017, brought to you by splunk. >> Welcome back to Washington DC, Nations capital. Here for dotconf2017 as the Cube continues our coverage. The flagship broadcast of silicon idol tv. Along with Dave Alonte, I am John Walls. Glad to have you with us after we've had a little lunch break. Feeling good? >> Feel great, good conversation with customers, dug into the pricing model, got some good information. >> What did you learn at lunch? >> Well talk about it at the end of the day. >> Alright, good, look forward to it. Let's talk healthcare right now. Derek Merck is with us right now. He is the director of computer vision and imaging analytics at the Rhode Island Hospital. Which is the teaching hospital for Brown University. Derek thanks for joining us here on the Cube. Good to see ya. >> Absolutely, very excited to be here. >> So, well and as are we to have you. Director of computer vision and image analytics, so let's talk about that. What falls under your portfolio, and tell us where does Splunk come into that picture? >> It's been an interesting journey, Rhode Island hospital is a huge clinical service. Takes really good care of the people of Rhode Island. I'm in diagnostic imaging, so I work with all the CT scans, the MR's, radiography, ultrasonography, and what I try to do is automate the data that is coming off all of these machines as much as possible. So, you know typically the patient will come in, they'll get imaged for some reason, the physician will take a look at that image and make a diagnosis, and then that image goes into an archive. It may be used again later if the patient comes back but other than that it is not really used at all. With these sort of emergence of computer vision access to training images, sets of data, has become really critical. Diagnostic imaging has become really interested in taking better account of what imaging they have so that they can try to answer questions like what's alike about these images. What is different about these images, and automate diagnosis. What's similar about all the images of patients who have cancer, versus patients who don't have cancer. Which is basically what a radiologist job is, is to go and look at this patients image and figure out does this patient have cancer or not. SO that is the way you would teach a computer how to do it in an automated fashion. SO I spent a lot of time trying to figure out how do you keep, how do you take, keep better track of what is available and be able to ask these sort of population based questions about what we have in our portfolio of data, our data portfolio. I spent a lot of time writing systems by hand in python, or other kinds of scripting tools. I spent a lot of time trying to interface with the hospital informatics systems, the electronic medical record. The electronic medical record again really meant for taking care of patients it is not meant for population analytics. We ended up basically building our own health care analytic system just to keep track of what we had. What were the doctors saying about different cases. Show me all the cases where the doctors think that some particular thing happened. And be able to ask these questions in real time, generate huge data sets, anonymize them, run them through computer vision algorithms, train classifiers. Diagnostic imaging is really excited about this kind of technology. There has been a lot of interesting side projects as well. One of the most, one of the things that administration is the most interested is because of these kinds of systems we are keeping a lot better track of radiation exposure, per image, so the CT scanners will tell you how much radiation was used for an individual study. But again our analytic systems historically you have no way of saying what's the average? What's high, what's low? Its months of latency, six months of latency between when you run a scan and when American College of Radiology comes back and says some of your scans were a little high in radiation exposure. Whereas now because we keep track of all this data we have this real time dashboards and that is the kind of thing we use Splunk for. WE keep track of all the data we are collecting and then we create these dashboards and give them to people who haven't had access to this kind of analytics before. For looking at utilization, optimizing work flow, things like that. >> I am just kind of curious when you mention like x-rays and maybe Dave you know more about this than I do. But it seems like it is kind of a standard practice you have a certain amount of exposure for a certain amount of test, and that data I don't know how but it sounds like it is more critical to have that kind of data than someone a layman might think. I was curious of the analytics of that. What are you using to determine there in terms of that exposure? >> There's always a trade off with radiation based imaging. There is a lot of non radiation based imaging. Like you may have heard of magnetic resonance imaging, or MR. Those are thought to be perfectly safe. You can get MR's all day long. If fact they do give MR's to people all day long for research purposes sometimes. >> You climb in the tube, I don't want to climb in the tube. >> You get a little claustrophobic >> They are expensive >> That is the thing, we don't have very many of them. They are very slow but they're safe. Ultrasounds very safe, we give ultrasounds to pregnant women all the time very safe, but they don't give you very quality images back. They give you a very small field of view and things are wiggling around. A CT scan is super fast and it gives a physician all the information they need in a snap shot. CT scanners are so fast now they can freeze your beating heart. They can make a revolution around your body of thickness so they can capture your heart while it is in motion. You know like with anything if you have a camera and you take a picture of someone running across the screen you don't see the person you just see this sort of blur, right? Now with modern fast aperture cameras you can take a picture of nutrinos and things that are impossibly fast. I don't know that that's actually true. You might wand to edit that out. (laughing) >> But conceptually >> A CT scan is the same sort of thing. Your heart is beat all the time, your lungs are moving all the time. Your bowls are moving all the time. Your blood is coursing through your veins all the time. It is so fast it can freeze it and give you this volumetric data back. They use that for all kinds of different things. They're not able to do with other kinds of imaging modalities The downside is that they're potentially somewhat dangerous, right? People have known since the 1890's when x-rays were first discovered by Wilhome Rankin that if you put somebody under an x-ray beam for too long, your hair will fall out, you'll get skin burns, all kinds of things that these early pioneers of x-ray did to themselves without realizing it. Documenting all of these problems that can happen, and a CT can uses ionizing radiation if you get too many CT scans you'll get skin reactions, or other kinds of things. It is really important to keep track of the risk to benefit ratio there. People give you a CT scan if you fall down and you hurt your head. They give you a CT scan cause they're worried that you are going to die if you don't get the CT scan. Along with that is this idea of how do you track how many CT scans an individual patient gets in a year. Right now the hospital has a hard time keeping track if somebody comes into the emergency room of automatically identifying oh this patients already had six CT's should we put them in line for a MR instead of another CT. Again these are the kinds of things that we are able to get at through using, through better management of our data and organization of our data. >> You mentioned that you're doing more of this real time analysis, Splunk is obviously a tool that helps do that. Other tooling, are you using cloud based tools? >> We have to be really careful about cloud based stuff. There is this protected health information that everyone's really concerned about. Working with data at the hospital is really walking a fine line you need to be very conscious of security. There really reluctant to let non anonymized data out to cloud sources for storage. There are some ways of getting around that, but basically we run all of our servers in house. There's a couple of big data centers down in the basement of the hospital. Mostly they have clinical duties but we have a number of research servers that are installed down there as well. They're managed by the same IT staff in this sort of hardened architecture. I actually can't do any work from home which is an unusual kind of experience, I am used to being able to log in remotely. >> Oh darn (laughing) >> Or you spend too much time on the job. >> Some times you'd like to >> I'm ambivalent about it, there's goods and bads about it. >> So how do you deal with that streaming infrastructure and real time analysis. Do you guys sort of build your own? Any kind of resource tools, or >> I use a lot of open source tools. Traditionally the hospital wants to pay for everything. They feel like if they pay for things then it comes with uptime guarantees. When I build my systems though, because I'm working on shoestring budgets, And because I believe in open source. I use open source where ever I can. I wanted to mention we're actually for a lot of the work that we do supported through Splunk for good. So I don't pay for a full Splunk license, Cory Marshal who runs Splunk for good, has sort of recognized the value of some of the stuff that we're doing with dealing with non traditional data. It's not the sort of standard things that the other people who are working in the healthcare space with splunk are working with. We are working with imaging data. We are working with patient bedside telemetry data, you know the EKG signals and the heart rate signals. And aggregating all this stuff in to one place to make more sensible alerts and alarms. Oh this patient set off an alarm three times in the last hour I should send a page to the nurse who is taking care of this person. It's different that the kind of business optimism that I think a lot of people in the healthcare space are using splunk for. >> SO you have your core mission around diagnostic imaging. As we sort of touched on you have all these other peripheral factors in your industry. The affordable care act, obviously there's HIPPA, there's EMR, there's meaningful use. How much does that affect your mission? Does it get in the way? Is it something you have to be cognizant of like constantly, obviously HIPPA. Other factors? >> I try to just be cognoscente, I try not to let anything get in my way. Almost all of these things that you talk about they're really meant to protect the patient. I make sure that everything that I do is working with data is that we are anonymizing things, were using data securely, and we are trying to help the patients. I think I just have this moral check in my head of what is what I am doing right now good for my department, good for my institution, good for my patient. Then because I am aware of all these other rules they are very complicated and hard to navigate. At the end of the day I can say I understood that rule, I followed that rule, and what I did was the appropriate thing to do. >> It's like house rules. >> Yeah >> Okay, talk a little bit more about splunk, how are you using it, what it does for your mission, for your operation. >> What I came to the conference this year to talk about is this dose management system that we built that I think is really important. We've had vendors coming in and telling us that medicare isn't going to pay hospitals, or is going to reduce reimbursement to hospitals who can't prove that they're using ionizing radiation imaging appropriately. So what does that mean? No body quite knows exactly what that means. How do I tell whether my hospital is adhering to these rules that are ill defined and these vendors are coming in and they're trying to sell us solutions that are like a hundred thousand dollar a year licenses. Administration is taking this seriously, they're trying to figure out which of these vendors are we going to give money to. In the mean time a bunch of the CT technology staff and I basically put together a system that answers all these questions for them using Splunk. We use splunk to collect meta information about how all the scanners system wide are being use. We have 12 CT scanners, they shoot 90,000 different studies every year. Each one of those studies may be hundreds or even thousands of slices of data in these volumetric data sets. It's a huge amount of data to keep track of. Your not using Splunk to keep track of the imaging per se. Your using splunk to keep track of what imaging you collected. So it is a small fraction, it is just the metadata about each one of the studies. That metadata comes with a bunch of interesting information about what the radiation exposure for each one of those studies was. Splunk has these wonderfully adaptable easy to use tools. That once we covert our strange dicom, device independent communications in medicine data, we flatten it, normalize it, turn it into generic data, it is Json, it's dictionary files. Then splunk has these great tools that can be applied instead of to business analytics and optimization to image analytics and optimization. We build our dashboards on top of splunk to show per institution what was the average dose? Per protocol, per body type, you can track which technologist have the lower doses and higher doses. We found all kinds of interesting things. My favorite story the chief technologist was just telling me. I was putting together my slides for this presentation that I did here about this. I said we need an example of a does outlier. Some time when we had a higher than expected radiation event. We never have dangerously high radiation events. >> Good caveat, thank you. >> All the machines care about is whether you're harming some one and we never harm anyone. The machines don't track, this one is a little higher than you would expect it so that you can say why is that, what happened there? But now we do using our splunk dashboards. So I asked him can you get me an example for my slide deck. He literally just looked over to the monitor that he had open and he says oh right here. Here is a patient who had a 69. These numbers are irrelevant, they're supposed to be 50. He knows what the numbers are supposed to be, to me numbers are just numbers. This patient had a 69 and he picks up the phone, this was 5 minutes ago, he calls down to the control room. He says I'm not blaming anyone but why did Mrs So and So have a little bit higher radiation dose? 69 is not dangerous by the way, alarms don't go off until like 75 or 80 or something like that. So he just called and he asked what was going on with this patient. She had a dislocated arm. Okay I understand. This was a head scan, I was like Scott what does a dislocated arm have to do with a head scan? He said well she went through the CT bore with her arm up over her head which is not the way but it was the only way she would tolerate. So the CT thought she was this big and it had to raise the amount of radiation that it was putting into her to go through a larger object. So he documented that, he put it down, and again we used splunk for ticketing for outlier identification. So he put this one into the outlier identification database that we have, he picked other for the reason because we don't have a drop down menu with dislocated arm. Marked it as closed and it is justified, so when the JCO Joint commission on hospital accreditation comes trough and they say well what do you do to manage your higher than expected radiation exposures? We can both say well we never have unsafe radiation exposures it is all documented right here. When it is higher than usual this is the way we document it, and here are examples of ten or twenty of these odd instances where something happened. Either it was completely justified like this lady where the machines were used appropriately, that was appropriate. Or very occasionally we'll find something strange like an improper head holder was being used at one site for a while. It was resulting in these head CT's should usually be around 45 or 50 and instead they were 55 or 60. They went and they took the metal head holder and replaced it with a carbon fiber head holder that they should have been using and then all of a sudden our doses came down, and we documented it. >> It was a dislocated arm, let's leave it at that alright and we are happy with that. Derek thanks for being with us >> Oh absolutely >> Appreciate the time here on the cube and glad to have you here. Continued good luck with your work at Rhode Island. >> Thank you very much, you guys have a good day. >> Very good thank you. Derek Merck joining us here on the cube. We'll continue live from Washington DC right after this. (upbeat music)

>> Announcer: Live from Santa Fe, California, it's the Cube covering Big Data Silicon Valley 2017. >> Welcome, back, everyone. We're here live in Silicon Valley in San Jose for Big Data SV in conjunct with stratAHEAD Hadoop part two. Three days of coverage here in Silicon Valley and Big Data. It's our eighth year covering Hadoop and the Hadoop ecosystem. Now expanding beyond just Hadoop into AI, machine learning, IoT, cloud computing with all this compute is really making it happen. I'm John Furrier with my co-host George Gilbert. Our next guest is Arik Pelkey who is the senior director of product marketing at Pentaho that we've covered many times and covered their event at Pentaho world. Thanks for joining us. >> Thank you for having me. >> So, in following you guys I'll see Pentaho was once an independent company bought by Hitachi, but still an independent group within Hitachi. >> That's right, very much so. >> Okay so you guys some news. Let's just jump into the news. You guys announced some of the machine learning. >> Exactly, yeah. So, Arik Pelkey, Pentaho. We are a data integration and analytics software company. You mentioned you've been doing this for eight years. We have been at Big Data for the past eight years as well. In fact, we're one of the first vendors to support Hadoop back in the day, so we've been along for the journey ever since then. What we're announcing today is really exciting. It's a set of machine learning orchestration capabilities, which allows data scientists, data engineers, and data analysts to really streamline their data science processes. Everything from ingesting new data sources through data preparation, feature engineering which is where a lot of data scientists spend their time through tuning their models which can still be programmed in R, in Weka, in Python, and any other kind of data science tool of choice. What we do is we help them deploy those models inside of Pentaho as a step inside of Pentaho, and then we help them update those models as time goes on. So, really what this is doing is it's streamlining. It's making them more productive so that they can focus their time on things like model building rather than data preparation and feature engineering. >> You know, it's interesting. The market is really active right now around machine learning and even just last week at Google Next, which is their cloud event, they had made the acquisition of Kaggle, which is kind of an open data science. You mentioned the three categories: data engineer, data science, data analyst. Almost on a progression, super geek to business facing, and there's different approaches. One of the comments from the CEO of Kaggle on the acquisition when we wrote up at Sylvan Angle was, and I found this fascinating, I want to get your commentary and reaction to is, he says the data science tools are as early as generations ago, meaning that all the advances and open source and tooling and software development is far along, but now data science is still at that early stage and is going to get better. So, what's your reaction to that, because this is really the demand we're seeing is a lot of heavy lifing going on in the data science world, yet there's a lot of runway of more stuff to do. What is that more stuff? >> Right. Yeah, we're seeing the same thing. Last week I was at the Gardener Data and Analytics conference, and that was kind of the take there from one of their lead machine learning analysts was this is still really early days for data science software. So, there's a lot of Apache projects out there. There's a lot of other open source activity going on, but there are very few vendors that bring to the table an integrated kind of full platform approach to the data science workflow, and that's what we're bringing to market today. Let me be clear, we're not trying to replace R, or Python, or MLlib, because those are the tools of the data scientists. They're not going anywhere. They spent eight years in their phD program working with these tools. We're not trying to change that. >> They're fluent with those tools. >> Very much so. They're also spending a lot of time doing feature engineering. Some research reports, say between 70 and 80% of their time. What we bring to the table is a visual drag and drop environment to do feature engineering a much faster, more efficient way than before. So, there's a lot of different kind of desperate siloed applications out there that all do interesting things on their own, but what we're doing is we're trying to bring all of those together. >> And the trends are reduce the time it takes to do stuff and take away some of those tasks that you can use machine learning for. What unique capabilities do you guys have? Talk about that for a minute, just what Pentaho is doing that's unique and added value to those guys. >> So, the big thing is I keep going back to the data preparation part. I mean, that's 80% of time that's still a really big challenge. There's other vendors out there that focus on just the data science kind of workflow, but where we're really unqiue is around being able to accommodate very complex data environments, and being able to onboard data. >> Give me an example of those environments. >> Geospatial data combined with data from your ERP or your CRM system and all kinds of different formats. So, there might be 15 different data formats that need to be blended together and standardized before any of that can really happen. That's the complexity in the data. So, Pentaho, very consistent with everything else that we do outside of machine learning, is all about helping our customers solve those very complex data challenges before doing any kind of machine learning. One example is one customer is called Caterpillar Machine Asset Intelligence. So, their doing predictive maintenance onboard container ships and on ferry's. So, they're taking data from hundreds and hundreds of sensors onboard these ships, combining that kind of operational sensor data together with geospatial data and then they're serving up predictive maintenance alerts if you will, or giving signals when it's time to replace an engine or complace a compressor or something like that. >> Versus waiting for it to break. >> Versus waiting for it to break, exactly. That's one of the real differentiators is that very complex data environment, and then I was starting to move toward the other differentiator which is our end to end platform which allows customers to deliver these analytics in an embedded fashion. So, kind of full circle, being able to send that signal, but not to an operational system which is sometimes a challenge because you might have to rewrite the code. Deploying models is a really big challenge within Pentaho because it is this fully integrated application. You can deploy the models within Pentaho and not have to jump out into a mainframe environment or something like that. So, I'd say differentiators are very complex data environments, and then this end to end approach where deploying models is much easier than ever before. >> Perhaps, let's talk about alternatives that customers might see. You have a tool suite, and others might have to put together a suite of tools. Maybe tell us some of the geeky version would be the impendent mismatch. You know, like the chasms you'd find between each tool where you have to glue them together, so what are some of those pitfalls? >> One of the challenges is, you have these data scientists working in silos often times. You have data analysts working in silos, you might have data engineers working in silos. One of the big pitfalls is not really collaborating enough to the point where they can do all of this together. So, that's a really big area that we see pitfalls. >> Is it binary not collaborating, or is it that the round trip takes so long that the quality or number of collaborations is so drastically reduced that the output is of lower quality? >> I think it's probably a little bit of both. I think they want to collaborate but one person might sit in Dearborn, Michigan and the other person might sit in Silicon Valley, so there's just a location challenge as well. The other challenge is, some of the data analysts might sit in IT and some of the data scientists might sit in an analytics department somewhere, so it kind of cuts across both location and functional area too. >> So let me ask from the point of view of, you know we've been doing these shows for a number of years and most people have their first data links up and running and their first maybe one or two use cases in production, very sophisticated customers have done more, but what seems to be clear is the highest value coming from those projects isn't to put a BI tool in front of them so much as to do advanced analytics on that data, apply those analytics to inform a decision, whether a person or a machine. >> That's exactly right. >> So, how do you help customers over that hump and what are some other examples that you can share? >> Yeah, so speaking of transformative. I mean, that's what machine learning is all about. It helps companies transform their businesses. We like to talk about that at Pentaho. One customer kind of industry example that I'll share is a company called IMS. IMS is in the business of providing data and analytics to insurance companies so that the insurance companies can price insurance policies based on usage. So, it's a usage model. So, IMS has a technology platform where they put sensors in a car, and then using your mobile phone, can track your driving behavior. Then, your insurance premium that month reflects the driving behavior that you had during that month. In terms of transformative, this is completely upending the insurance industry which has always had a very fixed approach to pricing risk. Now, they understand everything about your behavior. You know, are you turning too fast? Are you breaking too fast, and they're taking it further than that too. They're able to now do kind of a retroactive look at an accident. So, after an accident, they can go back and kind of decompose what happened in the accident and determine whether or not it was your fault or was in fact the ice on the street. So, transformative? I mean, this is just changing things in a really big way. >> I want to get your thoughts on this. I'm just looking at some of the research. You know, we always have the good data but there's also other data out there. In your news, 92% of organizations plan to deploy more predictive analytics, however 50% of organizations have difficulty integrating predictive analytics into their information architecture, which is where the research is shown. So my question to you is, there's a huge gap between the technology landscapes of front end BI tools and then complex data integration tools. That seems to be the sweet spot where the value's created. So, you have the demand and then front end BI's kind of sexy and cool. Wow, I could power my business, but the complexity is really hard in the backend. Who's accessing it? What's the data sources? What's the governance? All these things are complicated, so how do you guys reconcile the front end BI tools and the backend complexity integrations? >> Our story from the beginning has always been this one integrated platform, both for complex data integration challenges together with visualizations, and that's very similar to what this announcement is all about for the data science market. We're very much in line with that. >> So, it's the cart before the horse? Is it like the BI tools are really driven by the data? I mean, it makes sense that the data has to be key. Front end BI could be easy if you have one data set. >> It's funny you say that. I presented at the Gardner conference last week and my topic was, this just in: it's not about analytics. Kind of in jest, but it drove a really big crowd. So, it's about the data right? It's about solving the data problem before you solve the analytics problem whether it's a simple visualization or it's a complex fraud machine learning problem. It's about solving the data problem first. To that quote, I think one of the things that they were referencing was the challenging information architectures into which companies are trying to deploy models and so part of that is when you build a machine learning model, you use R and Python and all these other ones we're familiar with. In order to deploy that into a mainframe environment, someone has to then recode it in C++ or COBOL or something else. That can take a really long time. With our integrated approach, once you've done the feature engineering and the data preparation using our drag and drop environment, what's really interesting is that you're like 90% of the way there in terms of making that model production ready. So, you don't have to go back and change all that code, it's already there because you used it in Pentaho. >> So obviously for those two technologies groups I just mentioned, I think you had a good story there, but it creates problems. You've got product gaps, you've got organizational gaps, you have process gaps between the two. Are you guys going to solve that, or are you currently solving that today? There's a lot of little questions in there, but that seems to be the disconnect. You know, I can do this, I can do that, do I do them together? >> I mean, sticking to my story of one integrated approach to being able to do the entire data science workflow, from beginning to end and that's where we've really excelled. To the extent that more and more data engineers and data analysts and data scientists can get on this one platform even if their using R and WECCA and Python. >> You guys want to close those gaps down, that's what you guys are doing, right? >> We want to make the process more collaborative and more efficient. >> So Dave Alonte has a question on CrowdChat for you. Dave Alonte was in the snowstorm in Boston. Dave, good to see you, hope you're doing well shoveling out the driveway. Thanks for coming in digitally. His question is HDS has been known for mainframes and storage, but Hitachi is an industrial giant. How is Pentaho leveraging Hitatchi's IoT chops? >> Great question, thanks for asking. Hitatchi acquired Pentaho about two years ago, this is before my time. I've been with Pentaho about ten months ago. One of the reasons that they acquired Pentaho is because a platform that they've announced which is called Lumata which is their IoT platform, so what Pentaho is, is the analytics engine that drives that IoT platform Lumata. So, Lumata is about solving more of the hardware sensor, bringing data from the edge into being able to do the analytics. So, it's an incredibly great partnership between Lumata and Pentaho. >> Makes an eternal customer too. >> It's a 90 billion dollar conglomerate so yeah, the acquisition's been great and we're still very much an independent company going to market on our own, but we now have a much larger channel through Hitatchi's reps around the world. >> You've got IoT's use case right there in front of you. >> Exactly. >> But you are leveraging it big time, that's what you're saying? >> Oh yeah, absolutely. We're a very big part of their IoT strategy. It's the analytics. Both of the examples that I shared with you are in fact IoT, not by design but it's because there's a lot of demand. >> You guys seeing a lot of IoT right now? >> Oh yeah. We're seeing a lot of companies coming to us who have just hired a director or vice president of IoT to go out and figure out the IoT strategy. A lot of these are manufacturing companies or coming from industries that are inefficient. >> Digitizing the business model. >> So to the other point about Hitachi that I'll make, is that as it relates to data science, a 90 billion dollar manufacturing and otherwise giant, we have a very deep bench of phD data scientists that we can go to when there's very complex data science problems to solve at customer sight. So, if a customer's struggling with some of the basic how do I get up and running doing machine learning, we can bring our bench of data scientist at Hitatchi to bear in those engagements, and that's a really big differentiator for us. >> Just to be clear and one last point, you've talked about you handle the entire life cycle of modeling from acquiring the data and prepping it all the way through to building a model, deploying it, and updating it which is a continuous process. I think as we've talked about before, data scientists or just the DEV ops community has had trouble operationalizing the end of the model life cycle where you deploy it and update it. Tell us how Pentaho helps with that. >> Yeah, it's a really big problem and it's a very simple solution inside of Pentaho. It's basically a step inside of Pentaho. So, in the case of fraud let's say for example, a prediction might say fraud, not fraud, fraud, not fraud, whatever it is. We can then bring that kind of full lifecycle back into the data workflow at the beginning. It's a simple drag and drop step inside of Pentaho to say which were right and which were wrong and feed that back into the next prediction. We could also take it one step further where there has to be a manual part of this too where it goes to the customer service center, they investigate and they say yes fraud, no fraud, and then that then gets funneled back into the next prediction. So yeah, it's a big challenge and it's something that's relatively easy for us to do just as part of the data science workflow inside of Pentaho. >> Well Arick, thanks for coming on The Cube. We really appreciate it, good luck with the rest of the week here. >> Yeah, very exciting. Thank you for having me. >> You're watching The Cube here live in Silicon Valley covering Strata Hadoop, and of course our Big Data SV event, we also have a companion event called Big Data NYC. We program with O'Reilley Strata Hadoop, and of course have been covering Hadoop really since it's been founded. This is The Cube, I'm John Furrier. George Gilbert. We'll be back with more live coverage today for the next three days here inside The Cube after this short break.