>> Live from New York. It's theCUBE. Covering theCUBE New York City, 2018. Brought to you by SiliconANGLE Media, and it's ecosystem partners. >> I'm John Furrier with Peter Burris, our next two guests are Jim Franklin with Dell EMC Director of Product Management Anant Chintamaneni, who is the Vice President of Products at BlueData. Welcome to theCUBE, good to see you. >> Thanks, John. >> Thank you. >> Thanks for coming on. >> I've been following BlueData since the founding. Great company, and the founders are great. Great teams, so thanks for coming on and sharing what's going on, I appreciate it. >> It's a pleasure, thanks for the opportunity. >> So Jim, talk about the Dell relationship with BlueData. What are you guys doing? You have the Dell-ready solutions. How is that related now, because you've seen this industry with us over the years morph. It's really now about, the set-up days are over, it's about proof points. >> That's right. >> AI and machine learning are driving the signal, which is saying, 'We need results'. There's action on the developer's side, there's action on the deployment, people want ROI, that's the main focus. >> That's right. That's right, and we've seen this journey happen from the new batch processing days, and we're seeing that customer base mature and come along, so the reason why we partnered with BlueData is, you have to have those softwares, you have to have the contenders. They have to have the algorithms, and things like that, in order to make this real. So it's been a great partnership with BlueData, it's dated back actually a little farther back than some may realize, all the way to 2015, believe it or not, when we used to incorporate BlueData with Isilon. So it's been actually a pretty positive partnership. >> Now we've talked with you guys in the past, you guys were on the cutting edge, this was back when Docker containers were fashionable, but now containers have become so proliferated out there, it's not just Docker, containerization has been the wave. Now, Kubernetes on top of it is really bringing in the orchestration. This is really making the storage and the network so much more valuable with workloads, whether respective workloads, and AI is a part of that. How do you guys navigate those waters now? What's the BlueData update, how are you guys taking advantage of that big wave? >> I think, great observation, re-embrace Docker containers, even before actually Docker was even formed as a company by that time, and Kubernetes was just getting launched, so we saw the value of Docker containers very early on, in terms of being able to obviously provide the agility, elasticity, but also, from a packaging of applications perspective, as we all know it's a very dynamic environment, and today, I think we are very happy to know that, with Kubernetes being a household name now, especially a tech company, so the way we're navigating this is, we have a turnkey product, which has containerization, and then now we are taking our value proposition of big data and AI and lifecycle management and bringing it to Kubernetes with an open source project that we launched called Cube Director under our umbrella. So, we're all about bringing stateful applications like Hadoop, AI, ML to the community and to our customer base, which is some of the largest financial services in health care customers. >> So the container revolution has certainly groped developers, and developers have always had a history of chasing after the next cool technology, and for good reason, it's not like just chasing after... Developers tend not to just chase after the shiny thing, they chased after the most productive thing, and they start using it, and they start learning about it, and they make themselves valuable, and they build more valuable applications as a result. But there's this interesting meshing of creators, makers, in the software world, between the development community and the data science community. How are data scientists, who you must be spending a fair amount of time with, starting to adopt containers, what are they looking at? Are they even aware of this, as you try to help these communities come together? >> We absolutely talk to the data scientists and they're the drivers of determining what applications they want to consume for the different news cases. But, at the end of the day, the person who has to deliver these applications, you know data scientists care about time to value, getting the environment quickly all prepared so they can access the right data sets. So, in many ways, most of our customers, many of them are unaware that there's actually containers under the hood. >> So this is the data scientists. >> The data scientists, but the actual administrators and the system administrators were making these tools available, are using containers as a way to accelerate the way they package the software, which has a whole bunch of dependent libraries, and there's a lot of complexity our there. So they're simplifying all that and providing the environment as quickly as possible. >> And in so doing, making sure that whatever workloads are put together, can scaled, can be combined differently and recombined differently, based on requirements of the data scientists. So the data scientist sees the tool... >> Yeah. >> The tool is manifest as, in concert with some of these new container related technologies, and then the whole CICD process supports the data scientist >> The other thing to think about though, is that this also allows freedom of choice, and we were discussing off camera before, these developers want to pick out what they want to pick out what they want to work with, they don't want to have to be locked in. So with containers, you can also speed that deployment but give them freedom to choose the tools that make them best productive. That'll make them much happier, and probably much more efficient. >> So there's a separation under the data science tools, and the developer tools, but they end up all supporting the same basic objective. So how does the infrastructure play in this, because the challenge of big data for the last five years as John and I both know, is that a lot of people conflated. The outcome of data science, the outcome of big data, with the process of standing up clusters, and lining up Hadoop, and if they failed on the infrastructure, they said it was a failure overall. So how you making the infrastructure really simple, and line up with this time of value? >> Well, the reality is, we all need food and water. IT still needs server and storage in order to work. But at the end of the day, the abstraction has to be there just like VMware in the early days, clouds, containers with BlueData is just another way to create a layer of abstraction. But this one is in the context of what the data scientist is trying to get done, and that's the key to why we partnered with BlueData and why we delivered big data as a service. >> So at that point, what's the update from Dell EMC and Dell, in particular, Analytics? Obviously you guys work with a lot of customers, have challenges, how are you solving those problems? What are those problems? Because we know there's some AI rumors, big Dell event coming up, there's rumors of a lot of AI involved, I'm speculating there's going to be probably a new kind of hardware device and software. What's the state of the analytics today? >> I think a lot of the customers we talked about, they were born in that batch processing, that Hadoop space we just talked about. I think they largely got that right, they've largely got that figured out, but now we're seeing proliferation of AI tools, proliferation of sandbox environments, and you're psyched to see a little bit of silo behavior happening, so what we're trying to do is that IT shop is trying to dispatch those environments, dispatch with some speed, with some agility. They want to have it at the right economic model as well, so we're trying to strike a better balance, say 'Hey, I've invested in all this infrastructure already, I need to modernize it, and that I also need to offer it up in a way that data scientists can consume it'. Oh, by the way, we're starting to see them start to hire more and more of these data scientists. Well, you don't want your data scientists, this very expensive, intelligent resource, sitting there doing data mining, data cleansing, detail offloads, we want them actually doing modeling and analytics. So we find that a lot of times right now as you're doing an operational change, the operational mindset as you're starting to hire these very expensive people to do this very good work, at the corest of the data, but they need to get productive in the way that you hired them to be productive. >> So what is this ready solution, can you just explain what that is? Is it a program, is it a hardware, is it a solution? What is the ready solution? >> Generally speaking, what we do as a division is we look for value workloads, just generally speaking, not necessarily in batch processing, or AI, or applications, and we try and create an environment that solves that customer challenge, typically they're very complex, SAP, Oracle Database, it's AI, my goodness. Very difficult. >> Variety of tools, using hives, no sequel, all this stuff's going on. >> Cassandra, you've got Tensorflow, so we try fit together a set of knowledge experts, that's the key, the intellectual property of our engineers, and their deep knowledge expertise in a certain area. So for AI, we have a sight of them back at the shop, they're in the lab, and this is what they do, and they're serving up these models, they're putting data through its paces, they're doing the work of a data scientist. They are data scientists. >> And so this is where BlueData comes in. You guys are part of this abstraction layer in the ready solutions. Offering? Is that how it works? >> Yeah, we are the software that enables the self-service experience, the multitenancy, that the consumers of the ready solution would want in terms of being able to onboard multiple different groups of users, lines of business, so you could have a user that wants to run basic spark, cluster, spark jobs, or you could have another user group that's using Tensorflow, or accelerated by a special type of CPU or GPU, and so you can have them all on the same infrastructure. >> One of the things Peter and I were talking about, Dave Vellante, who was here, he's at another event right now getting some content but, one of the things we observed was, we saw this awhile ago so it's not new to us but certainly we're seeing the impact at this event. Hadoop World, there's now called Strata Data NYC, is that we hear words like Kubernetes, and Multi Cloud, and Istio for the first time. At this event. This is the impact of the Cloud. The Cloud has essentially leveled the Hadoop World, certainly there's some Hadoop activity going on there, people have clusters, there's standing up infrastructure for analytical infrastructures that do analytics, obviously AI drives that, but now you have the Cloud being a power base. Changing that analytics infrastructure. How has it impacted you guys? BlueData, how are you guys impacted by the Cloud? Tailwind for you guys? Helpful? Good? >> You described it well, it is a tailwind. This space is about the data, not where the data lives necessarily, but the robustness of the data. So whether that's in the Cloud, whether that's on Premise, whether that's on Premise in your own private Cloud, I think anywhere where there's data that can be gathered, modeled, and new insights being pulled out of, this is wonderful, so as we ditched data, whether it's born in the Cloud or born on Premise, this is actually an accelerant to the solutions that we built together. >> As BlueData, we're all in on the Cloud, we support all the three major Cloud providers that was the big announcement that we made this week, we're generally available for AWS, GCP, and Azure, and, in particular, we start with customers who weren't born in the Cloud, so we're talking about some of the large financial services >> We had Barclays UK here who we nominated, they won the Cloud Era Data Impact Award, and what they're actually going through right now, is they started on Prem, they have these really packaged certified technology stacks, whether they are Cloud Era Hadoop, whether they are Anaconda for data science, and what they're trying to do right now is, they're obviously getting value from that on Premise with BlueData, and now they want to leverage the Cloud. They want to be able to extend into the Cloud. So, we as a company have made our product a hybrid Cloud-ready platform, so it can span on Prem as well as multiple Clouds, and you have the ability to move the workloads from one to the other, depending on data gravity, SLA considerations. >> Compliancy. >> I think it's one more thing, I want to test this with you guys, John, and that is, analytics is, I don't want to call it inert, or passive, but analytics has always been about getting the right data to human beings so they can make decisions, and now we're seeing, because of AI, the distinction that we draw between analytics and AI is, AI is about taking action on the data, it's about having a consequential action, as a result of the data, so in many respects, NCL, Kubernetes, a lot of these are not only do some interesting things for the infrastructure associated with big data, but they also facilitate the incorporation of new causes of applications, that act on behalf of the brand. >> Here's the other thing I'll add to it, there's a time element here. It used to be we were passive, and it was in the past, and you're trying to project forward, that's no longer the case. You can do it right now. Exactly. >> In many respects, the history of the computing industry can be drawn in this way, you focused on the past, and then with spreadsheets in the 80s and personal computing, you focused on getting everybody to agree on the future, and now, it's about getting action to happen right now. >> At the moment it happens. >> And that's why there's so much action. We're passed the set-up phase, and I think this is why we're hearing, seeing machine learning being so popular because it's like, people want to take action there's a demand, that's a signal that it's time to show where the ROI is and get action done. Clearly we see that. >> We're capitalists, right? We're all trying to figure out how to make money in these spaces. >> Certainly there's a lot of movement, and Cloud has proven that spinning up an instance concept has been a great thing, and certainly analytics. It's okay to have these workloads, but how do you tie it together? So, I want to ask you, because you guys have been involved in containers, Cloud has certainly been a tailwind, we agree with you 100 percent on that. What is the relevance of Kubernetes and Istio? You're starting to see these new trends. Kubernetes, Istio, Cupflow. Higher level microservices with all kinds of stateful and stateless dynamics. I call it API 2.0, it's a whole other generation of abstractions that are going on, that are creating some goodness for people. What is the impact, in your opinion, of Kubernetes and this new revolution? >> I think the impact of Kubernetes is, I just gave a talk here yesterday, called Hadoop-la About Kubernetes. We were thinking very deeply about this. We're thinking deeply about this. So I think Kubernetes, if you look at the genesis, it's all about stateless applications, and I think as new applications are being written folks are thinking about writing them in a manner that are decomposed, stateless, microservices, things like Cupflow. When you write it like that, Kubernetes fits in very well, and you get all the benefits of auto-scaling, and so control a pattern, and ultimately Kubernetes is this finite state machine-type model where you describe what the state should be, and it will work and crank towards making it towards that state. I think it's a little bit harder for stateful applications, and I think that's where we believe that the Kubernetes community has to do a lot more work, and folks like BlueData are going to contribute to that work which is, how do you bring stateful applications like Hadoop where there's a lot of interdependent services, they're not necessarily microservices, they're actually almost close to monolithic applications. So I think new applications, new AI ML tooling that's going to come out, they're going to be very conscious of how they're running in a Cloud world today that folks weren't aware of seven or eight years ago, so it's really going to make a huge difference. And I think things like Istio are going to make a huge difference because you can start in the cloud and maybe now expand on to Prem. So there's going to be some interesting dynamics. >> Without hopping management frameworks, absolutely. >> And this is really critical, you just nailed it. Stateful is where ML will shine, if you can then cross the chasma to the on Premise where the workloads can have state sharing. >> Right. >> Scales beautifully. It's a whole other level. >> Right. You're going to the data into the action, or the activity, you're going to have to move the processing to the data, and you want to have nonetheless, a common, seamless management development framework so that you have the choices about where you do those things. >> Absolutely. >> Great stuff. We can do a whole Cube segment just on that. We love talking about these new dynamics going on. We'll see you in CF CupCon coming up in Seattle. Great to have you guys on. Thanks, and congratulations on the relationship between BlueData and Dell EMC and Ready Solutions. This is Cube, with the Ready Solutions here. New York City, talking about big data and the impact, the future of AI, all things stateful, stateless, Cloud and all. It's theCUBE bringing you all the action. Stay with us for more after this short break.

(bright orchestra music) >> Hi, I'm Peter Burris. Welcome to a special digital community event brought to you by Wikibon and theCUBE. Sponsored by Dell EMC. Today we're gonna spend quite some time talking about some of the trends in the relationship between hardware and AI. Specifically, we're seeing a number of companies doing some masterful work incorporating new technologies to simplify the infrastructure required to take full advantage of AI options and possibilities. Now at the end of this conversation, series of conversations, we're gonna run a CrowdChat, which will be your opportunity to engage your peers and engage thought leaders from Dell EMC and from Wikibon SiliconANGLE and have a broader conversation about what does it mean to be better at doing AI, more successful, improving time to value, et cetera. So wait 'til the very end for that. Alright, let's get it kicked off. Tom Burns is my first guest. And he is the Senior Vice President and General Manager of Networking Solutions at Dell EMC. Tom, it's great to have you back again. Welcome back to theCUBE. >> Thank you very much. It's great to be here. >> So Tom, this is gonna be a very, very exciting conversation we're gonna have. And it's gonna be about AI. So when you go out and talk to customers specifically, what are you hearing then as they describe their needs, their wants, their aspirations as they pertain to AI? >> Yeah, Pete, we've always been looking at this as this whole digital transformation. Some studies say that about 70% of enterprises today are looking how to take advantage of the digital transformation that's occurring. In fact, you're probably familiar with the Dell 2030 Survey, where we went out and talked to about 400 different companies of very different sizes. And they're looking at all these connected devices and edge computing and all the various changes that are happening from a technology standpoint, and certainly AI is one of the hottest areas. There's a report I think that was co-sponsored by ServiceNow. Over 62% of the CIO's and the Fortune 500 are looking at AI as far as managing their business in the future. And it's really about user outcomes. It's about how do they improve their businesses, their operations, their processes, their decision-making using the capability of compute coming down from a class perspective and the number of connected devices exploding bringing more and more data to their companies that they can use, analyze, and put to use cases that really make a difference in their business. >> But they make a difference in their business, but they're also often these use cases are a lot more complex. They're not, we have this little bromide that we use that the first 50 years of computing were about known process, unknown technology. We're now entering into an era where we know a little bit more about the technology. It's gonna be cloud-like, but we don't know what the processes are, because we're engaging directly with customers or partners in much more complex domains. That suggests a lot of things. How are customers dealing with that new level of complexity and where are they looking to simplify? >> You actually nailed it on the head. What's happening in our customers' environment is they're hiring these data scientists to really look at this data. And instead of looking at analyzing the data that's being connected, that's being analyzed and connected, they're spending more time worried about the infrastructure and building the components and looking about allocations of capacity in order to make these data scientists productive. And really, what we're trying to do is help them get through that particular hurdle. So you have the data scientists that are frustrated, because they're waiting for the IT Department to help them set up and scale the capacity that they need and infrastructure that they need in order to do their job. And then you got the IT Departments that are very frustrated, because they don't know how to manage all this infrastructure. So the question around do I go to the cloud? Do I remain on-prem? All of this is things that our companies, our customers, are continuing to be challenged with. >> Now, the ideal would be that you can have a cloud experience but have the data reside where it most naturally resides, given physics, given the cost, given bandwidth limitations, given regulatory regimes, et cetera. So how are you at Dell EMC helping to provide that sense of an experience based on what the work load is and where the data resides, as opposed to some other set of infrastructure choices? >> Well, that's the exciting part is that we're getting ready to announce a new solution called the Ready Solutions for AI. And what we've been doing is working with our customers over the last several years looking at these challenges around infrastructure, the data analytics, the connected devices, but giving them an experience that's real-time. Not letting them worry about how am I gonna set this up or management and so forth. So we're introducing the Ready Solutions for AI, which really focuses on three things. One is simplify the AI process. The second thing is to ensure that we give them deep and real-time analytics. And lastly, provide them the level of expertise that they need in a partner in order to make those tools useful and that information useful to their business. >> Now we want to not only provide AI to the business, but we also wanna start utilizing some of these advanced technologies directly into the infrastructure elements themselves to make it more simple. Is that a big feature of what the ready system for AI is? >> Absolutely, as I said, one of the key value propositions is around making AI simple. We are experts at building infrastructure. We have IP around compute, storage, networking, infinity band. The things that are capable of putting this infrastructure together. So we have tested that based upon customers' input, using traditional data analytics, libraries, and tool sets that the data scientists are gonna use, already pre-tested and certified. And then we're bringing this to them in a way which allows them through a service provisioning portal to basically set up and get to work much faster. The previous tools that were available out there, some from our competition. There were 15, 20, 25 different steps just to log on, just to get enough automation or enough capability in order to get the information that they need. The infrastructure allocated for this big data analytics through this service portal we've actually gotten it down to around five clicks with a very user-friendly GUI, no CLI required. And basically, again, interacting with the tools that they're used to immediately right out of the gate like in stage three. And then getting them to work in stage four and stage five so that they're not worried about the infrastructure, not worried about capacity, or is it gonna work. They basically are one, two, three, four clicks away, and they're up and working on the analytics that everyone wants them to work on. And heaven knows, these guys are not cheap. >> So you're talking about the data scientists. So presumably when you're saying they're not worried about all those things, they're also not worried about when the IT Department can get around to doing it. So this gives them the opportunity to self-provision. Have I got that right? >> That's correct. They don't need the IT to come in and set up the network to do the CLI for the provisioning, to make sure that there is enough VM's or workloads that are properly scheduled in order to give them the capacity that they need. They basically are set with a preset platform. Again, let's think about what Dell EMC is really working towards and that's becoming the infrastructure provider. We believe that the silos, the service storage, and networking are becoming eliminated, that companies want a platform that they can enable those capabilities. So you're absolutely right. The part about the simplicity or simplifying the AI process is really giving the data scientists the tools they need to provision the infrastructure they need very quickly. >> And so that means that the AI or rather the IT group can actually start acting more like a DevOps organization as opposed to a specialist in one or another technology. >> Correct, but we've also given them the capability by giving the usual automation and configuration tools that they're used to coming from some of our software partners, such as Cloudera. So in other words, you still want the IT Department involved, making sure that the infrastructure is meeting the requirements of the users. They're giving them what they want, but we're simplifying the tools and processes around the IT standpoint as well. >> Now we've done a lot of research into what's happening in the big data now is likely to happen in the AI world. And a lot of the problems that companies had with big data was they conflated or they confused the objectives, the outcome of a big data project, with just getting the infrastructure to work. And they walked away often, because they failed to get the infrastructure to work. So it sounds though what you're doing is you're trying to take the infrastructure out of the equation while at the same time going back to the customer and saying, "Wherever you want this job "to run or this workload to run, you're gonna get the same "experience irregardless." >> Correct, but we're gonna get an improved experience as well. Because of the products that we've put together in this particular solution, combined with our compute, our scale-out mass solution from a storage perspective, our partnership with Mellon Oshman infinity band or ethernet switch capability. We're gonna give them deeper insights and faster insights. The performance and scalability of this particular platform is tremendous. We believe in certain benchmark studies based upon the Reznik 50 benchmark. We've performed anywhere between two and half to almost three times faster than the competition. In addition from a storage standpoint, all of these workloads, all of the various characteristics that happen, you need a ton of IOPS. >> Yeah. >> And there's no one in the industry that has the IOP performance that we have with our All-Flash Isilon product. The capabilities that we have there we believe are somewhere around nine times the competition. Again, the scale-out performance while simplifying the overall architecture. >> Tom Burns, Senior Vice President of Networking and Solutions at Dell EMC. Thanks for being on theCUBE. >> Thank you very much. >> So there's some great points there about this new class of technology that dramatically simplifies how hardware can be deployed to improve the overall productivity and performance of AI solutions. But let's take a look at a product demo. >> Every week, more customers are telling us they know AI is possible for them, but they don't know where to start. Much of the recent progress in AI has been fueled by open source software. So it's tempting to think that do-it-yourself is the right way to go. Get some how-to references from the web and start building out your own distributive deep-learning platform. But it takes a lot of time and effort to create an enterprise-class AI platform with automation for deployment, management, and monitoring. There is no easy solution for that. Until now. Instead of putting the burden of do-it-yourself on your already limited staff, consider Dell EMC Ready Solutions for AI. Ready Solutions are complete software and hardware stacks pre-tested and validated with the most popular open source AI frameworks and libraries. Our professional services with proven AI expertise will have the solution up and running in days and ready for data scientists to start working in weeks. Data scientists will find the Dell EMC data science provisioning portal a welcome change for managing their own hardware and software environments. The portal lets data scientists acquire hardware resources from the cluster and customize their software environment with packages and libraries tested for compatibility with all dependencies. Data scientists choose between JupyterHub notebooks for interactive work, as well as terminal sessions for large-scale neural networks. These neural networks run across a high-performance cluster of power-edge servers with scalable Intel processors and scale-out Isilon storage that delivers up to 18 times the throughput of its closest all-flash competitor. IT pros will experience that AI is simplified as Bright Cluster Manager monitors your cluster for configuration drift down to the server BIOS using exclusive integration with Dell EMC's open manage API's for power-edge. This solution provides comprehensive metrics along with automatic health checks that keep an eye on the cluster and will alert you when there's trouble. Ready Solutions for AI are the only platforms that keep both data center professionals and data scientists productive and getting along. IT operations are simplified and that produces a more consistent experience for everyone. Data scientists get a customizable, high-performance, deep-learning service experience that can eliminate monthly charges spent on public cloud while keeping your data under your control. (upbeat guitar music) >> It's always great to see the product videos, but Tom Burns mentioned something earlier. He talked about the expansive expertise that Dell EMC has and bringing together advanced hardware and advanced software into more simple solutions that can liberate business value for customers, especially around AI. And so to really test that out, we sent Jeff Frick, who's the general manager and host of theCUBE down to the bowels of Dell EMC's operations in Austin, Texas. Jeff went and visited the Dell EMC HPC and AI Innovation Lab and met with Garima Kochhar, who's a tactical staff Senior Principal Engineer. Let's hear what Jeff learned. >> We're excited to have with us our next guest. She's Garima Kochhar. She's on the tactical staff and the Senior Principal Engineer at Dell EMC. Welcome. >> Thank you. >> From your perspective what kinda changing in the landscape from high-performance computing, which has been around for a long time, into more of the AI and machine learning and deep learning and stuff we hear about much more in business context today? >> High-performance computing has applicability across a broad range industries. So not just national labs and supercomputers, but commercial space as well. And our lab, we've done a lot of that work in the last several years. And then the deep learning algorithms, those have also been around for decades. But what we are finding right now is that the algorithms and the hardware, the technologies available, have hit that perfect point, along with industries' interest with the amount of data we have to make it more, what we would call, mainstream. >> So you can build an optimum solution, but ultimately you wanna build industry solutions. And then even subset of that, you invite customers in to optimize for what their particular workflow or their particular business case which may not match the perfect benchmark spec at all, right? >> That's exactly right. And so that's the reason this lab is set up for customer access, because we do the standard benchmarking. But you want to see what is my experience with this, how does my code work? And it allows us to learn from our customers, of course. And it allows them to get comfortable with their technologies, to work directly with the engineers and the experts so that we can be their true partners and trusted advisors and help them advance their research, their science, their business goals. >> Right. So you guys built the whole rack out, right? Not just the fun shiny new toys. >> Yeah, you're right. So typically, when something fails, it fails spectacularly. Right, so I'm you've heard horror stories where there was equipment on the dock and it wouldn't fit in the elevator or things like that, right? So there are lots of other teams that handle, of course Dell's really good at this, the logistics piece of it, but even within the lab. When you walk around the lab, you'll see our racks are set up with power meters. So we do power measurements. Whatever best practices in tuning we come up with, we feed that into our factories. So if you buy a solution, say targeted for HPC, it will come with different BIOS tuning options than a regular, say Oracle, database workload. We have this integration into our software deployment methods. So when you have racks and racks of equipment or one rack of equipment or maybe even three servers, and you're doing an installation, all the pieces are baked-in already and everything is easy, seamless, easy to operate. So our idea is... The more that we can do in building integrated solutions that are simple to use and performant, the less time our customers and their technical computing and IT Departments have to spend worrying about the equipment and they can focus on their unique and specific use case. >> Right, you guys have a services arm as well. >> Well, we're an engineering lab, which is why it's really messy, right? Like if you look at the racks, if you look at the work we do, we're a working lab. We're an engineering lab. We're a product development lab. And of course, we have a support arm. We have a services arm. And sometimes we're working with new technologies. We conduct training in the lab for our services and support people, but we're an engineering organization. And so when customers come into the lab and work with us, they work with it from an engineering point of view not from a pre-sales point of view or a services point of view. >> Right, kinda what's the benefit of having the experience in this broader set of applications as you can apply it to some of the newer, more exciting things around AI, machine learning, deep learning? >> Right, so the fact that we are a shared lab, right? Like the bulk of this lab is High Performance Computing and AI, but there's lots of other technologies and solutions we work on over here. And there's other labs in the building that we have colleagues in as well. The first thing is that the technology building blocks for several of these solutions are similar, right? So when you're looking at storage arrays, when you're looking at Linux kernels, when you're looking at network cards, or solid state drives, or NVMe, several of the building block technolgies are similar. And so when we find interoperability issues, which you would think that there would never be any problems, you throw all these things together, they always work like-- >> (laughs) Of course (laughs). >> Right, so when you sometimes, rarely find an interoperability issue, that issue can affect multiple solutions. And so we share those best practices, because we engineers sit next to each other and we discuss things with each other. We're part of the larger organization. Similarly, when you find tuning options and nuances and parameters for performance or for energy efficiency, those also apply across different domains. So while you might think of Oracle as something that it's been done for years, with every iteration of technology there's new learning and that applies broadly across anybody using enterprise infrastructure. >> Right, what gets you excited? What are some of the things that you see, like, "I'm so excited that we can now apply "this horsepower to some of these problems out there?" >> Right, so that's a really good point, right? Because most of the time when you're trying to describe what you do, it's hard to make everybody understand. Well, not what you're doing, right? But sometimes with deep technology it's hard to explain what's the actual value of this. And so a lot of work we're doing in terms of excess scale, it's to grow like the... Human body of knowledge forward, to grow the science happening in each country moving that forward. And that's kind of, at the higher end when you talk about national labs and defense and everybody understands that needs to be done. But when you find that your social media is doing some face recognition, everybody experiences that and everybody sees that. And when you're trying to describe the, we're all talking about driverless cars or we're all talking about, "Oh, it took me so long, "because I had this insurance claim and then I had "to get an appointment with the appraisor "and they had to come in." I mean, those are actual real-world use cases where some of these technologies are going to apply. So even industries where you didn't think of them as being leading-edge on the technical forefront in terms of IT infrastructure and digital transformation, in every one of these places you're going to have an impact of what you do. >> Right. >> Whether it's drug discovery, right? Or whether it's next-generation gene sequencing or whether it's designing the next car, like pick your favorite car, or when you're flying in an aircraft the engineers who were designing the engine and the blades and the rotors for that craft were using technologies that you worked with. And so now it's everywhere, everywhere you go. We talked about 5G and IoT and edge computing. >> Right. >> I mean, we all work on this collectively. >> Right. >> So it's our world. >> Right. Okay, so last question before I let you go. Just being, having the resources to bear, in terms of being in your position, to do the work when you've got the massive resources now behind you. You have Dell, the merger of EMC, all the subset brands, Isilon, so many brands. How does that help you do your job better? What does that let you do here in this lab that probably a lot of other people can't do? >> Yeah, exactly. So when you're building complex solutions, there's no one company that makes every single piece of it, but the tighter that things work together the better that they work together. And that's directly through all the technologies that we have in the Dell technologies umbrella and with Dell EMC. And that's because of our super close relationships with our partners that allows us to build these solutions that are painless for our customers and our users. And so that's the advantage we bring. >> Alright. >> This lab and our company. >> Alright, Garima. Well, thank you for taking a few minutes. Your passion shines through. (laughs) >> Thank you. >> I really liked hearing about what Dell EMC's doing in their innovation labs down at Austin, Texas, but it all comes together for the customer. And so the last segment that we wanna bring you here is a great segment. Nick Curcuru, who's the Vice President of Big Data Analytics at Mastercard is here to talk about how some of these technologies are coming together to speed value and realize the potential of AI at Mastercard. Nick, welcome to theCUBE. >> Thank you for letting me be here. >> So Mastercard, tell us a little bit about what's going on at Mastercard. >> There's a lot that's going on with Mastercard, but I think the most exciting things that we're doing out of Mastercard right now is with artificial intelligence and how we're bringing the ability for artificial intelligence to really allow a seamless transition when someone's actually doing a transaction and also bringing a level of security to our customers and our banks and the people that use Mastercards. >> So AI to improve engagement, provide a better experience, but that's a pretty broad range of things. What specifically kinds of, when you think about how AI can be applied, what are you looking to? Especially early on. >> Well, let's actually take a look at our core business, which is being able to make sure that we can secure a payment, right? So at this particular point, people are used to, we're applying AI to biometrics. But not just a fingerprint or a facial recognition, but actually how you interact with your device. So you think of like the Internet of Things and you're sitting back saying, "I'm using, "I'm swiping my device, my mobile device, "or how I interact with a keyboard." Those are all key signatures. And we, with our company, new data that we've just acquired are taking that capability to create a profile and make that a part of your signature. So it's not just beyond a fingerprint. It's not just beyond a facial. It's actually how you're interacting so that we know it's you. >> So there's a lot of different potential sources of information that you can utilize, but AI is still a relatively young technology and practice. And one of the big issues for a lot of our clients is how do you get time to value? So take us through, if you would, a little bit about some of the challenges that Mastercard and anybody would face to try to get to that time to value. >> Well, what you're really seeing is looking for actually a good partner to be with when you're doing artificial intelligence, because again, at that particular point, you try to get to scale. For us, it's always about scale. How can we roll this across 220 countries? We're 165 million transactions per hour, right? So what we're looking for is a partner who also has that ability to scale. A partner who has the global presence, who's learning. So that's the first step. That's gonna help you with your time to value. The other part is actually sitting back and actually using those particular partners to bring their expertise that they're learning to combine with yours. It's no longer just silos. So when we talk about artificial intelligence, how can we be learning from each other? Those open source systems that are out there, how do we learn from that community? It's that community that allows you to get there. Again, those that are trying to do it on their own, trying to do it by themselves, they're not gonna get to the point where they need to be. In other words, in a six month time to value it's gonna take them years. We're trying to accelerate that, you say, "How can we get out of those algorithms operating for us "the way we need them to provide the experiences "that people want quickly." And that's with good partners. >> 165 million transactions per hour is only likely to go up over the course of the next few years. That creates an operational challenge. AI is associated with a probabilistic set of behaviors as opposed to categorical. Little bit more difficult to test, little bit more difficult to verify, how is the introduction of some of these AI technologies impacting the way you think about operations at Mastercard? >> Well, for the operations, it's actually when you take a look there's three components, right? There's right there on the edge. So when someone's interacting and actually doing the transaction, and then we'll look at it as we have a core. So that core sits there, right? Basically, that's where you're learning, right? And then there's actually, what we call, the deep learning component of it. So for us, it's how can we move what we need to have in the core and what we need to have on the edge? So the question for us always is we want that algorithm to be smart. So what three to four things do we need that algorithm to be looking for within that artificial intelligence needs to know that it then goes back into the core and retrieves something, whether that's your fingerprint, your biometrics, how you're interacting with that machine, to say, "Yes, that's you. "Yes, we want that transaction to go through." Or, "No, stop it before it even begins." It's that interaction and operational basis that we're always have a dynamic tension with, but it's how we get from the edge to the core. And it's understanding what we need it to do. So we're breaking apart what we have to have that intelligence to be able to create a decision for us. So that's how we're trying to manage it, as well as of course, the hardware that goes with it and the tools that we need in order to make that happen. >> When we get on the hardware just a little bit, so that historically different applications put pressure on different components within a stack. One of the observations that we've made is that the transition from spinning disk to flash allows companies like Mastercard to think about just persisting data to actually delivering data. >> Yeah. >> Much more rapidly. How does some of the, how does these AI technologies, what kinda new pressures do they put on storage? >> Well, they put a tremendous pressure, because that's actually again, the next tension or dynamics that you have to play with. So what do you wanna have on disk? What do you need flash to do? Again, if you look at some people, everyone's like, "Oh, flash will take over everything." It's like no, flash has, there's a reason for it to exist, and understanding what that reason is and understanding, "Hey, I need that to be able to do this "in sub-seconds, nanoseconds," I've heard the term before. That's what you're asking flash to do. When you want deep learning, that, I want it on disk. I want to be taking all those millions of billions of transactions that we're gonna see and learn from them. All the ways that people will be trying to attack me, right? The bad guys, how am I learning from everything that I'm having that can sit there on disk and let it continue to run, that's the deep learning. The flash is when I wanna create a seamless transaction with a customer, or a consumer, or from a business to business. I need to have that decision now. I need to know it is you who is trying to swipe or purchase something with my mobile device or through the, basically through the Internet. Or how am I actually even swiping or inserting, tipping my card in that particular machine at a merchant. That's we're looking at how we use flash. >> So you're looking at perhaps using older technologies or different classes technologies for some of the training elements, but really moving to flash for the interfacing piece where you gotta deliver the real-time effort right now. >> And that's the experience. And that's what you're looking for. And that's you're looking, you wanna be able to make sure you're making those distinctions. 'Cause again there's no longer one or the other. It's how they interact. And again, when you look at your partners, it's the question now is how are they interacting? Am I actually, has this been done at scale somewhere else? Can you help me understand how I need to deploy this so that I can reduce my time to value, which is very, very important to create that seamless, frictionless transaction we want our consumers to have. >> So Nick, you talked about how you wanna work with companies that demonstrate that they have expertise, because you can't do it on your own. Companies that are capable of providing the scale that you need to provide. So just as we talk about how AI is placing pressure on different parts of the technology stack, it's got also to be putting pressure on the traditional relationships you have with technology suppliers. What are you looking for in suppliers as you think about these new classes of applications? >> Well, the part is you're looking at, for us it's do you have that scale that we're looking at? Have you done this before, that global scale? Again, in many cases you can have five guys in a garage that can do great things, but where has it been tested? When we say tested, it's not just, "Hey, we did this "in a pilot." We're talking it's gotta be robust. So that's one thing that you're looking for. You're looking for also a partner we can bring, for us, additional information that we don't have ourselves, right? In many cases, when you look at that partner they're gonna bring something that they're almost like they are an adjunct part of your team. They are your bench strength. That's what we're looking for when we look at it. What expertise do you have that we may not? What are you seeing, especially on the technology front, that we're not privy to? What are those different chips that are coming out, the new ways we should be handling the storage, the new ways the applications are interacting with that? We want to know from you, because again, everyone's, there's a talent, competition for talent, and we're looking for a partner who has that talent and will bring it to us so that we don't have to search it. >> At scale. >> Yeah, especially at scale. >> Nick Curcuro, Mastercard. Thanks for being on theCUBE. >> Thank you for having me. >> So there you have a great example of what leading companies or what a leading company is doing to try to take full advantage of the possibilities of AI by utilizing infrastructure that gets the job done simpler, faster, and better. So let's imagine for a second how it might affect your life. Well, here's your opportunity. We're now gonna move into the CrowdChat part of the event, and this is your chance to ask peers questions, provide your insights, tell your war stories. Ultimately, to interact with thought leaders about what it means to get ready for AI. Once again, I'm Peter Burris, thank you for watching. Now let's jump into the CrowdChat.

>> Announcer: Live from Las Vegas It's theCube covering Dell Technologies World 2018 brought to you by Dell EMC and its ecosystem partners. >> Welcome back to theCube's live coverage of the Inaugural Dell Technologies World 2018 here in Las Vegas. Getting to the end of three days of wall-to-wall live coverage from two sets I'm Stu Miniman, joined by my co-host John Troyer, and for those of you that haven't attended one of these shows, sometimes like "Oh, you're going to Vegas, this is some boondoggle," but I'm really happy, I've got a customer, one of the Dell EMC employees, here. A lot of stuff goes on. There's learning, there's lotsa meetings, there's, you know, you come here, you kind of, you know, get as much out of it as you can. So, first, Stephen Herzig, who's the Director of Enterprise Systems at the University of Arkansas, >> Correct, yes. >> Stu: You had a busy week so far. >> I have. >> Thank you for joining us >> You bet. >> Stu: Also, Andrew McDaniel, who's the Senior Director of Ready Solutions for VDI with Dell EMC, thank you for joining us-- >> Thanks guys >> Alright, so, Stephen, first of all, give us a little bit about your background and University of Arkansas, I think most people know the Razorbacks-- >> Stephen: That's right, the Razorbacks! >> Talk about your org and your role there. >> Yeah, I'm Director of Enterprise Systems, as you mentioned. We're an R1 University, we have about 27,000 students, about 5,000 faculty and staff in the university. And, so my organization is responsible for maintaining, as I said, all the enterprise systems, essentially everything in the data center on the floor to support all the educational activities. Now there is some distributed or commonly known as shadow IT organizations throughout the university and we work quite closely with them, too. >> Okay, you stamp out all that shadow IT stuff and pull it all back in, right? >> Stephen: (laughs) No, no! No, absolutely not. >> We'll get a, Andrew, before we get into more about the university, tell us a little bit about your role and your org, inside Dell EMC. >> So my organization basically develops the end-to-end VDI solutions that Dell EMC sell globally. So, we work with partners such as VMware and Citrix, to put together the industry leading solutions for VDI. Tested, validated, engineered, to give real good confidence in the solution the customer's going to buy. >> Okay, John and I spent many years looking at these, you know, memes in the industry, all that, you know, but uh, Stephen, before we get into the VDI piece, give us, what are some of the challenges that you're facing in the University? We've had, you know, from an IT standpoint, we know the technology requirements are more than ever. While tuitions go up, budgets are always a challenge. So, when you're talking to your peers, what are the things you're all commiserating about or, you know, working at. >> Yeah, like any IT organization, it's a challenge to do more with less. We're constantly being required to support more systems, more technology, and technology is becoming more and more an integral part of the educational process. We also have students coming from very diverse backgrounds, and so the kinds of computing devices that they're able to bring to the university with them, some can afford high-end, some not, and so, it's a challenge for us to deliver that, the applications to them, no matter what kind of device they happen to bring. >> Alright, so, sounds like VDI is something that fits there-- >> Yes >> Before we get into the actual solution, tell us, what was the struggle you were facing, what led to that, what was there, was there a mandate? How did you get to the solution that you were-- >> Well, really, we were struggling with those challenges We're a very small IT team, and as those things grew, we knew we had to find a way to reduce the number of resources that we're supporting, all the end points, all the machines in the labs, all the machines on faculty and staff desks, and again, like I said, the students bring their own devices, which we had to support as well. >> Alright, so, you ended up choosing a Dell Solution, maybe give us a little bit about that, that process and walk us through the project some. >> Yeah, we really needed a solution. We could not go out and assemble pieces, parts, from a lot of different vendors, and we needed a solution that was tailored to our needs, that fit, VDI is complex by its nature, but some vendors made it really complex. So, we had to find one that was right for our environment, for what we were trying to achieve, and of course, at the right price point. Higher education, we're not flush with cash. >> That's always been really hard, I think that's been the hard thing about VDI, right? It's always been kind of complicated and hard to do, at least back in the day, and then when you did it, half the things didn't work, and the things that didn't work were really weird, and the user was very confused. "This application works, but this one doesn't." And, "where's my cursor?" and "Everything went wonky all of a sudden and I can't login at 9am." I mean, I'm kind of curious, what is necessary maybe, from eye-level in a modern VDI solution stack, that makes it easy? You know, is it the hypervisor, the end clients? >> I think, John, you know we've seen such great advances in the software side of it, right? So, if you look at Horizon, as a broker, VMware Horizon, the advances that they've made in things like protocols, right, so Blast Extreme, for example, one of the big challenges that we've always had, is things like Link or Skype, in a VDI environment. It was, it made a disaster for many customers, right? So, that has been solved by VMware and the advances that they did, above and beyond what was capable in PC over IP. So, that's one of the things. From a hardware perspective, you know, one of the challenges we frequently had in VDI, was poor user experience, right? And it was typically because the graphics requirement for the application could not be delivered by the CPU alone, right, so GPUs, Nvidia, K1, K2's, then it went to the M10, M60's, and moving forward into the P4 and P40's, they've really helped us to improve that user experience, and it's starting to get to a point where GPUs are a standard delivery within any VDI employment. So, you get really good experience moving forward. And as you know, if you can't deliver a good user experience, the project is dead before it even starts. Alright, so that's a big challenge. >> Stephen, do you have any commentary on some of the challenges that we faced before? What was your experience like? >> Yeah, it, that's exactly right. We made the decision early on to include GPU in every session that we served up. And we weren't quite sure, 'cause it is an additional expense, but it was one of the best decisions that we've made. It really does make all the difference. >> Was there something specific from the application or user-base, and how they were using it, that led you to that? >> Well, we are all Windows 10, and Windows 10 just looks better, it runs better, the video, scrolling through a Word document, the text, some are very nuanced, but it makes a big difference in the user experience. And of course, we have higher-end users using CAD programs, things like that, you know, in the School of Engineering, they needed the GPU for what they were doing. >> Andrew, wondering if you could give us, little bit of an update on the stack, So, I think back to, on the EMC side, I watched everything from the Flash on the converge side. On the Dell side, there was the Wyse acquisition of course, EMC and VM were coming together, so, a long journey, but even the first year we did theCube, you know, Dell had some big customers doing large scale, cost-effective VDI, because, had that, you know, to give some of the marketing terms I've heard here, it's end to end, but you add the devices all the way through. So, bring us up to 2018. >> Yeah, so, I guess, you know, one of the challenges that Stephen spoke about is the, previously, the hassle of having to go and buy each of the individual components from multiple different vendors. So, you're buying your storage from one vendor, compute from another, GPUs from another, hypervisor from another, broker from another, and so on. So, it gets very complicated to manage all of that. And so, we had lots of customers who had run into scenarios where, say a BIAS firmware and a driver revision were not compatible, and so we'd run into those kinds of problems that we were talking about earlier on, right? So, I think, you know, bringing all of that together, in Dell Technologies, we can now deliver every single aspect of what you need for a VDI deployment. So, we created a bundle called VDI Complete. It uses vSAN ReadyNodes or VxRail, right? So, hyper-converged, massive from a VDI perspective, and I'll come back to that in a second. It pairs then, Horizon Advanced or Horizon Enterprise, with those base platforms, and the Dell Wyse Thin clients. So, every aspect, true end to end, is delivered by Dell Technologies, and there's simply no other vendor in the market who can do that. So, what that basically does is it gives the customer confidence that everything that has been tested can be owned, from a support perspective, by Dell Technologies. Alright, so, if you've got a problem, we're not going to hand you off to another company to go solve that issue, or lay blame with somebody else. It's fully our stack, and as a result, we take full responsibility for it. And that's one of the benefits that we have with customers like University of Arkansas. >> And that was important to us. That single point of contact for support was really important to us. >> Stephen, I wonder if you could talk about, from an operational standpoint, you said, you've got a small team. One of the challenges, at least years ago, was like "Oh, wait! I have the guy that walked around "and did the desktops, now I centralized it, "who owns it, you know, how do we sort through this? "You know, we've got a full stack there. "Simplicity's one of the big messages of HCI," but what was the reality for your team and the roles, how did you change? >> Well one of the first areas, or actually, the first area that we implemented VDI in was in the labs. Hundreds of end points across the campus. And, before VDI, you would walk into the lab, and a certain percentage of the machines would always be down. They needed updating, there was a virus, somebody spilled a coffee on the machine, you know, that kind of thing. After VDI, when you walked into the lab, 100% of the end points were always up, and there was no noise in the lab, except when somebody printed. So, the maintenance required, the resources for my team, and these distributed IT teams was reduced drastically. As a matter of fact, some of the distributed teams had 50% of their resources reduced. That could then go and do more high-value projects and deliver high-value services to their colleges. >> From the student and faculty perspective, it sounds like the uptake has been good, and the satisfaction level high. I mean, user experience is everything with VDI, right? >> Yeah, absolutely, the students came, we installed during spring break, and they came back from spring break, went into the labs with these beautiful new 27-inch monitors, sat down, logged on, and it looked almost the same as before. Which was exactly what we were after. We wanted that same high-quality experience in VDI that they had with a laptop or a desktop. >> The monitors are an important thing to consider, right, 'cause a lot of customers will think about the data center side of VDI, right, so, get lots of compute, good, high-performing storage, good network, and then they put a really poorly designed thin client or an old desktop PC, or something like that, on the end, and wonder why they're not getting good performance, right? So, we just launched yesterday the Dell Wyse 5070. It's the first thin client in the market that can have six monitors attached to it, four of those can be 4K, and two 2K, right? So, it's immense from a display perspective, and this is what our customers are demanding. Especially in financial services, for example, or in automotive design, you know, in CAD labs, for example, you need three or four really good, high-quality screens attached. >> Well, I'm saying, I'll date myself, I wish I had that when I was playing Doom when I was in college in the labs. >> That too! >> That does bring into question, your upgrade and scenarios, moving on to the future, right? You used to have all those janky old PC's that you'd kind of, maybe they'd slide out the back door, maybe they'd get recycled, or whatever, but now it's a different refreshed cycle, and maybe even different use cases. >> Yeah, the lifespan of the endpoints is much longer with the VDI solution. >> John: It's got to be good, yeah. I was curious, you mentioned the converged infrastructure, too, Andrew. I mean, how does that play into it? (muffled) >> Yeah, so I mean, you know, traditionally, a SAN infrastructure was used in VDI, alright? So, for us, that would have been Equallogic Compellent, historically. Now, we're seeing that VDI market almost totally transition to hyperconverged. Alright, so vSAN has really revolutionized VDI, okay? I'd say, you know, a good 30, 35% of all VxRail and vSan deployments that we do, are in the VDI space. So, it's really, and I would say about 90, 95% of our VDI deployments are on hyperconverged rather than a traditional SAN infrastructure. That's really where VDI has moved now. 'Cause it gives customers the ability to scale on demand. Instead of having to go and buy another half-million dollar storage rate, add another thousand users, you can simply add in a couple of more compute nodes with the storage built in. For us, hybrid works very well. So, a hybrid-disc configuration is working very well in most VDI deployments. Some customers require all flash, it depends on the applications and the other kind of performance that they want to get from it. But for a majority of customers, hyperconverged with the hybrid configuration works brilliantly. >> So, Stephen, I want to give you the final word. Sounds like everything went really well, but one of the things we always like to understand, when you're talking with your peers, they said "Hey, what did you learn? "What would you do a little different, "either internally, or configuration-wise, or roll-out," What would you tell your peers? >> Well, when we implemented VDI it was just before VDI Complete came out. So, the work that's done in the VDI Complete solution, we didn't have. So, as we look to the future, and we want to expand, and grow our environment, VDI Complete will be a huge help. Had we had that, it only took us about four months to stand it up, which, considering what we accomplished, was very short time, but, if we had had VDI Complete, that time would've been much more compressed. So, looking to the future, we're looking to expand using VDI Complete. >> Just to, Andrew, maybe you can tie the knot on this bow for us, is sounds like this could, if I've got VDI, I don't have to start brand new, it can fit with existing environments, how does that all work? >> Absolutely, I mean we've got lots of customers who've already done Citrix or VMware deployments, right? Ideally, you want to connect with one broker. So you want to stick with one broker. But, we can bring in a hyperconverged VDI solution into your existing user estate, and merge into that. So, that's pretty common. >> Alright, well, Andrew and Stephen, thank you so much for sharing the story. Really great to always get the customer stories. We're getting towards the end of three days of live coverage here at the Sands Convention Center in Las Vegas, at Dell Technologies World 2018. For John Troyer, I'm Stu Miniman, thanks for watching theCube. (techno music)

>> Announcer: Live from Las Vegas, it's the Cube. Covering Dell Technologies World 2018. Brought to you by Dell EMC, and its ecosystem partners. >> Welcome back to SiliconANGLE media's coverage of Dell Technologies World 2018. I'm Stu Miniman here with my cohost Keith Townsend, happy to welcome back to the program Tom Burns, who's the SVP of Networking and Solutions at Dell EMC. Tom, great to see ya. >> Great to see you guys as well. Good to see you again. >> All right, so I feel like one of those CNBC guys. It's like, Tom, I remember back when Force10 was acquired by Dell and all the various pieces that have gone on and converged in infrastructure, but of course with the merger, you've gotten some new pieces to your toy chest. >> Tom: That's correct. >> So maybe give us the update first as to what's under your purview. >> Right, right, so I continue to support and manage the entire global networking business on behalf of Dell EMC, and then recently I picked up what we called our converged infrastructure business or the VxBlock, Vscale business. And I continue also to manage what we call Enterprise Infrastructure, which is basically any time our customers want to extend the life of their infrastructure around memory, storage, optics, and so forth. We support them with Dell EMC certified parts, and then we add to that some third-party componentry around rack power and cooling, software, Cumulus, Big Switch, things like that. Riverbed, Silver Peak, others. And so with that particular portfolio we also cover what we call the Dell EMC Ready Solutions, both for the service provider, but then also for traditional enterprises as well. >> Yeah, well luckily there's no change in any of those environments. >> Tom: No, no. >> Networking's been static for decades. I mean they threw a product line that I mean last I checked was somewhere in the three to four billion dollar range. With the VxBlock under what you're talking there. >> Yeah it's a so, yeah-- >> Maybe you could talk, what does this mean? 'Cause if I give you your networking guy. >> Right. >> Keith and I are networking guys by background, obviously networking's a piece of this, but give us a little bit of how the sausage is made inside to-- >> Tom: Sure. >> Get to this stuff. >> Well I think when you talk about all these solutions, Cloud, Hybrid Cloud, Public Cloud, when you think about software-defined X, the network is still pretty darn important, right? I often say that if the network's not working, it's going to be a pretty cloudy day. It's not going to connect. And so the fabric continues to remain one of the most critical parts of the solution. So the thought around the VxBlock and moving that in towards the networking team is the importance of the fabric and the capability to scale out and scale up with our customers' workloads and applications. So that's probably the reason primarily the reason. And then we can also look at how we can work very closely with our storage division 'cause that's the key IP component coming from Dell EMC on the block side. And see how we can continue to help our customers solve their problems when it comes to this not your do-it-yourself but do-it-for-me environment. >> All right, I know Keith wants to jump in, but one just kind of high-level question for you. I look at networking, we've really been talking about disaggregation of what's going on. It's really about disaggregated systems. And then you've got convergence, and there's other parts of the group that have hyper convergence. How do you square the circle on those two trends and how do those go together? >> Well, I think it's pretty similar on whether you go hyper converge, converge, or do-it-yourself, you build your own block so to speak. There's a set of buyers that want everything to be done for them. They want to buy the entire stack, they want it pre-tested, they want it certified, they want it supported. And then there's a set of customers that want to do it themselves. And that's where we see this opportunity around disaggregation. So we see it primarily in hyperscale and Cloud, but we're seeing it more and more in large enterprise, medium enterprise, particular verticals where customers are in essence looking for some level of agility or capability to interchange their solutions by a particular vendor or solutions that are coming from the same vendor but might be a different IP as an example. And I'm really proud of the fact that Dell EMC really kicked off this disaggregation of the hardware and software and networking. Some 4 1/2 years ago. Now you see some of the, let's say, larger industry players starting to follow suit. And they're starting to disaggregate their software as well. >> Yeah, I would have said just the commonality between those two seemingly opposed trends it's scale. >> Right. >> It's how do customers really help scale these environments? >> Exactly, exactly. It depends a lot around the customer environment and what kind of skill sets do they have. Are they willing to help go through some of that do-it-yourself type of process. Obviously Dell EMC services is there to help them in those particular cases. But we kind of have this buying conundrum of build versus buy. I think my old friend, Chad Sakac, used to say, there's different types of customers that want a VxRail or build-it-themselves, or they want a VxBlock. We see the same thing happen in a networking. There's those customers that want disaggregated hardware and software, and in some cases even disaggregated software. Putting those protocols and features on the switch that they actually use in the data center. Rather than buying a full proprietary stack, well we continue to build the full stack for a select number of customers as well because that's important to that particular sector. >> So again, Tom, two very different ends of the spectrum. I was at ONS a couple of months ago, talked to the team. Dell is a huge sponsor of the Open Source community. And I don't think many people know that. Can you talk about the Open Source relationship or the relationship that Dell Networking has with the Open Source community? >> Absolutely, we first made our venture in Open Source actually with Microsoft in their SONiC work. So they're creating their own network operating software, and we made a joint contribution around the switch abstraction interface, or side. So that was put into the Open Compute Project probably around 3 1/2, maybe four years ago. And that's right after we announced this disaggregation. We then built basically an entire layer of what we call our OS10 base, or what's known in the Linux foundation as OPX. And we contributed that to the OPX or to the Linux foundation, where basically that gives the customer the capability through the software that takes care of all the hardware, creates this switch subtraction interface to gather the intelligence from the ASIC and the silicon, and bringing it to a control plane, which allows APIs to be connected for all your north-bound applications or your general analysis that you want to use, or a disaggregated analysis, what you want to do. So we've been very active in Linux. We've been very active in OCP as well. We're seeing more and more of embracing this opportunity. You've probably seen recently AT&T announced a rather large endeavor to replace tens of thousands of routers with basically white box switches and Open Source software. We really think that this trend is moving, and I'm pretty proud that Dell EMC was a part of getting that all started. >> So that was an awful lot of provider talk. You covered both the provider's base and the enterprise space. Talk to us about where the two kind of meet. You know the provider space, they're creating software, they're embracing OpenStack, they're creating plug-ins for disaggregated networking. And then there's the enterprise. There's opportunity there. Where do you see the enterprise leveraging disaggregation versus the service provider? >> Well, I think it's this move towards software-defined. If you heard in Michael's keynote today, and you'll hear more tomorrow from Jeff Clarke. The whole world is moving to software-defined. It's no longer if, it's when. And I think the opportunity for enterprises that are kind of in that transformation stage, and moving from traditional software-defined, or excuse me, traditional data centers to the software-defined, they could look at disaggregation as an opportunity to give them that agility and capability. In a manner of which they can kind of continue to manage the old world, but move forward into the new world of disaggregation software-defined with the same infrastructure. You know it's not well-known that Dell EMC, we've made our switching now capable of running five different operating softwares. That's dependent upon workloads and use cases, and the customer environment. So, traditional enterprise, they want to look at traditional protocols, traditional features. We give them that capability through our own OS. We can reduce that with OS partners, software coming from some of our OS partners, giving them just the protocols and features that they need for the data center or even out to the edge. And it gives them that flexibility and change. So I think it really comes at this point of when are they going to move towards moving from traditional networking to the next generation of networking. And I'm very happy, I think Dell Technologies is leading the way. >> So I'm wondering if you could expand a little bit about that. When I think about Dell and this show, I mean it is a huge ecosystem. We're sitting right near the Solutions Expo, which will be opening in a little bit, but on the networking side, you've got everything from all the SD-WAN pieces, to all the network operating systems that can sit on top. Maybe, give us kind of the update on the overview, the ecosystem, where Dell wins. >> Yeah, yeah I mean, if you think about 30-something years ago when Michael started the company and Dell started, what was it about. It was really about transforming personal computing, right? It was about taking something that was kind of a traditional proprietary architecture and commoditizing it, making sure it's scalable and supportable. You think of the changes that's occurred now between the mainframe and x86. This is what we think's happening in networking. And at Dell Technologies in the networking area whether it's Dell EMC or to VMware, we're really geared towards this SDX type of market. Virtualization, Layer two, day or three disaggregated switching in the data center. Now SD-WAN with the acquisition of Velocloud by VMware. We're really hoping customers transform at the way networking is being managed, operated, supported to give them much more flexibility and agility in a software-defined market. That being said, we continue to support a multitude of other partners. We have Cumulus, Big Switch, IP infusion, and Pluribus as network operating software alternatives. We have our own, and then we have them as partners. On the SD-WAN area while we lead with Velocloud, we have Silver Peak and we also have Versa Technology, which is getting a lot of upkick in the area. Both in the service provider and in the enterprise space. Huge area of opportunity for enterprises to really lower their cost of connectivity and their branch offices. So, again, we at Dell, we want to have an opinion. We have some leading technologies that we own, but we also partner with some very good, best-of-breed solutions. But being that we're open, and we're disaggregated, and we have an incredible scaling and service department or organization, we have this capability to bring it together for our customers and support them as they go through their IT transformation. >> So, Dell EMC is learning a lot of lessons as you guys start to embrace software-defined. Couple of Dell EMC World's ago, big announcement Chad talked about, ScaleIO, and abstracting, and giving away basically, ScaleIO as a basic solution for free. Then you guys pulled back. And you said, you know what, that's not quite what customers want. They want a packaged solution. So we're talking on one end, total disaggregation and another end, you know what, in a different area of IT, customers seem to want packaged solutions. >> Tom: Yeah. >> Can you talk to the importance of software-defined and packaged solutions? >> Right, it's kind of this theory of appliances, right? Or how is that software going to be packaged? And we give that flexibility in either way. If you think of VxRail or even our vSAN operating or vSAN ready node, it gives that customer the capability to know that we put that software and hardware together, and we tested it, we certified it, most importantly we can support it with kind of one throat to choke, one single call. And so I think the importance for customers are again, am I building it myself or do I want to buy a stack. If I'm somewhere in the middle maybe I'm doing a hybrid or perhaps a Rail type of solution, where it's just compute and storage for the most part. Maybe I'm looking for something different on my networking or connectivity standpoint. But Dell EMC, having the entire portfolio, can help them at any point of the venture or at any part of the solution. So I think that you're absolutely right. The customer buying is varied. You've got those that want everything from a single point, and you got others that are saying I want decision points. I think a lot of the opportunity around the cost savings, mostly from an Opex standpoint are those that are moving towards disaggregated. It doesn't lock 'em in to a single solution. It doesn't get 'em into that long life cycle of when you're going to do changes and upgrades and so forth. This gives them a lot more flexibility and capability. >> Tom, sometimes we have the tendency to get down in the weeds on these products. Especially in the networking space. One of my complaints was, the whole SDN wave, didn't seem to connect necessarily to some of the big businesses' challenges. Heard in the keynote this morning a lot of talk about digital transformation. Bring us up to speed as to how networking plays into that overall story. What you're hearing from customers and if you have any examples we'd love to hear. >> Yeah, no so, I think networking plays a critical part of the IT transformation. I think if you think of the first move in virtualization around compute, then you have the software-defined storage, the networking component was kind of the lagger. It was kind of holding back. And in fact today, I think some analysts say that even when certain software-defined storage implementations occur, interruptions or issues happen in the network. Because the network has then been built and architected for that type of environment. So the companies end up going back and re-looking at how that's done. And companies overall are I think are frustrated with this. They're frustrated with the fact that the network is holding them back from enabling new services, new capabilities, new workloads, moving towards a software-defined environment. And so I think this area again, of disaggregation, of software-defined, of offering choice around software, I think it's doing well, and it's really starting to see an uptick. And the customer experiences as follows. One is, open networking where it's based upon standard commodity-based hardware. It's simply less expensive than proprietary hardware. So they're going to have a little bit of savings from the CapEx standpoint. But because they moved towards this disaggregated model where perhaps they're using one of our third-party software partners that happens to be based in Linux, or even our own OS10 is now based in Linux. Look at that, the tools around configuration and automation are the same as compute. And the same as storage. And so therefore I'm saving on this configuration and automation and so forth. So we have examples such as Verizon that literally not only saves about 30% cost savings on their CapEx, they're saving anywhere between 40 and 50% on their Opex. Why? They can roll out applications much faster. They can make changes to their network much faster. I mean that's the benefit of virtualization and NSX as well, right? Instead of having this decisions of sending a network engineer to a closet to do CLI, down in the dirt as you would say, and reconfigure the switch, a lot of that now has been attracted to a software lever, and getting the company much more capability to make the changes across the fabric, or to segregate it using NSX micro segmentation to make the changes to those users or to that particular environment that needs those changes. So, just the incredible amount of flexibility. I think SDN let's say six, seven years ago, everyone thought it was going to be CapEx. You know, cheaper hardware, cheaper ASICs, et cetera. It's all about Opex. It's around flexibility, agility, common tool sets, better configuration, faster automation. >> So we all have this nirvana idea that we can take our traditional stacks, whether it's pre-packaged CI configurations that's pre-engineered, HCI, SDN, disaggregated networking. Add to that a software layer this magical automation. Can you unpack that for us a little bit? What are you seeing practically whether it's in the server provider perspective or on the enterprise. What are those crucial relationships that Dell EMC is forming with the software industry to bring forth that automation? >> Well obviously we have a very strong relationship with VMware. >> Keith: Right. >> And so you have vRealize and vROps and so forth, and in fact in the new VxBlock 1000, you're going to see a lot of us gearings, a lot of our development towards the vRealize suite, so that helps those customers that are in a VMware environment. We also have a very strong relationship with Red Hat and OpenStack, where we've seen very successful implementations in the service provider space. Those that want to go a little bit more, a little bit more disaggregated, a little bit more open, even it from the storage participation like SAP and so forth. But then obviously we're doing a lot of work with Ansible, Chef, and Puppet, for those that are looking for more of a common open source set of tools across server, compute, networking storage and so forth. So I think the real benefit is kind of looking at it at that 25,000-foot view on how we want to automate. Do you want to go towards containers, do you want to go traditional? What are the tool sets that you've been using in your compute environment, and can those be brought down to the entire stack? >> All right, well Tom Burns, really appreciate catching up with you. I know Keith will be spending a little time at Interop this week too. I know, I'm excited that we have a lot more networking here at this end of the strip also this week. >> Appreciate it. Listen to Pat's talk this afternoon. I think we're going to be hearing even more about Dell Technology's networking. >> All right. Tom Burns, SVP of Networking and Solutions at Dell EMC. I'm Stu Miniman and this is Keith Townsend. Thanks for watching The Cube. (upbeat music)

>> Announcer: From Denver, Colorado, it's theCUBE, covering Super Computing 17. Brought to you by Intel. (soft electronic music) Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're gettin' towards the end of the day here at Super Computing 2017 in Denver, Colorado. 12,000 people talkin' really about the outer limits of what you can do with compute power and lookin' out into the universe and black holes and all kinds of exciting stuff. We're kind of bringin' it back, right? We're all about democratization of technology for people to solve real problems. We're really excited to have our last guest of the day, bringin' the energy, Armughan Ahmad. He's SVP and GM, Hybrid Cloud and Ready Solutions for Dell EMC, and a many-time CUBE alumni. Armughan, great to see you. >> Yeah, good to see you, Jeff. So, first off, just impressions of the show. 12,000 people, we had no idea. We've never been to this show before. This is great. >> This is a show that has been around. If you know the history of the show, this was an IEEE engineering show, that actually turned into high-performance computing around research-based analytics and other things that came out of it. But, it's just grown. We're seeing now, yesterday the super computing top petaflops were released here. So, it's fascinating. You have some of the brightest minds in the world that actually come to this event. 12,000 of them. >> Yeah, and Dell EMC is here in force, so a lot of announcements, a lot of excitement. What are you guys excited about participating in this type of show? >> Yeah, Jeff, so when we come to an event like this, HBC-- We know that HBC is also evolved from your traditional HBC, which was around modeling and simulation, and how it started from engineering to then clusters. It's now evolving more towards machine learning, deep learning, and artificial intelligence. So, what we announced here-- Yesterday, our press release went out. It was really related to how our strategy of advancing HBC, but also democratizing HBC's working. So, on the advancing, on the HBC side, the top 500 super computing list came out. We're powering some of the top 500 of those. One big one is TAC, which is Texas Institute out of UT, University of Texas. They now have, I believe, the number 12 spot in the top 500 super computers in the world, running an 8.2 petaflops off computing. >> So, a lot of zeros. I have no idea what a petaflop is. >> It's very, very big. It's very big. It's available for machine learning, but also eventually going to be available for deep learning. But, more importantly, we're also moving towards democratizing HBC because we feel that democratizing is also very important, where HBC should not only be for the research and the academia, but it should also be focused towards the manufacturing customers, the financial customers, our commercial customers, so that they can actually take the complexity of HBC out, and that's where our-- We call it our HBC 2.0 strategy, off learning from the advancements that we continue to drive, to then also democratizing it for our customers. >> It's interesting, I think, back to the old days of Intel microprocessors getting better and better and better, and you had Spark and you had Silicon Graphics, and these things that were way better. This huge differentiation. But, the Intel I32 just kept pluggin' along and it really begs the question, where is the distinction now? You have huge clusters of computers you can put together with virtualization. Where is the difference between just a really big cluster and HBC and super computing? >> So, I think, if you look at HBC, HBC is also evolving, so let's look at the customer view, right? So, the other part of our announcement here was artificial intelligence, which is really, what is artificial intelligence? It's, if you look at a customer retailer, a retailer has-- They start with data, for example. You buy beer and chips at J's Retailer, for example. You come in and do that, you usually used to run a SEQUEL database or you used to run a RDBMS database, and then that would basically tell you, these are the people who can purchase from me. You know their purchase history. But, then you evolved into BI, and then if that data got really, very large, you then had an HBC cluster, would which basically analyze a lot of that data for you, and show you trends and things. That would then tell you, you know what, these are my customers, this is how many times they are frequent. But, now it's moving more towards machine learning and deep learning as well. So, as the data gets larger and larger, we're seeing datas becoming larger, not just by social media, but your traditional computational frameworks, your traditional applications and others. We're finding that data is also growing at the edge, so by 2020, about 20 billion devices are going to wake up at the edge and start generating data. So, now, Internet data is going to look very small over the next three, four years, as the edge data comes up. So, you actually need to now start thinking of machine learning and deep learning a lot more. So, you asked the question, how do you see that evolving? So, you see an RDBMS traditional SQL evolving to BI. BI then evolves into either an HBC or hadoop. Then, from HBC and hadoop, what do you do next? What you do next is you start to now feed predictive analytics into machine learning kind of solutions, and then once those predictive analytics are there, then you really, truly start thinking about the full deep learning frameworks. >> Right, well and clearly like the data in motion. I think it's funny, we used to make decisions on a sample of data in the past. Now, we have the opportunity to take all the data in real time and make those decisions with Kafka and Spark and Flink and all these crazy systems that are comin' to play. Makes Hadoop look ancient, tired, and yesterday, right? But, it's still valid, right? >> A lot of customers are still paying. Customers are using it, and that's where we feel we need to simplify the complex for our customers. That's why we announced our Machine Learning Ready Bundle and our Deep Learning Ready Bundle. We announced it with Intel and Nvidia together, because we feel like our customers either go to the GPU route, which is your accelerator's route. We announced-- You were talking to Ravi, from our server team, earlier, where he talked about the C4140, which has the quad GPU power, and it's perfect for deep learning. But, with Intel, we've also worked on the same, where we worked on the AI software with Intel. Why are we doing all of this? We're saying that if you thought that RDBMS was difficult, and if you thought that building a hadoop cluster or HBC was a little challenging and time consuming, as the customers move to machine learning and deep learning, you now have to think about the whole stack. So, let me explain the stack to you. You think of a compute storage and network stack, then you think of-- The whole eternity. Yeah, that's right, the whole eternity of our data center. Then you talk about our-- These frameworks, like Theano, Caffe, TensorFlow, right? These are new frameworks. They are machine learning and deep learning frameworks. They're open source and others. Then you go to libraries. Then you go to accelerators, which accelerators you choose, then you go to your operating systems. Now, you haven't even talked about your use case. Retail use case or genomic sequencing use case. All you're trying to do is now figure out TensorFlow works with this accelerator or does not work with this accelerator. Or, does Caffe and Theano work with this operating system or not? And, that is a complexity that is way more complex. So, that's where we felt that we really needed to launch these new solutions, and we prelaunched them here at Super Computing, because we feel the evolution of HBC towards AI is happening. We're going to start shipping these Ready Bundles for machine learning and deep learning in first half of 2018. >> So, that's what the Ready Solutions are? You're basically putting the solution together for the client, then they can start-- You work together to build the application to fix whatever it is they're trying to do. >> That's exactly it. But, not just fix it. It's an outcome. So, I'm going to go back to the retailer. So, if you are the CEO of the biggest retailer and you are saying, hey, I just don't want to know who buys from me, I want to now do predictive analytics, which is who buys chips and beer, but who can I sell more things to, right? So, you now start thinking about demographic data. You start thinking about payroll data and other datas that surround-- You start feeding that data into it, so your machine now starts to learn a lot more of those frameworks, and then can actually give you predictive analytics. But, imagine a day where you actually-- The machine or the deep learning AI actually tells you that it's not just who you want to sell chips and beer to, it's who's going to buy the 4k TV? You're makin' a lot of presumptions. Well, there you go, and the 4k-- But, I'm glad you're doin' the 4k TV. So, that's important, right? That is where our customers need to understand how predictive analytics are going to move towards cognitive analytics. So, this is complex but we're trying to make that complex simple with these Ready Solutions from machine learning and deep learning. >> So, I want to just get your take on-- You've kind of talked about these three things a couple times, how you delineate between AI, machine learning, and deep learning. >> So, as I said, there is an evolution. I don't think a customer can achieve artificial intelligence unless they go through the whole crawl walk around space. There's no shortcuts there, right? What do you do? So, if you think about, Mastercard is a great customer of ours. They do an incredible amount of transactions per day, (laughs) as you can think, right? In millions. They want to do facial recognitions at kiosks, or they're looking at different policies based on your buying behavior-- That, hey, Jeff doesn't buy $20,000 Rolexes every year. Maybe once every week, you know, (laughs) it just depends how your mood is. I was in the Emirates. Exactly, you were in Dubai (laughs). Then, you think about his credit card is being used where? And, based on your behaviors that's important. Now, think about, even for Mastercard, they have traditional RDBMS databases. They went to BI. They have high-performance computing clusters. Then, they developed the hadoop cluster. So, what we did with them, we said okay. All that is good. That data that has been generated for you through customers and through internal IT organizations, those things are all very important. But, at the same time, now you need to start going through this data and start analyzing this data for predictive analytics. So, they had 1.2 million policies, for example, that they had to crunch. Now, think about 1.2 million policies that they had to say-- In which they had to take decisions on. That they had to take decisions on. One of the policies could be, hey, does Jeff go to Dubai to buy a Rolex or not? Or, does Jeff do these other patterns, or is Armughan taking his card and having a field day with it? So, those are policies that they feed into machine learning frameworks, and then machine learning actually gives you patterns that they can now see what your behavior is. Then, based on that, eventually deep learning is when they move to next. Deep learning now not only you actually talk about your behavior patterns on the credit card, but your entire other life data starts to-- Starts to also come into that. Then, now, you're actually talking about something before, that's for catching a fraud, you can actually be a lot more predictive about it and cognitive about it. So, that's where we feel that our Ready Solutions around machine learning and deep learning are really geared towards, so taking HBC to then democratizing it, advancing it, and then now helping our customers move towards machine learning and deep learning, 'cause these buzzwords of AIs are out there. If you're a financial institution and you're trying to figure out, who is that customer who's going to buy the next mortgage from you? Or, who are you going to lend to next? You want the machine and others to tell you this, not to take over your life, but to actually help you make these decisions so that your bottom line can go up along with your top line. Revenue and margins are important to every customer. >> It's amazing on the credit card example, because people get so pissed if there's a false positive. With the amount of effort that they've put into keep you from making fraudulent transactions, and if your credit card ever gets denied, people go bananas, right? The behavior just is amazing. But, I want to ask you-- We're comin' to the end of 2017, which is hard to believe. Things are rolling at Dell EMC. Michael Dell, ever since he took that thing private, you could see the sparkle in his eye. We got him on a CUBE interview a few years back. A year from now, 2018. What are we going to talk about? What are your top priorities for 2018? >> So, number one, Michael continues to talk about that our vision is advancing human progress through technology, right? That's our vision. We want to get there. But, at the same time we know that we have to drive IT transformation, we have to drive workforce transformation, we have to drive digital transformation, and we have to drive security transformation. All those things are important because lots of customers-- I mean, Jeff, do you know like 75% of the S&P 500 companies will not exist by 2027 because they're either not going to be able to make that shift from Blockbuster to Netflix, or Uber taxi-- It's happened to our friends at GE over the last little while. >> You can think about any customer-- That's what Michael did. Michael actually disrupted Dell with Dell technologies and the acquisition of EMC and Pivotal and VMWare. In a year from now, our strategy is really about edge to core to the cloud. We think the world is going to be all three, because the rise of 20 billion devices at the edge is going to require new computational frameworks. But, at the same time, people are going to bring them into the core, and then cloud will still exist. But, a lot of times-- Let me ask you, if you were driving an autonomous vehicle, do you want that data-- I'm an Edge guy. I know where you're going with this. It's not going to go, right? You want it at the edge, because data gravity is important. That's where we're going, so it's going to be huge. We feel data gravity is going to be big. We think core is going to be big. We think cloud's going to be big. And we really want to play in all three of those areas. >> That's when the speed of light is just too damn slow, in the car example. You don't want to send it to the data center and back. You don't want to send it to the data center, you want those decisions to be made at the edge. Your manufacturing floor needs to make the decision at the edge as well. You don't want a lot of that data going back to the cloud. All right, Armughan, thanks for bringing the energy to wrap up our day, and it's great to see you as always. Always good to see you guys, thank you. >> All right, this is Armughan, I'm Jeff Frick. You're watching theCUBE from Super Computing Summit 2017. Thanks for watching. We'll see you next time. (soft electronic music)

>> Announcer: Live from Las Vegas, it's theCUBE. Covering VMworld 2017, brought to you by VMware, and it's Ecosystem partners. >> Welcome back to VMworld 2017 in Las Vegas. We're at the Mandalay Bay Convention Center. My name is Dave Vellante, and I'm here with my co-host, Peter Burris. Mike Arterbury is here, he's the Vice President of Technology Alliances at DELLEMC, Mike, welcome to theCUBE. >> Dave, thanks. Peter, great to see you guys again. >> The Ecosystem is absolutely exploding. VMware is on fire, the data center is on fire, you know, the technology business is just, as Pat and Michael were saying this morning, this is going to be the most boring time ever, relative to the future. >> Right. >> What's happening in the Ecosystem, bring us up to date. >> Well I think as Michael and Pat have talked about this week, pretty proficiently, is the fact that technology transitions are all out in front of us right? It's a new world, the opportunity for both On-prem and Off-prem infrastructure is all out in front of the companies, the family of companies, and we're putting together some of the best offers, the best combinations of technology. The DELLEMC infrastructure, VMware, Infrastructure Management, and a host of ISVs that, whose workloads we combined with all of our infrastructure and platform technology, to drive customer outcomes. >> It, it-- >> Well actually, let me jump in on this real quick if I may. So one of the things about Ecosystems, is that there's a danger in the metaphor of the Ecosystem, because an Ecosystem evolves in response to a number of things. But Ecosystems in the Tech industry require care and feeding. They have to be set up, they have to, contracts have to be written, programs have to be put in place. This is really, really hard work, and it's undervalued by customers too much. So tell us, talk to us a little bit about the work that goes into forming an Ecosystem, sustaining an Ecosystem, and then creating new degrees of freedom, in some of these partnerships, as these new problems emerge, and these new types of complexities, and these interesting problems, get to be solved. >> Well, so I'll start at the start, which is, we've got the vestige of two great partnering companies, Dell classically, and EMC classically. EMC classically partnered much more deeply at a technology level, and Dell partnered at a go-to-market level right? They could monetize in a high-velocity way, these partnerships. When you put the two companies together, you get both the monetization and the technology, the deep technical alignment between the partner, and Dell, but you can start at the very simplest instantiation of a solution. A vSAN Ready Node, or a VxRail, a hyper-converged appliance. Basically we take VMware, goodness, in their storage, software-defined storage offer, and we combine that with a very tightly configured set of offers from our PowerEdge Server lineup, and our VxRail lineup, and we test the configurations, and we test them and tighten them, and test them and tighten them, so that we can give customers a very prescriptive understanding of what their outcome's going to be, when they deploy a hyper-converged offer, running on DELLEMC infrastructure. >> So for what that means, if I can, sorry Dave, what that means is that in your... So the partnership then becomes measured, not just in terms of whether the logos are together, but whether or not it's been engineered together. Whether it's been tested together-- >> Tested together, validated, you bet. >> Whether it's court regimes are put in place, and that's different from how we used to think about partnerships. >> Well, and that's a critical point, and Andy Jassy on stage this week, basically said, "Hey, this is not a Barney deal," Barney being I love you, you love me, let's do a press release. It's got substantive engineering going on, and so that's really what differentiates a core partnership that has teeth, versus one that's just what we call a Barney deal. But I wanted to ask you to go back to the sort of different cultural nuances that you mentioned, Dell, high-volume, high-velocity, EMC, very high-touch, bring those two worlds together. Are there inherent conflicts there, or were you able to, are you in the process of sort of re-engineering how you form those partnerships? >> You know, interestingly enough, in the 2000s, I managed the partnership between Dell and EMC from the Dell side, and we created a lot of good customer outcomes, by combining their storage platforms, our go-to-market prowess but, but it was all learnings that we could transform the business with, when we actually did a hard-core marriage between the two companies. So I would tell you then, it was two companies trying to play nice together. Now, it's one company, and we're playing really effectively together so-- >> Well, I tell ya something there, I talked to Chad about this at a show recently, and asked Michael Dell about it as well. If you look back, that was an epic partnership that you entered, and the outcomes were tremendous, and I argued that in fact, if Michael had had a mulligan, that he would've just bought EMC sooner, and drive the, drove that integration sooner, he essentially said, "Yeah I wish I could've "Bought EMC sooner." But I think that to your point, you had, you know a partnership, and now that you're one company, you can really drive some outcomes that you couldn't through you know, smaller tuck-in acquisitions are you seeing that? >> Absolutely, so what we have today, because of the combination, because of the marriage, is one portfolio. Compute, storage, networking, combined with the goodness from our family of offers, whether it's VMware or Pivotal, you heard a great announcement about Pivotal today, and what they're doing with Kubernetes. So we're going to be able to combine all of those things, the breadth of our portfolio, in a way that we never could before when it was a partnership based on siloed offerings. Now we can really build solutions, and we've got a whole family of products, which we call Ready Solutions, that combine cross business unit portfolios, and our family of products as well. >> So can you talk about the scope of some of the technology partnerships, maybe get specific on, on some of the ones that you're exited about, I mean, I know you're excited about them all but-- >> Sure, sure-- >> In the time we have maybe you could address it-- >> So principally, I would start with VMware. While VMware is a family member of ours, we still manage the relationship much like we would a partnership, where you have to play team ball, and drive your technologies together, and test them, and ruggedize them. But once you get past the infrastructural software of virtualization, customers don't stop at virtualization, they run workloads on that virtual infrastructure, so you look at SAP, and what we're doing with Hanna, and IoT, and Leonardo. You look at what we're doing frankly, with Microsoft, what we're doing with some of the public Cloud providers, in connecting both our infrastructure on premises, with the capabilities of the Public Cloud, in a way that leverages the most appropriate Cloud to run a particular piece of software, and a particular workload in. Those workloads are going to gravitate towards the best usage model for a customer, but we want to have a full compliment of offering so that we can offer the right Cloud for the right customer at the right time, and the right price. >> So I got two quick questions for you, and one draws off of what you just said, and that is the, I really like that notion of technology partnership, and go-to-market partnership, and the expertise at EMC, and the expertise at Dell. Customers want invention, which is the engineering element, the technology partnership. But they also want the innovation side, which is, it's been applied to my business, and I'm adopting it, and it's creating business value for me, and I'm finding, are you finding, that as Dell broadens it's, or DELLEMC broadens this notion that even the technology partnerships are becoming informed by the innovation, or the go-to-market partnerships, so that it's making the technology side that much more successful? >> Absolutely, so we want, every day in the hardware business you have to fight commoditization, you fight that by simply adding value right? It could be business model value, it could be technology value. We add innovation everywhere we can. So those combinations both of our technology, and our partners technology, but in a way that doesn't just combine it, it doesn't make it any easier to buy, it makes it easier to operate. It makes it easier to understand, it makes it easier for a seller to sell it, and a customer to buy it and consume it. So you'll hear Chad talk about an easy button a lot. That is our mission in solutions, is to combine those things in a way that makes it easy for everyone. >> So here's the second question I have. And it's going to be a challenge out to you, because increasingly as companies become more digital businesses, and recognize the role that these technologies play in driving their business models and their go forward, they are struggling with this question of partnership. They are still driving with procurement, driving with taking cost out, et cetera, when in fact, they have to find ways to drive with partnership and strategy, and whatnot. What can DELLEMC do to train this industry about how to do partnership better? >> Well, I think you demonstrate the value that you create for a customer, for a partner, for a seller in these combinations. If you can show that you create real value through your innovation, through your great partnering, then that's value that any one of those constituents can align to. You create value, you let them harvest that value, and they will come back to you again, and again, and again. >> So we have to go Mike, but last question is, how do you see, or do you see your Ecosystem of technology partners sort of reforming, not only to the new DELLEMC, but also to this new Cloud reality, that I'm not going to put everything in the Cloud, I'm going to bring the Cloud model to my data? >> Right, I think VMware's going to play a pivotal role in that right, because they are the-- >> Unintended right? >> They are unintended. They are the kings of workload management today, and what customers really want, from the Public Cloud, or the Private Cloud, is a way to move those virtual machines around in a very seamless way. So at the end of the day, it doesn't really matter where you operate that workload, you're going to operate it in the location that it best serves your mission as a customer. And so I think they're going to play a very instrumental role in how we do that going forward. >> Mike Arterbury, thanks very much for coming to theCUBE. But really Peter, to your point, this is hard work, and customers generally undervalue it, but they expect it, and it adds a lot of value, so thanks very much for sharing your perspectives. >> Thank you guys. >> Your welcome, alright keep it right there everybody, we're going wall to wall, this is day two, two sets here at SiliconANGLE, theCUBE, and WikiBond. We'll be back, right after this short break. (alternative music)