>> 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.

(upbeat 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. (soft electronic music) >> And welcome back here on The Cube which, of course, is the flagship broadcast of Silicon Angle TV. Proud to be here at Dell Technologies World 2018. We've been live Monday, now today Tuesday, back live again tomorrow. Hope you join us for all three days of coverage. Along with Keith Townsend, I'm John Walls. We're joined by Jim Franklin who's the director of solution management at Dell EMC. Jim, good to see you this afternoon. >> Hey, nice to see you as well. >> How's the show been for you so far? >> Fantastic, and there's always a lot of energy at Dell World. It's always exciting to be around, see our partners, our customers, hear our executives speak, gives us some clarity on what we're doing at my level. [Men Laugh] So it's a fun time, it's energetic, it's Vegas. >> Get's ya. >> Good combination right? >> Yeah, so. (laughs) Get you energized. >> So before we jump in, what are you hearing from customers now? Because we've been talking to a lot of folks in your shoes at Dell and just kind of curious what are people bending your ear about? What are they most curious about? >> Yeah, so a lot of our customers and our partners are interested in, I'll call them hot trends. So what from my perspective are we seeing, where are there problems? So for that, things like how do I continue to try and outpace the data that keeps coming, because like death and taxes, the data keeps growing and growing and growing so they're looking at it going, how do I start to consume all this data? Can you help out? Hey but what about this cloud and how do I make the cloud a reality? Several of them haven't actually even started on a cloud strategy, so they're saying, hey what's the best way to look at that? And then they're looking at it saying, if they're the infrastructure guy or if they're the backup administrator, they're saying, how do I actually flip my economic model from a cost model to a profit model? So these are the sorts of conversations we're seeing, not only with our customers, but our partners are trying to help them out as well. >> So take us back. Let's go to the most simple or at least maybe the most elementary stage and say they're not even thinking of a cloud strategy yet or they're just now embarking on that. >> Jim: Just sniffing out. >> Yeah, walk us through that. What do you do because you would think by now obviously, their awareness is viable. We should be there, but they don't know where to go. >> So most customers know that this is now trusted technology, a trusted operating model. The problem has to be is how do you actually get there? What does that journey look like and what choices do I have? So even those early adopters that jumped out to public cloud for sort of a quick fix, we see them especially for my area, which is business critical applications, SAP Oracle, Splunk, they're coming back to an on-premise cloud for reasons like being able to recover out of that or this now they've discovered is their intellectual property and there's a little bit of reluctance just to go send that out to sort of an unknown place, so we see a lot of customers that are not bringing it back, but they now learn how that economic model can work, so they're trying to go in with sort of a cloud mentality. So still do the operational, the show back, the charge back, but maybe bring that in house, so you're more comfortable with it, so you can innovate on that. >> So as we're talking about these traditional, mission-critical apps, SAP, Splunk, Oracle Suite, these applications that are very rigid. The cash register, SAP, the cash register of the world. We don't want to change, you get the product guy. He's like, hey we want access to the mission-critical data. We want to be able to change it on the fly. You have the SAP guys going back and saying, no, no, no. >> Jim: Wait a minute, yeah. >> Wait a minute, we'll give you N+1 environment to develop in and then you prove to us, but it takes nine months to get an N+1 environment so you can do the development. How is Dell EMC, Dell Technologies, helping solve that agility problem for these legacy applications? >> So the first thing that we have to do, if you're going to keep it on premise is we advise our customers, modernize the infrastructure, because a lot of times you'll come up on a server or a storage refresh, right? This is the plumbing, right? This is underneath the guts of the house. It's not exactly attractive stuff, so if you can actually move to speed based technologies, things like Flash, right, fantastic technology. If you can virtualize it, if you can start to consider scale out and scale up technologies that are ready to go. Software Define has been a boon for these things. SAP is now adopting this like Software Define. That's fantastic for our folks, I guess. You guys know the advantages of Software Define. It can spin up, spin out, scale up, scale, in a much more pragmatic, quicker way. So these are sort, see now we're entering into things like VX Rack, VX Rail, and they have the resiliency, the stability, the scale in order to support these applications. They're built now solid enough that you can trust them to run, so now you get those operational efficiencies, you get that ability to scale, you get the performance, and you get it at a little bit better price point as well, so I think that's where customers are starting to be less reluctant to move those big humongous SAP, Oracle workloads, because it can be trusted. It's now that technology's aged enough and is resilient enough, then now customers are doing it and they're doing it quite rapidly. >> So step two of this is once I get some agility, what I thought was, traditional rational, you know what, Dell should never move SAP to the cloud, because it's static, it doesn't change, and it's costly. Well I now have these use cases where I'm spinning up N+1s all the time and I'm bringing them down. That's elastic. That sounds like the cloud. How do you help make that transition? >> So SAP actually, as one of the trigger points is this move to HANA, the memory database. And the economic model was, it's a little pricey, that software, right? So SAP has actually gone in with a cloud-first mentality. So they've actually helped us out here. They've promoted them as, so HANA enterprise cloud, for instance, is a way for you to get in on HANA at a price point that's a little better, the subscription based model. And you can start to migrate some, like a BW app, something a little smaller. Remember back in the days when we first virtualized? You wouldn't virtualize your mission-critical app right off the bat. You picked something small that you could eat. We don't eat our meal one big hunk at a time, right, we eat little bites of it, so we're doing the same thing with-- >> Keith: Unless you have four brothers. >> What's that? Unless-- (laughs) >> You have four brothers. >> You eat quickly. >> You use those. >> You do it all, right. >> Or you get real quick with your elbows. So we advise our customers, take a small BW app that you got on Oracle right now, flop it over there, put it in the cloud. You'll be able to cost-justify this much, much better and then with the work on tangible use cases, start to pull in more data-rich, hydrate that really fast, awesome analytics engine, and start to use it for the power of good. It's a super hero. It's a super hero technology, so we want to invoke it. We want to bring it alive. We want to apply it towards new innovations and that's what our customers are doing now. Financial services, health care, the retail market. So now our customers are starting to say, hey how can I apply this super awesome, super hero technology to my retail space. How can I inflate my tires 5 PSI more so I save my company 10 million dollars? So these, all these use cases now are coming. Now I call this, my personal thing, I call it now cool IT. We're no longer in the trenches doing the plumbing for SAP, we're now moving on to cool IT where we can start to do data analytics, we can start to apply use cases, start to ingest more data, maybe that oil rig out there in the gulf, I can start to pull in more of that data, I can start to do analytics on it. I can start to show the business that I'm meaningful, that I am a profit center, I know what's going on. >> Yeah, what's from the big jump there in terms of opening people's eyes, opening a company's eyes to how rich that data is for them and how applicable it is and how actionable it is, because that's been one of the bugaboos, right? People were like, I got all this data, where there's treasure there. >> Jim: There is. >> You got to find it, you got to get there. >> Right, right. So that advancement, some of the technology, like HANA's a hardened database now, not hardened in terms of its access, but hardened in terms of the technology itself, so I can actually put more in it and ingest it. The other thing that's happened is we've moved out to the edge, things like the gateways and things like that. Now I can apply that technology, but I don't have to suck it all in. And we'll go back to the original point, the cloud has enabled a lot of this traffic, the data traffic to go out there and what we see our customers now doing is now they're able to actually quiesce the data and just, we always could do this, but it never came together in such a way that it was cohesive, that I could have universal translators of all this different data coming in and I could actually quiesce it. And now, to me, the part that always matters, the UI work, like I can actually visualize and then SAP, and Oracle, and all the, they can now make it visual. I think that's the key. So if I'm a CFO or I'm a CEO and I'm talking to my CIO and I don't need to talk about numbers. I can literally visualize the data on my screen, on my iPad or whatever device I have. That now, what we see with our eyes, is much more believable than what we hear with our ears. >> John: Absolutely. >> So you can see it. And that's, I think, that's the big differentiator I've seen is we don't do customer presentations anymore. We show them with their own data. So we used to do that design thinking way back in the day, but now you can actually apply that with the technology we have and I can visualize it. >> John: Seeing is believing, right? >> Immediately customers, you don't have to do a business justification. They see it. They see it right there in front of their own eyes. It's fantastic. >> So, talking about design theory or design approach, there has to be a point where industry-wide or even within your practice where you're at the 50, 60% of the solution for most customers and there's a customization point. Where are you guys at in that? Is it 50, 60, 70, 80% at that point? What-- >> Well that's what makes it fun for a guy like me, because in solutions we can validate, we can do performance optimization and that's, for the most part you're talking servers, network storage, stuff we've always done and we can optimize that to a large extent, but once you flip the script and you look from the application down, you can start to tune from that perspective, so we can get about 70, 80% of this well constructed. It's that last 20% where the customer's saying, hey I'm a financial services arm and I'm trying to catch the flashboys or the stock traders that are manipulating the market. Well that requires a new set of tools, right, a new set of approach to how to do this, how to analyze your data, how to introduce automation, so for us, the last mile, particularly with our SI partners, who are really good at doing this. SAP is really good at doing this design thinking session. We could sit down with a customer now, we could ask them where do they want to make money. How do you want to invest in IT so that your analytics is fully realized, your data is fully realized, and they have wonderful use cases. So now we're not talking about how does widget X work with application Y, we're talking about how do I apply this data in the direction of the use case you're trying to solve for and that's the last 20% or something like that. >> Is that where art meets science in a way? All of the sudden, like you said, you've got your 80%, this is the way it's going to be. >> Now, now. >> This stuff works. >> Now we're going to fine tune. >> Jim: Yeah. >> So there is some art maybe that comes into play there. >> There is. We found that it tends to be vertical specific and there is an art form to it, which is why our global system integrators are wonderful, because they're artists. We could go in with them and we could have that conversation. We could sit down for, you could even sit down just for a couple hours and pretty soon you're having a great conversation, understanding really what the customer's business is like and then targeting that particular use case and making it tangible. >> So that's pretty interesting. You say you sit down. Who exactly are you sitting down with, because traditionally Dell EMC, Dell Technologies, talked to the infrastructure group. You're talking about a completely different level. This sounds like application level folks, analysts, not the traditional Dell contact. >> Yeah, which makes us a little bit specialized. So you still want to sell to the back of the house, the infrastructure guys, the folks that are-- >> Keith: It's going to need a PowerMax. >> Right, and it's a completely different conversation though and I'll connect the two in just a minute, but we go in and we'll talk to the VP of applications, we'll talk to the DBA. These are the folks that actually, they're not worried about the widget, the disc behind it. We'll sell them a VMAX, or a PowerMax, excuse me, at the end of the day, but they're not so worried about that. They're worried about how do I get fiduciary responsibility out of this? How do I control my regulations? What do I do about data locality? How do I look at the pressure on that oil rig out in the Gulf of Mexico and make sure it's not going to burst? How do I proactively send out my maintenance man, not on every month, but when I know on the 5,000th open of that train door, that I need to proactively go do that, because at 5,000 open and closes, it's going to fail. We've done that with analytics. We know that. So for us, most of those conversations tend to be at the, you look for the DBA or the VP of applications or the CIO and in this way, this is the beauty of how this all, we're actually going in with the Rainmaker ISV. So we're going in with SAP, we're going in with Oracle, and now we combine what traditionally has been Dell, the infrastructure guys with SAP and we never used to call, we used to call six months detached from each other. Not anymore. Design thinking, IOT, use case, data analytics has brought us right together and we're in the glide path together now. It's a much different partnership now with those guys. >> Yeah, good recipe, right? >> It's fabulous. >> It really is. >> It's great, it's a fun time. >> Yeah I can tell, I can tell. And thank you for being with us. We appreciate the birds-eye view but as you said, this is kind of an exciting time, right? Because you're able to, you're transforming your business and other businesses at the same time. >> Jim: Yeah, best thing to do, yeah, love it. >> Very cool. Jim, thanks for being with us, appreciate your time. >> Yeah, appreciate it, thanks for having me. >> Joining us for Dell EMC. Back with more from Dell Technologies World 2018. We're live here in Las Vegas. (soft upbeat electronic music)