 from Washington D.C., it's theCUBE covering .next conference. Brought to you by Nutanix. Welcome back to the district everybody. This is Nutanix, next conf, hashtag next conf. And this is theCUBE, the leader in live tech coverage. Stephanie Sheros is here, she's the vice president of IBM Power Systems offering management. And she's joined by Raja Mukapadhyay, who is the VP of product management at Nutanix. Great to see you guys again. Thanks for coming on. Thank you. Thanks for having us. So Stephanie, you're welcome. So Stephanie, I'm excited about you guys getting into this whole hyper-converged space, but I'm also excited about the Cognitive Systems Group. It's kind of a new play on power. Give us the update on what's going on. Yeah, so we've been through some interesting changes here. We, IBM Power Systems, while we still maintain that branding around our architecture, from a division standpoint, we're now IBM Cognitive Systems. We've been through a change in leadership. We have now Senior Vice President Bob Picciano leading IBM Cognitive Systems, which is foundationally built upon the technology that comes from power systems. So our portfolio remains IBM Power Systems, but really what it means is we've set our sights on how to take our technology into really those cognitive workloads. It's a focus on clients going to the Cognitive Era and driving their business into the Cognitive Era. It's changed everything we do from how we deliver and pull together our offerings. We have offerings like Power AI, which is an offering built upon a differentiated accelerated product with power technology inside, has NVIDIA GPUs, it has NVLink capability, and we have all the optimized frameworks. So you have Cafe, Torch, TensorFlow, Chain, or Theano. All of those are optimized for the server, downloadable, right, in a binary. So it's really about how do we bring ease of use for cognitive workloads and allow clients to work in machine learning and deep learning. So, Roger, again, part of the reason I'm so excited is IBM has a $15 billion analytics business. You guys talked to the analysts this morning about one of the next waves of workloads, is this data-oriented AI machine learning workload? IBM obviously has a lot of experience in that space. How did this relationship come together and let's talk about what it brings to customers? It's all customer-driven, right? So our customers, they told us that, look, Nutanix, we have used your software to bring really unprecedented levels of agility and simplicity to our data center infrastructure. But they run a certain set of workloads on sort of non-IBM platforms, but a lot of mission-critical applications, a lot of the cognitive applications, they want to leverage IBM for that, and they said, look, can we get the same Nutanix one-click simplicity all across my data center? And that is the promise that we see. Can we bring all of the age-v goodness that abstracts the underlying platform, no matter whether you're running on x86 or your cognitive applications or your mission-critical applications on IBM Power, it's a fantastic thing for a joint customer. So, Stevi, come on. Couldn't you reach somewhere into the IBM portfolio and sort of pull out a hyper-converged solution? Why Nutanix? Why, it's love it. Look what the hyper-converged market is doing. It's growing at incredible rates, and clients love Nutanix, right? We see incredible repurchases around Nutanix, clients buy three, next they buy 10. Those repurchases is a real sign that clients like the experience. Now, you can take that experience and under the same simplicity and elegance right of the prism platform for clients, you can pull in and choose the infrastructure that's best for your workload. So I look at a single prism experience, if I'm running a database, I can pull that on to a power-based offering. If I'm running a BDI, I can pull that on to an alternative, but I can now with a simplicity of action under prism, right, for clients who love that look and feel, pick the best infrastructure for their workload you're running, simply. That's the beauty of it. Right, Rajah. Nutanix has spread beyond the initial platform that you had, you used SuperMaker inside, you've got a few OEVs. This one was a little different. Can you bring us inside a little bit? What kind of engineering work had to happen here? And then I want to understand from a workload perspective, it used to be, okay, well, kind of general purpose, what do you want on power and what should you say isn't for power? Yeah, yeah. It's actually, I think a power to, it speaks to the power of our engineering teams that the level of abstraction that they were able to sort of imbue into our software, the transition from supporting X86 platform to making the leap onto power, it has not been a significant lift from an engineering standpoint. So because the right abstractions were put in from the get go, you know, literally within a matter of like mere months, something like six to eight months, we were able to have our software put it onto the IBM power platform. And that is kind of the promise that our customers saw that look, for the first time as they're going through a replatforming of the data center, they see the power in Nutanix as software to abstract all these different platforms. Now in terms of the applications that they are hoping to run, I think we are at the cost of a big transition. If you look at enterprise applications, you could have framed them as systems of record and systems of engagement. If you look forward the next 10 years, we'll see this big shift and this new class of applications around systems of intelligence. And that is what a lot of So say it again systems of intelligence, right? And that is where a lot of like IBM power platform and the things that the power architecture provides, you know, things around better GPU capabilities, it's going to drive those applications. So our, you know, customers are thinking of running both the classical mission critical applications that IBM is known for, but as well as the more sort of, you know, forward leaning cognitive and data analytics driven applications. So Stephanie, in one hand, I look at this just as an extension of what IBM has done for years with Linux, but why is it more, what's it going to accelerate from your customers and what applications that they want to deploy? So for us, one of the additional reasons why Nutanix was key to us is they support the Acropolis platform, which is KVM based. Very much supports our focus on being open around our playing in the Linux space, playing in the KVM space supporting open. So now as you've seen throughout, since we launched Power 8 back in early 2014, we went little lendy and we've been very focused on getting a strategic set of ISVs ported to the platform, right, Portnworks, MongoDB, EnterpriseDB. Now it's about being able to take the value propositions that we have and, you know, we're pretty bullish on our value propositions. We have a two X price performance guarantee on MongoDB that runs better on power than it runs on the alternative competition. So we're pretty bullish. Now for clients who have taken a stance that their data center will be a hyper-converged data center because they like the simplicity of it, now they can pull in that value in a seamless way. To me, it's really all about compatibility. Pick the best architecture and all compatible within your data center. So you talked about six to eight months you were able to do the integration. Was that open power that allowed you to do that? Was it little endian, you know, advancements? I think it was a combination of both, right? We have done a lot from our Linux side to be compatible within the broad Linux ecosystem, particularly around KVM. That was critical for this integration into Acropolis. So we've done a lot from the bottoms up to be, you know, Linux is Linux is Linux. And just as Rajah said, right? They've done a lot in their platform to be able to abstract from the underlying and provide a seamless experience that, you know, I think you guys use the term invisible infrastructure, right? The experience to a client is simple, right? And in a simple way, pick the best, right? For the workload I run. You talked about systems of intelligence. Bob Pitchell, a lot of times we talk about the insight economy. And so we're, you know, you're right. We have the systems of record, systems of engagement, systems of intelligence. Let's talk about those workloads a little bit. I infer from that, that you're essentially basically affecting outcomes while the transaction is occurring. Maybe it's bringing transactions and analytics together and doing so in a fashion that maybe humans aren't as involved. Maybe they're not involved at all. What do you mean by systems of intelligence and how do your joint solutions address those? Yeah, so, you know, one way to look at it is, I mean, so far if you look at how sort of decisions are made and insights are gathered, it's we look at data and between a combination of mostly, you know, we try to get structured data and then we try to draw inferences from it. And mostly it's human beings drawing the inferences. If you look at the promise of technologies like machine learning and deep learning, it is precisely that you can throw unstructured data where no patterns are obvious and software will find patterns therein. And what we mean by systems of intelligence is imagine you're going through your business and literally hundreds of terabytes of your transactional data is flowing through a system. The software would be able to come up with insights that would be very hard for human beings to otherwise kind of, you know, input, right? So that's one dimension and it speaks to kind of the fact that there needs to be a more real time aspect to that sort of system. Is part of your strategy to drive specific solutions, I mean, integrating certain IBM software on power or are you sort of stepping back and say, okay, customers, do whatever you want? Let me talk about that. No, we're very keen to take this up to a solution value level, right? We have architected our ISV strategy, we have architected our software strategy for this space, right? It is all around the cognitive workloads that we're focused on. But it's about not just being a platform and an infrastructure platform, it's about being able to bring that solution level above and target it. So when a client runs that workload, they know this is the infrastructure they should put it on. Great, what's the impact on the go to market then for that offering? So from a solutions level or when the application? You know, it's more complicated than the traditional, okay, here's your platform for infrastructure. You know, what channel, maybe it's a question for Raja, but yeah. Yeah, sure, so clearly, you know, the product would be sold by, you know, the community of Nutanix and channel partners always as IBM's channel partner, right? So, and you know, we'll both make the appropriate investments to make sure that the, you know, the daughter, you know, channel community is enabled around how they essentially talk about the value proposition of the solution in front of a joint customer. All right, we have to leave them there, Stephanie and Raja, thanks so much for coming back to theCUBE. It's great to see you guys. Thank you. Thank you. All right, keep it right there, buddy. We'll be back with our next guest, we're live from DC, Nutanix dot next. Right back.