>> Live from San Francisco, it's theCUBE. Covering Google Cloud Next 2018. Brought to you by Google Cloud, and it's ecosystem partners. >> Hey, welcome back everyone. This is theCUBE live in San Francisco for coverage of Google Cloud Next 18, #GoogleCloudNext18 I'm John Furrier, Dave Vellante. Your next guest, Anthony Lye, Senior Vice President and General Manager of Cloud Data Services Business Unit at NetApp. Yes, Business Unit at NetApp, storage in the cloud. Anthony, welcome to theCUBE, Good to see you. >> Thank you very much. nice to see you guys again. >> Great to have you on, we have been, first of all, very complimentary of NetApp over the years. We've had some critical analysis, but one thing I will say that you guys were early on cloud. I remember talking to Tom Georgans years ago, >> Yup. >> You listened to the customers, and you saw cloud, and there was some work going on. Now, you're here at Google Cloud, you're in Amazon, kind of not conventional wisdom for a storage company selling boxes to be living in a cloud where there's serverless, and, some would argue, storageless soon. >> Well, you know-- >> How did this happen? How did this business unit happen? (mumbled speech) >> Well, I think George Kurian, our CEO, probably now about five years ago, I think saw that cloud computing had just too much, I think, going for it not for us to pay attention to it. And he took the top ten engineers at NetApp, and said, you know our flagship operating system ONTAP that runs on our engineered systems, he said, port it to Amazon. And so we spent time porting the operating system over directly to Amazon and today, now, it's a real business. Fully funded, staffed, growing, and you know to your point, you know, who'd have thought NetApp would be calling the cloud. Google chose us. >> Big announcement today, in the keynote-- >> Yup. >> Right >> Oh yeah. >> I mean it's-- >> Key partner >> Turns out that enterprises need enterprise level files, whether that's NFS or SMB, and we're the best in the business to do it. >> So talk about that a little more, because a lot of people get confused, and they say, well wait a minute, why do I need NetApp on Google Cloud or AWS? Why don't I just use whatever object store the cloud provider gives me? Explain that. >> So I think there's a number of use cases, certainly if you look at legacy, there's a lot of applications, databases, that need and demand file. And customers would rather not have to do all the work to translate them over to something like object. Now, you know, object is a very descriptive storage protocol, but it's not as fast as file. So, there are distinct advantages to file that I think the cloud companies have realized they need, to win the enterprise business, whether it's the lift-and-shift business, there's a lot of applications. If you look at oil and gas, all that seismic data is in a file in a volume. You look at CAD-CAM, all of those applications demand file. Oracle database runs incredibly fast on file, so file is certainly not to be discounted, and I think it's very much now a hot topic in public cloud. >> And there's more to this story than just running in the public cloud. THere's a whole business model around the economics, >> Yup. >> the pricings, can you explain that? >> The way we think about cloud is we think that we can build a business that's just in the cloud. We basically monetize a service, a set of services that we offer to our customers to help them manage their data, protect their data, secure their data, integrate and orchestrate their data. Whether it's on one cloud or many. Whether it's a combination of onprem and cloud. And we charge very, very simply based on capacity or API call. We provide a full service. And that's what I think the cloud has done is democratized and empowered many, many people to consume technology that, prior to these big public clouds, you'd have to go to IT and wait six months and get charged a lot of money. The clouds make everything instantly available. It's wonderful. >> You guys have a great history, and again we've been, not critical but complementary of NetApp. You listen to customers, got a very loyal customer base. No matter what the trend is against you, by the pundits, you guys persevere as a company. And it's been great to watch, classic Silicon Valley success story. But you got Solify, you got Flash, you've been doing some kicking the tires early in cloud, now you created a business unit out of it. As you listen to customers, you see DevOps, you see (mumble) Infrastructures go, massive amounts of new proliferation, there's going to be a renaissance in software development, it's coming very fast. You almost see it coming very, very fast. What are the use cases for NetApp in the cloud, what are some of the things that customers are talking to you about, what are the top use cases, and where do you think they're going to be? >> Yeah, yeah, yeah. Well, so people have been very ... in Google we've been in preview phase onboarding customers to test the system out, sort of flush water through the pipes. And we've been very lucky at Google, we've had really every use case that we wanted to test tested. At the low end, it can be as simple as just home directories shared across ... whether it's POSIX or Windows, people need access to those file systems and NetApp is the only company that offers that sort of dual protocol access. So we have home directories at the low end, all the way up to genome sequencing databases, big data, relational databases, data warehouses at the high end. And what's nice about our service is we have service level objectives. So we, for the first time, have actually put a performance guarantee on the volumes. And what's nice about that is the customer knows that that's something that we stand to. What's really nice is the customer can dial up or dial down, either the capacity that they want or the performance that they want. So they may say, Monday through Friday we want to run the volumes at this basic service level, and then over the weekend, through an API, we're going to crank them up and make them run at 128 MB/sec. So, we really are, I think, providing incredible value for all workload types. >> You just described what I consider chew software, defined strategy, programmable through an API, I mean that's something that is nuanced but dramatically simplified-- >> Oh, you know, I'm an application developer. >> I was going to say. >> And I can tell you the last thing application developers want to do is talk to IT. Second to last thing application developers want to do is mess around with UI's. So, you know, the cloud, where there are lots of pretty demos of Google Console, which is a very, very, I think, well written user interface. What we really want is the API. We want the code or application code to tell the cloud what to do and how to do it. And so, everything behind our cloud business is API first. >> The programmable aspect is critical. >> Yup. >> And this is where we're starting to see microservices >> Absolutely. >> Become interesting phenomenon. Because now you can have pure application developers, >> Yup. >> Never talking to anyone but other developers in collaboration space. They just collaborate, and they go play in open source communities, and they're-- >> Absolutely. >> Happy as a clam. >> We've now got NFS persisting in containers, so we've done ... we worked on a project called Trident. Which is an open source project and we contribute to that. On Google, you'll be able to mount file systems directly into containers. And persist storage now, with all the cool, new (mumble) things that Google brings. So, you know, the files are a very integral part, I think, of technology and strategy. And we seem to have, according to Google, the best one. What are the go-to-market aspects of your relationship with Google? Well that's the other thing I tell you I'm incredibly pleased with is Google sells our product. Google supports our product. Google bills the customers for our product. >> That's good. >> Google has kind of chosen us, and Google wants it to be part of Google. So, the experience is completely native to the console. We encapsulate all of the permissions, access control lists, it looks and feels exactly like any native Google service. >> And what's next now, obviously great relation with Google. You're almost embedded/operationalized with them. Congratulations. >> Thank you. >> What's next, what's going on, what's the agenda for you guys? >> For us it's really increased investment in two dimensions. I think the first dimension is now the roll-out. We've got a very aggressive schedule to roll this out to all the major Google data centers to support all their major regions. And that's probably a never ending task, cause Google ups its ante and increases its data centers, so that keeps us busy, making the service available. The second thing then is sort of integrating that service with more of our own services. And integrating our service into some of the other Google services like BigQuery, or Spanner, or obviously there's a huge opportunity for people to bring file based data into Google Cloud and take advantage of AI and ML. (overlapping voices) >> That's interesting, integration into Spanner, I mean you've pointed out, Anthony, that Oracle runs really well on file. You guys, decade ago or so, made that happen. We had a conversation yesterday with a customer that basically moved from Oracle to Spanner. So that level of integration is one to really watch, from a transaction/database in the cloud standpoint. >> Our mission is to make file a first class protocol. >> It was interesting, also, about this, and George Kurian was talking about this on the scene, I haven't yet interviewed him yet, I'll do that next time on theCUBE, but I've heard him speak publicly, I've seen comments, software is critical. You're a software company, >> Yeah, exactly. >> you happen to have hardware here and there. So this is actually ... >> We don't make the hardware, you know. >> You don't bend the metal. >> Right. >> Google loves software. >> Yeah. >> So, interesting, so you have a lot of range, potentially, looking out in the future. >> I tell you, you know, George asked me to come to NetApp, and he gave me a blank canvass, and told me to paint whatever picture I wanted. And so, as an application developer, I wanted to have a rich set of services to help me manage my data, and I wanted to be able to do it in the cloud. >> And you want to do it without storage. >> Yeah, I mean at the end of the day ... >> You're a developer, you just want it to be there working. >> Exactly right. You expect it to be like dial tone. When you pick up the phone, at home, you don't ask yourself, how does it work? >> Nor do you want to ask the operator to connect it for you. >> Exactly right. >> And that's what's been unique, I've been following NetApp since they took on Auspex. Early on, we realized that this is a company who, basically, has storage services, and makes calls to those storage services as required, like a software developer would. >> Exactly. >> Not things that are locked into some piece of hardware. >> No, I tell you, I think what the other thing that I'm particularly proud of is I think that all of those loyal customers who have built their careers on NetApp and ONTAP, we've now given them the next part of their journey. >> Yeah. >> We've now made all of their skills relevant for Google. >> That's another 20 year lease. >> Well, the other thing ... >> It's a beautiful thing. >> The other thing you've done is, by integrating with the cloud, you bring scale that has always been a challenge for clustered systems that the cloud resolves. It was a barrier to the adoption of the cluster concept. >> I tell you the other thing that customers say more than anything else is, you know, NetApp really provides probably the industry's best insurance. I mean, any customer that makes an onpremise decision, of which there are still many, are choosing NetApp onpremise because NetApp is in the cloud. >> That's interesting, because you see Oracle's marketing with same/same but Oracle's storage products are deficient. So (laughs) >> Well, when are we start to see storage functions and terms like storageless? We have serverless. I mean ... (laughs) >> We have some, let me tell you, we have some pretty cool tricks up our sleeve. We're not going to show our hand just yet, but the stuff we're doing with the Google guys, you know, I wouldn't underestimate the amount of work the teams have put into this. This is a amazing collaboration at the development level. It's something that I don't think Google has ever done before. And I think Google, like NetApp, we see each other as very, very strong partners at a very, very deep level. >> So you're talking about engineering resources that you're providing. Can you help us understand that? Or quantify that in any way? >> Oh yeah, so ... >> Couple of guys and a laptop, or we talking about ... >> It's a very large team, and a growing team. You know, my team at NetApp, just building software on the cloud, is six-seven hundred people strong now, all product managers and developers. I mean, we take this business very, very seriously. >> This is the future of NetApp. This is a competitive strategy for you guys. >> I think NetApp is cloud first. Just imagine, did you ever think you'd hear NetApp say we're a cloud first company? Because that's what we are. >> We don't hear your competitors saying that, I can tell you that right now. >> This is NetApp's fifth life. Like I said, I've been following this company a long time. It started with workstations, you brought file to dot-com. Then you went hard after that, dot-com blew up. You went hard into the enterprise. Bet the farm on virtualization. Now you're betting the farm on cloud. >> You know, I tell you the one thing that I've been at NetApp, as I said, for about 18 months. And the company has passion and conviction and belief. And what it does so amazingly well is it leans into the things that people think are going to kill it. >> Yeah. And there ... >> And you've met Dave, right? He's a wonderful guy. He founded the company, he's still involved in the company. He's here, he's learning cloud, and he loves it. >> We saw him last night, he's a great entrepreneur. And again, that's the kind of leadership, when the founders stay around, companies succeed. I've always said that, I wrote about it. And it statistically is proven. Lean in to anything you think will probably kill you, you'll probably come out stronger. And that's really an entrepreneurial lesson. >> I tell you, the other thing that I would say, more than anything else, and it was really the biggest part of my decision to join NetApp, is a technical CEO. >> Yeah. >> You have to have a technical CEO. No disrespect to sales guys that become CEO's, or finance guys that become CEO's, they're just not as good as the technical ones. And George is an engineer. >> Yup. And he gets it. He's very passionate and committed about the product. And that, that to me, I think-- >> More than ever now in a changing tide where technology decisions, the bets can be company killing or company making, about little things, how you deal with service meshes, >> Exactly right. >> How you deal with provisioning storage through software now, these are new things. >> You know, this stuff doesn't happen overnight, right. It takes a lot of time and a lot of effort. Software engineering, you know, is something that takes time. >> Well Anthony we really appreciate you taking the time to come on theCUBE. We love covering NetApp, we've been following your journey again, we see you at all the events, you guys are part of theCUBE community. We really appreciate that. And more than ever, we want to follow what you guys are doing in the cloud. We think it's competitive advantage vis-a-vis the competition. And want to see how it turns out. So... >> We're having so much fun. >> Let's keep in touch. >> So much fun. Thanks guys very much. >> Storageless is a big trend coming, trust me you heard it here first on theCUBE. I don't think they use that term yet, Dave. We'll be back with more live coverage, Day Two is coming to a close. Couple more segments, stay with us, for our three days of coverage of Google Cloud Google Next 2018. Be right back. (techno music)

>> Announcer: Live from Washington D.C, it's theCUBE. Covering .NEXT Conference. Bought to you by Nutanix. >> Welcome back to .NEXT everybody inside the district kind of. This is Dave Vellante and Stu Miniman. We're with theCUBE, the leader in live tech coverage. This is day two of NEXTConf. Aaditya Sood is here, he's the director of, Senior Director of Engineering and Products at Nutanix and Binny Gill who is the Chief Architect at the company. Gentlemen welcome to theCUBE. >> Both: Thank you. >> Good to see you again. >> Nice to be here. >> So you guys had the great keynote I love how, Dheerraj interacts with you on the stage. Asks you these Columbo questions even though he has deep knowledge of what's goin' on it's really quite good. But Binny let me start with you, first of all what's the show like this year we've been now this is our third year doing. >> Yeah. >> NEXT, we've seen quite an evolution. From your standpoint from the hardcore product side what are you seeing? >> I think it's exciting to be here, this year we are seeing almost every year doubling of the attendance in the conference and also the excitement that we hear from our Nutanix technical champions and our customers is a lot more visceral. In terms of how much we are doing in terms of vision and mission and as we are executing on our vision that has always been are we sure you can do it kind of thing, but as we deliver every year there's more conviction that we are seeing from all these people here so it's really exciting. >> Aaditya I wonder if you could talk about Calm a little bit it is your baby. People say Calm I'm calm, is the joke goin' around today what is Calm? >> Well Calm is many things at once, but most of all it's a control plane at the application layer. How do you build this multi-cloud hybrid cloud technology together? And manage the entire life cycle, compliance, governance, provisioning, costing, visibility all of these things together? >> And Aaditya there's many companies that have tried to attack this challenge why did you when you help co-found Calm think that you could address this and bring us up to speed as to the acquisition last year what is being part of Nutanix what did that do to the product itself to lead us to today? >> I think addressing the first part of your question why we think this is different many people have tried to do this, blueprints and these things over the many decades automation has been around. But I think this is fundamentally 10X to a 100X better because of the logical approach we are taking, the data model we have built which models applications and tries to keep the logical layer separate from the physical layer or the virtual layer. Thinking about the Nutanix integration was homecoming. This was, practically nothing changed from our point of view except that we got, became part of a much bigger family, a lot of warmth there a lot of technical goodness, a lot of I would say systems level and platform goodness that got leveraged in. So it's been all pretty great the last say what nine months or so. >> Alright Binny we want to get your view point of course Nutanix has been growing its ecosystem. We've got a big expo hall with a lot of partners there, what were the goals that you wanted to make sure you hit? By the time we came to this show with Calm to meet your customers and this growing ecosystem. >> Yeah, I think what we're looking at is explaining how we are building an operating system. An operating system is a platform for running applications and now the platform for running applications in fact changing earlier it used to be Linux and Windows now it's Clouds. Cloud is an OS, and when you talk about building OSs it's about ecosystem, it's about the drivers, it's about the partners that build stuff for you. Calm is a way of inviting them to a marketplace. A repository for where you put your stuff. Calm is also your YUM in app kit tool, one click deployment of any application the more simple you make it the more a person will be attracted to your operating system so the message here is look we are developing a marketplace bringing Calm to the picture, giving an operating system that can run on any hardware. Hardware meaning any cloud, any hypervice and so on. >> How much can I ask Stu, how much that one click if you think about the the lifecycle of what has to occur in that one click and then by the way everything else that you don't do how much time do you think you're saving people? I mean take database as a service, yes one click but then you got to do regression testing, you got to do some recovery testing but you're taking away a lot of the planning presumably. >> Yeah absolutely. >> Do you have any sense as to if it takes 100 how much you just shaved off with one click? >> Yeah, I mean I think there's the best person to answer that question his team has been working on this for seven years. >> And this is something that again touching back on what Calm does differently that it understands that provisioning your application is only the beginning of your problems. You have an application now you have to grow it, scale it, it's going to blow up, you need to upgrade it, test it, certify it and all that stuff. And that's where the application lifecycle management part comes in. As a rough estimate I would say at least 70 to 80% of the complexity has gone away. There is still some complexity because there is an essential complexity in every problem cannot reduce it beyond that. But I'd say off the ballpark 70, 80. >> I like that number so I kind of baited you, the practitioner that we had on today said at least, he's being conservative because that's what IT guys do he said at least 50% so we can fairly say let's say 50 to 70% you've just taken off the table so they can now focus on higher quality, testing, recovery and even in the fun stuff as Stu likes to say. (laughs) >> Absolutely, and Aadiyta there was the joke in the keynote that this is not an app store so what do you see the one click is obviously is a critical piece but how's this different from think of the Amazon Marketplace or other we've talked about do we need an enterprise app store, how is this different, how do customers perceive this? What early feedback have you been getting? >> I think one of the problems that we are trying to solve and how we are looking at it is every platform let's say every public cloud, every private cloud they have some amount of tooling and automation already built in. But from a customer point of view these are all just different independent silos. So if you go look at any marketplace it's on EWS only. And what we are trying to do is this is means to an end. I'm running five different kind of infrastructure stacks I want a single app store for my consumers internally. My business user doesn't care where the application is provisioned as long as we can write them the right SLAs and cost ROI for their internal application. >> What's important about this, I wonder if we can riff on it for a bit so we weren't the first to say this I think it was probably Benioff that more non-tech companies will be SaaS companies than tech companies. So that says that they need a stack to build their SaaS. So that marketplace that you showed you had Cassandra, Mongo, MySQL and Redes and TensorFlow and all of these tools. That'll allow a SaaS provider to build their own stack. So I wonder if we could talk about this a little bit in terms of the vision of the next generation company, not just tech company. And how you see yourselves fitting into that as an enabler. Comments. >> I think as an analogy I'd like to take how they the electronics industry evolved. Back in the 70's and 80's the semi-conductor explosion happened. And any kind of functionality that I wanted I looked at the catalog, I looked at the cost, the yield rates, the functionality that chip provided. I just went and bought those chips and I plugged them in, and I built my (mumbles) out of it. And this is how I think this is going to evolve. That we are just going to move the level of abstraction one higher level. Have usable fundamental components, compose them together and have a faster time to market. Do as an application developer do I really need to understand how Bongo scales and how it's provisioned and how it's backed up and everything? I want a single EPI or a one click experience equal in my view to just plug it into my application and then go on from there. And then take this my application and deliver it as a unit to my user which can then go ahead and just like Lego blocks keep building higher and higher layers of functionality. >> Yeah Aaditya brought up a key point there with APIs right. Anytime an OS is developed after some point you talk about what is the standardization of APIs on top of this OS? Like Posix was a standard of APIs in an operating system. This new cloud operating system is actually asking for a standardization of API so that the applications built on top of it can enjoy a guaranteed stable API that'll be portable across various hardwares and clouds. We are seeing the beginning of the that kind of API with the work that Aaditya's team is doing around our blueprints, our lifecycle that's coming to the floor right now. >> Aaditya one of the things I usually hear after a company gets acquired by a bigger company is the amount of feedback they get from the customers. Nutanix is a little bit self-selecting customers that are usually looking to try something different. What's been your experience with the Nutanix customer base? Has that impacted or shifted where you were looking to drive the product? >> It has certainly, informed the product roadmap but I wouldn't say it has fundamentally changed it. Because one of the key things and one of the great things about Nutanix is that we are building open systems. Which is why even in the keynote that you saw when we are going and provisioning an application we are not saying this is Nutanix only, we are treating each of the computer platform an independent equivalent level. And that was our vision right from the start to bring the goodness at the top layer and then leverage some deep platform stuff like we can obviously work best on Nutanix because we get underlying data from the storage systems, the virtualization systems and we'll run on that. But, yeah fundamentally it has not really changed anything. >> Ambitious. >> Yeah. (laughs) >> Binny the question I have for you is if I look at the public cloud they all want to own the applications in one way or another. Google's pretty a little bit more open but Microsoft lots of business apps, Amazon have dealt with next generation apps, how does Nutanix look at that app ownership? Obviously you come from the infrastructure side but how does your view point differ say from some other clouds? >> Our viewpoint is more like how players like Apple look at owning the app. I mean you have an app store or a marketplace but that is sort of democratic I do have my own apps I could have my mail app, my camera app but I am neutral in the sense that I enable others to create a better app if they can because it only helps my platform. So we are in the business of creating the best in class operating system we're calling it Enterprise Cloud Operating System and then enabling that cloud operating system to run on any farm factor any hypervice and or hardware or going all the way to the edge as you might have talked to others in this conference our cloud operating system can run on a single node now down from three nodes, to two nodes to one node to a intel node in a drone. That is where we're going, enable everybody. And on these various farm factors different applications would run. I would say a small fraction of the applications that are key to most customers to get to 80% of the simple use cases might come from us but the majority of the use cases would come from outside. And eventually we look at this as we primarily building the OS and the world building an app, app store or marketplace on top of us. >> Alright gents we have to leave it there. Thanks so much for coming on theCUBE. >> Thank you. >> It was really a pleasure seeing you again. >> As always take care. >> Okay keep it right there Stu and I will be back with our next guest right after this short break. This is theCUBE we're live from NEXTConf in D.C.. We'll be right back. (exciting music)

>> Narrator: Covering DataWorks Summit, Europe 2017, brought to you by Hortonworks. >> Welcome back to Munich everybody. This is The Cube. We're here live at DataWorks Summit, and we are the live leader in tech coverage. Steve Roberts is here as the offering manager for big data on power systems for IBM. Steve, good to see you again. >> Yeah, good to see you Dave. >> So we're here in Munich, a lot of action, good European flavor. It's my second European, formerly Hadoop Summit, now DataWorks. What's your take on the show? >> I like it. I like the size of the venue. It's the ability to interact and talk to a lot of the different sponsors and clients and partners, so the ability to network with a lot of people from a lot of different parts of the world in a short period of time, so it's been great so far and I'm looking forward to building upon this and towards the next DataWorks Summit in San Jose. >> Terri Virnig VP in your organization was up this morning, had a keynote presentation, so IBM got a lot of love in front of a fairly decent sized audience, talking a lot about the sort of ecosystem and that's evolving, the openness. Talk a little bit about open generally at IBM, but specifically what it means to your organization in the context of big data. >> Well, I am from the power systems team. So we have an initiative that we have launched a couple years ago called Open Power. And Open Power is a foundation of participants innovating from the power processor through all aspects, through accelerators, IO, GPUs, advanced analytics packages, system integration, but all to the point of being able to drive open power capability into the market and have power servers delivered not just through IBM, but through a whole ecosystem of partners. This compliments quite well with the Apache, Hadoop, and Spark philosophy of openness as it relates to software stack. So our story's really about being able to marry the benefits of open ecosystem for open power as it relates to the system infrastructure technology, which drives the same time to innovation, community value, and choice for customers as it relates to a multi-vendor ecosystem and coupled with the same premise as it relates to Hadoop and Spark. And of course, IBM is making significant contributions to Spark as part of the Apache Spark community and we're a key active member, as is Hortonworks with the ODPi organization forwarding the standards around Hadoop. So this is a one, two combo of open Hadoop, open Spark, either from Hortonworks or from IBM sitting on the open power platform built for big data. No other story really exists like that in the market today, open on open. >> So Terri mentioned cognitive systems. Bob Picciano has recently taken over and obviously has some cognitive chops, and some systems chops. Is this a rebranding of power? Is it sort of a layer on top? How should we interpret this? >> No, think of it more as a layer on top. So power will now be one of the assets, one of the sort of member family of the cognitive systems portion on IBM. System z can also be used as another great engine for cognitive in certain clients, certain use cases where they want to run cognitive close to the data and they have a lot of data sitting on System z. So power systems as a server really built for big data and machine learning, in particular our S822LC for high performance computing. This is a server which is landing very well in the deep learning, machine learning space. It offers the Tesla P100 GPU and with the NVIDIA NVLink technology can offer up to 2.8x bandwidth benefits CPU to GPU over what would be available through a PCIe Intel combination today. So this drives immediate value when you need to ensure that not just you're exploiting GPUs, but you of course need to move your data quickly from the processor to the GPU. >> So I was going to ask you actually, sort of what make power so well suited for big data and cognitive applications, particularly relative to Intel alternatives. You touched on that. IBM talks a lot about Moore's Law starting to hit its peak, that innovation is going to come from other places. I love that narrative 'cause it's really combinatorial innovation that's going to lead us in the next 50 years, but can we stay on that thread for a bit? What makes power so substantially unique, uniquely suited and qualified to run cognitive systems and big data? >> Yeah, it actually starts with even more of the fundamentals of the power processors. The power processor has eight threads per core in contrast to Intel's two threads per core. So this just means for being able to parallelize your workloads and workloads that come up in the cognitive space, whether you're running complex queries and need to drive SQL over a lot of parallel pipes or you're writing iterative computation, the same data set as when you're doing model training, these can all benefit from highly parallelized workloads, which can benefit from this 4x thread advantage. But of course to do this, you also need large, fast memory, and we have six times more cache per core versus Broadwell, so this just means you have a lot of memory close to the processor, driving that throughput that you require. And then on top of that, now we get to the ability to add accelerators, and unique accelerators such as I mentioned the NVIDIA in the links scenario for GPU or using the open CAPI as an approach to attach FPGA or Flash to get access speeds, processor memory access speeds, but with an attached acceleration device. And so this is economies of scale in terms of being able to offload specialized compute processing to the right accelerator at the right time, so you can drive way more throughput. The upper bounds are driving workload through individual nodes and being able to balance your IO and compute on an individual node is far superior with the power system server. >> Okay, so multi-threaded, giant memories, and this open CAPI gives you primitive level access I guess to a memory extension, instead of having to-- >> Yeah, pluggable accelerators through this high speed memory extension. >> Instead of going through, what I often call the horrible storage stack, aka SCSI, And so that's cool, some good technology discussion there. What's the business impact of all that? What are you seeing with clients? >> Well, the business impact is not everyone is going to start with supped up accelerated workloads, but they're going to get there. So part of the vision that clients need to understand is to begin to get more insights from their data is, it's hard to predict where your workloads are going to go. So you want to start with a server that provides you some of that upper room for growth. You don't want to keep scaling out horizontally by requiring to add nodes every time you need to add storage or add more compute capacity. So firstly, it's the flexibility, being able to bring versatile workloads onto a node or a small number of nodes and be able to exploit some of these memory advantages, acceleration advantages without necessarily having to build large scale out clusters. Ultimately, it's about improving time to insights. So with accelerators and with large memory, running workloads on a similar configured clusters, you're simply going to get your results faster. For example, recent benchmark we did with a representative set of TPC-DS queries on Hortonworks running on Linux and power servers, we're able to drive 70% more queries per hour over a comparable Intel configuration. So this is just getting more work done on what is now similarly priced infrastructure. 'Cause power family is a broad family that now includes 1U, 2U, scale out servers, along with our 192 core horsepowers for enterprise grade. So we can directly price compete on a scale out box, but we offer a lot more flexible choice as clients want to move up in the workload stack or to bring accelerators to the table as they start to experiment with machine learning. >> So if I understand that right, I can turn two knobs. I can do the same amount of work for less money, TCO play. Or, for the same amount of money, I can do more work. >> Absolutely >> Is that fair? >> Absolutely, now in some cases, especially in the Hadoop space, the size of your cluster is somewhat gated by how much storage you require. And if you're using the classic scale up storage model, you're going to have so many nodes no matter what 'cause you can only put so much storage on the node. So in that case, >> You're scaling storage. >> Your clusters can look the same, but you can put a lot more workload on that cluster or you can bring in IBM, a solution like IBM Spectrum Scale our elastic storage server, which allows you to essentially pull that storage off the nodes, put it in a storage appliance, and at that point, you now have high speed access to storage 'cause of course the network bandwidth has increased to the point that the performance benefit of local storage is no longer really a driving factor to a classic Hadoop deployment. You can get that high speed access in a storage appliance mode with the resiliency at far less cost 'cause you don't need 3x replication, you just have about a 30% overhead for the software erasure coding. And now with your compete nodes, you can really choose and scale those nodes just for your workload purposes. So you're not bound by the number of nodes equal total storage required by storage per node, which is a classic, how big is my cluster calculation. That just doesn't work if you get over 10 nodes, 'cause now you're just starting to get to the point where you're wasting something right? You're either wasting storage capacity or typically you're wasting compute capacity 'cause you're over provisioned on one side or the other. >> So you're able to scale compute and storage independent and tune that for the workload and grow that resource efficiently, more efficiently? >> You can right size the compute and storage for your cluster, but also importantly is you gain the flexibility with that storage tier, that data plan can be used for other non-HDFS workloads. You can still have classic POSIX applications or you may have new object based applications and you can with a single copy of the data, one virtual file system, which could also be geographically distributed, serving both Hadoop and non-Hadoop workloads, so you're saving then additional replicas of the data from being required by being able to onboard that onto a common data layer. >> So that's a return on asset play. You got an asset that's more fungible across the application portfolio. You can get more value out of it. You don't have to dedicate it to this one workload and then over provision for another one when you got extra capacity sitting here. >> It's a TCO play, but it's also a time saver. It's going to get you time to insight faster 'cause you don't have to keep moving that data around. The time you spend copying data is time you should be spending getting insights from the data, so having a common data layer removes that delay. >> Okay, 'cause it's HDFS ready I don't have to essentially move data from my existing systems into this new stovepipe. >> Yeah, we just present it through the HDFS API as it lands in the file system from the original application. >> So now, all this talk about rings of flexibility, agility, etc, what about cloud? How does cloud fit into this strategy? What do are you guys doing with your colleagues and cohorts at Bluemix, aka SoftLayer. You don't use that term anymore, but we do. When we get our bill it says SoftLayer still, but any rate, you know what I'm talking about. The cloud with IBM, how does it relate to what you guys are doing in power systems? >> Well the cloud is still, really the born on the cloud philosophy of IBM software analytics team is still very much the motto. So as you see in the data science experience, which was launched last year, born in the cloud, all our analytics packages whether it be our BigInsights software or our business intelligence software like Cognos, our future generations are landing first in the cloud. And of course we have our whole arsenal of Watson based analytics and APIs available through the cloud. So what we're now seeing as well as we're taking those born in the cloud, but now also offering a lot of those in an on-premise model. So they can also participate in the hybrid model, so data science experience now coming on premise, we're showing it at the booth here today. Bluemix has a on premise version as well, and the same software library, BigInsights, Cognos, SPSS are all available for on prem deployment. So power is still ideal place for hosting your on prem data and to run your analytics close to the data, and now we can federate that through hybrid access to these elements running in the cloud. So the focus is really being able to, the cloud applications being able to leverage the power and System z's based data through high speed connectors and being able to build hybrid configurations where you're running your analytics where they most make sense based upon your performance requirements, data security and compliance requirements. And a lot of companies, of course, are still not comfortable putting all their jewels in the cloud, so typically there's going to be a mix and match. We are expanding the footprint for cloud based offerings both in terms of power servers offered through SoftLayer, but also through other cloud providers, Nimbix is a partner we're working with right now who actually is offering our Power AI package. Power AI is a package of open source, deep learning frameworks, packaged by IBM, optimized for Power in an easily deployed package with IBM support available. And that's, could be deployed on premise in a power server, but also available on a pay per drink purpose through the Nimbix cloud. >> All right, we covered a lot of ground here. We talked strategy, we talked strategic fit, which I guess is sort of a adjunct to strategy, we talked a little bit about the competition and where you differentiate, some of the deployment models, like cloud, other bits and pieces of your portfolio. Can we talk specifically about the announcements that you have here at this event, just maybe summarize for use? >> Yeah, no absolutely. As it relates to IBM, and Hadoop, and Spark, we really have the full stack support, the rich analytics capabilities that I was mentioning, deep insight, prescriptive insights, streaming analytics with IBM Streams, Cognos Business Intelligence, so this set of technologies is available for both IBMs, Hadoop stack, and Hortonworks Hadoop stack today. Our BigInsights and IOP offering, is now out for tech preview, their next release their 4.3 release, is available for technical preview will be available for both Linux on Intel, Linux on power towards the end of this month, so that's kind of one piece of new Hadoop news at the analytics layer. As it relates to power systems, as Hortonworks announced this morning, HDP 2.6 is now available for Linux on power, so we've been partnering closely with Hortonworks to ensure that we have an optimized story for HDP running on power system servers as the data point I shared earlier with the 70% improved queries per hour. At the storage layer, we have a work in progress to certify Hortonworks, to certify Spectrum Scale file system, which really now unlocks abilities to offer this converged storage alternative to the classic Hadoop model. Spectrum Scale actually supports and provides advantages in both a classic Hadoop model with local storage or it can provide the flexibility of offering the same sort of multi-application support, but in a scale out model for storage that it also has the ability to form a part of a storage appliance that we call Elastic Storage Server, which is a combination of power servers and high density storage enclosures, SSD or spinning disk, depending upon the, or flash, depending on the configuration, and that certification will now have that as an available storage appliance, which could underpin either IBM Open Platform or HDP as a Hadoop data leg. But as I mentioned, not just for Hadoop, really for building a common data plane behind mixed analytics workloads that reduces your TCO through converged storage footprint, but more importantly, provides you that flexibility of not having to create data copies to support multiple applications. >> Excellent, IBM opening up its portfolio to the open source ecosystem. You guys have always had, well not always, but in the last 20 years, major, major investments in open source. They continue on, we're seeing it here. Steve, people are filing in. The evening festivities are about to begin. >> Steve: Yeah, yeah, the party will begin shortly. >> Really appreciate you coming on The Cube, thanks very much. >> Thanks a lot Dave. >> You're welcome. >> Great to talk to you. >> All right, keep it right there everybody. John and I will be back with a wrap up right after this short break, right back.