>> Narrator: Live from San Jose, in the heart of Silicon Valley, it's theCUBE! Covering Data Works Summit 2017. Brought to you by Hortonworks. >> Welcome back to theCUBE. I'm Lisa Martin with my co-host George Gilbert. We are live on day one of the Data Works Summit in San Jose in the heart of Silicon Valley. Great buzz in the event, I'm sure you can see and hear behind us. We're very excited to be joined by a couple of fellows from IBM. A very longstanding Hortonworks partner that announced a phenomenal suite of four new levels of that partnership today. Please welcome Asad Mahmood, Analytics Cloud Solutions Specialist at IBM, and medical doctor, and Linton Ward, Distinguished Engineer, Power Systems OpenPOWER Solutions from IBM. Welcome guys, great to have you both on the queue for the first time. So, Linton, software has been changing, companies, enterprises all around are really looking for more open solutions, really moving away from proprietary. Talk to us about the OpenPOWER Foundation before we get into the announcements today, what was the genesis of that? >> Okay sure, we recognized the need for innovation beyond a single chip, to build out an ecosystem, an innovation collaboration with our system partners. So, ranging from Google to Mellanox for networking, to Hortonworks for software, we believe that system-level optimization and innovation is what's going to bring the price performance advantage in the future. That traditional seamless scaling doesn't really bring us there by itself but that partnership does. >> So, from today's announcements, a number of announcements that Hortonworks is adopting IBM's data science platforms, so really the theme this morning of the keynote was data science, right, it's the next leg in really transforming an enterprise to be very much data driven and digitalized. We also saw the announcement about Atlas for data governance, what does that mean from your perspective on the engineering side? >> Very exciting you know, in terms of building out solutions of hardware and software the ability to really harden the Hortonworks data platform with servers, and storage and networking I think is going to bring simplification to on-premises, like people are seeing with the Cloud, I think the ability to create the analyst workbench, or the cognitive workbench, using the data science experience to create a pipeline of data flow and analytic flow, I think it's going to be very strong for innovation. Around that, most notable for me is the fact that they're all built on open technologies leveraging communities that universities can pick up, contribute to, I think we're going to see the pace of innovation really pick up. >> And on that front, on pace of innovation, you talked about universities, one of the things I thought was really a great highlight in the customer panel this morning that Raj Verma hosted was you had health care, insurance companies, financial services, there was Duke Energy there, and they all talked about one of the great benefits of open source is that kids in universities have access to the software for free. So from a talent attraction perspective, they're really kind of fostering that next generation who will be able to take this to the next level, which I think is a really important point as we look at data science being kind of the next big driver or transformer and also going, you know, there's not a lot of really skilled data scientists, how can that change over time? And this is is one, the open source community that Hortonworks has been very dedicated to since the beginning, it's a great it's really a great outcome of that. >> Definitely, I think the ability to take the risk out of a new analytical project is one benefit, and the other benefit is there's a tremendous, not just from young people, a tremendous amount of interest among programmers, developers of all types, to create data science skills, data engineering and data science skills. >> If we leave aside the skills for a moment and focus on the, sort of, the operationalization of the models once they're built, how should we think about a trained model, or, I should break it into two pieces. How should we think about training the models, where the data comes from and who does it? And then, the orchestration and deployment of them, Cloud, Edge Gateway, Edge device, that sort of thing. >> I think it all comes down to exactly what your use case is. You have to identify what use case you're trying to tackle, whether that's applicable to clinical medicine, whether that's applicable to finance, to banking, to retail or transportation, first you have to have that use case in mind, then you can go about training that model, developing that model, and for that you need to have a good, potent, robust data set to allow you to carry out that analysis and whether you want to do exploratory analysis or you want to do predictive analysis, that needs to be very well defined in your training stage. Once you have that model developed, then we have certain services, such as Watson Machine Learning, within data science experience that will allow you to take that model that you just developed, just moments ago, and just deploy that as a restful API that you can then embed into an application and to your solution, and in that solution you can basically use across industry. >> Are there some use cases where you have almost like a tiering of models where, you know, there're some that are right at the edge like, you know, a big device like a car and then, you know, there's sort of the fog level which is the, say, cell towers or other buildings nearby and then there's something in the Cloud that's sort of like, master model or an ensemble of models, I don't assume that's like, Evel Knievel would say you know, "Don't try that at home," but sort-of, is the tooling being built to enable that? >> So the tooling is already in existence right now. You can actually go ahead right now and be able to build out prototypes, even full-level, full-range applications right on the Cloud, and you can do that, you can do that thanks to Data Science Experience, you can do that thanks to IBM Bluemix, you can go ahead and do that type of analysis right there and not only that, you can allow that analysis to actually guide you along the path from building a model to building a full-range application and this is all happening on the Cloud level. We can talk more about it happening on on-premise level but on the Cloud level specifically, you can have those applications built on the fly, on the Cloud and have them deployed for web apps, for moblie apps, et cetera. >> One of the things that you talked about is use cases in certain verticals, IBM has been very strong and vertically focused for a very long time, but you kind of almost answered the question that I'd like to maybe explore a little bit more about building these models, training the models, in say, health care or telco and being able to deploy them, where's the horizontal benefits there that IBM would be able to deliver faster to other industries? >> Definitely, I think the main thing is that IBM, first of all, gives you that opportunity, that platform to say that hey, you have a data set, you have a use case, let's give you the tooling, let's give you the methodology to take you from data, to a model, to ultimately that full range application and specifically, I've built some applications specific to federal health care, specifically to address clinical medicine and behavioral medicine and that's allowed me to actually use IBM tools and some open source technologies as well to actually go out and build these applications on the fly as a prototype to show, not only the realm, the art of the possible when it comes to these technologies, but also to solve problems, because ultimately, that's what we're trying to accomplish here. We're trying to find real-world solutions to real-world problems. >> Linton, let me re-direct something towards you about, a lot of people are talking about how Moore's law slowing down or even ending, well at least in terms of speed of processors, but if you look at the, not just the CPU but FPGA or Asic or the tensor processing unit, which, I assume is an Asic, and you have the high speed interconnects, if we don't look at just, you know what can you fit on one chip, but you look at, you know 3D what's the density of transistors in a rack or in a data center, is that still growing as fast or faster, and what does it mean for the types of models that we can build? >> That's a great question. One of the key things that we did with the OpenPOWER Foundation, is to open up the interfaces to the chip, so with NVIDIA we have NVLink, which gives us a substantial increase in bandwidth, we have created something called OpenCAPI, which is a coherent protocol, to get to other types of accelerators, so we believe that hybrid computing in that form, you saw NVIDIDA on-stage this morning, and we believe especially for deploring the acceleration provided for GPUs is going to continue to drive substantial growth, it's a very exciting time. >> Would it be fair to say that we're on the same curve, if we look at it, not from the point of view of, you know what can we fit on a little square, but if we look at what can we fit in a data center or the power available to model things, you know Jeff Dean at Google said, "If Android users "talk into their phones for two to three minutes a day, "we need two to three times the data centers we have." Can we grow that price performance faster and enable sort of things that we did not expect? >> I think the innovation that you're describing will, in fact, put pressure on data centers. The ability to collect data from autonomous vehicles or other N points is really going up. So, we're okay for the near-term but at some point we will have to start looking at other technologies to continue that growth. Right now we're in the throws of what I call fast data versus slow data, so keeping the slow data cheaply and getting the fast data closer to the compute is a very big deal for us, so NAND flash and other non-volatile technologies for the fast data are where the innovation is happening right now, but you're right, over time we will continue to collect more and more data and it will put pressure on the overall technologies. >> Last question as we get ready to wrap here, Asad, your background is fascinating to me. Having a medical degree and working in federal healthcare for IBM, you talked about some of the clinical work that you're doing and the models that you're helping to build. What are some of the mission critical needs that you're seeing in health care today that are really kind of driving, not just health care organizations to do big data right, but to do data science right? >> Exactly, so I think one of the biggest questions that we get and one of the biggest needs that we get from the healthcare arena is patient-centric solutions. There are a lot of solutions that are hoping to address problems that are being faced by physicians on a day-to-day level, but there are not enough applications that are addressing the concerns that are the pain points that patients are facing on a daily basis. So the applications that I've started building out at IBM are all patient-centric applications that basically put the level of their data, their symptoms, their diagnosis, in their hands alone and allows them to actually find out more or less what's going wrong with my body at any particular time during the day and then find the right healthcare professional or the right doctor that is best suited to treating that condition, treating that diagnosis. So I think that's the big thing that we've seen from the healthcare market right now. The big need that we have, that we're currently addressing with our Cloud analytics technology which is just becoming more and more advanced and sophisticated and is trending towards some of the other health trends or technology trends that we have currently right now on the market, including the Blockchain, which is tending towards more of a de-centralized focus on these applications. So it's actually they're putting more of the data in the hands of the consumer, of the hands of the patient, and even in the hands of the doctor. >> Wow, fantastic. Well you guys, thank you so much for joining us on theCUBE. Congratulations on your first time being on the show, Asad Mahmood and Linton Ward from IBM, we appreciate your time. >> Thank you very much. >> Thank you. >> And for my co-host George Gilbert, I'm Lisa Martin, you're watching theCUBE live on day one of the Data Works Summit from Silicon Valley but stick around, we've got great guests coming up so we'll be right back.

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by Silicon Angle. Hi, this is David Linton. You're watching the Cubes coverage of the Cube on Cloud, our own virtual event where we're trying to understand the future of cloud, where we've come from and where we're going. And we're bringing in visionaries to really have that detailed conversation. Daniel Jones is here. He's the CEO of automation specialist. You. I path Daniel. Thanks for coming on and sharing your insights here. >>Thank you so much for inviting me. They've appreciated. >>That's always a pleasure to get together with folks that have started companies with a seed of a vision and have exploded in tow. You know, great success. And when I wanna go back to the the the founding days of you, I path 2005. It was a pre cloud. There's certainly pre cloud as we know it today. A w s came out in 2006. Aw, and then we saw the clouds Ascendancy. But but your original founding premise there was no cloud, you know, it wasn't like a startup could just spend up stuff in the cloud. But what you've seen that evolution. So when you first started to see cloud evolved, What did you think? Did you think Oh, well, we'll see what happens. Or did you? Did you know at the time that this was gonna be a bigas? It actually has become. What were your thoughts back then? >>Well, I honestly, I thought that we are kind of agent. And maybe it's stupid to not to pie foot in tow, The new trends in technology like Cloud Mobile social and I we kept, you know, working on this computer vision technology that 15 years ago, war was not really hot. But with the evolution of self driving cars and the latest development in AI, we we've been able to capture our investments in the domain that was not hot. But suddenly, you know, became the word the of the greatest minds in I t. And we definitely we specialize Our computer vision toe a narrow use case, but still, it's the It's the key of what we've done in, uh, in the end, the robots are powered by computer vision technology. This kind of a robot emulate how human user work. So obviously we use vision a lot in our day by day work and having the best technology that allows our robots to interact with the computer screen more like human user is quintessential and, uh, making our business reliable and easy to use. So we were lucky. But I always felt that maybe I should change it. And we were feeling I remember you know, many discussions with my, you know, initial developers because we like what you're doing. What we felt a bit left outside my door. What way? Got lucky in the end. >>So So I have a premise here and that when you go back to the early days of cloud, what they got right was they were attacking the human labor problem and they automate it was storage. It was it was networking. It was compute. But really the automation that they brought toe i t. And the quality that that drove and the flexibility was, you know, a game changer. Of course, we know that now. And you know, many of us at the time were very excited about Cloud. I'm not sure we predicted the impact that it had, but my premise is that there's a parallel in your business with the automation that you're driving into the business. We've talked toe people, for instance, that some of your customers have said, You know I can't do Six Sigma. I can't afford to do six Sigma before things like R P. A. For business process. I do that for Mission critical things, but now I can apply six Sigma thinking across my entire business that drives quality. It takes costs out of my business. So what do you think about that premise? That there's a parallel between the early days of cloud taking human labor out of the equation and driving quality and flexibility, cost saving speed and revenue, etcetera and what you're doing on the business side, >>it is clearly a parallel. I can tell that the cloud was built by looking at ICTY Automation use cases first of all, because this is all software engineers understand the most software engineers. Let's be you little on this. They don't understand the business work. They don't understand all how the rial work is performing a big enterprise and they don't care. Sometimes when in my own discussions with our CFO, he is surprised that I don't know all the use cases in the world. Yes, of course. I don't know exactly how an insurance company work All the processes in a health care, all the banking processes. I have intellectual curiosity how they were. But what interests me the most is our computer vision technology that works uniformly well across different. That was the same from the cloud. So initially they built and they build a cow cloud one toe, help them when what they know the best. And now, for we were put in the face of having great technology, this computer region technology, but without having a great use case in the I t world that we understood. And when we when I'm speaking about our early days like 12, 13, 14, I believe this technology has a lot less applicable bility in the real world. Because again, we were thinking of some sorts of small I T automation gigs that were not possible just doing the AP ice. But when I discovered the messy world of business processes and how important is to emulate people when you think automation, that was a big ah ha moment. So I believe that we can do for business processes what the cloud has done for I t processes on. We are really patient now about this business processes on helping people toe eliminate all the repetitive work that is their delegate. This work two robots and have the people that are required to do this work do do better. A smaller number of tasks every day. Everyone has own, as on her or him played today like, let's say, 10, 20 different activities. Some of them can be completely delegated to rob to robots, and they are the low value type of activities, while they can focus on the high value activities like interaction with people, creativity, decision making and this type of human like things that we as humans really love. >>I love that you shared that story, but you thought it was a very narrow, sort of set of use cases when you first started and then, you know, that's that's just an awesome founders, you know, really ization. I love it when we've often said in the Cube that, you know, for decades we've marched to the tune of Moore's Law. That was the innovation engine. No longer is that case. It's a combination of of data, applied machine intelligence and cloud for scale. And I guess the computer vision pieces How you in just the data you've you've made some investments in a I and there's many more to come the industry in general and the cloud is sort of the piece of that equation that we see for scale. So I wonder how you see those pieces fitting to your business. Uh, and how important is the cloud for your scale? At last? Uh, at last year, I path forward. There was a lot of talk amongst your customers about scaling. Is the cloud critical for that scale? >>Yeah, I believe so. And we are thinking of clouds in tow. Distinct ways number one. We're offering Onda manage automation service in our own close, using where we host everything by ourselves, including our orchestrator, and then be next to have the plans to include our the robots that execute the automation And people simply can't connect to our cloud building automation and just scheduled to run without any maintainers. And they will have access to oh, great analytics, Everything integrated. So this is a major force to us, and the way we launching G a. This cloud offering in April this year, and I can tell you that until now, 20% of our customers already are in a shape or another in this type of offering, not 20% dollar amount, but 20% of our customers. And it's clear that at this point this has mawr applicability into the long tail, a smaller customers than in the on our biggest customers. But the second, this thing type of cloud offering that we focus on is toe have best in class support and best in class multi cloud support for the cloud of choice of our customers. For instance, if you go in if you go in a w, g, c, p usher and you buy a subscription there, you wear buildings. Specialized editions were with one click. You will be able to install our technology in those clouds and you'll be ableto scale up and down your robots. You can connect your robots to our many service were within your tenant, but basically the angle is toe lesson. Ah lot the administration, the maintainers footprint of your installation, either on our own cloud, even on your cloud of choice. I'm a strong believer that we will see an accelerated transition from the completely on Prem Workloads into these two source of cloud workloads. >>I wanna ask you, is a a technologist if you see. So you mentioned that you're gonna take your products and your support. Multiple clouds will run on any cloud in A lot of companies are talking about that, you know, for their respective whether it's a database or, you know, whatever storage device, etcetera. Do you see the day where you'll actually start? You're collaborating across clouds. Where the user, uh, maybe maybe the user today doesn't know, but maybe a developer does know which cloud it's running on. But do you see any value in actual, you know, connecting across clouds where the data and one cloud is relevant for the data? Another cloud is I know there are latent see issues. Is that you know, technically feasible. And is it it? Will it drive business value? What do you think about that cross cloud connection? >>I believe it is already happening. There is a mesh between between various services and who knows in which cloud they are awful. Already. I feel the Leighton see is less and less of a problem as much as the biggest cloud provider have have a very distributed geographically president. So as long as I can playing AWS in East Coast, on on Asia in East Coast, it's not such a big Leighton see issue. Uh huh. Frankly, in the past, our customers at least start telling us they seen how it is to be completely looking toe one technology on people would like Toa have optionality. It's not necessarily that I will use three clothes, but I would like to use the vendor that gives me optionally even. And this is what we're trying to offer. >>Do you, when you think about the future of work? I mean, e said before the cloud one dato was infrastructure storage, networking, computing Uh, it seems like to Dato we're bringing in more ai new workloads. We're seeing, you know, analytics and machine intelligence applied to the data and then, you know, distributed at scale self serve to the business. How do you see the future of work specifically as it relates toe automation affecting that, uh on you know what role does cloud play there? What's your vision? >>So as the workloads will move to cloud. It's absolutely critical that the processes will move to cloud, so there is no way back. I think, that moving in tow, moving from home for and software into cloud will make even easier toe automate this type of workloads into the cloud. It's gonna be less maintain us. You will deal less with legacy applications that require some special care. It's kind of a bit more easier to automate modern Onley, Web based type of application so that Z we'll see an acceleration on the moving to cloud. But again, there will be different sorts of cloud from a completely manage automation service from us toe managing yourself the automation in your cloud tenant, but not on prayer. I'm not a big believer that we will accept unless very few critical sectors I don't think that we will see home Primor roads in the past five years. >>I mean, I agree in this case, the business case for on Prem just gets, you know, less and less. I mean, it'll be a certain applications for sure. My last question is, when thinking about from a software developer standpoint, you obviously you're gonna wanna run in a W S and G, C P and Azure. Uh, perhaps Alibaba, Uh, do you look at other clouds? Whether their regional clouds, of course. You got your own cloud. Maybe Oracle. IBM. How do you think about those? Do you just sort of evaluated on a case by case basis? You let customers, you know, tell you where you need to be. >>Yeah, way focus on the on the three big clouds today, but we're building on the top off Q Burnett is most of our way. We have a big shift in tow building que Burnett is micro services. And my guess is that all mother clouds would offer fantastic support for kubernetes. So what What it takes when you create a new edition for another cloud is toe is toe have the underlying services. Like if we plan to use snowflake, for instance in our analytics offering, you better have snowflake in another cloud. Otherwise, probably the the analytics will will have toe be delayed or use a less of one part technology. So it's not only about what we are building, but it's also, you know, the vast availability of other set of technologies that we try toe use when you choose a technology. Now, first of all, we are looking. We need to choose something that is multi cloud. There's who's dedicated from one cloud vendor. That's that's our first priority. This is why I've mentioned snowflake and then when when we moved into a cloud. We are limited by the offerings that are there, but I my belief is in the main clouds, probably in the US I don't know one of the region's what's gonna happen, but in the main crowds in the U. S. In I believe that they will. In the end, they will catch up in terms off offering and convincing of other defenders toe have kind of kind of similar offering on their own. I don't know if, besides, the Big Three, or you'll see someone and that is able to compete could be too much fragmented. Maybe they will be dedicated clouds for certain services. But for General Cloud, I think three is more than enough. >>Yeah, and so, you know, in the early days of cloud, people talked about dial tone, and essentially, that's what's becoming. It's the it's the value that's running on top of the cloud from software companies like ey Path and others that is really driving. So the cloud to Dato the next generation Daniel Dennett is thanks so much for sharing your vision on participating in the Cuban cloud. Really appreciate it. >>My pleasure, Dave. Thank you so much for inviting. >>You're welcome. You always great to talk to you. And thank you for watching everybody keep it right there. We'll be back with our next guest right into this short break. This is Dave Volonte for the Cube. Yeah.