>>on March 17th at 8 a.m. >>Pacific. The >>Cube is hosting Israel Day with support from Hewlett Packard. Enterprise I am really excited about is moral. It's H. P s set of solutions that will allow containerized apps and workloads to run >>anywhere. Talking on Prem in the public cloud across clouds >>are really anywhere, including the emergent edge you can think of, as well as a data fabric and a platform to allow you to manage work across all >>these domains. >>That is more all day. We have an exciting lineup of guests, including Kirk Born, who was a famed >>astrophysicist and >>extraordinary data scientist. >>He's from Booz >>Allen. Hamilton will also be joined by my longtime friend Kumar. Sorry >>Conte, who is CEO >>and head of software at HP. In addition, you'll hear from Robert Christiansen >>of HPV will discuss >>data strategies that make sense >>for you, >>and we'll hear from >>customers and partners from around the globe who >>are using as moral >>capabilities to >>create and deploy transformative >>products and solutions that are >>impacting lives every single day. We'll also give you a chance to have a few breakout rooms >>and go deeper on specific topics >>that are important to you, and we'll give you a demo toward the end. So you want to hang around now? Most of all, we >>have a team of experts >>standing by to answer any questions that you may have. >>So please >>do join in on the chat room. It's gonna be a great event. So grab your coffee, your tea or your favorite beverage and grab a note >>pad. We'll see >>you there. March 17th at 8 a.m. >>8 a.m. Pacific >>on the Cube.

>>from around the globe. It's the Q. With digital coverage >>of exa scale day made possible by Hewlett Packard Enterprise. We're back at the celebration of Exa Scale Day. This is Dave Volant, and I'm pleased to welcome to great guests Brian Dance Berries Here. Here's what The ISS Program Science office at the Johnson Space Center. And Dr Mark Fernandez is back. He's the Americas HPC technology officer at Hewlett Packard Enterprise. Gentlemen, welcome. >>Thank you. Yeah, >>well, thanks for coming on. And, Mark, Good to see you again. And, Brian, I wonder if we could start with you and talk a little bit about your role. A T. I s s program Science office as a scientist. What's happening these days? What are you working on? >>Well, it's been my privilege the last few years to be working in the, uh, research integration area of of the space station office. And that's where we're looking at all of the different sponsors NASA, the other international partners, all the sponsors within NASA, and, uh, prioritizing what research gets to go up to station. What research gets conducted in that regard. And to give you a feel for the magnitude of the task, but we're coming up now on November 2nd for the 20th anniversary of continuous human presence on station. So we've been a space faring society now for coming up on 20 years, and I would like to point out because, you know, as an old guy myself, it impresses me. That's, you know, that's 25% of the US population. Everybody under the age of 20 has never had a moment when they were alive and we didn't have people living and working in space. So Okay, I got off on a tangent there. We'll move on in that 20 years we've done 3000 experiments on station and the station has really made ah, miraculously sort of evolution from, ah, basic platform, what is now really fully functioning national lab up there with, um, commercially run research facilities all the time. I think you can think of it as the world's largest satellite bus. We have, you know, four or five instruments looking down, measuring all kinds of things in the atmosphere during Earth observation data, looking out, doing astrophysics, research, measuring cosmic rays, X ray observatory, all kinds of things, plus inside the station you've got racks and racks of experiments going on typically scores, you know, if not more than 50 experiments going on at any one time. So, you know, the topic of this event is really important. Doesn't NASA, you know, data transmission Up and down, all of the cameras going on on on station the experiments. Um, you know, one of one of those astrophysics observatory's you know, it has collected over 15 billion um uh, impact data of cosmic rays. And so the massive amounts of data that that needs to be collected and transferred for all of these experiments to go on really hits to the core. And I'm glad I'm able toe be here and and speak with you today on this. This topic. >>Well, thank you for that, Bryan. A baby boomer, right? Grew up with the national pride of the moon landing. And of course, we've we've seen we saw the space shuttle. We've seen international collaboration, and it's just always been something, you know, part of our lives. So thank you for the great work that you guys were doing their mark. You and I had a great discussion about exa scale and kind of what it means for society and some of the innovations that we could maybe expect over the coming years. Now I wonder if you could talk about some of the collaboration between what you guys were doing and Brian's team. >>Uh, yeah, so yes, indeed. Thank you for having me early. Appreciate it. That was a great introduction. Brian, Uh, I'm the principal investigator on Space Born computer, too. And as the two implies, where there was one before it. And so we worked with Bryant and his team extensively over the past few years again high performance computing on board the International Space Station. Brian mentioned the thousands of experiments that have been done to date and that there are currently 50 orm or going on at any one time. And those experiments collect data. And up until recently, you've had to transmit that data down to Earth for processing. And that's a significant amount of bandwidth. Yeah, so with baseball and computer to we're inviting hello developers and others to take advantage of that onboard computational capability you mentioned exa scale. We plan to get the extra scale next year. We're currently in the era that's called PETA scale on. We've been in the past scale era since 2000 and seven, so it's taken us a while to make it that next lead. Well, 10 years after Earth had a PETA scale system in 2017 were able to put ah teraflop system on the International space station to prove that we could do a trillion calculations a second in space. That's where the data is originating. That's where it might be best to process it. So we want to be able to take those capabilities with us. And with H. P. E. Acting as a wonderful partner with Brian and NASA and the space station, we think we're able to do that for many of these experiments. >>It's mind boggling you were talking about. I was talking about the moon landing earlier and the limited power of computing power. Now we've got, you know, water, cool supercomputers in space. I'm interested. I'd love to explore this notion of private industry developing space capable computers. I think it's an interesting model where you have computer companies can repurpose technology that they're selling obviously greater scale for space exploration and apply that supercomputing technology instead of having government fund, proprietary purpose built systems that air. Essentially, you use case, if you will. So, Brian, what are the benefits of that model? The perhaps you wouldn't achieve with governments or maybe contractors, you know, kind of building these proprietary systems. >>Well, first of all, you know, any any tool, your using any, any new technology that has, you know, multiple users is going to mature quicker. You're gonna have, you know, greater features, greater capabilities, you know, not even talking about computers. Anything you're doing. So moving from, you know, governor government is a single, um, you know, user to off the shelf type products gives you that opportunity to have things that have been proven, have the technology is fully matured. Now, what had to happen is we had to mature the space station so that we had a platform where we could test these things and make sure they're gonna work in the high radiation environments, you know, And they're gonna be reliable, because first, you've got to make sure that that safety and reliability or taken care of so that that's that's why in the space program you're gonna you're gonna be behind the times in terms of the computing power of the equipment up there because, first of all and foremost, you needed to make sure that it was reliable and say, Now, my undergraduate degree was in aerospace engineering and what we care about is aerospace engineers is how heavy is it, how big and bulky is it because you know it z expensive? You know, every pound I once visited Gulfstream Aerospace, and they would pay their employees $1000 that they could come up with a way saving £1 in building that aircraft. That means you have more capacity for flying. It's on the orders of magnitude. More important to do that when you're taking payloads to space. So you know, particularly with space born computer, the opportunity there to use software and and check the reliability that way, Uh, without having to make the computer, you know, radiation resistance, if you will, with heavy, you know, bulky, um, packaging to protect it from that radiation is a really important thing, and it's gonna be a huge advantage moving forward as we go to the moon and on to Mars. >>Yeah, that's interesting. I mean, your point about cots commercial off the shelf technology. I mean, that's something that obviously governments have wanted to leverage for a long, long time for many, many decades. But but But Mark the issue was always the is. Brian was just saying the very stringent and difficult requirements of space. Well, you're obviously with space Born one. You got to the point where you had visibility of the economics made sense. It made commercial sense for companies like Hewlett Packard Enterprise. And now we've sort of closed that gap to the point where you're sort of now on that innovation curve. What if you could talk about that a little bit? >>Yeah, absolutely. Brian has some excellent points, you know, he said, anything we do today and requires computers, and that's absolutely correct. So I tell people that when you go to the moon and when you go to the Mars, you probably want to go with the iPhone 10 or 11 and not a flip phone. So before space born was sent up, you went with 2000 early two thousands computing technology there which, like you said many of the people born today weren't even around when the space station began and has been occupied so they don't even know how to program or use that type of computing. Power was based on one. We sent the exact same products that we were shipping to customers today, so they are current state of the art, and we had a mandate. Don't touch the hardware, have all the protection that you can via software. So that's what we've done. We've got several philosophical ways to do that. We've implemented those in software. They've been successful improving in the space for one, and now it's space born to. We're going to begin the experiments so that the rest of the community so that the rest of the community can figure out that it is economically viable, and it will accelerate their research and progress in space. I'm most excited about that. Every venture into space as Brian mentioned will require some computational capability, and HP has figured out that the economics air there we need to bring the customers through space ball into in order for them to learn that we are reliable but current state of the art, and that we could benefit them and all of humanity. >>Guys, I wanna ask you kind of a two part question. And, Brian, I'll start with you and it z somewhat philosophical. Uh, I mean, my understanding was and I want to say this was probably around the time of the Bush administration w two on and maybe certainly before that, but as technology progress, there was a debate about all right, Should we put our resource is on moon because of the proximity to Earth? Or should we, you know, go where no man has gone before and or woman and get to Mars? Where What's the thinking today, Brian? On that? That balance between Moon and Mars? >>Well, you know, our plans today are are to get back to the moon by 2024. That's the Artemus program. Uh, it's exciting. It makes sense from, you know, an engineering standpoint. You take, you know, you take baby steps as you continue to move forward. And so you have that opportunity, um, to to learn while you're still, you know, relatively close to home. You can get there in days, not months. If you're going to Mars, for example, toe have everything line up properly. You're looking at a multi year mission you know, it may take you nine months to get there. Then you have to wait for the Earth and Mars to get back in the right position to come back on that same kind of trajectory. So you have toe be there for more than a year before you can turn around and come back. So, you know, he was talking about the computing power. You know, right now that the beautiful thing about the space station is, it's right there. It's it's orbiting above us. It's only 250 miles away. Uh, so you can test out all of these technologies. You can rely on the ground to keep track of systems. There's not that much of a delay in terms of telemetry coming back. But as you get to the moon and then definitely is, you get get out to Mars. You know, there are enough minutes delay out there that you've got to take the computing power with you. You've got to take everything you need to be able to make those decisions you need to make because there's not time to, um, you know, get that information back on the ground, get back get it back to Earth, have people analyze the situation and then tell you what the next step is to do. That may be too late. So you've got to think the computing power with you. >>So extra scale bring some new possibilities. Both both for, you know, the moon and Mars. I know Space Born one did some simulations relative. Tomorrow we'll talk about that. But But, Brian, what are the things that you hope to get out of excess scale computing that maybe you couldn't do with previous generations? >>Well, you know, you know, market on a key point. You know, bandwidth up and down is, of course, always a limitation. In the more computing data analysis you can do on site, the more efficient you could be with parsing out that that bandwidth and to give you ah, feel for just that kind of think about those those observatory's earth observing and an astronomical I was talking about collecting data. Think about the hours of video that are being recorded daily as the astronauts work on various things to document what they're doing. They many of the biological experiments, one of the key key pieces of data that's coming back. Is that video of the the microbes growing or the plants growing or whatever fluid physics experiments going on? We do a lot of colloids research, which is suspended particles inside ah liquid. And that, of course, high speed video. Is he Thio doing that kind of research? Right now? We've got something called the I s s experience going on in there, which is basically recording and will eventually put out a syriza of basically a movie on virtual reality recording. That kind of data is so huge when you have a 360 degree camera up there recording all of that data, great virtual reality, they There's still a lot of times bringing that back on higher hard drives when the space six vehicles come back to the Earth. That's a lot of data going on. We recorded videos all the time, tremendous amount of bandwidth going on. And as you get to the moon and as you get further out, you can a man imagine how much more limiting that bandwidth it. >>Yeah, We used to joke in the old mainframe days that the fastest way to get data from point a to Point B was called C Tam, the Chevy truck access method. Just load >>up a >>truck, whatever it was, tapes or hard drive. So eso and mark, of course space born to was coming on. Spaceport one really was a pilot, but it proved that the commercial computers could actually work for long durations in space, and the economics were feasible. Thinking about, you know, future missions and space born to What are you hoping to accomplish? >>I'm hoping to bring. I'm hoping to bring that success from space born one to the rest of the community with space born to so that they can realize they can do. They're processing at the edge. The purpose of exploration is insight, not data collection. So all of these experiments begin with data collection. Whether that's videos or samples are mold growing, etcetera, collecting that data, we must process it to turn it into information and insight. And the faster we can do that, the faster we get. Our results and the better things are. I often talk Thio College in high school and sometimes grammar school students about this need to process at the edge and how the communication issues can prevent you from doing that. For example, many of us remember the communications with the moon. The moon is about 250,000 miles away, if I remember correctly, and the speed of light is 186,000 miles a second. So even if the speed of light it takes more than a second for the communications to get to the moon and back. So I can remember being stressed out when Houston will to make a statement. And we were wondering if the astronauts could answer Well, they answered as soon as possible. But that 1 to 2 second delay that was natural was what drove us crazy, which made us nervous. We were worried about them in the success of the mission. So Mars is millions of miles away. So flip it around. If you're a Mars explorer and you look out the window and there's a big red cloud coming at you that looks like a tornado and you might want to do some Mars dust storm modeling right then and there to figure out what's the safest thing to do. You don't have the time literally get that back to earth have been processing and get you the answer back. You've got to take those computational capabilities with you. And we're hoping that of these 52 thousands of experiments that are on board, the SS can show that in order to better accomplish their missions on the moon. And Omar, >>I'm so glad you brought that up because I was gonna ask you guys in the commercial world everybody talks about real time. Of course, we talk about the real time edge and AI influencing and and the time value of data I was gonna ask, you know, the real time, Nous, How do you handle that? I think Mark, you just answered that. But at the same time, people will say, you know, the commercial would like, for instance, in advertising. You know, the joke the best. It's not kind of a joke, but the best minds of our generation tryingto get people to click on ads. And it's somewhat true, unfortunately, but at any rate, the value of data diminishes over time. I would imagine in space exploration where where you're dealing and things like light years, that actually there's quite a bit of value in the historical data. But, Mark, you just You just gave a great example of where you need real time, compute capabilities on the ground. But but But, Brian, I wonder if I could ask you the value of this historic historical data, as you just described collecting so much data. Are you? Do you see that the value of that data actually persists over time, you could go back with better modeling and better a i and computing and actually learn from all that data. What are your thoughts on that, Brian? >>Definitely. I think the answer is yes to that. And, you know, as part of the evolution from from basically a platform to a station, we're also learning to make use of the experiments in the data that we have there. NASA has set up. Um, you know, unopened data access sites for some of our physical science experiments that taking place there and and gene lab for looking at some of the biological genomic experiments that have gone on. And I've seen papers already beginning to be generated not from the original experimenters and principal investigators, but from that data set that has been collected. And, you know, when you're sending something up to space and it to the space station and volume for cargo is so limited, you want to get the most you can out of that. So you you want to be is efficient as possible. And one of the ways you do that is you collect. You take these earth observing, uh, instruments. Then you take that data. And, sure, the principal investigators air using it for the key thing that they designed it for. But if that data is available, others will come along and make use of it in different ways. >>Yeah, So I wanna remind the audience and these these these air supercomputers, the space born computers, they're they're solar powered, obviously, and and they're mounted overhead, right? Is that is that correct? >>Yeah. Yes. Space borne computer was mounted in the overhead. I jokingly say that as soon as someone could figure out how to get a data center in orbit, they will have a 50 per cent denser data station that we could have down here instead of two robes side by side. You can also have one overhead on. The power is free. If you can drive it off a solar, and the cooling is free because it's pretty cold out there in space, so it's gonna be very efficient. Uh, space borne computer is the most energy efficient computer in existence. Uh, free electricity and free cooling. And now we're offering free cycles through all the experimenters on goal >>Eso Space born one exceeded its mission timeframe. You were able to run as it was mentioned before some simulations for future Mars missions. And, um and you talked a little bit about what you want to get out of, uh, space born to. I mean, are there other, like, wish list items, bucket bucket list items that people are talking about? >>Yeah, two of them. And these air kind of hypothetical. And Brian kind of alluded to them. Uh, one is having the data on board. So an example that halo developers talk to us about is Hey, I'm on Mars and I see this mold growing on my potatoes. That's not good. So let me let me sample that mold, do a gene sequencing, and then I've got stored all the historical data on space borne computer of all the bad molds out there and let me do a comparison right then and there before I have dinner with my fried potato. So that's that's one. That's very interesting. A second one closely related to it is we have offered up the storage on space borne computer to for all of your raw data that we process. So, Mr Scientist, if if you need the raw data and you need it now, of course, you can have it sent down. But if you don't let us just hold it there as long as they have space. And when we returned to Earth like you mentioned, Patrick will ship that solid state disk back to them so they could have a new person, but again, reserving that network bandwidth, uh, keeping all that raw data available for the entire duration of the mission so that it may have value later on. >>Great. Thank you for that. I want to end on just sort of talking about come back to the collaboration between I S s National Labs and Hewlett Packard Enterprise, and you've got your inviting project ideas using space Bourne to during the upcoming mission. Maybe you could talk about what that's about, and we have A We have a graphic we're gonna put up on DSM information that you can you can access. But please, mark share with us what you're planning there. >>So again, the collaboration has been outstanding. There. There's been a mention off How much savings is, uh, if you can reduce the weight by a pound. Well, our partners ice s national lab and NASA have taken on that cost of delivering baseball in computer to the international space station as part of their collaboration and powering and cooling us and giving us the technical support in return on our side, we're offering up space borne computer to for all the onboard experiments and all those that think they might be wanting doing experiments on space born on the S s in the future to take advantage of that. So we're very, very excited about that. >>Yeah, and you could go toe just email space born at hp dot com on just float some ideas. I'm sure at some point there'll be a website so you can email them or you can email me david dot volonte at at silicon angle dot com and I'll shoot you that that email one or that website once we get it. But, Brian, I wanna end with you. You've been so gracious with your time. Uh, yeah. Give us your final thoughts on on exa scale. Maybe how you're celebrating exa scale day? I was joking with Mark. Maybe we got a special exa scale drink for 10. 18 but, uh, what's your final thoughts, Brian? >>Uh, I'm going to digress just a little bit. I think I think I have a unique perspective to celebrate eggs a scale day because as an undergraduate student, I was interning at Langley Research Center in the wind tunnels and the wind tunnel. I was then, um, they they were very excited that they had a new state of the art giant room size computer to take that data we way worked on unsteady, um, aerodynamic forces. So you need a lot of computation, and you need to be ableto take data at a high bandwidth. To be able to do that, they'd always, you know, run their their wind tunnel for four or five hours. Almost the whole shift. Like that data and maybe a week later, been ableto look at the data to decide if they got what they were looking for? Well, at the time in the in the early eighties, this is definitely the before times that I got there. They had they had that computer in place. Yes, it was a punchcard computer. It was the one time in my life I got to put my hands on the punch cards and was told not to drop them there. Any trouble if I did that. But I was able thio immediately after, uh, actually, during their run, take that data, reduce it down, grabbed my colored pencils and graph paper and graph out coefficient lift coefficient of drag. Other things that they were measuring. Take it back to them. And they were so excited to have data two hours after they had taken it analyzed and looked at it just pickled them. Think that they could make decisions now on what they wanted to do for their next run. Well, we've come a long way since then. You know, extra scale day really, really emphasizes that point, you know? So it really brings it home to me. Yeah. >>Please, no, please carry on. >>Well, I was just gonna say, you know, you talked about the opportunities that that space borne computer provides and and Mark mentioned our colleagues at the I S s national lab. You know, um, the space station has been declared a national laboratory, and so about half of the, uh, capabilities we have for doing research is a portion to the national lab so that commercial entities so that HP can can do these sorts of projects and universities can access station and and other government agencies. And then NASA can focus in on those things we want to do purely to push our exploration programs. So the opportunities to take advantage of that are there marks opening up the door for a lot of opportunities. But others can just Google S s national laboratory and find some information on how to get in the way. Mark did originally using s national lab to maybe get a good experiment up there. >>Well, it's just astounding to see the progress that this industry is made when you go back and look, you know, the early days of supercomputing to imagine that they actually can be space born is just tremendous. Not only the impacts that it can have on Space six exploration, but also society in general. Mark Wayne talked about that. Guys, thanks so much for coming on the Cube and celebrating Exa scale day and helping expand the community. Great work. And, uh, thank you very much for all that you guys dio >>Thank you very much for having me on and everybody out there. Let's get the XO scale as quick as we can. Appreciate everything you all are >>doing. Let's do it. >>I've got a I've got a similar story. Humanity saw the first trillion calculations per second. Like I said in 1997. And it was over 100 racks of computer equipment. Well, space borne one is less than fourth of Iraq in only 20 years. So I'm gonna be celebrating exa scale day in anticipation off exa scale computers on earth and soon following within the national lab that exists in 20 plus years And being on Mars. >>That's awesome. That mark. Thank you for that. And and thank you for watching everybody. We're celebrating Exa scale day with the community. The supercomputing community on the Cube Right back

>>from around the globe. It's the Cube with digital coverage of data automated and event. Siri's brought to you by Iot. Tahoe. Okay, we're back with Adam Worthington. Who's the CTO and co founder of Ethos Adam. Good to see you. How are things across the pond? >>Thank you. I'm sure that a little bit on your side. >>Okay, so let's let's set it up. Tell us about yourself. What your role is a CTO and give us the low down on those. >>Sure, So we get automatic. As you said CTO and co founder of A were pretty young company ourselves that we're in our sixth year and we specialize in emerging disruptive technologies within the infrastructure Data center kind of cloud space. And my role is the technical lead. So it's kind of my job to be an expert in all of the technologies that we work with, which can be a bit of a challenge if you have a huge portfolio, is one of the reasons we deliberately focusing on on also kind of a validation and evaluation of new technologies. Yeah, >>so you guys are really technology experts, data experts and probably also expert in process and delivering customer outcomes. Right? >>That's a great word there, Dave Outcomes. That's a lot of what I like to speak to customers about on. Sometimes I get that gets lost, particularly with within highly technical field. I like the virtualization guy or a network like very quickly start talking about the nuts and bolts of technology on I'm a techie. I'm absolutely a nerd, like the best tech guitar but fundamentally reporting in technologies to meet. This is outcomes to solve business problems on on to enable a better way. >>Love it. We love tech, too, but really, it's all about the customer. So let's talk about smart data. You know, when you when you throw in terms like this is it kind of Canfield Buzz Wordy. But let's let's get into the meat on it. What does that mean to you? One of the critical aspects of so called smart data >>cool probably hoped to step back a little bit and set the scene a little bit more in in terms of kind of where I came from, the types of problems that I'm really an infrastructure solution architect trace on what I kind of benefits. We organically But over time my personal framework, I focused on three core design principles whatever it was I was designing. And obviously they need different things. Depending on what technology area is that we're working with. That's pretty good on. And what I realized that we realized we started with those principles could be it could be used more broadly in the the absolute best of breed of technologies. And those really disrupt, uh, significantly improve upon the status quo in one or more of those three areas. Ideally or more simple, more on if we look at the data of the challenges that organizations, enterprises organizations have criticized around data and smart fail over the best way. Maybe it's good to reflect on what the opposite end of the story is kind of why data is often quite dumb. The traditional approaches. We have limited visibility into the data that we're up to the story using within our infrastructure as what we kind of ended up with over time, through no fault of the organizations that have happened silos, everyone silos of expertise. So whether that be, that's going out. Specialized teams, socialization, networking. They have been, for example, silos of infrastructure, which trade state of fragmentation copies of data in different areas of the infrastructure on copies of replication in that data set or reputation in terms of application environments. I think that that's kind of what we tend to focus on, what it's becoming, um, resonating with more organizations. There's a survey that one of the vendors that we work with actually are launched vendor 5.5 years ago, a medical be gone. They work with any company called Phantom Born a first of a kind of global market, 900 respondents, all different vectors, a little different countries, the U. S. And Germany. And what they found was shocking. It was a recent survey so focused on secondary data, but the lessons learned the information taken out a survey applies right across the gamut of infrastructure data organizations. Just some stats just pull out the five minutes 85% off the organization surveyed store between two and five stores data in 3 to 5 clouds. 63% of organizations have between four and 16 coffees of exactly the same data. Nearly nine out of 10 respondents believe that organizations, secondly, data's fragmented across silos are touched on is would become nearly impossible to manage over the long term on. And 91% of the vast majority of organizations leadership were concerned about the level of visibility their teams. So they're the kind of areas that a smart approach to data will directly address. So reducing silos that comes from simplifying so moving away from complexity of infrastructure, reducing the amount of copies of data that we have across the infrastructure and reducing the amount of application environment. I mean, Harry, so smarter we get with data is in my eyes. Anyway, the further we moved away from this, >>there was a lot in that answer, but I want to kind of summarize it if I can talk. You started with simplicity, flexibility, efficiency. Of course, that's what customers want. And then I was gonna ask you about you know, what challenges customers are facing, and I think you laid it out here. But I want to I want to pick on a couple of some of the data that you talked about the public cloud treat that adds complexity and diversity in skill requirements. The copies of data is so true, like data is just like like if rebels, If you Star Trek franchise, they just expand and replicate. So that's an expense, and it adds complexity. Silo data means you spend a lot of time trying to figure out who's got the right data. What's the real truth with a lot of manual processes involved in the visibility is obviously critical. So those are the problems on. But course you talked about how you address those, But But how does it work? I mean, how do you know what's what's involved in injecting smarts into your data? Lifecycle >>that plane, Think about it. So insurance of the infrastructure and say they were very good reasons why customers are in situations they have been in this situation because of the limits are traditional prices. So you look at something is fundamental. So a great example, um on applications that utilize the biggest fundamentally back ups are now often what that typically required is completely separate infrastructure to everything else. But when we're talking about the data set, so what would be a perfect is if we could back up data on use it for other things, and that's where a, uh, a technology provider like So So although it better technology is incredibly simple, it's also incredibly powerful and allows identification, consolidation. And then, if you look at just getting insight out of that fundamentally tradition approaches to infrastructure, they're put in a point of putting a requirement. And therefore it wasn't really incumbent exposed any information out of the data that's stored within the division, which makes it really tricky to do anything else outside of the application. That that's where something like Iot how come in in terms of abstracting away the complexity more directly, I So these are the kind of the area. So I think one of my I did not ready, but generally one of my favorite quotes from the French philosopher and a mathematician, Blaise Pascal, he says, I get this right. I have written a short letter, but I didn't have time. But Israel. I love that quite for lots of reasons, that computation of what we're talking about, it is actually really complicated to develop a technology capability to make things simple, more directly meet the needs of the business. So you provide self service capabilities that they just need to stop driving. I mean making data on infrastructure makes sense for the business users. Music. It's My belief is that the technology shouldn't mean that the users of the technology has to be a technology expert what we really want them to be. And they should be a business experts in any technology that you should enable on demand for the types of technologies to get me excited. They're not necessarily from a ftt complicated technology perspective, but those are really focused on impressive the capability. >>Yeah. Okay, so you talked about back up, We're gonna hear from Kohi City a little bit later and beyond backup data protection, Data Management, That insight piece you talked earlier about visibility, and that's what the Iot Tahoe's bringing table with its software. So that's another component of the tech stack, if you will, Um, and then you talk about simplicity. We're gonna hear from pure storage. They're all about simple storage. They call it the modern data experience. I think so. So those are some of the aspects and your job. Correct me. If I'm wrong is to kind of put that all together in a solution and then help the customer realize that we talked about earlier that business out. >>Yeah, it's that they said, in understanding both sides so that it keeps us on our ability to be able to deliver on exactly what you just said. It's being experts in the capabilities and new and better ways to do things but also having the kind of business under. I found it to be able to ask the right questions, identify how new a better price is positions and you touched on. Yet three vendors that we work with that you have on the panel are very genuinely of. I think of the most exciting around storage and pure is a great one. So yes, a lot of the way that they've made their way. The market is through impressive C and through producing data redundancy. But another area that I really like is with that platform, you can do more with less. And that's not just about using data redundancy. That's about creating application environment, that conservative, then the infrastructure to service different requirements are able to do that the random Io thing without getting too kind of low level as well as a sequential. So what that means is that you don't necessarily have to move data from application environment a do one thing. They disseminate it and then move it to the application environment. Be that based environment three in terms of an analytics on the left to right work. So keep the data where it is, use it for different requirements within the infrastructure and again do more with less. And what that does is not just about simplicity and efficiency. It significantly reduces the time to value. Well at that again resonates that I want to pick up a soundbite that resonates with all of the vendors we have on the panel later. This is the way that they're able todo a better a better TCO better our alliance significantly reduce the value of data. But to answer your question, yeah, you're exactly right. So it's key to us to kind of position, understand? Customer climbs, position the right technology. >>Adam. I wonder if you could give us your insights based on your experience with customers in terms of what success looks like. I'm interested in what they're measuring. I'm big on and end cycle times and taking a systems view, but of course you know customers. They want to measure everything, whether it's the productivity of developers or, you know, time to insights, etcetera. What >>are >>they? One of the KP eyes that are driving success and outcomes? >>Those capabilities on historically in our space have always been a bit really. When you talk about total cost of ownership, talk about return on investment, you talk about time to value on. I've worked in many different companies, many different infrastructure, often quite complicated environments and infrastructure. I'm being able to put together anything Security realistic gets proven out. One solution gets turned around our alliance TCO is challenging. But now with these new, a better approach is that more efficient, enables you to really build a true story and on replicate whatever you want. Obviously ran kind of our life, and the key thing is to say from data, But now it's time to value. So what we what? We help in terms of the scoping on in terms of the understanding what the requirements are, we specifically called out business outcomes what organizations are looking to achieve and then back on those metrics, uh, to those outcomes. What that does is a few different things, but it provides a certain success criteria. Whether that's success criteria within a proof of concept of the mobile solutions on being able to speak that language on before, more directly meet the needs of the business kind of crystallized defined way is we're only really be able to do that. Now we work with >>Yeah, So when you think about the business case, they are a why benefit over cost benefit obviously lower tco you lower the denominator, you're going to increase the output in the value. And then I would I would really stress that I think the numerator, ultimately especially in a world of data, is the most important. And I think the TCO is fundamental. It's really becoming table stakes. You gotta have simple. You've gotta have efficient. You've got to be agile. But it enables that that numerator, whether that's new customer revenue, maybe, you know, maybe cost savings across the business. And again that comes from taking that systems view. Do you >>have >>examples that you can share with us even if they're anonymous, eyes the customers that you work with that or maybe a little further down on the journey, or maybe not things that you can share with us that are proof points here. >>Sure, it's quite easy and very gratifying when you've spoken to a customer. We know you've been doing this for 20 years, and this is the way that your infrastructure if you think about it like this, if we implemented that technology or this new approach, then we will enable you to get simple, often ready, populous. Reduce your back. I worked on a project where a customer accused that back book from I think it was. It was nine. Just under 10. It was nine fully loaded. Wraps back. We should just for the it you're providing the fundamental underlying storage architectures. And they were able to consolidate that that down on, provide additional capacity. Great performance. The less than half Uh huh. Looking at the you mentioned data protection earlier. So another organization. This is a project which is just kind of nearing completion of the moment. Huge organization. They're literally petabytes of data that was servicing their back up in archive. And what they have is not just the reams of data, they have the combined thing. I different backup. Yeah, that they have dependent on the what area of infrastructure they were backing up. So whether it was virtualization that was different, they were backing up. Pretty soon they're backing up another database environment using something else in the cloud. So a consolidated approach that we recommended to work with them on they were able to significantly reduce complexity and reduce the amount of time that it system what they were able to achieve. And this is again one of the clients have they've gone above the threshold of being able to back up. When they tried to do a CR, you been everything back up into in a second. They want people to achieve it. Within the timescales is a disaster recovery, business continuity. So with this, we're able to prove them with a proof up. Just before they went into production and the our test using the new approach. And they were able to recover everything the entire interest in minutes instead of a production production, workloads that this was in comparison to hours and that was those hours is just a handful of workloads. They were able to get up and running with the entire estate, and I think it was something like an hour on the core production systems. They were up and running practically instantaneously. So if you look at really stepping back what the customers are looking to the chief, they want to be able to if there is any issues recover from those issues, understand what they're dealing with. Yeah, On another, we have customers that we work with recently what they had huge challenges around and they were understandably very scared about GDP are. But this is a little while ago, actually, a bit still no up. A conversation has gone away. Just everybody are still speaks to issues and concerns around GDP are applying understanding whether they so put in them in us in a position to be able to effectively react. Subject That was something that was a key metric. A target for on infrastructure solution that we work with and we were able to provide them with the insight into their data on day enables them to react to compliance. And they're here to get a subject access request way created in significantly. I'm >>awesome. Thank you for that. I want to pick up on a little bit. So the first example you get your infrastructure in order to bust down those silos and what I've when I talk to customers. And I've talked to a number of banks, insurance companies, other financial services of manufacturers when they're able to sort of streamline that data lifecycle and bring in automation and intelligence, if you will. What they tell me is now they're able to obviously compress the time to value, but also they're loading up on way more initiatives and projects that they can deliver for the business. And you talk for about about the line of business having self served. The businesses feel like they actually are really invested in the data, that it's their data that it's not, you know, confusing and a lot of finger pointing. So so that's that's huge on. And I think that your other example is right on as well of really clear business value that organizations are seeing. So thanks for those you know. Now is the time really, t get these houses in order, if you will, because it really drives competitive advantage, especially take your second example in this isolation economy, you know, being able to respond things like privacy are just increasingly critical. Adam, give us the final thoughts. Bring us home in this segment, >>not the farm of built, something we didn't particularly touch on that I think it's It's fairly fairly hidden. It isn't spoken about as much as I think it is that digital approaches to infrastructure we've already touched on there could be complicated on lack of efficiency, impact, a user's ability to be agile, what you find with traditional approaches. And you already touched on some of the kind of benefits and new approaches that they're often very prescriptive, designed for a particular as the infrastructure environment, the way that it served up to the users in a kind of A packaged either way means that they need to use it in that whatever way, in places. So that kind of self service aspect that comes in from a flexibility standpoint that for me in this platform approach, which is the right way to address technology in my eyes enables it's the infrastructure to be used effectively so that the business uses of the data users what we find in this capability into their hand and start innovating in the way that they use that on the way that they bring benefits a platform to prescriptive, and they are able to do that. So what you're doing with these new approaches is all of the metrics that we touched on fantastic from a cost standpoint, from a visibility standpoint. But what it means is that the innovators in the business want to really, really understand what they're looking to achieve and now tools to innovate with us. Now, I think I've started to see that with projects that were completed, you could do it in the right way. You articulate the capability and empower the business users in the right way. Then very significantly better position. Take advantage of this on really match and significantly bigger than their competition. >>Super Adam in a really exciting space. And we spent the last 10 years gathering all this data, you know, trying to slog through it and figure it out. And now, with the tools that we have and the automation capabilities, it really is a new era of innovation and insights. So, Adam or they didn't thanks so much for coming on the Cube and participating in this program >>Exciting times. And thank you very much today. >>Alright, Stay safe and thank you. Everybody, this is Dave Volante for the Cube. Yeah, yeah, yeah, yeah

>>Yeah. >>From the Cube Studios in Palo Alto and Boston. It's the Cube covering IBM. Think brought to you by IBM. >>Welcome back to IBM. Think Digital 2020. This is the Cube, and we're really excited to have two great guests on Michael Jordan is the distinguished engineer with IBM Z Security. Michael, good to see you again. Welcome back. >>Thank you. It's good to be back. >>And, Matt, what Born is the program director and offering lead for Z 15. Good to see that. >>Thank you for having me, >>guys. Easy. Easy is a good place to be. Great corner, 61% growth. You got to love it. Regulations. It'll be feeling pretty good. I mean, other than what we're going through. But from a business standpoint, Z powered through, didn't it? >>It did. I mean, we're really pleased with the contribution that Z continues to make for our clients. Especially right now, given everything that's going on, business continuity, scale, resilient security. They're just so important for our clients in the platform. >>Yes. So we're gonna We're gonna talk a lot about this. Maybe Matt could start with you just in terms of, you know, you talk about. Ah, cyber resiliency. Hear that a lot? Um, e I think it may be. Means a lot of different things to a lot of different people. What does it mean? Busy? >>Yeah, for us. I mean, you know, we kind of start in many ways with, like that, this definition on that which talks about the ability to anticipate, withstand, recover, adapt all of these adverse conditions, might face or stresses compromises in attacks in your systems and your just cyber results. It's so it's a really important top of mind talking point from other clients who are thinking about this both from, I guess, the resilience when it comes to the systems and also the data as well. From our standpoint, you know, Z has been at the forefront of resilience for many, many generations. Now, whether that's the scale that systems we're able to provide, the ability to tap into more capacity is needed, whether on a temporary or permanent basis, cause you never know when a when a spike might be occurring on day, especially with clients going through digital transformation as well. The fact that we can talk about solutions being designed for seven nines of availability on. But the reason why clients like Tesco or alliances for their resilient banking platform or Department of Treasury in Puerto Rico depend on us or for a highly available solution. So it's never been more important for by us. >>So, Michael, from a technical standpoint, um, I mean, I go back to the rack f days and and I I used to ask, why is it that, you know, the mainframe had, you know, such good security, and it was explained to me years ago? Well, cause you knew everything that went on who touched what? You know, there was a clear understanding of that clear visibility of that. Um, but maybe you could explain just for laypeople from just from a technical standpoint. Why is it that Z has such strong cyber resiliency? >>Sure. So So some of it, I think, is there's 22 aspects that I want to mention first is, you know, culture, right? You know, the IBM Z, you know, development team and broader, you know, design team. We have in our culture to build systems that are secure and robust, that that's kind of part of our DNA. And so it's that mindset when you look at, you know, technologies like parallel system, flex and geographic geographically dispersed, parallel, parallel suspects, GPS. You know, those are ingrained in those technologies, but the other capability that we have or I should say, um, you know, benefit that we we have is we own the whole stack, right? We own, you know, the hardware we own the firmware, um, and we own the software that sits on top of there in the middle, where and so whether it's resiliency or whether it's security when we want to design and build solutions, you know, to make optimal solutions, you know any of those spaces we can actually design and architect the solutions, you know, both at the right point in the stack and across the stack as needed to really deliver on these capabilities. >>So, Matt, one of our partners, ET are holds these CEO roundtables, and one of the CEO said we really weren't ready from a resiliency standpoint. We're too focused on on er and kind of missed the boat on business continuity to narrow focus. I presume you're hearing a lot of that these days. I wonder if you could just tell us about some of the things that you're seeing with clients, Maybe the conversations you're having and how you're helping Sort of broaden that capability. >>Yeah, sure. I mean, to your point. I mean, nobody really could have quite predicted. You know what we're dealing with right now, but, you know, we have had over many generations of the Z platform, you know, clients deeply partnered with us to try and make sure they have a a highly available environment for business continuity. And, you know, just thinking about things from a Dell perspective. You know what they can do to fortify and make their solution sort of more resilient on the day by day basis. I mean, one of the things you might be talking about, some of the inherent capabilities we have a hassle. The fact that we build, you know, our systems with the additional capacity kind of baked in. Which means that for so many of our clients, you know, in the first in the first quarter, where they were seeing the huge amounts of peak workload kind of coming in, that they needed to be able to deal with the fact that we design our systems to be able to just kind of gobble up that work. With that we call dark capacity to be turned on at the drop of a hat. It's tremendously important because not only need to be offsite, just resilient in terms of the applications, but you need to get a deal with growth. You're going through that. The other aspect, which is a new capability with the 15 that kind of builds on what we could do with that dark past thing is this concept of instant recovery. But what we're actually helping clients do there in terms of fortifying and making their environment more resilient, is letting them attack into that dark capacity when they're going through restart activities of partitions, not just thinking about unplanned scenarios, but actually planned out just as well. So what that really helps with is because you always have to do planned maintenance. You know, when your systems, you know when you're partitions your your system because the environment. So what we're doing is saying when you're going through that restart sort of process, whether it's the shutdown, whether it's to bring up of the partition or the middleware or even in fact, actually helping you catch up. Kind of for what? You what you lost one weren't sort of processing workflow. We turn on that extra capacity in the system automatically for this boost window that were that we're helping our clients with. Not only we do that. Mike's point about owning a stack means that we can deliver that in a way that there's no increase in IBM software cost a reliever. So we're always kind of looking about what we can do to kind of move the ball forward to make a client's environment even more resilient as well. >>I've always, I learned from my mainframe days many, many years ago. And what when a vendor comes in and shows a new product, they always ask you what happens when something goes wrong? It's all about recovery that's always been one of the main frame strength. Mike, I want to ask you about data protection. I mean, it's a topic that again means a lot of things to a lot of people you know doesn't mean backup. There's data privacy. There's data Providence. There's data sovereignty. We talk about data protection from a Z prism. >>Sure, so So our point of view on data protection is is we view it as a as a multi layered proposition. It's not. It's not just one thing. In effect, we viewed the lens of a broader, you know, layered cybersecurity strategy where you know, data protection. And, you know, in this case, you know, talking about encryption and being another encrypt data on a massive scale is the foundation for, you know, a layered cyber security strategy, um, and providing capabilities for appliance. Do you protect data at the disk level with the 15? We also introduced the ability of actually being able to protect the data as it flows through their storage area network through something we call fibre channel endpoint security and then layering on top of that, you know, host based encryption capabilities, you know, in the operating system, whether it's, you know, buy or or data set level encryption and you know, then on top of that, they can layer additional capabilities for things like multi factor authentication to protect your privileged identities from being compromised or being able to do damage to your system and then, you know, building and layering. On top of that things like security, intelligence and being able to monitor and understand You know what, what's happening across the system. >>So I was talking with Developer the other day in cloud app pretty, you know, non mission critical. But ask them to use encryption and he said, Yeah, we could, but we don't cause it slows us down a little bit. So I'm wondering how you deal with that trade off performance versus Protection Z. How does he deal with that? >>Sure, So that's that. That's a great That's a great question. And that actually goes back to you know what we did with with our Z 14 so that the generation before and I think we've we've improved that with with the 15 and then I'll get to that in a bit. But one of the barriers that we recognized is exactly what you said is the You know, the cost of doing encryption is prohibitive, Um, and what we did is we have, ah, a cryptographic accelerator that's integrated into our micro processor that's capable of encrypting so each or it's capable of encrypting up to 14 gigabytes of data per second. And if you multiply that by the number of cores that you have. You know, a fully configured you nosy 15 met. What does it have any cores? Do we have in that 100 >>90 with >>190 So So do the math right? 190 times, you know, 14 gigabytes per second. It's an encryption powerhouse, and that can all be done synchronously with extremely low latency. So we have the horsepower to do encryption on a very broad scale with very, very low overhead. And that's what our clients are leveraging and taking advantage of. And with the Zy 15. That being we announced it and made available last year. We actually have now compression that's built into the micro processor so you can actually compress the data, Um, first and then encrypted. And there's a twofold benefits that first is now. I have less data to encrypt, so I have lowered my encryption overhead, and at the same time I've managed to preserve my storage efficiency. So it's a It's a twofold benefit there, >>you know. People talk off about Z, they talk about it, it's open. It's kind of all started back when you guys brought in Lennox. And now, of course, it's It's much more than that. Um, but I'm wondering how open plays into this notion of cyber resiliency in some respects there. Counter poised. But But how do you sort of square that circle for me? >>Yeah, I mean, it's kind of look at it is when it comes to openness and digital transformation, it's kind of doing it without compromise on. That's kind of the way I look at the Z platform because you're right. I mean the fact that we have the likes of open shift support on the seat platform or you can use, you know, answerable for for doing automation. I mean, were always looking to try and make sure that we support from A from a management standpoint or development standpoint. We'll use whichever tool frameworks languages are appropriate on the platform and integrated to a hyper cloud wherever you want to go. That's why when we look at it from the perspective of what it really means to have mission critical applications and why, it's why that is the key point about banks. Insurance companies, etcetera continue to trust. Z is there is the home for their system of record because they want to get the benefits. You know, the best of both worlds. So they want to be able to have the security, the resilience and the scale of the platform. But the same time they want to have flexibility to be able to use cloud native technologies to be able to deploy them on our platform. And then this micro sort of talking about the exciting thing for us is even going one step further. That says, if you do want your data to move around your hybrid cloud for very good reasons for certain scenarios, being able to have that capability to protect the data, not just encrypted that manage the privacy over the data as it flows out and see to kind of take those characteristics into the hybrid cloud is something that a lot of that clients been really, really excited to take advantage of it. It's >>about this conference. You might get certain >>charting Matt into a security guide. You see that? >>Yeah, >>I think everybody's got to be a security person these days. I want to ask about zero trust. You know, that term is thrown around a lot of, uh, you know, you can get kind of buzz, wordy. You see, people always have substance. I want to ask you guys what zero trust means the Io. >>So So I think there's, you know, my view of zeros where we're at from an industry from from zero. Trust is is very similar to where we're at with cloud, you know, going back a handful of years where if you ask 10 different people what you know, cloud was you get 10 different answers. Um, and none of them were probably wrong. And so I think, you know, we're very similar state in terms of our understanding and, you know, market maturity around zero trust. But there's, you know, at its for, you know, the the the The idea is, you know, we've been focused on protecting, you know, our environments using a castle and moat of approach. Um, and, you know, you know, protecting the perimeter. Yeah, and then trusting everything inside of inside of that. You know that that mode, if you will, um and what the zero trust is a recognition that that's not sufficient. And, you know, and then if you look at that in the context of our evolving and changing in environment and moving to hybrid multi clouds where, um, the notion of a perimeter is gone. You know that that strategy and approach for protection, it doesn't hold up. And so we need to evolve that, um And we need to have, you know, you know, move from the notion of, um, operational trust to a notion of technical trust and building, you know, building more sophisticated mechanisms for doing authentication, understanding broader what's happening across the environment and feeding that into, you know, decisions that are made in terms of who gets to access. What data. So, >>yeah, good, Matt, bring us home overnight. You know, this pandemic has really heightened our awareness of cyber resiliency. Business continuity have changed our our mindset and definition of those two things. But give us your final thoughts on this top. >>I think it's probably just been into sharp focus, really what? It what it means to have mission critical applications that are right at the heart of your of your business. And, you know, you come to realize very quickly. But if those services are not available to your clients, I mean it can have such a long lasting implications So I think people embittering you know their strategy when it comes to, you know, millions off applications with infrastructure and all of that in the context of business continuity, I think people are gonna gonna have a much sharper focus in the future to really see, you know, what is what does it mean? And it's the lifeblood of their business is not able todo operate and serve their clients. And probably as well, more and more applications that maybe weren't considered mission critical in the past will be considered mission critical now because it's not just the back end services, but it's the way the community a reply. It's so a lot of that, I think, is going to play out the way that people think about their business continuity strategy in the future. >>Yeah, you're right. Video conferencing has become mission critical, isn't it? Guys, thanks so much for coming on the Cube again. You know, keep up the good work. Uh, I really appreciate your time and your insights. Always, always great talking, talking Z. So thanks again. >>Thank you. >>All right. Thank you for watching. Everybody. This is Dave Volante for the Cube. Our wall to wall coverage of the think 2020 digital event experience. Keep right there. Right back after this short break. >>Yeah, yeah, yeah.

>> Live from Times Square in New York City, it's theCUBE, covering IBM's Change the Game, Winning with AI, brought to you by IBM. >> Hi everybody, welcome back to theCUBE's special presentation. We're here at the Western Hotel and the theater district covering IBM's announcements. They've got an analyst meeting today, partner event. They've got a big event tonight. IBM.com/winwithAI, go to that website, if you're in town register. You can watch the webcast online. You'll see this very cool play of Vince Lombardy, one of his famous plays. It's kind of a power sweep right which is a great way to talk about sort of winning and with X's and O's. So anyway, Daniel Hernandez is here the vice president of IBM analytics, long time Cube along. It's great to see you again, thanks for coming on. >> My pleasure Dave. >> So we've talked a number of times. We talked earlier this year. Give us the update on momentum in your business. You guys are doing really well, we see this in the quadrants and the waves, but your perspective. >> Data science and AI, so when we last talked we were just introducing something called IBM Club Private for data. The basic idea is anybody that wants to do data science, data engineering or building apps with data anywhere, we're going to give them a single integrated platform to get that done. It's going to be the most efficient, best way to do those jobs to be done. We introduced it, it's been a resounding success. Been rolling that out with clients, that's been a whole lot of fun. >> So we talked a little bit with Rob Thomas about some of the news that you guys have, but this is really your wheelhouse so I'm going to drill down into each of these. Let's say we had Rob Beerden on yesterday on our program and he talked a lot about the IBM Red Hat and Hortonworks relationship. Certainly they talked about it on their earnings call and there seems to be clear momentum in the marketplace. But give us your perspective on that announcement. What exactly is it all about? I mean it started kind of back in the ODPI days and it's really evolved into something that now customers are taking advantage of. >> You go back to June last year, we entered into a relationship with Hortonworks where the basic primacy, was customers care about data and any data driven initiative was going to require data science. We had to do a better job bringing these eco systems, one focused on kind of Hadoop, the other one on classic enterprise analytical and operational data together. We did that last year. The other element of that was we're going to bring our data science and machine learning tools and run times to where the data is including Hadoop. That's been a resounding success. The next step up is how do we proliferate that single integrated stack everywhere including private Cloud or preferred Clouds like Open Shift. So there was two elements of the announcement. We did the hybrid Cloud architecture initiative which is taking the Hadoop data stack and bringing it to containers and Kubernetes. That's a big deal for people that want to run the infrastructure with Cloud characteristics. And the other was we're going to bring that whole stack onto Open Shift. So on IBM's side, with IBM Cloud Private for data we are driving certification of that entire stack on OpenShift so any customer that's betting on OpenShift as their Cloud infrastructure can benefit from that and the single integrated data stack. It's a pretty big deal. >> So OpenShift is really interesting because OpenShift was kind of quiet for awhile. It was quiest if you will. And then containers come on the scene and OpenShift has just exploded. What are your perspectives on that and what's IBM's angle on OpenShift? >> Containers of Kubernetes basically allow you to get Cloud characteristics everywhere. It used to be locked in to kind of the public Cloud or SCP providers that were offering as a service whether PAS OR IAS and Docker and Kubernetes are making the same underline technology that enabled elasticity, pay as you go models available anywhere including your own data center. So I think it explains why OpenShift, why IBM Cloud Private, why IBM Club Private for data just got on there. >> I mean the Core OS move by Red Hat was genius. They picked that up for the song in our view anyway and it's really helped explode that. And in this world, everybody's talking about Kubernetes. I mean we're here at a big data conference all week. It used to be Hadoop world. Everybody's talking about containers, Kubernetes and Multi cloud. Those are kind of the hot trends. I presume you've seen the same thing. >> 100 percent. There's not a single client that I know, and I spend the majority of my time with clients that are running their workloads in a single stack. And so what do you do? If data is an imperative for you, you better run your data analytic stack wherever you need to and that means Multi cloud by definition. So you've got a choice. You can say, I can port that workload to every distinct programming model and data stack or you can have a data stack everywhere including Multi clouds and Open Shift in this case. >> So thinking about the three companies, so Hortonworks obviously had duped distro specialists, open source, brings that end to end sort of data management from you know Edge, or Clouds on Prim. Red Hat doing a lot of the sort of hardcore infrastructure layer. IBM bringing in the analytics and really empowering people to get insights out of data. Is that the right way to think about that triangle? >> 100 percent and you know with the Hortonworks and IBM data stacks, we've got our common services, particularly you're on open meta data which means wherever your data is, you're going to know about it and you're going to be able to control it. Privacy, security, data discovery reasons, that's a pretty big deal. >> Yeah and as the Cloud, well obviously the Cloud whether it's on Prim or in the public Cloud expands now to the Edge, you've also got this concept of data virtualization. We've talked about this in the past. You guys have made some announcements there. But let's put a double click on that a little bit. What's it all about? >> Data virtualization been going on for a long time. It's basic intent is to help you access data through whatever tools, no matter where the data is. Traditional approaches of data virtualization are pretty limiting. So they work relatively well when you've got small data sets but when you've got highly fragmented data, which is the case in virtually every enterprise that exists a lot of the undermined technology for data virtualization breaks down. Data coming through a single headnote. Ultimately that becomes the critical issue. So you can't take advantage of data virtualization technologies largely because of that when you've got wide scale deployments. We've been incubating technology under this project codename query plex, it was a code name that we used internally and that we were working with Beta clients on and testing it out, validating it technically and it was pretty clear that this is a game changing method for data virtualization that allows you to drive the benefits of accessing your data wherever it is, pushing down queries where the data is and getting benefits of that through highly fragmented data landscape. And so what we've done is take that extremely innovated next generation data virtualization technology include it in our data platform called IBM Club Private for Data, and made it a critical feature inside of that. >> I like that term, query plex, it reminds me of the global sisplex. I go back to the days when actually viewing sort of distributed global systems was very, very challenging and IBM sort of solved that problem. Okay, so what's the secret sauce though of query plex and data virtualization? How does it all work? What's the tech behind it? >> So technically, instead of data coming and getting funneled through one node. If you ever think of your data as kind of a graph of computational data nodes. What query plex does is take advantage of that computational mesh to do queries and analytics. So instead of bringing all the data and funneling it through one of the nodes, and depending on the computational horsepower of that node and all the data being able to get to it, this just federates it out. It distributes out that workload so it's some magic behind the scenes but relatively simple technique. Low computing aggregate, it's probably going to be higher than whatever you can put into that single node. >> And how do customers access these services? How long does it take? >> It would look like a standard query interface to them. So this is all magic behind the scenes. >> Okay and they get this capability as part of what? IBM's >> IBM's Club Private for Data. It's going to be a feature, so this project query plex, is introduced as next generation data virtualization technology which just becomes a part of IBM Club Private for Data. >> Okay and then the other announcement that we talked to Rob, I'd like to understand a little bit more behind it. Actually before we get there, can we talk about the business impact of query plex and data virtualization? Thinking about it, it dramatically simplifies the processes that I have to go through to get data. But more importantly, it helps me get a handle on my data so I can apply machine intelligence. It seems like the innovation sandwich if you will. Data plus AI and then Cloud models for scale and simplicity and that's what's going to drive innovation. So talk about the business impact that people are excited about with regard to query plex. >> Better economics, so in order for you to access your data, you don't have to do ETO in this particular case. So data at rest getting consumed because of this online technology. Two performance, so because of the way this works you're actually going to get faster response times. Three, you're going to be able to query more data simply because this technology allows you to access all your data in a fragmented way without having to consolidate it. >> Okay, so it eliminates steps, right, and gets you time to value and gives you a bigger corporate of data that you can the analyze and drive inside. >> 100 percent. >> Okay, let's talk about stack overflow. You know, Rob took us through a little bit about what that's, what's going on there but why stack overflow, you're targeting developers? Talk to me more about that. >> So stack overflow, 50 million active developers each month on that community. You're a developer and you want to know something, you have to go to stack overflow. You think about data science and AI as disciplines. The idea that that is only dermained to AI and data scientists is very limiting idea. In order for you to actually apply artificial intelligence for whatever your use case is instead of a business it's going to require multiple individuals working together to get that particular outcome done including developers. So instead of having a distinct community for AI that's focused on AI machine developers, why not bring the artificial intelligence community to where the developers already are, which is stack overflow. So, if you go to AI.stackexchange.com, it's going to be the place for you to go to get all your answers to any question around artificial intelligence and of course IBM is going to be there in the community helping out. >> So it's AI.stackexchange.com. You know, it's interesting Daniel that, I mean to talk about digital transformation talking about data. John Furrier said something awhile back about the dots. This is like five or six years ago. He said data is the new development kit and now you guys are essentially targeting developers around AI, obviously a data centric. People trying to put data at the core of the organization. You see that that's a winning strategy. What do you think about that? >> 100 percent, I mean we're the data company instead of IBM, so you're probably asking the wrong guy if you think >> You're biased. (laughing) >> Yeah possibly, but I'm acknowledged. The data over opinions. >> Alright, tell us about tonight what we can expect? I was referencing the Vince Lombardy play here. You know, what's behind that? What are we going to see tonight? >> We were joking a little bit about the old school power eye formation, but that obviously works for your, you're a New England fan aren't you? >> I am actually, if you saw the games this weekend Pat's were in the power eye for quite a bit of the game which I know upset a lot of people. But it works. >> Yeah, maybe we should of used it as a Dallas Cowboy team. But anyways, it's going to be an amazing night. So we're going to have a bunch of clients talking about what they're doing with AI. And so if you're interested in learning what's happening in the industry, kind of perfect event to get it. We're going to do some expert analysis. It will be a little bit of fun breaking down what those customers did to be successful and maybe some tips and tricks that will help you along your way. >> Great, it's right up the street on the west side highway, probably about a mile from the Javis Center people that are at Strata. We've been running programs all week. One of the themes that we talked about, we had an event Tuesday night. We had a bunch of people coming in. There was people from financial services, we had folks from New York State, the city of New York. It was a great meet up and we had a whole conversation got going and one of the things that we talked about and I'd love to get your thoughts and kind of know where you're headed here, but big data to do all that talk and people ask, is that, now at AI, the conversation has moved to AI, is it same wine, new bottle, or is there something substantive here? The consensus was, there's substantive innovation going on. Your thoughts about where that innovation is coming from and what the potential is for clients? >> So if you're going to implement AI for let's say customer care for instance, you're going to be three wrongs griefs. You need data, you need algorithms, you need compute. With a lot of different structure to relate down to capture data wasn't captured until the traditional data systems anchored by Hadoop and big data movement. We landed, we created a data and computational grid for that data today. With all the advancements going on in algorithms particularly in Open Source, you now have, you can build a neuro networks, you can do Cisco machine learning in any language that you want. And bringing those together are exactly the combination that you need to implement any AI system. You already have data and computational grids here. You've got algorithms bringing them together solving some problem that matters to a customer is like the natural next step. >> And despite the skills gap, the skill gaps that we talked about, you're seeing a lot of knowledge transfer from a lot of expertise getting out there into the wild when you follow people like Kirk Born on Twitter you'll see that he'll post like the 20 different models for deep learning and people are starting to share that information. And then that skills gap is closing. Maybe not as fast as some people like but it seems like the industry is paying attention to this and really driving hard to work toward it 'cause it's real. >> Yeah I agree. You're going to have Seth Dulpren, I think it's Niagara, one of our clients. What I like about them is the, in general there's two skill issues. There's one, where does data science and AI help us solve problems that matter in business? That's really a, trying to build a treasure map of potential problems you can solve with a stack. And Seth and Niagara are going to give you a really good basis for the kinds of problems that we can solve. I don't think there's enough of that going on. There's a lot of commentary communication actually work underway in the technical skill problem. You know, how do I actually build these models to do. But there's not enough in how do I, now that I solved that problem, how do we marry it to problems that matter? So the skills gap, you know, we're doing our part with our data science lead team which Seth opens which is telling a customer, pick a hard problem, give us some data, give us some domain experts. We're going to be in the AI and ML experts and we're going to see what happens. So the skill problem is very serious but I don't think it's most people are not having the right conversations about it necessarily. They understand intuitively there's a tech problem but that tech not linked to a business problem matters nothing. >> Yeah it's not insurmountable, I'm glad you mentioned that. We're going to be talking to Niagara Bottling and how they use the data science elite team as an accelerant, to kind of close that gap. And I'm really interested in the knowledge transfer that occurred and of course the one thing about IBM and companies like IBM is you get not only technical skills but you get deep industry expertise as well. Daniel, always great to see you. Love talking about the offerings and going deep. So good luck tonight. We'll see you there and thanks so much for coming on theCUBE. >> My pleasure. >> Alright, keep it right there everybody. This is Dave Vellanti. We'll be back right after this short break. You're watching theCUBE. (upbeat music)