>>from >>Around the globe. It's the cube with digital coverage of IBM think 2021 brought to you by IBM. >>Welcome back to the cubes ongoing coverage of IBM Think 2021 virtual cube, you know, the pandemic has caused us to really rethink this this whole concept of operational resilience and we're gonna dig into that and talk about the importance of constructing a holistic resilience plan and get the perspective of some really great domain experts. Alan Downs is the vice president, global Cloud security and resiliency services at IBM and he's joined by MS Michelle what? Weston who is the director of cloud security and resiliency offerings at IBM folks. Welcome to the cube. Thanks for coming on. >>Thank you. >>Now before we get into it, I said IBM but I want to ask you, alan about an announcement you made last month about Kendrell new spin out from IBM. What can you tell us? >>Very excited about the name? I think there's a lot of meaning in the name centered around new growth and censored around partnership and relationship. So if you look at the name that was announced I think it really does typify what we set out to be as a trusted partner in the industry. All born around new growth centered around strong partnership and relationship. So very pleased and excited and look forward to the opportunity we have going forward. >>Yeah congratulations on that. Had some clarity martin schroder. New ceo Cubillan. Great executive love it. So good luck. Um Alan let me stay with you for a second. I mean operational resilience it means different things to different people and we know from speaking with C. IOS in our community during the pandemic. It doesn't just mean disaster recovery. In fact a lot of C. I. O. Said that their business continuing strategy were too focused on on D. R. Ellen. What does operational resilience mean from your perspective? >>So I'll answer it this way. Operational resiliency risk is defined as the quantifiable steps that any client needs to take in order to respond, recover from an unplanned outage. It sits squarely within operational risk. And if you think about it, operational risk is the kind of non financial element of risk. And defined within that category, operational resiliency risk is trying to identify those steps both pre active and reactive that a client needs to consider that they would have to take in the event of an unplanned disruption or an unplanned outage that would impact their ability to serve their clients or to serve their organization. That's how I define operational resiliency risk. >>Great and I wonder Michelle if you can add to that but I think you know I sometimes say that the pandemic was like a forced march to digital and part of that was business resilience. But You know, where do we go from here? You know, we had 14 months shoved into our face and now we have some time to think about. So how should clients think about evolving their strategies in this regard? >>Yeah, Well, certainly with respect to what was called Newco now, Kendrell, um our approach has been advisory led. Uh we will help clients along this journey. Uh, one thing that I'd like to point out in one of the journeys that we've been taking over the last couple of years is it really is about security and resiliency together. If you think of that planning and how to mitigate your operational risk, the security and resiliency go hand in hand through the same people within the organization that are planning for that and worried about it. And so we had already started about three years ago to pull the two together and to have a unified value proposition for clients around security and resiliency, both being advisory lead, doing everything for a client from project based to the digital consumption world which we know clients live in today to a fully managed service all around security and resiliency together. >>Yeah, so I mean it's really important topic. I mean you heard Chair Powell last month. He was he was on 60 minutes saying well yeah worried about inflation, were way more worried about security. So so alan you know, were let's say you're in the virtual, you know, conference room with the board of directors. What's that conversation like? Uh where does it start? >>I think there is a huge concern right now with regard to security and obviously resiliency as well. But if you just think about what we've all been through and what's transpired in the last 12 months, the what we call the threat landscape has broadened significantly and therefore clients have had to go through a rapid transformation not just by moving employees to home base, but also their clients having a much higher expectation in terms of access to systems, access to transactions which are all digital. So you referred to it earlier. But the transformation, our clients have had to go on driving a higher dependence on those systems that enable them to serve their clients digitally and enable them to allow the employees to work remotely in this period has increased the dependencies that they have across the environment that are running many of the critical business processes. So the discussion in the boardroom is very much are we secure? Are we safe? How do we know how safe and secure and resilient should we be? And based on that fact about how safe and secure should we be? Where are we today as an organization? And I think these are the questions that are at the boardroom is basically from a resiliency security perspective, where should we be that supports our strategy vision and our client expectation? And then the second question is very much where are we today? How do we know that we are secure? How do we know that we can recover from any unplanned or unforeseen disruption to our environments? >>So Michelle, I mean I just mentioned the threat surface is expanding and we're just getting started, everybody's like crazy about five G leaning in the edge Iot and that's just uh this could be orders of magnitude by the end of the decade compared to where it is today. So how do you think about the key steps that organizations should should take to ensure operational resilience, you know, not only today, but also putting in a road map. >>Yeah, yeah. And and one thing that we do know from our clients is those that have actually planned for resiliency and security at the forefront. They tend to do that more effectively and more efficiently. Um It's much better to do that than to try to do that after an outage. You certainly learn a lot. Um but that's not the experience that you want to go through. You want to have that planning and strategy in the forefront. As Alan said in terms of the threat vector, the pandemic brought that on as well. We saw surgeons Of cyberattacks, opportunistic attacks. Um you know, we saw the best of people in the pandemic as well as the worst in people. Some of those attacks were on agencies that we're trying to recover. We're trying to treat the public with respect to the COVID-19 pandemic. So none of us can let our guard down here. I think we can anticipate that that's only going to increase. And with the emergence of these new technologies like cloud, we know that there's been such a massive benefit to clients. In fact those that were cloud enabled to sustain their businesses during the pandemic full stop. But with that comes a lot more complexity. Those threat vectors increase five G. I expect to be the same. So again, resilience and security have never been more relevant. More important, we see a lot of our clients putting budget there and those that plan for it with a strategic mindset and understand that whatever they have today may be good enough, but in the future they're going to have to invest and continue to evolve that strategy. Are those that have done the best. >>Yeah, the bolt on strategy doesn't doesn't really work that well, but and I wonder if you think about when we talk to CSOS for example, and you ask them what's your biggest challenge? They'll say things like lack of talent. We got too many tools. It's just as we're on the hamsters on wheels. So I would think that's, you know, unfortunately for some, but it's good for your, your business. That's that's a dynamic that you can help with. I mean you're a services organization, you got deep expertise in this. So I wonder if you could, could talk a little bit about that, that lack of talent, that skills gap and how you guys address that. >>I think this is really the fit for managed services providers like Kendrell, um, certainly with some of our largest clients, if we look at Peta as an example, that notion of phone a friend is really important when it starts to go down and you're not sure what you're gonna do next. You want the expertise, you want to be able to phone someone and you want to be able to rely on them to help you recover your most critical data. One of the things clients have also been asking us for is a vaulted capability, almost like the safe deposit box for your data and your critical applications. Being able to put them somewhere and then in the event of needing to recover, um, you certainly could call someone to help you do exactly that >>Ellen. I wonder if you can address this. I mean, I like IBM I was I'm a customer. I trust IBM. What's your relationship? Are you still gonna, you know, be able to allow me to tap the pieces that that I like and maybe you guys can be more agile in some respects, maybe you can talk about that a little bit. >>She has Sure, Dave and many of our clients, we have a long history with a very positive experience of delivering, you know, market leading and high high quality of services and product the relationship continue. So we will remain very close to IBM and we will continue to work with many of IBM's customers as will IBM work with our customers going forward. So the relationship, I believe whilst a different dynamic will continue and I believe engenders an opportunity for growth and you know, we mentioned earlier the very name signifies the fact that it's new growth and I do think that that partnership will continue and we'll continue together to deliver the type of service, the quality of products and services that our clients have, you know, enjoyed from IBM over the last number of years, >>Michelle my, one of my takeaway from your earlier comments as you guys are hands on consultative in nature. Um, and I think about the comment I made about a lot of Ceo said we were way too d our focus. But when I think about d are a lot of times it was a checkbox to the board. Hey, we got it. But it was last time you tested it. Well, we don't test it because it's too risky to test. You know, we, we do fail over, but we don't fail back because it's just too risky. Can I stress test, you know, my environment, we, at the point now where technology and expertise will allow us to do that is that part of what you bring to the table? >>It is exactly exactly what we bring to the table. So from a first of all, from a compliance and regulatory perspective, you no longer have that option. A lot of the auditors are asking you to demonstrate your d our plan. We have technology and I think we've talked about this before about the automation that we have in our portfolio with resiliency orchestration that allows you to see the risk in your environment on a day to day basis. Proactively manage it. I tried to recover this, there's a there's a failure and then you're able to proactively address it. I also give the example from a resiliency orchestration perspective in this very powerful software automation that we have for D. R. We've had clients that have come in scheduled A. D. R. Test, it was to be all day they've ordered in lunch And the D. R. test fail over failed back took 22 minutes and lunch was canceled. >>I love >>it. Very powerful and very powerful with an auditor. >>That's awesome. Okay guys, we've got to leave it there. Really great to get the update. Best of luck to you and congratulations. Thanks for coming on. >>Thank you so much >>and thank you for watching. This is Dave Volonte for the cubes continuous coverage of IBM think 2021 right back. >>Mhm.

>>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 the Silicon Angle Media Office in Boston, Massachusetts. It's the cue Now, here's your host. Day Volonte. >> Hi, buddy. This is David Lantz. Welcome to this cube. Conversation with Dr Rico is the CMO of infinite out. It's still I still have a hard time saying that doctor or an engineer and I love having you on because we could talk storage. We could go deep and we could talk trends and marketing trends, too. But so welcome. Thanks for coming on my sled. So tell me what's new since the scale to win launch that you guys had. Tell me what you know. Is everything shipping Now What's the uptake been like with customers? And the reaction? Yeah, >> they're the reaction has been phenomenal. This, as you may recall, you were there. It was biggest launch in our history, which was fantastic. And the reaction has just been overwhelmingly positive, with customers with partners with analysts. Human scum cases with competitors is an interesting you know, we had a lot of things that were already shipping. They were an early customer release. There were a few things that we had started shipping in December on the things that we said we'd be coming in three Q. We G eight on time. So there, there now all generally available except the stuff that we talked about that would be available in 2020 which right now looks like it's on track. It's doing very, very well. >> So VM wear VM world eyes coming up later on this month, things are obviously changing. There was announcement recently that that VM wears gonna choir pivotal. So a little bit of financial engineering going on stock stock rose 77% on the day when the Dow dropped 800. So okay, the funny money. But things are changing in the V m where ecosystem you certainly saw we we This is our 10th year the M world. We go back and you hear Tod Nielsen back in the day, talk about for every dollar spent on a V M where lice and 15 was spent a Negro system, you know, we're kinda del izing vm wear now, which is sort of interesting, but I'm curious as to what you're seeing what that all means to you. I mean, still half a million 600,000 customers, you've got to be there you guys have great success at that show. So your thoughts what's going on? But VM world this year? Yeah, I >> kind of kind of loaded their first of all congratulations on the milestone. That's great. 10 years is super. Remember, probably seeing you with the 1st 1 there. Of course we knew each other longer. Uh, you know, and sure I get the incestuous, you know, money changing of hand there, I think I think it's it's good in one respect. You certainly CBM where, you know, making big inroads with VM wear on AWS. And this isn't now with Pivotal will be a good launching platform for Della's well, a svm where to be a little bit more in control of their own destiny. And it's certainly the way a lot of people are going. We're doing a lot of that ourselves. Not so much, in a sense. We don't have a cloud platform that we sell is a total encompassing platform. But of course, with new tricks cloud on big players and then certainly a large portion of our our customer base, our cloud service providers, they love our stuff. It helps them compete. It actually gives them in some respects, a competitive advantage, but VM world itself. Lots going on there. We have amplified our presence once again because VM where does represent a large portion of our customer base? So we're we're very proud of that. We're very proud to be a technology alliance partner of the M wears Andi. We're expecting to see a really good show in a really good cloud. A cloud crowd has they return back to their home base in San Francisco for us this year, it's It's gonna be a different experience. Were tellingme or of the software story, more of the portfolio story more about how you scare scale the win. We have a virtual presence this year, which is going to be very helpful in telling that story. Customers can come in and they can see more than just a ah box that in our world is really not important because it's for us. It's all about the software and stuff we do. We even in Booth Theater, we have some private meeting spaces well, to take people into a bigger, deeper drill down. But the virtual experience will allow them to touch and feel stuff that maybe they didn't get to do before, and that's gonna be kind of exciting as well. >> So you mentioned C S P s. We had Michael Gray thrive on a while back, and you know, he was saying that Look, he likes your product because it allows him to do other things. And don't worry about, you know, the old sort of tuning and managing and ableto re shift labor. I felt like that was an interesting discussion, primarily because you've got all these cloud service providers that everybody thought aws was just gonna kill. And if anything, it's elevated them. What are you seeing in the CSP space? Yeah, you know, >> Michael had a lot of interesting things to say that definitely love the fact that we enable multiple workloads without them having to do lots of cautious planning and re planning and shifting and shuffling. And we are seeing C S P is becoming more value. Add to a lot of businesses, especially the mid market and the smaller enterprise where people may want more than just infrastructure. You know, they don't they need that application level support and companies like thrive in some of our other really good customer, US signal and you know they're all capable of Flex Central's. Another one they're all capable of providing service is beyond the hardware they're capable of providing that application support the guidance and, in the case of Thrive, the cybersecurity guidance especial Really, which is really, really critical. So they're growing, and they're also, by the way, working with eight of us and Google and Azure to provide that capabilities well, when necessary. >> Well, that leads me to the sort of multi cloud discussion in our industry. We tend to have this alphabet soup of acronyms like another reason I like talking to you because we can kind of cut through that. And, you know, I love the marketing. I think marketing helps people understand what's going on differentiate. It gives you an indication of where the industry is going, and multi cloud is one of those things that I mean. I've kind of said it's a symptom of multi vendor and more so than a strategy. But increasingly it seems like it's becoming a strategy with customers, and you just gave an example of thrive working with multiple cloud vendors. Clearly, VM where wants to be in that business. What your thoughts on multi cloud and and hybrid. What does it mean for for infinite at What's your strategy there? You know, it's it's interesting because I >> just read an article the other day about you know, the definition of multi cloud on whether it's being abused and, you know, I I look at it as someone just trying to tell their story and give it. Give it some favor. I think at the end of the day, uh, every business is going to be talking to multiple platforms whether they want to or not. You know, there are many customers and companies out there, businesses who are in our customers who have gone the way of the cloud and repatriated. Certain things is they've they found that it it may work. It may not work, and there are many cloud providers who were trying to do things to accelerate migration of applications because they see that certain applications don't work. You know, we got one of the cloud providers buying Ah, now as provider, another one buying very recently, you know, an envy me based flash company to try to pick up those loose workloads where they might struggle today. But the end of the day everybody's going to be multiple. And whether it's because they're using cloud service is from from a software perspective or whether they just need to basically broker and maintain sort of that that independence so that they can maintain some cost control, availability, control, security, control and in some cases it will remain on premises. And some of things will be off just so they could get the applications closer to their end users. So you know what is multi Cloud? Multi Cloud really is just one of those terms that literally means what it says. It's your business running in multiple places. It doesn't have to necessarily be simultaneously by the same application. >> A big part of your value proposition is the simplicity. We've heard that from your customers, and you guys obviously push that out there. I want to ask you because you mentioned repatriation and you know, Cloud keeps growing like crazy. Sure, and the on prem not so much. You guys are smaller company. You're growing your stealing share, So yep. So maybe is that simplicity thing. Here's my question. So it's around automation. The cloud providers, generally an Amazon specifically have have driven automation. They've attacked the IittIe labor problem and they're able to charge for that on Dhe. So my question is, are you seeing that you're able to attack that labor problem in a similar sense and bring forth the value proposition to customers is Look, we can create a cloud like experience on Prem if you want MacLeod. Great. But if you want to stay on Prem, you're gonna get the benefit of being able to shift. Resource is two more strategic things and not have to worry about all this heavy, heavy lifting. You You seeing tangible evidence of that? >> We're seeing significant tangible evidence of that on and, you know, a couple of things. You know, you talk about growth, right? And I think when we did the launch, you know, only a few months ago we were at about 4.6 exabytes of capacity shipped. We just passed 5.1. That's some significant growth in in just a few months. It's like a 33% growth just from the same time last year, which is which is fairly significant. And of course, if you're familiar with the way we talk, you know you have an engineer is the head of marketing. We like to tell the truth. You know, we don't like to mask, do many things and confuse people. We don't like talking about effective storage because effective capacity doesn't really mean much to some people. So that's, you know, this is what we This is what we shipped and it's growing rapidly. And a lot of that is growing, in part because of the significance of the message and in part because of this need to control costs, contain costs and really operate in a more modern way. So get back to your comments about cloud and cloud operation. That's really what people want. People like the consumption model of cloud. They don't always like the cost on hidden costs. So simplifying that, but giving them the flexibility Thio have either an op X or cap ex that allows him to grow and shrink as they move workloads around. Because everybody grows even on Prem is growing. It's just, you know, it's the law of numbers, right? Cloud is growing, absolutely. But on Prem really is growing. And then the other thing I want is they want the operational flexibility. And that's what we talked about in our elastic data fabric. They don't like constantly having to re jigger and re balance workloads. Infinite box by itself. The platform of infinite Box takes away a lot of that mystery and magic, because it it kind of hides all of the complexity of that workload. And it, you know, we take the randomness out of the I o. I think maybe Craig Hibbert mentioned in his video is he was describing in detail how that happens. Remember Michael Gray talking about that as well, you know, So those those things come out in a single infinite box. But even if you said well, I still want to move my workload from, uh, you know this data center to an adjacent data center or perhaps a data center in another facility. Um, excuse me, Another city. So that's closer to the end user. Making that transparent to the applications is critically important. >> Yes, he talked about growth in about 1/2 a PETA bite. Sorry, half an exabyte in just a few months. A couple months? Really Right. That's that's growth. But I want to ask you about petabytes. Petabytes scales. Kind of key of companies that don't do that in a year day, eh? Exactly. So that's a petabytes scale. Is big party of marketing two questions? Why is that relevant? Or is that relevant to VM? Where customers? Why so and then, does it scare some people owe you? Asked a great question. >> It absolutely scared some people. And I know that there are some pundits out their industry pundits who who basically don't agree with our messaging. But this is this is the business problem that we we targeted the solve rate. Um, there are a lot of people out there who don't think they're petabytes scale yet because maybe they're individual applications aren't petabytes scale. But when you add it up, they get there and a lot of our customers are existing. Customers didn't start with infinite at at petabytes scale. They started a couple 100 terabytes, perhaps, but they're petabytes skill now. In fact, over 80% of the customers and systems that we have out there today or above the petty bite. We have customers that are in the tens of petabytes. We have customers that are in the hundreds of petabytes. They grow, they grow rapidly on. Why is that? Well, to two factors. Really. Number one, if you go back to. Probably when I first met you back when I had your hair, at least in quantity, way had way. Were kind of crusting that terabyte mark. Right? Right. And what was the problem? The problem was nobody could figure out how to deal with the performance. Nobody wanted to put that much risk on a single platform, so they couldn't deal with the availability. And they really didn't know how to deal with even the serviceability of that scale. So terabyte was a problem solved No, 25 years ago, and then things were rapidly from there. Now we're at the same juncture, just three orders of magnitude later. Right? >> Well, that's interesting, because, you know, you're right. People didn't want to put all all that capacity under an actuator that cost performance problems. They were concerned about, you know, just availability. And then two things happen so simultaneously, flash comes along. And, you know, you would say was put sort of a Band aid to some of the performance problems. Sure. And you guys came up with, like, this magic sauce to actually use spinning disc and get the same performance or better performance you would argue with flash. And so as a result, you were now able to do a lot Maur with the data, the concerns about that much date under the actuator somewhat attenuated because, I mean, you've got now so much data, you've got to do something that's almost that's flywheel effective. You've got tons of data machine intelligence and a I. Now, coming into the picture, you've got Cloud, which has been this huge tail when for the industry and for data creation in general. And so I see. You know, you see, like the I. D. C numbers and for forecasting growth of data and storage could be low. I mean, the curve could be bending, you know, kind of more than exponentially your thoughts on that. >> Yeah, it's an interesting, interesting observation. I think what it really comes down to is our storyline is math is greater than media, all right? And when you when you look at the flash being, you know, the panacea to performance it was just a step in the evolution, right? You go back and and say, spinning disc was the same solution to the performance problem 20 years ago. 25 years ago, even it was 5400 rpm discs and then very rapidly. Servers got faster. The interconnects got a little bit faster. They were still mostly differential. Scuzzy. There was 7200 rpm discs. And I promise you, by the way, that if you're running 5400 rpm desk, you install 7200 rpm. All yours performance problems will go away until the day you install it. And then it was 10,000 rpm discs and I was 15,000 rpm disc, and it still wasn't getting fast enough because, you know, you went to Fibre Channel One Gig Fibre channel and then to Geek Fibre, Channel four, Gig fibre, Channel eight, gig fibre channel. The unified connects got faster. The servers got faster. That was more cash on the servers. Then this thing came along, cuts called solid state disc. Right. And then it was it was SLC single layer cell technology. But don't worry about it's very expensive. Not a problem. You only need 4% of your application, right? Jerry? No, no, I'm sorry. percent. No, I'm sorry. 30%. What the heck? You know, M l c is now a little bit more reliable, so let's just make make it all slash. Right? So that was the end of the story, right? No. Servers continue to get faster. Uh, the media continue to get faster and denser, right? So now the interconnect isn't fast enough, So envy me. Is that the answer to life? The universe and everything? Well, wait. I got a better answer for your test. CIA storage class memory in parallel with that. By the way, there are some vendors out there who said that's still not fast enough. We want to put more d ram and the servers and do things in memory. We went in memory databases. I guarantee whatever you do from a media perspective on my personal guarantee to you, it's obsolete by the time you're up and running. By the time you get your applications migrated, configured and running with business value, it's already obsolete. Some vendors got something better coming out. The right answers. This stuff you talked about, the right answer is everything that you're doing for your business. APs. It's a it's a Mel. It's solving the problems in software and, you know, you said we use disc and make it fast. It's not despite itself, of course, right? It's D Bram. It's a lot of the Ram, which, by the way, is orders of magnitude faster than flash the NAND flash. And even if its ECM and still orders of magnitude faster than that, what we use the disk for today in the architecture is the cost factor. We take the random ization out in the flash and we take the >> end and in the in the diagram >> and we used the SAS in the back end to manage costs. But we use it in a way that it performs well, which is highly sequential, massively parallel. And we take full advantage of that Beck and Ben with to do that with that massive dear am front end. Our cash ratios are unparalleled in the industry and and we use it even more effectively that way. But if architecture already evolves, so if if SCM becomes more stable and becomes more cost effective, we can replace that that S S D layer with the cm. And if you know, if the economics of Q L C or something beyond that. Come down will replace the back end with that, do you? Do >> you ever look at what you're doing today as sort of a modern day symmetric. So I mean, a lot of things you just said. I mean, you've got a lot of memory. You've got a massive back end. You know, those were two of the characteristics of symmetric snow. Of course. Fast forward. Whatever. 30 years, right. But a lot of it was sort of intelligence and understanding. Sure. So how data works, is it Is it a fair sort of, or is it radically different? Well, in terms of mindset, I mean, I know the implementation is >> right, right? >> Yeah. I mean, it's not an unfair comparison. I mean, tiered storage was around before some metrics. Right? So it's certainly existed existed then, too. It was just at the time. It was a significant innovation course to layer at the time, right? A big cash front, ending some slower media and then taking advantage of the media on the back end. The big difference today is that if you look at what some metrics became through its Evolution's DMX and V Max and now Power Max. It's still tiered storage, you know, you still have some cash. That's that's for unending some faster media with power. Max, you're you're dealing now with us with an SS a back end. But what happened with those types of architectures is the tearing became more automated. But you're still moving information around. You're still moving Information from one said it This to another set of this leader in the cycle. You're still trying to promote things you know, to to the cash up front. We're doing it in real time. We're >> doing it by analyzing >> the data on the way it comes in. We're reassembling it again, taking the random ization out we're reassembling it and storing it across multiple disks in a way that it it increases our probability of pulling that information associated information back when we need it later. So there's there's no movement. Once its place, we don't have to replace it. You know it's already associated with other data that makes sense, and that gives us a lot of value. >> And secret sauce is the outcome of the secret sauce is you're able to very efficiently. Well, historically, you haven't been able to do a lot of garbage collection, a lot of data movement, and that just kills performance. There's >> really no garbage collection necessary in our in our world way. Also use very modern data structures or patents. Ah, lot of them on our neural cash Deal with the fact that we use a try data structure. So we're not using old fashioned hash tables and you know, l are you algorithms, You know it Sze very, very rapid traverse a ll of these trees >> and you're taking advantage of machine intelligence inside the software architecture. That really is some of the new innovation that really wasn't around to be able to take advantage of that 20 years ago. Maybe it was it was just not cost effective. Do the math was there, put it that the math of the mouth was there and >> there there There's been lots of evolutions of that over the years, a swell, but we continue to evolve and innovate. And, you know, one of the one of the cool things I think about working infinite at is is the multiple multiple generations of engineer where you've got people who understand that math they understand the real nuances of what it means to operate in a world of storage, which is quite a bit different than operating, saying networks or proceed be used because data integrity is paramount. There's lots of lots of things that go on there as well. But we also have younger generations, generations who like new challenges and like to re invent things so they find newer and greater ways to do things. >> This is exciting. So systems, thinkers and I mean server thinkers. I mean, people who understand, you know, systems designed it all the way through and and, you know, newbies who are super smart like you say, wanna learn and solve problems? Go back to the petabytes scale discussion, >> solve problems at petabytes scale, right? Even if the customer doesn't need that necessarily to solve that problem is critically important because even if you look at Les, just take, you know NFS, for example, most NFS systems deal with thousands of objects. Hundreds to thousands of objects are an F s. Implementation deals with billions, right? Do you need billions? How many applications you know that have billions of objects, But being able to do that in a way where performance doesn't degrade over time and also do it in a way where we say our nlm implementation isn't impacted by any any type of service events, we can take a note out, and it doesn't impact in ln There's no no degradation and performance. There's no impact or outage in service. All that's important. Even when you're dealing with smaller application sizes because they add up, they really do add up. He also brought up the point about, you know, density and actually intensity. Great. You know, back 25 years ago, when we were dealing with, you know, the first terabyte storage system, you know, how much how much stories did you have on your laptop? How much you have today, right? You know, you're probably more than a terabyte. They were laughing about putting things terabyte on the floor. And now you get more than a terabyte on your laptop. Things changing? >> Yeah. Um, I wanna ask you where you see the competition. We talked about all flash. We've had a long conversation, long, many conversations in the past about this, But you really, you know, the all flashy kind of described it as a Band Aid, essentially my words, but it was sort of a step function. Okay, great. Um, you have one company, really us who achieve escape velocity in that business in terms of pure But is that where you see in competition and you're seeing it from, you know, the hyper scale er's where you Yeah, you know, >> it's interesting. You know, you look at companies like, you know, we admire what they dio, especially with regard to marketing. They do a really good job of that. They also, um I have some really interesting ideas innovating the media, which is which is great. It helps us in the long run as well. Um, we just look at it as a component of our system, not these system, which makes it different. We don't really see the A f a. You know, the small scale a FAA is are the majority of our competition. We do run into them, but typically it the lower end of the opportunity. Even within the bigger companies that have competitors to those products, we run into them and smaller opportunities, not bigger opportunities where we run into them where there's a significant performance advantage as long as you don't mind the scale out approach to solving the problem. Unfortunately, when you're using a phase two skill out, you know you're putting all of the intelligence requirements on some poor storage administrator or system administrator to figure out what those where right, we take all of that away. So once it starts to scale, that's where we come in a plan. We don't see tons of competition there. Certainly, we're seeing competition from the clouds. And the competition from the clouds is more born of customer mandates and company mandates. Sometimes they I'm not quite sure that everybody knows why there who think to the cloud and we're problem they're trying to solve. But once they start to see a story that says, Hey, if the reasons are and you do understand those reasons, if the reasons are agility and financial flexibility and operational agility not as well as his acquisition agility, you know, we have answers to that and it starts to become a little bit more interesting and compelling. >> All right. One of the highlights of the M world each year is your dinner. Your customer I crashed in a couple of years ago when there were no other analysts there. And then last year again, it was in Vegas. Shows a nice steak house. This year we're in San Francisco, but But I had some great conversations with customers. I remember speaking to one customer about juxtaposing the sand thio to infinite debts platform. And you know the difference. The Sands taken off doing really well, but But he helped me understand the thinking from their standpoint of how they're applying it to solve problems and why v san wasn't a good fit. Your system was, um that was just one of many conversations last year had again other great conversations with customers. What do you do in this year? You have a customer dinner. We are? Yeah. We love to have you in and gave the invitation there. Yeah, the invitation. Is that definitely there? You know, a couple of >> years ago we didn't invite analysts, and you know what it was? It was a mistake. We and we learned that lesson into a large part. We credit you for for showing us how wrong we are. Our customers are very loyal. They're some of the most loyal in the industry. Don't take my word for it going. The gardener Pierre Insights and and look at our numbers compared to everybody else's any pick. Pick a vendor. We're at the top of the list with regard to not only the ratings but, more importantly, the customers willingness to recommend in every category, too. By the way, it's It's not just product quality and performance, and it's it's service support. It's easy doing business. It's an entirely different experience. So we love having the customers there, and the customers love having you there, too. They love having you and your appears in the industry there because they love learning from you and they love answering the questions and getting new insights. And we'd love to have you there. We're gonna be in the Mint this year. San Francisco meant not the not the current one that that's pretty coins, but the original historical site on duh. You know we have. We have invitations out thio to about 130 people because there's only so much room we have it at the event, but we're looking forward to a great time and a great meal and good conversation. >> That's great. Well, VM World is obviously one of the marquee events in our industry. It's the It's the fat middle of where the IittIe pro goes on dhe We're excited. Used to be Labor Day started the fall season. Now it's VM world. Well, Doc will see you out there. Thanks very much for your good to see you. All right. Excellent. All right. Thank you for watching everybody. This is day Volonte in the Cube will see you next time we'll see you at the M World 2019.

>> Live from San Francisco, it's theCUBE covering IBM Think 2019. Brought to you by IBM. >> Welcome back, we're here at Moscone North. You're watching theCUBE, the leader in live tech coverage. This is day four of our wall to wall coverage of IBM the Think. The second annual IBM Think, first year at Moscone. Dave Vellante here with Stu Miniman. Eric Herzog is here, he's the CMO of IBM Storage and Sam Werner is the VP of Offering Management for Storage Software at IBM. Guys welcome back to theCUBE. Always good to see ya both. >> Thanks >> Thank you. >> So we were joking yesterday and today, of course multi cloud, the clouds opened, it's been raining, it's been sunny today, so multi cloud is all the rage. Evidently you guys have done some work in multi cloud. Some research that you can share with us. >> Yeah, so couple things. First of all, the storage vision in multi cloud at IBM for years. We work with all the cloud providers including IBM cloud, but we work with Amazon and we work with Azure, we work with Google cloud and in fact our Spectrum Protect, modern data protection product, has about 350 small and medium cloud providers across the world that use it for the engine for their back up as a service. So we've been doing that for a long time, but I think what you're getting is, what we found in a survey multi cloud and I actually had had a panel yesterday and all three of my panelists, including Aetna, use a minimum of five different public cloud providers. So what we're seeing is hybrid is a subset of that, right? On and off, but even if someone is saying, I'm using cloud providers, they're using between five and 10, not counting software as a service because many of the people in the survey didn't realize software as a service is theoretically a type of cloud deployment, right? >> So that's obviously not just the big three or the big five, we're talking about a lot of small guys. Some of the guys maybe you could have used in your Spectrum Protect for back up, local cloud providers, right? And then add sas to that, you could probably double or triple it, right? >> Right, well we've have been very successful with sas providers so for example, one of people on the panel, a company called Follett, they're a privately held, in the mid close to a billion dollars, they provide services to universities and school districts and they have a software package for universities for the bookstores to manage the textbooks and another software as a service for school districts across the United States. They have 1,500 and it's all software service. No on prem licensing and that's an example. That's in my mind, that's a cloud deployment, right? >> Ginni talked Tuesday about chapter two how chapter one was kind of, I call it commodity cloud, but you know, apps that are customer facing, chapter two, a lot of chapter two anyways, is going to be about hybrid and multi cloud. I feel like to date it's largely been, not necessarily a purposeful strategy to go multi cloud, it's just we're multi vendor. Do you see customers actually starting to think about a multi cloud strategy? If so, what's behind that and then more specifically, what are you guys doing from a software stand point to support that? >> Yeah, so in the storage space where we are, we find customers are now trying to come up with a data management strategy in a multi cloud model, especially as they want to bring all their data together to come up with insights. So as they start wanting to build an AI strategy and extend what they're doing with analytics and try to figure out how to get value out of the data they're building a model that's able to consolidate the data, allow them to ingest it and then actually build out AI models that can gain insights from it. So for our software portfolio, we're working with the different types of service providers. We're working closely with all the big cloud providers and getting our software out there and giving our customers flexible ways to move and manage their data between the clouds and also have clear visibility into all the data so they can bring it together. >> You know, I wonder sort of what the catalyst is there? I wrote an article that's going up on SiliconANGLE later and I talked about how the first phase was kind of tire kicking of cloud and then when the down turn hit, people went from capex to opex. It was sort of a CFO mandate and then coming out of the down turn, the lines of business were like, whoa agility, I love this. So shadow IT and then IT sort of bought in and said, "we got to clean up this mess." and that seems to be why, at least one catalyst, for companies saying, "hey, we want a single data management strategy." Are you seeing that or is there more to it? >> Well I think first of all, we're absolutely seeing it and there's a lot of drivers behind it There's absolutely IT realizing they need to get control over this again. >> Governance, compliance, security, edix >> And think about all the new regulations. GDPR's had a huge impact. All a sudden, these IT organizations need to really track the data and be able to take action on it and now you have all these new roles in organizations, like data scientists who want to get their hands on data. How do you make sure that you have governance models around that data to ensure you're not handing them things like pi? So they realized very quickly that they need to have much better control. The other thing you've seen is, the rise of the vulnerabilities. You see much more public attacks on data. You've seen C level executives lose their jobs over this. So there's a lot more stress about how we're keeping all this data safe. >> You're right. Boards are gettin' flipped and it's a big, big risk these days >> Well the other thing you're seeing is legal issues. Canada, the data has to stay in Canada. So if you're multi national and you're a Japanese company, all your Canadian offices, the data has to be some cloud of ours got an office in Canada. So if you're a Japanese headquarter company, using NTT cloud, then you got to use IBM or Amazon or Azure, 'cause you have to have a data center inside the country just to have the cloud data. You also have shier maturity in the market. I would argue, the cloud used to be called the web and before it was the web, it was called the internet and so now that you're doing that, what happens in the bigger companies, procurement is involved, just the way they've been involved in storage servers and networking for a long time. Great you're using CISCO for the network. You did get a quote from HP or using IBM storage, but make sure you get at least one other quote so as that influences aside from definitely getting the control is when procurement get involved, everything goes out for RFP or RFQ or at ten dure, as they say in Europe and you have to have multiple vendors and you sometimes may end up for purely, we need the way to club 'em on price so we need IBM cloud and Microsoft so we can keep 'em honest. So when everyone rushed the cloud, they didn't necessarily do that, but now that it's maturing >> Yeah, it's a sign of maturity. >> It's a sign of maturity that people want to control pricing. >> Alright, so one of the other big themes we've been talking a lot about this week is AI. So Eric talks about, when we roll back the clock, I think back to the storage world, we've been talking about intelligence in storage for longer than my career. So Sam, maybe you can tell us what's different about AI in storage than the intelligence we've been talking and what's the latest about how AI fits into the portfolio? >> Yeah, that's a great question and actually a lot of times we talk about AI and how storage is really important to make the data available for AI, but we're also embedding AI in our storage products. If you think about it, if you have a problem with your storage product, you don't just take down one application. You can take down an entire company, so you've got to make sure your storage is really resilient. So we're building AI in that can actually predict failures before they happen so that our storage never takes any outages or has any down time. We can also predict by looking at behavior out in the network, we can predict or identify issues that a host might be causing on the network and proactively tell a customer before they get the call that the applications are slowing down and we can point out exactly which host is causing the problem. So we're actually proactively finding problems out on the storage network before they become an issue. >> Yeah and Eric, what is it about the storage portfolio that IBM has that makes it a good solution for customers that are deploying AI as an application in use cases? >> Yeah so we look at all, so one is AI, in the box if you will, in the array and we've done a ton of work there, but the other is as the underlying foundation for AI workloads and applications so a couple things. Clearly, AI often is performance dependent and we're focused on all flash. Second thing as Sam already put it out, resilience and availability. If you're going to use AI in an automotive factory to control the supply chain and to control the actual factory floor, you can't have it go down because they could be out tens of millions, hundreds of millions of year just for that day of building Mercedes or Toyotas or whatever they're building if you have an automated factory. The other areas we've created what we call, the data pipeline and it involves three, four members of our storage software family. Our Spectrum Scale, a highly parallel file system that allows incredible performance for AI. Our Spectrum Discover which allows you to use meta data which is information about the data to more accurately plan and the AI software from any vendor can use an API and go in and see this meta data information to make the AI software more efficient that they would use. Our IBM Cloud Object Storage and our Spectrum Archive, you have to archive the data, but easily bring it back because AI is like a human. We are, smart humans are learning non-stop, whether you're five, whether you're 25, or whether you're 75, you're always learning. You read the newspaper, you see of course theCUBE and you learn new things, but you're always comparing that to what you used to know. Are the Russians our friends or our enemies? It depends on your point in time. Do we love what's going on in Germany? It depends on your point in time. In 1944, I'd say probably not. Today you'd say, what a great Democratic country, but you have to learn and so this data pipeline, this loop, our software is on our storage arrays and allows it to be used. We'll even sell the software without our storage arrays for use on any AI server platform, so that softwares really the huge differentiator for us. >> So can you, as a follow up to that, can you address the programmability of your portfolio? Whether it's through software or maybe the infrastructure as well. Infrastructure, I'm thinking infrastructure's code. You mentioned you know API's. You mentioned the ability to go into like Spectrum Discover for example, access meta data. How programmable is your infrastructure and how are you enabling that? >> I mean across our entire portfolio, we build restful API's to make our infrastructure completely extensible. We find that more and more enterprises are looking to automate the deployment of the infrastructure and so we provide API's for programming and deploying that. We're also moving towards containerizing most of our storage products so that as enterprises move towards cubernetes type clusters, we work with both Red Hat and with our own ICP and as customers move towards those deployment models and automate the deployment of their clusters, we're making all of our storage's available to be deployed within those environments. >> So do you see an evolution of the role of a storage admin, from one that's sort of provisioning luns to one that's actually becoming a coder, maybe learning Python, learning how to interact through API's, maybe even at some point developing applications for automation? Is that happening? >> I think there's absolutely a shift in the skills. I think you've got skills going in two directions. One, in the way of somebody else to administer hardware and replace parts as they fail. So you have lower skilled jobs on that side and then I believe that yes, people who are managing the infrastructure have to move up and move towards coding and automating the infrastructure. As the amount of data grows, it becomes too difficult to manage it in the old manual ways of doing it. You need automation and intelligence in the storage infrastructure that can identify problems and readjust. For example, in our storage infrastructure, we have automated data placement that puts it on the correct tier. That use to be something a storage administrator had to do manually and figure out how to place data. Now the storage can do it themselves, so now they need to move up into the automation stack. >> Yeah, so we've been talking about automation and storage also for a lot of years. Eric, how are enterprises getting over that fear that either I'm going to lose my job or you know, this is my business we're talking about here. How do I let go and trust? I love, I saw downstairs, there was a in the automation booth for IBM, it was free the humans, so we understand that we need to go there. We can't not put automation with the scale and how things are moving, but what's the reality out in the field? >> So I think that the big difference is and this is going to sound funny, but the economic down turn of seven, eight and nine, when downturn hit and certainly was all over the IT press, layoff, layoff, layoff, layoff, layoffs, so we also know that storage is growing exponentially, so for example, if I'm Fortune 500 company x and I had 100 people doing storage across the planet. If I laid off 50 of them and now I'm recovered. I'm making tons of money, my IT budget is back up. I didn't go to the CIO and say, you can hire the 50 storage people back. You can hire 50 people back, but no more than five or six can be storage people. Everything else has to be dev ops or something else. So what that means is, they are managing an un-Godly amounts of more storage every year with essentially the same people they had in 2008 or maybe a tiny bit more. So what matters is, you don't manage a peta bite or in the old days, half a peta bite. Now, one storage admin or back up admin or anyone in that space, they want you to manage 20 peta bites and if you don't have automation, that will never happen. >> Stu and I were interviewing Steven Hill from KPMG yesterday and he was talking about the macro numbers show we're not (stutters) as globally and even in the US, we're not seeing productivity gains. I'm saying yeah, you're not looking at the storage business you know, right? Because if you look at anybody who's running storage, they're doing way more with much less, to your point. >> Which is why, so for example when Sam talked about our easy tier, we can tier, not only as AI base. So in the old days, when you guys weren't even born yet, when I was doing it. >> Well I don't know about that >> What was it? It was move the data after 90, so first it was manual movement, then it was set up something, a policy. Remember policy automation was the big deal 10 years ago? Automatically move the data when its 90, 60, or 30 days old. AI based, what we have an easy tier, automatically will determine what tier it should go on, whether when the data's hot or when the data's cold and on top of that, because we can tier over 440 arrays that are not IBM logo'd, multi vendor tiering, we can tier from our box to an EMC box. So if you have a flash array, you've got an old or all hard drive that you've moved into your back up in archive tier, we can automatically tier to that. We can tier from the EMC array out to the Cloud, but it's all done automatically. The admin doesn't do anything, it just says source and target and the AI does all the work. That's how you get the productivity that you're talking about, that you need in storage and back ups even worse because you got to keep everything now, which Sam mentioned GDPR, all these new regulations and the Federal Government its like keep the data forever. >> But in that case, the machine can determine whether or not it's okay to put it in the Cloud, if it's in Canada or Germany or wherever, the machine can adjudicate and make those decisions. >> And that's what the AI, so in that case you're using AI inside of the storage system versus what we talked about with our other software that makes our storage systems a great platform for other AI workloads that are not, if you will, AI for storage. AI for everything else, cars or hospitals or resume analysis. That's what the platform can, but we put all this AI inside of the system 'cause there aren't that big, giant, global, Fortune 500 has 55 storage admins and in 2007 or eight, they had 100, but they've quintupled the amount of storage easily if not 10x'd it, so who's going to manage that? Automation. >> Guys, good discussion. Not everyday, boring, old storage. It's talking about intelligence, real intelligence this time. Eric, Sam, thanks very much for coming to theCUBE. Great to see you guys again. >> Thank you. >> Thank you. >> You're welcome. Alright, keep it right there everybody. Stu and I will be back with our next guest shortly, right after this break. John Furrier is also here. IBM Think, Day four, you're watching theCUBE. Be right back. (tech music)

>> Announcer: Live from Las Vegas, it's The Cube, covering AWS re:Invent 2017, presented by AWS, Intel, and our ecosystem of partners. >> And we're back, here on the show floor in the exhibit hall at Sands Expo, live at re:Invent for AWS along with Justin Warren. I'm John Walls. We're joined by a couple of executives now from Weka IO, to my immediate right is Liran Zvibel, who is the co-founder and CEO and then Maor Ben Dayan who's the chief architect at IO. Gentleman thanks for being with us. >> Thanks for having us. >> Appreciate you being here on theCube. First off tell the viewers a little bit about your company and I think a little about the unusual origination of the name. You were sharing that with me as well. So let's start with that, and then tell us a little bit more about what you do. >> Alright, so the name is Weka IO. Weka is actually a greek unit, like mega and terra and peta so it's actually a trillion exobytes, ten to the power of thirty, it's a huge capacity, so it works well for a storage company. Hopefully we will end up storing wekabytes. It will take some time. >> I think a little bit of time to get there. >> A little bit. >> We're working on it. >> One customer at a time. >> Give a little more about what you do, in terms of your relationship with AWS. >> Okay, so at Weka IO we create the highest performance file system, either on prem or in the cloud. So we have a parallel file system over NVME. Like no previous generation file system did parallel work over hard drives. But these are 20 years old technology. We're the first file system to bring new paralleled rhythms to NVME so we get you lowest latency, highest throughput either on prem or in the cloud. We are perfect for machine learning and life sciences applications. Also you've mentioned media and entertainment earlier. We can run on your hardware on prem, we can run on our instances, I3 instances, in AWS and we can also take snapshots that are native performance so they don't take away performance and we also have the ability to take these snapshots and push them to S3 based object storage. This allows you to have DR or backup functionality if you look on prem but if your object storage is actually AWSS3, it also lets you do cloud bursting, so it can take your on prem cluster, connect it to AWSS3, take a snapshot, push it to AS3 and now if you have a huge amount of computation that you need to do, your local GPU servers don't have enough capacity or you just want to get the results faster, you would build a big enough cluster on AWS, get the results and bring them back. >> You were explaining before that it's a big challenge to be able to do something that can do both low latency with millions and millions of small files but also be able to do high throughput for some large files, like media and entertainment tends to be very few but very, very large files with something like genomics research, you'll have millions and millions of files but they're all quite tiny. That's quite hard, but you were saying it's actually easier to do the high throughput than it is for low latency, maybe explain some of that. >> You want to take it? >> Sure, on the one hand, streaming lots of data is easy when you distribute the data over many servers or instances in the AWS like luster dust or other solutions, but then doing small files becomes really hard. Now this is where Weka innovated and really solved this bottleneck so it really frees you to do whatever you want with the storage system without hitting any bottlenecks. This is the secret sauce of Weka. >> Right and you were mentioning before, it's a file system so it's an NFS and SMB access to this data but you're also saying that you can export to S3. >> Actually we have NFS, we have SMB, but we also have native posits so any application that you could up until now only run on the local file system such as EXT4 or ZFS, you can actually run in assured manner. Anything that's written on the many pages we do, so adjust works, locking, everything. That's one thing we're showing for life sciences, genomic workflows that we can scale their workflows without losing any performance, so if one server doing one kind of transformation takes time x, if you use 10 servers, it will take 10x the time to get 10x the results. If you have 100 servers, it's gonna take 100x servers to get 100x the results, what customers see with other storage solutions, either on prem or in the cloud, that they're adding servers but they're getting way less results. We're giving the customers five to 20 times more results than what they did on what they thought were high performance file systems prior to the Weka IO solution. >> Can you give me a real life example of this, when you talk about life sciences, you talk about genomic research and we talk about the itty bitty files and millions of samples and whatever, but exactly whatever, translate it for me, when it comes down to a real job task, a real chore, what exactly are you bringing to the table that will enable whatever research is being done or whatever examination's being done. >> I'll give you a general example, not out of specifically of life sciences, we were doing a POC at a very large customer last week and we were compared head to head with best of breed, all flash file system, they did a simple test. They created a large file system on both storage solutions filled with many many millions of small files, maybe even billions of small files and they wanted to go through all the files, they just ran the find command, so the leading competitor finished the work in six and a half hours. We finished the same work in just under two hours. More than 3x time difference compared to a solution that is currently considered probably the fastest. >> Gold standard allegedly, right? Allegedly. >> It's a big difference. During the same comparison, that customer just did an ALS of a directory with a million files that other leading solution took 55 seconds and it took just under 10 seconds for us. >> We just get you the results faster, meaning your compute remains occupied and working. If you're working with let's say GPU servers that are costly, but usually they are just idling around, waiting for the data to come to them. We just unstarve these GPU servers and let's you get what you paid for. >> And particularly with something like the elasticity of AWS, if it takes me only two hours instead of six, that's gonna save me a lot of money because I don't have to pay for that extra six hours. >> It does and if you look at the price of the P3 instances, for reason those voltage GPUs aren't inexpensive, any second they're not idling around is a second you saved and you're actually saving a lot of money, so we're showing customers that by deploying Weka IO on AWS and on premises, they're actually saving a lot of money. >> Explain some more about how you're able to bridge between both on premises and the cloud workloads, because I think you mentioned before that you would actually snapshot and then you could send the data as a cloud bursting capability. Is that the primary use case you see customers using or is it another way of getting your data from your side into the cloud? >> Actually we have a slightly more complex feature, it's called tiering through the object storage. Now customers have humongous name spaces, hundreds of petabytes some of them and it doesn't make sense to keep them all on NVME flash, it's too expensive so a big feature that we have is that we let you tier between your flash and object storage and let's you manage economics and actually we're chopping down large files and doing it to many objects, similarly to how a traditional file system treat hard drives so we treat NVMEs in a parallel fashion, that's world first but we also do all the tricks that a traditional parallel file system do to get good performance out of hard drives to the object storage. Now we take that tiering functionality and we couple it with our highest performance snapshotting abilities so you can take the snapshot and just push it completely into the object storage in a way that you don't require the original cluster anymore >> So you've mentioned a few of the areas that you're expertise now and certainly where you're working, what are some other verticals that you're looking at? What are some other areas where you think that you can bring what you're doing for maybe in the life science space and provide equal if not superior value? >> Currently. >> Like where are you going? >> Currently we focus on GPU based execution because that's where we save the most money to the customers, we give the biggest bang for the buck. Also genomics because they have severe performance problems around building, we've shown a huge semiconductor company that was trying to build and read, they were forced to building on local file system, it took them 35 minutes, they tried their fastest was actually on RAM battery backed RAM based shared file system using NFS V4, it took them four hours. It was too long, you only got to compile the day. It doesn't make sense. We showed them that they can actually compile in 38 minutes, show assured file system that is fully coherent, consistent and protected only took 10% more time, but it didn't take 10% more time because what we enabled them to do is now share the build cache, so the next build coming in only took 10 minutes. A full build took slightly longer, but if you take the average now their build was 13 or 14 minutes, so we've actually showed that assured file system can save time. Other use cases are media and entertainment, for rendering use cases, you have these use cases, they parallelize amazingly well. You can have tons of render nodes rendering your scenes and the more rendering nodes you have, the quicker you can come up with your videos, with your movies or they look nicer. We enable our customers to scale their clusters to sizes they couldn't even imagine prior to us. >> It's impressive, really impressive, great work and thanks for sharing it with us here on theCube, first time for each right? You're now Cube alumni, congratulations. >> Okay, thanks for having us. >> Thank you for being with us here. Again, we're live here at re:Invent and back with more live coverage here on theCube right after this time out.

>> Live from New York City, it's buck you. Here is your host, Jeff Frick. >> Hey, welcome back. I'm Jeff. Rick. We're here at the Cubes. Fifth birthday party. A big date in Icy in Manhattan is part of the big Date. A week. It's got Stratos cough, a dupe world. And, of course, big Aidan. I see. So now having our party, which is always good to have, and I'm joined department X gas. Kerry McFadden from Parodi Research. Carrie. Welcome. Well, thank you very much. So last last we saw he was actually a big data and twenty thirteen, So it's lots changing the year. >> Absolutely, Absolutely. I think the whole hoodoo thing is really taken off. And the thing that interests me the most about show or or the exhibitors at the show is that Bye. You could get a lot of data into Duke, but how do you get it out? How do you make it useful? What do you do with it when you get it out? You know, I said on structure data is structured. Date. Is that a combination? Is it ski Melis? >> All the above all the above, >> right? Exactly. So I think really, that's been on and actually have been Jeff to all the shows, right? Since the beginning, when it was just a new world. Okay, Cube started back. And I think two thousand ten two thousand filling our fifth birthday. Right? So at least at least at least twenty ten. So since then, you've seen, you know, progression off vendors coming in to provide services that actually enable Duke to do more than it does started is kind of a batch oriented type of solution that now, because of these other value added solutions can to really or near real time processing, you can take the data out of it a lot more easily. You can use do basically as a as a repository, right on DH. And a lot of the solutions out there are are evolving to the point where you can, uh, you could basically make a sense of the information, and I think that's a really important rights. Dated information information inside, right? That's where we want to go with this thing. Business decisions made in real time. Which way? Define as in time to do something about it. Right? Right. Yes. Some of the players, I mean, you've got the map. Our guys. You've got the act. Aeon folks that just bought pervasive software. So they've got the Predictive Analytics piece sort of covered. Obviously. That's stone breakers. Old company, you know, a variant of ing gris, right? You've got. Obviously, IBM is a player in this space. With their blue mix and their cloud capabilities and all of their information management pieces, every major vendor is got a piece of is part of the action, if you will. Trying to build something on top of a dupe to make it more useful and make it more valuable. Yeah, the floor was filled with little companies, big companies, and everyone is certainly jumping in. So let me get your prospectus that you've been coming for a lot of years on this thing. Where are we on the journey? How? How? You know, I think we're past the P E O C stage, right? People are getting stuff into production deployments, but it's still early days. You know, the Giants are playing tonight. Go Giants, are we? First inning, third inning, seventh inning. Where are we? I think we're probably in the second or third any second. I think we got a ways to go. And what's the next big hurdle to get us to the next inning. I think one of the problems is this storage issue, right? So you've got this issue of being able to scale out theoretically, exponentially, right? The nice thing about do piss If you need Teo, if you need more space, you just add No J had storage and whatnot, But what happens when you get too much information? You're into the pedal bike, multiple PETA right range now, and most of that data, you know you're not going to access. You may access only two percent of it overtime. I think they're a lot of figures around that. But actually, a wicked bon article that I read recently is very interesting, one called Flake Flake or what they were doing. Flake. I want to make sure he gets a slave by a herd where he said it to me off camera, right? It's a f L a P. It's a combination of flash and tape on DH. Basically, there's a great article on the Wicked Bond site by Wicked Bonds CTO, David's lawyer Okay, and his premises that at some point, relatively soon a cz thie as data grows exponentially into the multiple petabytes ranges and maybe even beyond The thing is gonna get squeezed is the traditional HDD or hardening is spinning disc, right? So tape has become much more, uh, much more resilient. Uh, tape last has a meat time failure of about twenty six or thirty years versus disc, which is about five. And obviously flash is much, much faster, right? Right in some cases don't get into all the nuances of almost feet feet, but flavor going to squeeze out disks and the men think so. And what that'll offer customers is a is a much lower TCO from managing those huge petabytes scale environments and also accessing it at a relatively quick speed. So I think that's that's a piece. It's interesting that the other part that's very interesting to me, Mr Cognitive Computing face. So I was at the no SQL event last week last month in in San Jose, and with that they had a cognitive computing component on DH. I think thie idea of trying to get machines to think more like people building neuro morphing chips to two. It's kind of mimic the way synapses or electricity, electricity in the brain, you know, works how neurons fire and so forth is very interesting. And I think once you Khun Get Dupe is the repository. You've got the data there. But how do you make use of it? And I think that's the challenge. That's going to be, well, paramount the next few years. Exciting days ahead. Well, Gary, thanks for taking a few minutes. We're at the fifth birthday party at the Cube. Were at Big Data and nice jefe. Rick, we're on the ground. Thanks for watching.