>>from around the globe. It's the Cube covering space and cybersecurity. Symposium 2020 hosted by Cal Poly >>Oven. Welcome back to the Space and Cyber Security Symposium. 2020 I'm John for your host with the Cuban silicon angle, along with Cal Poly, representing a great session here on industry success in developing space and cybersecurity. Resource is Got a great lineup. Brigadier General Steve Hotel, whose are also known as Bucky, is Call Sign director of Space Portfolio Defense Innovation Unit. Preston Miller, chief information security officer at JPL, NASA and Major General retired Clint Crozier, director of aerospace and satellite solutions at Amazon Web services, also known as a W s. Gentlemen, thank you for for joining me today. So the purpose of this session is to spend the next hour talking about the future of workforce talent. Um, skills needed and we're gonna dig into it. And Spaces is an exciting intersection of so many awesome disciplines. It's not just get a degree, go into a track ladder up and get promoted. Do those things. It's much different now. Love to get your perspectives, each of you will have an opening statement and we will start with the Brigadier General Steve Hotel. Right? >>Thank you very much. The Defense Innovation Unit was created in 2015 by then Secretary of Defense Ash Carter. To accomplish three things. One is to accelerate the adoption of commercial technology into the Department of Defense so that we can transform and keep our most relevant capabilities relevant. And also to build what we call now called the national Security Innovation Base, which is inclusive all the traditional defense companies, plus the commercial companies that may not necessarily work with focus exclusively on defense but could contribute to our national security and interesting ways. Um, this is such an exciting time Azul here from our other speakers about space on and I can't, uh I'm really excited to be here today to be able to share a little bit of our insight on the subject. >>Thank you very much. Precedent. Miller, Chief information security officer, Jet Propulsion Lab, NASA, Your opening statement. >>Hey, thank you for having me. I would like to start off by providing just a little bit of context of what brings us. Brings us together to talk about this exciting topic for space workforce. Had we've seen In recent years there's been there's been a trend towards expanding our space exploration and the space systems that offer the great things that we see in today's world like GPS. Um, but a lot of that has come with some Asian infrastructure and technology, and what we're seeing as we go towards our next generation expects of inspiration is that we now want to ensure that were secured on all levels. And there's an acknowledgement that our space systems are just a susceptible to cyber attacks as our terrestrial assistance. We've seen a recent space, uh, policy Directive five come out from our administration, that that details exactly how we should be looking at the cyber principle for our space systems, and we want to prevent. We want to prevent a few things as a result of that of these principles. Spoofing and jamming of our space systems are not authorized commands being sent to those space systems, lots of positive control of our space vehicles on lots of mission data. We also acknowledge that there's a couple of frameworks we wanna adopt across the board of our space systems levers and things like our nice miss cybersecurity frameworks. eso what has been a challenge in the past adopted somebody Cyber principles in space systems, where there simply has been a skill gap in a knowledge gap. We hire our space engineers to do a few things. Very well designed space systems, the ploy space systems and engineer space systems, often cybersecurity is seen as a after thought and certainly hasn't been a line item and in any budget for our spaces in racing. Uh, in the past in recent years, the dynamic started to change. We're now now integrating cyber principles at the onset of development of these life cycle of space. Systems were also taking a hard look of how we train the next generation of engineers to be both adequate. Space engineers, space system engineers and a cyber engineers, as a result to Mrs success on DWI, also are taking a hard look at What do we mean when we talk about holistic risk management for our space assistance, Traditionally risk management and missing insurance for space systems? I've really revolved around quality control, but now, in recent years we've started to adopt principles that takes cyber risk into account, So this is a really exciting topic for me. It's something that I'm fortunate to work with and live with every day. I'm really excited to get into this discussion with my other panel members. Thank you. >>You Preston. Great insight there. Looking forward. Thio chatting further. Um, Clint Closure with a W. S now heading up. A director of aerospace and satellite Solutions, formerly Major General, Your opening statement. >>Thanks, John. I really appreciate that introduction and really appreciate the opportunity to be here in the Space and Cybersecurity Symposium. And thanks to Cal Poly for putting it together, you know, I can't help, but as I think to Cal Poly there on the central California coast, San Luis Obispo, California I can't help but to think back in this park quickly. I spent two years of my life as a launch squadron commander at Vandenberg Air Force Base, about an hour south of Cal Poly launching rockets, putting satellites in orbit for the national intelligence community and so some really fond memories of the Central California coast. I couldn't agree more with the theme of our symposium this week. The space and cyber security we've all come to know over the last decade. How critical spaces to the world, whether it's for national security intelligence, whether it's whether communications, maritime, agriculture, development or a whole host of other things, economic and financial transactions. But I would make the case that I think most of your listeners would agree we won't have space without cybersecurity. In other words, if we can't guaranteed cybersecurity, all those benefits that we get from space may not be there. Preston in a moment ago that all the threats that have come across in the terrestrial world, whether it be hacking or malware or ransomware or are simple network attacks, we're seeing all those migrate to space to. And so it's a really important issue that we have to pay attention to. I also want to applaud Cow Pauling. They've got some really important initiatives. The conference here, in our particular panel, is about developing the next generation of space and cyber workers, and and Cal Poly has two important programs. One is the digital transformation hub, and the other is space data solutions, both of which, I'm happy to say, are in partnership with a W. S. But these were important programs where Cal Poly looks to try to develop the next generation of space and cyber leaders. And I would encourage you if you're interested in that toe. Look up the program because that could be very valuable is well, I'm relatively new to the AWS team and I'm really happy Thio team, as John you said recently retired from the U. S. Air Force and standing up the U. S. Space force. But the reason that I mentioned that as the director of the aerospace and satellite team is again it's in perfect harmony with the theme today. You know, we've recognized that space is critically important and that cyber security is critically important and that's been a W s vision as well. In fact, a W s understands how important the space domain is and coupled with the fact that AWS is well known that at a W s security is job zero and stolen a couple of those to fax A. W. S was looking to put together a team the aerospace and satellite team that focus solely and exclusively every single day on technical innovation in space and more security for the space domain through the cloud and our offerings there. So we're really excited to reimagine agree, envision what space networks and architectures could look like when they're born on the cloud. So that's important. You know, talk about workforce here in just a moment, but but I'll give you just a quick sneak. We at AWS have also recognized the gap in the projected workforce, as Preston mentioned, Um, depending on the projection that you look at, you know, most projections tell us that the demand for highly trained cyber cyber security cloud practitioners in the future outweighs what we think is going to be the supply. And so a ws has leaned into that in a number of ways that we're gonna talk about the next segment. I know. But with our workforce transformation, where we've tried to train free of charge not just a W s workers but more importantly, our customers workers. It s a W s we obsessed over the customer. And so we've provided free training toe over 7000 people this year alone toe bring their cloud security and cyber security skills up to where they will be able to fully leverage into the new workforce. So we're really happy about that too? I'm glad Preston raised SPD five space policy Directive five. I think it's gonna have a fundamental impact on the space and cyber industry. Uh, now full disclosure with that said, You know, I'm kind of a big fan of space policy directives, ESPN, Or was the space policy directive that directed to stand up of the U. S. Space Force and I spent the last 18 months of my life as the lead planner and architect for standing up the U. S. Space force. But with that said, I think when we look back a decade from now, we're going to see that s p d five will have as much of an impact in a positive way as I think SPD for on the stand up of the space Force have already done so. So I'll leave it there, but really look forward to the dialogue and discussion. >>Thank you, gentlemen. Clint, I just wanna say thank you for all your hard work and the team and the people who were involved in standing up Space force. Um, it is totally new. It's a game changer. It's modern, is needed. And there's benefits on potential challenges and opportunities that are gonna be there, so thank you very much for doing that. I personally am excited. I know a lot of people are excited for what the space force is today and what it could become. Thank you very much. >>Yeah, Thanks. >>Okay, So >>with >>that, let me give just jump in because, you know, as you're talking about space force and cybersecurity and you spend your time at Vanderburgh launching stuff into space, that's very technical. Is operation okay? I mean, it's complex in and of itself, but if you think about like, what's going on beyond in space is a lot of commercial aspect. So I'm thinking, you know, launching stuff into space on one side of my brain and the other side of brain, I'm thinking like air travel. You know, all the logistics and the rules of the road and air traffic control and all the communications and all the technology and policy and, you >>know, landing. >>So, Major General Clint, what's your take on this? Because this is not easy. It's not just one thing that speaks to the diversity of workforce needs. What's your reaction to that? >>Yeah. I mean, your observation is right on. We're seeing a real boom in the space and aerospace industry. For all the good reasons we talked about, we're recognizing all the value space from again economic prosperity to exploration to being ableto, you know, improve agriculture and in weather and all those sorts of things that we understand from space. So what I'm really excited about is we're seeing this this blossom of space companies that we sort of referred to his new space. You know, it used to be that really only large governments like the United States and a handful of others could operate in the space domain today and largely infused because of the technological innovation that have come with Cyber and Cyrus Space and even the cloud we're seeing more and more companies, capabilities, countries, all that have the ability, you know. Even a well funded university today can put a cube sat in orbit, and Cal Poly is working on some of those too, by the way, and so it's really expanded the number of people that benefits the activity in space and again, that's why it's so critically important because we become more and more reliant and we will become more and more reliant on those capabilities that we have to protect him. It's fundamental that we do. So, >>Bucky, I want you to weigh in on this because actually, you you've flown. Uh, I got a call sign which I love interviewing people. Anyone who's a call sign is cool in my book. So, Bucky, I want you to react to that because that's outside of the technology, you know, flying in space. There's >>no >>rule. I mean, is there like a rules? I mean, what's the rules of the road? I mean, state of the right. I mean, what I mean, what what's going? What's gonna have toe happen? Okay, just logistically. >>Well, this is very important because, uh and I've I've had access thio information space derived information for most of my flying career. But the amount of information that we need operate effectively in the 21st century is much greater than Thanet has been in the past. Let me describe the environment s so you can appreciate a little bit more what our challenges are. Where, from a space perspective, we're going to see a new exponential increase in the number of systems that could be satellites. Uh, users and applications, right? And so eso we're going we're growing rapidly into an environment where it's no longer practical to just simply evolved or operate on a perimeter security model. We and with this and as I was brought up previously, we're gonna try to bring in MAWR commercial capabilities. There is a tremendous benefit with increasing the diversity of sources of information. We use it right now. The military relies very heavily on commercial SAT com. We have our military capabilities, but the commercial capabilities give us capacity that we need and we can. We can vary that over time. The same will be true for remote sensing for other broadband communications capabilities on doing other interesting effects. Also, in the modern era, we doom or operations with our friends and allies, our regional partners all around the world, in order to really improve our interoperability and have rapid exchange of information, commercial information, sources and capabilities provides the best means of doing that. So that so that the imperative is very important and what all this describes if you want to put one word on it. ISS, we're involving into ah hybrid space architectures where it's gonna be imperative that we protect the integrity of information and the cyber security of the network for the things most important to us from a national security standpoint. But we have to have the rules that that allows us to freely exchange information rapidly and in a way that that we can guarantee that the right users are getting the right information at the right. >>We're gonna come back to that on the skill set and opportunities for people driving. That's just looking. There's so much opportunity. Preston, I want you to react to this. I interviewed General Keith Alexander last year. He formerly ran Cyber Command. Um, now he's building Cyber Security Technologies, and his whole thesis is you have to share. So the question is, how do you share and lock stuff down at the same time when you have ah, multi sided marketplace in space? You know, suppliers, users, systems. This is a huge security challenge. What's your reaction to this? Because we're intersecting all these things space and cybersecurity. It's just not easy. What's your reaction? >>Absolutely, Absolutely. And what I would say in response to that first would be that security really needs to be baked into the onset of how we develop and implement and deploy our space systems. Um, there's there's always going to be the need to collect and share data across multiple entities, particularly when we're changing scientific data with our mission partners. Eso with that necessitates that we have a security view from the onset, right? We have a system spaces, and they're designed to share information across the world. How do we make sure that those, uh, those other those communication channels so secure, free from interception free from disruption? So they're really done? That necessitates of our space leaders in our cyber leaders to be joining the hip about how to secure our space systems, and the communications there in Clinton brought up a really good point of. And then I'm gonna elaborate on a little bit, just toe invite a little bit more context and talk about some the complexities and challenges we face with this advent of new space and and all of our great commercial partners coming into therefore way, that's going to present a very significant supply chain risk management problems that we have to get our hands around as well. But we have these manufacturers developing these highly specialized components for the space instruments, Um, that as it stands right now, it's very little oversight And how those things air produced, manufactured, put into the space systems communication channels that they use ports protocols that they use to communicate. And that's gonna be a significant challenge for us to get get our hands around. So again, cybersecurity being brought in. And the very onset of these development thes thes decisions in these life cycles was certainly put us in a best better position to secure that data in our in our space missions. >>Yeah, E just pick up on that. You don't mind? Preston made such a really good point there. But you have to bake security in up front, and you know there's a challenge and there's an opportunity, you know, with a lot of our systems today. It was built in a pre cyber security environment, especially our government systems that were built, you know, in many cases 10 years ago, 15 years ago are still on orbit today, and we're thankful that they are. But as we look at this new environment and we understand the threats, if we bake cybersecurity in upfront weaken balance that open application versus the risk a long as we do it up front. And you know, that's one of the reasons that our company developed what we call govcloud, which is a secure cloud, that we use thio to manage data that our customers who want to do work with the federal government or other governments or the national security apparatus. They can operate in that space with the built in and baked in cybersecurity protocols. We have a secret region that both can handle secret and top secret information for the same reasons. But when you bake security into the upfront applications, that really allows you to balance that risk between making it available and accessible in sort of an open architecture way. But being sure that it's protected through things like ITAR certifications and fed ramp, uh, another ice T certifications that we have in place. So that's just a really important point. >>Let's stay high level for a man. You mentioned a little bit of those those govcloud, which made me think about you know, the tactical edge in the military analogy, but also with space similar theater. It's just another theater and you want to stand stuff up. Whether it's communications and have facilities, you gotta do it rapidly, and you gotta do it in a very agile, secure, I high availability secure way. So it's not the old waterfall planning. You gotta be fast is different. Cloud does things different? How do you talk to the young people out there, whether it's apparent with with kids in elementary and middle school to high school, college grad level or someone in the workforce? Because there are no previous jobs, that kind of map to the needs out there because you're talking about new skills, you could be an archaeologist and be the best cyber security guru on the planet. You don't have to have that. There's no degree for what, what we're talking about here. This >>is >>the big confusion around education. I mean, you gotta you like math and you could code you can Anything who wants to comment on that? Because I think this >>is the core issue. I'll say there are more and more programs growing around that educational need, and I could talk about a few things we're doing to, but I just wanna make an observation about what you just said about the need. And how do you get kids involved and interested? Interestingly, I think it's already happening, right. The good news. We're already developing that affinity. My four year old granddaughter can walk over, pick up my iPad, turn it on. Somehow she knows my account information, gets into my account, pulls up in application, starts playing a game. All before I really even realized she had my iPad. I mean, when when kids grow up on the cloud and in technology, it creates that natural proficiency. I think what we have to do is take that natural interest and give them the skill set the tools and capabilities that go with it so that we're managing, you know, the the interest with the technical skills. >>And also, like a fast I mean, just the the hackers are getting educated. Justus fast. Steve. I mean e mean Bucky. What do you do here? You CIt's the classic. Just keep chasing skills. I mean, there are new skills. What are some of those skills? >>Why would I amplify eloquent? Just said, First of all, the, uh, you know, cyber is one of those technology areas where commercial side not not the government is really kind of leading away and does a significant amount of research and development. Ah, billions of dollars are spent every year Thio to evolve new capabilities. And a lot of those companies are, you know, operated and and in some cases, led by folks in their early twenties. So the S O. This is definitely an era and a generation that is really poised in position. Well, uh, Thio take on this challenge. There's some unique aspects to space. Once we deploy a system, uh, it will be able to give me hard to service it, and we're developing capabilities now so that we could go up and and do system upgrades. But that's not a normal thing in space that just because the the technical means isn't there yet. So having software to find capabilities, I's gonna be really paramount being able to dio unique things. The cloud is huge. The cloud is centric to this or architectural, and it's kind of funny because d o d we joke because we just discovered the cloud, you know, a couple years ago. But the club has been around for a while and, uh, and it's going to give us scalability on and the growth potential for doing amazing things with a big Data Analytics. But as Preston said, it's all for not if if we can't trust the data that we receive. And so one of the concepts for future architectures is to evolve into a zero trust model where we trust nothing. We verify and authenticate everyone. And, uh, and that's that's probably a good, uh, point of departure as we look forward into our cybersecurity for space systems into the future. >>Block everyone. Preston. Your reaction to all this gaps, skills, What's needed. I mean it Z everyone's trying to squint through this >>absolutely. And I wanna want to shift gears a little bit and talk about the space agencies and organizations that are responsible for deploying these spaces into submission. So what is gonna take in this new era on, and what do we need from the workforce to be responsive to the challenges that we're seeing? First thing that comes to mind is creating a culture of security throughout aerospace right and ensuring that Azzawi mentioned before security isn't an afterthought. It's sort of baked into our models that we deploy and our rhetoric as well, right? And because again we hire our spaces in years to do it very highly. Specialized thing for a highly specialized, uh, it's topic. Our effort, if we start to incorporate rhetorically the importance of cybersecurity two missing success and missing assurance that's going to lend itself toe having more, more prepared on more capable system engineers that will be able to respond to the threats accordingly. Traditionally, what we see in organizational models it's that there's a cyber security team that's responsible for the for the whole kit kaboodle across the entire infrastructure, from enterprise systems to specialize, specialize, space systems and then a small pocket of spaces, years that that that are really there to perform their tasks on space systems. We really need to bridge that gap. We need to think about cybersecurity holistically, the skills that are necessary for your enterprise. I t security teams need to be the same skills that we need to look for for our system engineers on the flight side. So organizationally we need we need to address that issue and approach it, um todo responsive to the challenges we see our our space systems, >>new space, new culture, new skills. One of the things I want to bring up is looking for success formulas. You know, one of the things we've been seeing in the past 10 years of doing the Cube, which is, you know, we've been called the ESPN of Tech is that there's been kind of like a game ification. I want to. I don't wanna say sports because sports is different, but you're seeing robotics clubs pop up in some schools. It's like a varsity sport you're seeing, you know, twitch and you've got gamers out there, so you're seeing fun built into it. I think Cal Poly's got some challenges going on there, and then scholarships air behind it. So it's almost as if, you know, rather than going to a private sports training to get that scholarship, that never happens. There's so many more scholarship opportunities for are not scholarship, but just job opportunities and even scholarships we've covered as part of this conference. Uh, it's a whole new world of culture. It's much different than when I grew up, which was you know, you got math, science and English. You did >>it >>and you went into your track. Anyone want to comment on this new culture? Because I do believe that there is some new patterns emerging and some best practices anyone share any? >>Yeah, I do, because as you talked about robotics clubs and that sort of things, but those were great and I'm glad those air happening. And that's generating the interest, right? The whole gaming culture generating interest Robotic generates a lot of interest. Space right has captured the American in the world attention as well, with some recent NASA activities and all for the right reasons. But it's again, it's about taking that interested in providing the right skills along the way. So I'll tell you a couple of things. We're doing it a w s that we found success with. The first one is a program called A W s Academy. And this is where we have developed a cloud, uh, program a cloud certification. This is ah, cloud curriculum, if you will, and it's free and it's ready to teach. Our experts have developed this and we're ready to report it to a two year and four year colleges that they can use is part of the curriculum free of charge. And so we're seeing some real value there. And in fact, the governor's in Utah and Arizona recently adopted this program for their two year schools statewide again, where it's already to teach curriculum built by some of the best experts in the industry s so that we can try to get that skills to the people that are interested. We have another program called A W s educate, and this is for students to. But the idea behind this is we have 12 cracks and you can get up to 50 hours of free training that lead to A W s certification, that sort of thing. And then what's really interesting about that is all of our partners around the world that have tied into this program we manage what we call it ws educate Job board. And so if you have completed this educate program now, you can go to that job board and be linked directly with companies that want people with those skills we just helped you get. And it's a perfect match in a perfect marriage there. That one other piece real quickly that we're proud of is the aws Uh restart program. And that's where people who are unemployed, underemployed or transitioning can can go online. Self paced. We have over 500 courses they can take to try to develop those initial skills and get into the industry. And that's been very popular, too, So that those air a couple of things we're really trying to lean into >>anyone else want to react. Thio that question patterns success, best practices, new culture. >>I'd like Thio. The the wonderful thing about what you just touched on is problem solving, right, And there's some very, very good methodologies that are being taught in the universities and through programs like Hacking for Defense, which is sponsored by the National Security Innovation Network, a component of the I you where I work but the But whether you're using a lien methodologies or design school principals or any other method, the thing that's wonderful right now and not just, uh, where I work at the U. The Space force is doing this is well, but we're putting the problem out there for innovators to tackle, And so, rather than be prescriptive of the solutions that we want to procure, we want we want the best minds at all levels to be able to work on the problem. Uh, look at how they can leverage other commercial solutions infrastructure partnerships, uh, Thio to come up with a solution that we can that we can rapidly employ and scale. And if it's a dual use solution or whether it's, uh, civil military or or commercial, uh, in any of the other government solutions. Uh, that's really the best win for for the nation, because that commercial capability again allows us to scale globally and share those best practices with all of our friends and allies. People who share our values >>win win to this commercial. There's a business model potential financial benefits as well. Societal impact Preston. I want to come to you, JPL, NASA. I mean, you work in one of the most awesome places and you know, to me, you know, if you said to me, Hey, John, come working JP like I'm not smart enough to go there like I mean, like, it's a pretty It's intimidating, it might seem >>share folks out there, >>they can get there. I mean, it's you can get there if you have the right skills. I mean I'm just making that up. But, I mean, it is known to be super smart And is it attainable? So share your thoughts on this new culture because you could get the skills to get there. What's your take on all this >>s a bucket. Just missing something that really resonated with me, right? It's do it your love office. So if you put on the front engineer, the first thing you're gonna try to do is pick it apart. Be innovative, be creative and ways to solve that issue. And it has been really encouraging to me to see the ground welcome support an engagement that we've seen across our system. Engineers in space. I love space partners. A tackling the problem of cyber. Now that they know the West at risk on some of these cyber security threats that that they're facing with our space systems, they definitely want to be involved. They want to take the lead. They want to figure things out. They wanna be innovative and creative in that problem solving eso jpl We're doing a few things. Thio Raise the awareness Onda create a culture of security. Andi also create cyber advocates, cybersecurity advocates across our space engineers. We host events like hacked the lad, for example, and forgive me. Take a pause to think about the worst case scenarios that could that could result from that. But it certainly invites a culture of creative problem solving. Um, this is something that that kids really enjoy that are system engineers really enjoyed being a part off. Um, it's something that's new refreshing to them. Eso we were doing things like hosting a monthly cybersecurity advocacy group. When we talk about some of the cyber landscape of our space systems and invite our engineers into the conversation, we do outweighs programs specifically designed to to capture, um, our young folks, uh, young engineers to deceive. They would be interested and show them what this type of security has to offer by ways of data Analytic, since the engineering and those have been really, really successful identifying and bringing in new talent to address the skill gaps. >>Steve, I want to ask you about the d. O. D. You mentioned some of the commercial things. How are you guys engaging the commercial to solve the space issue? Because, um, the normalization in the economy with GPS just seeing spaces impacts everybody's lives. We we know that, um, it's been talked about. And and there's many, many examples. How are you guys the D o. D. From a security standpoint and or just from an advancement innovation standpoint, engaging with commercials, commercial entities and commercial folks? >>Well, I'll throw. I'll throw a, uh, I'll throw ah, compliment to Clint because he did such an outstanding job. The space forces already oriented, uh, towards ah, commercial where it's appropriate and extending the arms. Leveraging the half works on the Space Enterprise Consortium and other tools that allow for the entrepreneurs in the space force Thio work with their counterparts in a commercial community. And you see this with the, uh, you know, leveraging space X away to, uh, small companies who are doing extraordinary things to help build space situational awareness and, uh, s So it's it's the people who make this all happen. And what we do at at the D. O. D level, uh, work at the Office of Secretary defense level is we wanna make sure that they have the right tools to be able to do that in a way that allows these commercial companies to work with in this case of a space force or with cyber command and ways that doesn't redefine that. The nature of the company we want we want We want commercial companies to have, ah, great experience working with d o d. And we want d o d toe have the similar experience working, working with a commercial community, and and we actually work interagency projects to So you're going to see, uh, General Raymond, uh, hey, just recently signed an agreement with the NASA Esa, you're gonna see interagency collaborations on space that will include commercial capabilities as well. So when we speak as one government were not. You know, we're one voice, and that's gonna be tremendous, because if you're a commercial company on you can you can develop a capability that solves problems across the entire space enterprise on the government side. How great is that, Right. That's a scaling. Your solution, gentlemen. Let >>me pick you back on that, if you don't mind. I'm really excited about that. I mentioned new space, and Bucky talked about that too. You know, I've been flying satellites for 30 years, and there was a time where you know the U. S. Government national security. We wouldn't let anybody else look at him. Touch him. Plug into, um, anything else, right. And that probably worked at the time. >>But >>the world has changed. And more >>importantly, >>um, there is commercial technology and capability available today, and there's no way the U. S government or national security that national Intel community can afford economically >>to >>fund all that investment solely anymore. We don't have the manpower to do it anymore. So we have this perfect marriage of a burgeoning industry that has capabilities and it has re sources. And it has trained manpower. And we are seeing whether it's US Space Force, whether it's the intelligence community, whether it's NASA, we're seeing that opened up to commercial providers more than I've ever seen in my career. And I can tell you the customers I work with every day in a W s. We're building an entire ecosystem now that they understand how they can plug in and participate in that, and we're just seeing growth. But more importantly, we're seeing advanced capability at cheaper cost because of that hybrid model. So that really is exciting. >>Preston. You know you mentioned earlier supply chain. I don't think I think you didn't use the word supply chain. Maybe you did. But you know about the components. Um, you start opening things up and and your what you said baking it in to the beginning, which is well known. Uh, premise. It's complicated. So take me through again, Like how this all gonna work securely because And what's needed for skill sets because, you know, you're gonna open. You got open source software, which again, that's open. We live in a free society in the United States of America, so we can't lock everything down. You got components that are gonna be built anywhere all around the world from vendors that aren't just a certified >>or maybe >>certified. Um, it's pretty crazy. So just weigh in on this key point because I think Clint has it right. And but that's gonna be solved. What's your view on this? >>Absolutely. And I think it really, really start a top, right? And if you look back, you know, across, um in this country, particularly, you take the financial industry, for example, when when that was a burgeoning industry, what had to happen to ensure that across the board. Um, you know, your your finances were protected these way. Implemented regulations from the top, right? Yeah. And same thing with our health care industry. We implemented regulations, and I believe that's the same approach we're gonna need to take with our space systems in our space >>industry >>without being too directive or prescriptive. Instance she ating a core set of principles across the board for our manufacturers of space instruments for deployment and development of space systems on for how space data and scientific data is passed back and forth. Eso really? We're gonna need to take this. Ah, holistic approach. Thio, how we address this issue with cyber security is not gonna be easy. It's gonna be very challenging, but we need to set the guard rails for exactly what goes into our space systems, how they operate and how they communicate. >>Alright, so let's tie this back to the theme, um, Steve and Clint, because this is all about workforce gaps, opportunities. Um, Steve, you mentioned software defined. You can't do break fix in space. You can't just send a technician up in the space to fix a component. You gotta be software defined. We're talking about holistic approach, about commercial talk about business model technology with software and policy. We need people to think through, like you know. What the hell are you gonna do here, right? Do you just noticed road at the side of the road to drive on? There's no rules of engagement. So what I'm seeing is certainly software Check. If you wanna have a job for the next millennial software policy who solves two problems, what does freedom looked like in space Congestion Contention and then, obviously, business model. Can you guys comment on these three areas? Do you agree? And what specific person might be studying in grad school or undergraduate or in high school saying, Hey, I'm not a techie, but they can contribute your thoughts. I'll >>start off with, uh, speak on on behalf of the government today. I would just say that as policy goes, we need to definitely make sure that we're looking towards the future. Ah, lot of our policy was established in the past under different conditions, and, uh, and if there's anything that you cannot say today is that space is the same as it was even 10 years ago. So the so It's really important that our policy evolves and recognizes that that technology is going to enable not just a new ways of doing things, but also force us to maybe change or or get rid of obsolete policies that will inhibit our ability to innovate and grow and maintain peace with with a rapid, evolving threat. The for the for the audience today, Uh, you know, you want some job assurance, cybersecurity and space it's gonna be It's gonna be an unbelievable, uh, next, uh, few decades and I couldn't think of a more exciting for people to get into because, you know, spaces Ah, harsh environment. We're gonna have a hard time just dud being able differentiate, you know, anomalies that occur just because of the environment versus something that's being hacked. And so JPL has been doing this for years on they have Cem Cem great approaches, but but this is this is gonna be important if you put humans on the moon and you're going to sustain them there. Those life support systems are gonna be using, you know, state of the art computer technology, and which means, is also vulnerable. And so eso the consequences of us not being prepared? Uh, not just from our national security standpoint, but from our space exploration and our commercial, uh, economic growth in space over the long term all gonna be hinged on this cyber security environment. >>Clint, your thoughts on this too ill to get. >>Yeah. So I certainly agree with Bucky. But you said something a moment ago that Bucky was talking about as well. But that's the idea that you know in space, you can't just reach out and touch the satellite and do maintenance on the satellite the way you can't a car or a tank or a plane or a ship or something like that. And that is true. However, right, comma, I want to point out. You know, the satellite servicing industry is starting to develop where they're looking at robotic techniques in Cape abilities to go up in services satellite on orbit. And that's very promising off course. You got to think through the security policy that goes with that, of course. But the other thing that's really exciting is with artificial intelligence and machine learning and edge computing and database analytics and all those things that right on the cloud. You may not even need to send a robotic vehicle to a satellite, right? If you can upload and download software defined, fill in the blank right, maybe even fundamentally changing the mission package or the persona, if you will, of the satellite or the spacecraft. And that's really exciting to, ah, lot >>of >>security policy that you've gotta work through. But again, the cloud just opens up so many opportunities to continue to push the boundaries. You know, on the AWS team, the aerospace and satellite team, which is, you know, the new team that I'm leading. Now our motto is to the stars through the cloud. And there are just so many exciting opportunities right for for all those capabilities that I just mentioned to the stars through the cloud >>President, your thoughts on this? >>Yes, eso won >>a >>little bit of time talking about some of the business model implications and some of the challenges that exists there. Um, in my experience, we're still working through a bit of a language barrier of how we define risk management for our space systems. Traditionally traditionally risk management models is it is very clear what poses a risk to a flight mission. Our space mission, our space system. Um, and we're still finding ways to communicate cyber risk in the same terms that are system engineers are space engineers have traditionally understood. Um, this is a bit of a qualitative versus quantitative, a language barrier. But however adopting a risk management model that includes cybersecurity, a za way to express wish risk to miss the success, I think I think it would be a very good thing is something that that we have been focused on the J. P o as we Aziz, we look at the 34 years beyond. How do >>we >>risk that gap and not only skills but communication of cyber risk and the way that our space engineers and our project engineers and a space system managers understand >>Clinton, like Thio talk about space Force because this is the most popular new thing. It's only a couple of nine months in roughly not even a year, uh, already changing involving based on some of the reporting we've done even here at this symposium and on the Internet. Um, you know, when I was growing up, you know, I wasn't there when JFK said, you know, we're gonna get to the moon. I was born in the sixties, so, you know, when I was graduating my degree, you know, Draper Labs, Lincoln Lab, JPL, their pipeline and people wasn't like a surge of job openings. Um, so this kind of this new space new space race, you know, Kennedy also said that Torch has been passed to a new generation of Americans. So in a way that's happening right now with space force. A new generation is here is a digital generation. It's multi disciplinary generation. Could you take a minute and share, uh, for for our audience? And here at this symposium, um, the mission of Space Force and where you see it going because this truly is different. And I think anyone who's young e I mean, you know, if this was happening when I was in college would be like dropping everything. I'm in there, I think, cause there's so many areas thio jump into, um, it's >>intellectually challenging. >>It's intoxicating in some level. So can you share your thoughts? >>Yeah. Happy to do that. Of course. I I need to remind everybody that as a week ago I'm formally retired. So I'm not an official spokesman for US forces. But with that, you know, it said I did spend the last 18 months planning for it, designing and standing it up. And I'll tell you what's really exciting is you know, the commander of, uh, US Base Force General J. Raymond, who's the right leader at the right time. No question in my >>mind. But >>he said, I want to stand up the Space Force as the first fully digital service in the United States. Right? So he is trying >>to bake >>cloud baked cybersecurity, baked digital transformational processes and everything we did. And that was a guidance he gave us every day, every day. When we rolled in. He said, Remember, guys, I don't wanna be the same. I don't wanna be stale. I want new thinking, new capabilities and I want it all to be digital on. That's one of the reasons When we brought the first wave of people into the space force, we brought in space operations, right. People like me that flew satellites and launch rockets, we brought in cyber space experts, and we brought in intelligence experts. Those were the first three waves of people because of that, you know, perfect synergy between space and cyber and intel all wrapped in >>it. >>And so that was really, really smart. The other thing I'll say just about, you know, Kennedy's work. We're going to get to the moon. So here we are. Now we're going back to the Moon Project Artemus that NASA is working next man first woman on the moon by 2024 is the plan and >>then >>with designs to put a permanent presence on the moon and then lean off to march. So there was a lot to get excited about. I will tell you, as we were taking applications and looking at rounding out filling out the village in the U. S. Space Force, we were overwhelmed with the number of people that wanted, and that was a really, really good things. So they're off to a good start, and they're just gonna accomplishment major things. I know for sure. >>Preston, your thoughts on this new generation people out there were like I could get into this. This is a path. What's your what's your opinion on this? And what's your >>E could, uh, you so bold as to say >>that >>I feel like I'm a part of that new generation eso I grew up very much into space. Uh, looking at, um, listen to my, uh, folks I looked up to like Carl Sagan. Like like Neil Tyson. DeGrasse on did really feeling affinity for what What this country has done is for is a space program are focused on space exploration on bond. Through that, I got into our security, as it means from the military. And I just because I feel so fortunate that I could merge both of those worlds because of because of the generational, um, tailoring that we do thio promote space exploration and also the advent of cybersecurity expertise that is needed in this country. I feel like that. We are We are seeing a conversions of this too. I see a lot of young people really getting into space exploration. I see a lot of young people as well. Um uh, gravitating toward cybersecurity as a as a course of study. And to see those two worlds colliding and converse is something that's very near and dear to me. And again, I I feel like I'm a byproduct of that conversion, which is which, Really, Bothwell for space security in the future, >>we'll your great leader and inspiration. Certainly. Senior person as well. Congratulations, Steve. You know, young people motivational. I mean, get going. Get off the sidelines. Jump in Water is fine, Right? Come on in. What's your view on motivating the young workforce out there and anyone thinking about applying their skills on bringing something to the table? >>Well, look at the options today. You have civil space President represents you have military space. Uh, you have commercial space on and even, you know, in academia, the research, the potential as a as an aspiring cyber professional. All of you should be thinking about when we when we When? When we first invented the orbit, which eventually became the Internet, Uh, on Lee, we were, uh if all we had the insight to think Well, geez, you know whether the security implications 2030 years from now of this thing scaling on growing and I think was really good about today's era. Especially as Clint said, because we were building this space infrastructure with a cyber professionals at ground zero on dso the So the opportunity there is to look out into the future and say we're not just trying to secure independent her systems today and assure the free for all of of information for commerce. You know, the GPS signal, Uh, is Justus much in need of protection as anything else tied to our economy, But the would have fantastic mission. And you could do that. Uh, here on the ground. You could do it, uh, at a great companies like Amazon Web services. But you can also one of these states. Perhaps we go and be part of that contingency that goes and does the, uh, the se's oh job that that president has on the moon or on Mars and, uh, space will space will get boring within a generation or two because they'll just be seen as one continuum of everything we have here on Earth. And, uh, and that would be after our time. But in the meantime, is a very exciting place to be. And I know if I was in in my twenties, I wanna be, uh, jumping in with both feet into it. >>Yeah, great stuff. I mean, I think space is gonna be around for a long long time. It's super exciting and cybersecurity making it secure. And there's so many areas defeating on. Gentlemen, thank you very much for your awesome insight. Great panel. Um, great inspiration. Every one of you guys. Thank you very much for for sharing for the space and cybersecurity symposium. Appreciate it. Thank you very much. >>Thanks, John. Thank you. Thank you. Okay, >>I'm >>John for your host for the Space and Cybersecurity Symposium. Thanks for watching.

(upbeat music) >> Announcer: Live from Vancouver, Canada. It's The Cube covering Openstack Summit, North America, 2018. Brought to you by Red Hat, The Open Stack Foundation, and it's ecosystem partners. >> Welcome back to The Cube's coverage of Openstack Summit 2018 in Vancouver. I'm Stu Miniman with cohost of the week, John Troyer. Happy to welcome back to the program Stephan Fabel, who is the Director of Ubuntu product and development at Canonical. Great to see you. >> Yeah, great to be here, thank you for having me. Alright, so, boy, there's so much going on at this show. We've been talking about doing more things and in more places, is the theme that the Open Stack Foundation put into place, and we had a great conversation with Mark Shuttleworth, and going to dig in a little bit deeper in some of the areas with you. >> Stephan: Okay, absolutely. >> So we have the Cube, and we're go into all of the Kubernetes, Kubeflow, and all those other things that we'll mispronounce how they go. >> Stephan: Yes, yes, absolutely. >> What's your impression of the show first of all? >> Well I think that it's really, you know, there's a consolidation going on, right? I mean, we really have the people who are serious about open infrastructure here, serious about OpenStack. They're serious about Kubenetes. They want to implement, and they want to implement at a speed that fits the agility of their business. They want to really move quick with the obstrain release. I think the time for enterprise hardening delays an inertia there is over. I think people are really looking at the core of OpenStack, that's mature, it's stable, it's time for us to kind of move, get going, get success early, get it soon, then grow. I think most of the enterprise, most of the customers we talk to adopt that notion. >> One of the things that sometimes helps is help us lay out the stack a little bit here because we actually commented that some of the base infrastructure pieces we're not talking as much about because they're kind of mature, but OpenStack very much at the infrastructure level, your compute, storage, and network need to understand. But then we when we start doing things like Kubernetes as well, I can either do or, or on top of, and things like that, so give us your view as to what'd you put, what Canonical's seeing, and what customers-- how you lay out that stack? >> I think you're right, I think there's a little bit of path-finding here that needs to be done on the Kubernetes side, but ultimately, I think it's going to really converge around OpenStack being operative-centric, and operative-friendly, working and operating the infrastructure, scaling that out in a meaningful manner, providing multitenancy to all the different departments. Having Kubernetes be developer-centric and really help to on-board and accelerate the workload that option of the next gen initiatives, right? So, what we see is absolutely a use case for Kubernetes and OpenStack to work perfectly well together, be an extension of each other, possibly also sit next to each other without being too incumbenent there. But I think that ultimately having something like Kubernetes contain a based developer APIs that are providing that orchestration layer are the next thing, and they run just perfectly fine on Canonical OpenStack. >> Yeah, there certainly has been a lot of talk about that here at the show. Let's see, let's go a level above that, things we run on Kubernetes, I wanted to talk a little bit about ML and AI and Kubeflow. It seems like we're, I'd almost say that we're, this is like, if we were a movie, we're in a sequel like AI-5; this time, it's real. I really do see real enterprise applications incorporating these technologies into the workflow for what otherwise might be kind of boring, you know, line of business, can you talk a little bit about where we are in this evolution? >> You mean, John, only since we've been talking about it since the mid-1800s, so yeah. >> I was just about to point that out, I mean, AI's not new, right? We've seen it since about 60 years. It's been around for quite some time. I think that there is an unprecedented amount of sponsorship of new startups in this area, in this space, and there's a reason why this is heating up. I think the reason why ultimately it's there is because we're talking about a scale that's unprecedented, right? We thought the biggest problem we had with devices was going to be the IP addresses running out, and it turns out, that's not true at all, right? At a certain scale, and at a certain distributed nature of your rollout, you're going to have to deal with just such complexity and interaction between the underlying, the under-cloud, the over-cloud, the infrastructure, the developers. How do I roll this out? If I spin up 1000 BMs over here, why am I experiencing dropped calls over there? It's those types of things that need to be self-correlated. They need to be identified, they need to be worked out, so there's a whole operator angle just to be able to cope with that whole scenario. I think there's projects that are out there that are trying to ultimately address that, for example, Acumos (mumbles) Then, there is, of course, the new applications, right? Smart cities to connect to cars, all those car manufacturers who are, right now, faced with the problem: how do I deal with mobile, distributed inference rollout on the edge while still capturing the data continually, train my model, update, then again, distribute out to the edge to get a better experience. How do I catch up to some of the market leaders here that are out there? As the established car manufacturers are going to come and catch up, put more and more miles autonomously on the asphalt, we're going to basically have to deal with a whole lot more of proctization of machine-learning applications that just have to be managed at scale. And so we believe for all certain good company in that belief that having to manage large applications at scale, that containers and Kubernetes is a great way to do that, right? They did that for web apps. They did that for the next generation applications. This is one example where with the right operators in mind, the right CRDs, the right frameworks on top of Kubernetes managed correctly, you are actually in a great position to just go to market with that. >> I wonder if you might have a customer example that might go to walk us through kind of where they are in this discussion, talk to many companies, you know, the whole IOT even pieces were early in this. So what's actually real today, how much is planning, is this years we're talking before some of these really come to fruition? >> So yeah, I can't name a customer, but I can say that every single car manufacturer we're talking to is absolutely interested in solving the operational problem of running machine-learning frameworks as a service, making sure those are up running and up to speed at any given point in time, spin them up in a multitenant fashion, make sure that the GPU enablement is actually done properly at all layers of the virtualization. These are real operational challenges that they're facing today, and they're looking to solve with us. Pick a large car manufacturer you want. >> John: Nice. We're going down to something that I can type on my own keyboard then, and go to GitHub, right? That's one of the places to go where it is run, TensorFlow of machine-learning framework on Kubernetes is Kubeflow, and that little bit yesterday on stage, you want to talk about that maybe? >> Oh, absolutely, yes. That's the core of our current strategy right now. We're looking at Kubeflow as one of the key enablers of machine-learning frameworks as a service on top of Kubernetes, and I think they're a great example because they can really show how that as a service can be implemented on top of a virtualization platform, whether that be KVM, pure KVM, on bare metal, on OpenStack, and actually provide machine-learning frameworks such as TensorFlow, Pipe Torch, Seldon Core. You have all those frameworks being supported, and then basically start mix and matching. I think ultimately it's so interesting to us because the data scientists are really not the ones that are expected to manage all this, right? Yet they are the core of having to interact with it. In the next generation of the workloads, we're talking to PHDs and data scientists that have no interest whatsoever in understanding how all of this works on the back end, right? They just want to know this is where I'm going to submit my artifact that I'm creating, this is how it works in general. Companies pay them a lot of money to do just that, and to just do the model because that's where, until the right model is found, that is exactly where the value is. >> So Stephan, does Canonical go talk to the data scientists, or is there a class of operators who are facilitating the data scientists? >> Yes, we talk to the data scientists who understand their problems, we talk to the operators to understand their problems, and then we work with partners such as Google to try and find solutions to that. >> Great, what kind of conversations are you having here at the show? I can't imagine there's too many of those, great to hear if there are, but where are they? I think everybody here knows containers, very few know Kubernetes, and how far up the stack of building new stuff are they? >> You'd be surprised, I mean, we put this out there, and so far, I want to say the majority of the customer conversations we've had took an AI turn and said, this is what we're trying to do next year, this is what we're trying to do later in the year, this is what we're currently struggling with. So glad you have an approach because otherwise, we would spend a ton of time thinking about this, a ton of time trying to solve this in our own way that then gets us stuck in some deep end that we don't want to be. So, help us understand this, help us pave the way. >> John: Nice, nice. I don't want to leave without talking also about Microcades, that's a Kubernetes snap, you code some clojure download, Can we talk a little bit about that? >> Yeah, glad to. This was an idea that we conceived that came out of this notion of alright, well if I do have, talking to a data scientist, if I do have a data scientist, where does he start? >> Stu: Does Kubernetes have a learning curve to date? >> It does, yeah, it does. So here's the thing, as a developer, you have, what options do you have right when you get started? You can either go out and get a community stood up on one of the public clouds, but what if you're in the plane, right? You don't have a connection, you want to work on your local laptop. Possibly, that laptop also has a GPU, and you're a data scientist and you want to try this out because you know you're going to submit this training job now to a (mumbles) that runs un-prem behind the firewall with a limited training set, right? This is the situation we're talking about. So ultimately, the motivation for creating Microcades was we want to make this very, very equivalent. Now you can deploy Kubeflow on top of Microcades today, and it'll run just fine. You get your TensorBoard, you have Jupyter notebook, and you can do your work, and you can do it in a fashion that will then be compatible to your on-prem and public machine-learning framework. So that was your original motivation for why we went down this road, but then we noticed you know what, this is actually a wider need. People are thinking about local Kubernetes in many different ways. There are a couple of solutions out there. They tend to be cumbersome, or more cumbersome than developers would like it. So we actually said, you know, maybe we should turn this into a more general purpose solution. So hence, Microcades. It works like a snap on your machine, you kick that off, you have Kubernetes API, and under 30 seconds or little longer if your download speed plays a factor here, you enable DNS and you're good to go. >> Stephan, I just want to give you the opportunity, is there anything in the Queens Release that your customers have been specifically waiting for or any other product announcements before we wrap? >> Sure, we're very excited about the Queens Release. We think Queens Release is one of the great examples of the maturity of the code base and really the knot towards the operator, and that, I think was the big challenge beyond the olden days of OpenStack where the operators took a long time for the operators to be heard, and to establish that conversation. We'd like to say and to see that OpenStack Queens has matured in that respect, and we like things like Octavia. We're very exciting about (mumbles) as a service, taking its own life and being treated as a first-class citizen. I think that it was a great decision of the community to get on that road. We're supporting as a part of our distribution. >> Alright, well, appreciate the update. Really fascinating to hear about all, you know, everybody's thinking about it and really starting to move on all the ML and AI stuff. Alright, for John Troyer, I'm Tru Miniman. Lots more coverage here from OpenStack Summit 2018 in Vancouver. Thanks for watching The Cube. (upbeat music)

>> Announcer: From Copenhagen, Denmark, it's theCUBE. Covering KubeCon and CloudNativeCon Europe 2018. Brought to you by the Cloud Native Computing Foundation and its ecosystem partners. >> Hello and welcome back to the live CUBE coverage here in Copenhagen, Denmark, for KubeCon 2018, Kubernetes European conference. This is theCUBE, I'm John Furrier, my co-host Lauren Cooney here with Adrian Cockcroft who is the Vice President of Cloud Architecture and Strategy for Amazon Web Services, AWS. CUBE alumni, great to see you, a legend in the industry, great to have you on board today. Thanks for coming on. >> Thanks very much. >> Quick update, Amazon, we were at AWS Summit recently, I was at re:Invent last year, it gets bigger and bigger just continue to grow. Congratulations on successful great earnings. You guys posted last week, just continuing to show the scale and leverage that the cloud has. So, again, nothing really new here, cloud is winning and the model of choice. So you guys are doing a great job, so congratulations. Open source, you're handling a lot of that now. This community here, is all about driving cloud standards. >> Adrian: Yeah. >> Your guys position on that is? Standards are great, you do what customers want, as Andy Jassy always says, what's the update? I mean, what's new since Austin last year? >> Yeah, well, it's been great to be back on had a great video of us talking at Austin, it's been very helpful to get the message out of what we're doing in containers and what the open source team that I lead has been up to. It's been very nice. Since then we've done quite a lot. We were talking about doing things then, which we've now actually done and delivered on. We're getting closer to getting our Kubernetes service out, EKS. We hired Bob Wise, he started with us in January, he's the general manager of EKS. Some of you may know Bob has been working with Kubernetes since the early days. He was on the CNCF board before he joined us. He's working very hard, they have a team cranking away on all the things we need to do to get the EKS service out. So that's been major focus, just get it out. We have a lot of people signed up for the preview. Huge interest, we're onboarding a lot of people every week, and we're getting good feedback from people. We have demos of it in the booth here this week. >> So you guys are very customer-centric, following you guys closely as you know. What's the feedback that you're hearing and what are you guys ingesting from an intelligence standpoint from the field. Obviously, a new constituent, not new, but a major constituent is open source communities, as well as paying enterprise customers? What's the feedback? What are you hearing? I would say beyond tire kicking, there's general interest in what Kubernetes has enabled. What's Amazon's view of that? >> Yeah, well, open source in general is always getting a larger slice of what people want to do. Generally, people are trying to get off of their enterprise solutions and evolving into an open source space and then you kind of evolve from that into buying it as a service. So that's kind of the evolution from one trend, custom or enterprise software, to open source to as a service. And we're standing up all of these tools as a service to make them easier to consume for people. Just, everybody's happy to do that. What I'm hearing from customers is that that's what they're looking for. They want it to be easy to use, they want it to scale, they want it to be reliable and work, and that's what we're good at doing. And then they want to track the latest moves in the industry and run with the latest technologies and that's what Kubernetes and the CNCF is doing, gathering together a lot of technologies. Building the community around it, just able to move faster than we'd move on our own. We're leveraging all of those things into what we're doing. >> And the status of EKS right now is in preview? And the estimated timetable for GA? >> In the next few months. >> Next few months. >> You know, get it out then right now it's running in Oregon, in our Oregon data center, so the previews are all happening there. That gets us our initial thing and then everyone go okay, we want to in our other regions, so we have to do that. So another service we have is Fargate, which is basically say just here's a container, I want to run it, you don't have to declare a node or an instance to run it first. We launched that at re:Invent, that's already in production obviously, we just rolled that out to four regions. That's in Virginia, Oregon, Dublin and Ohio right now. A huge interest in Fargate, it lets you simplify your deployments a little bit. We just posted a new blog post that we have an open source blog, you can find if you want to keep up with what's going on with the open source team at AWS. Just another post this morning and it's a first pass at getting Fargate to work with Kubernetes using Virtual Kubelet which is a project that was kicked off by, it's an experimental project, not part of the core Kubernetes system. But it's running on the side. It's something that Microsoft came up with a little while ago. So we now have, we're working with them. We did a pull request, they accepted it, so that team and AWS and a few other customers and other people in the community, working together to provide you a way to start up Fargate as the underlying layer for provisioning containers underneath Kubernetes as the API for doing you know the management of that. >> So who do you work with mostly when you're working in open source? Who do you partner with? What communities are you engaging with in particular? >> It's all over. >> All over? >> Wherever the communities are we're engaging with them. >> Lauren: Okay, any particular ones that stand out? >> Other than CNCF, we have a lot of engagement with Apache Hadoop ecosystem. A lot of work in data science, there's many, many projects in that space. In AI and machine learning, we've sponsored, we've spend a lot of time working with Apache MXNet, we were also working off with TensorFlow by Torch and Caffe and there's a lot, those are all open source frameworks so there's lots of contributions there. In the serverless arena, we have our own SAM service application model. We've been open sourcing more of that recently ourselves and we're working with various other people. Across these different groups there's different conferences you go to, there's different things we do. We just sponsored Rails Conference. My team sponsors and manages most of the open source conference events we go to now. We just did RAILCON, we're doing a Rust conference, soon I think, there's Python conferences. I forget when all these are. There's a massive calendar of conferences that we're supporting. >> Make sure you email us that that list, we're interested actually in looking at what the news and action is. >> So the language ones, AltCon's our flagship one, we'll be top-level sponsor there. When we get to the U.S., CubeCon in Seattle, it's right there, it's two weeks after re:Invent. It's going to be much easier to manage. When we go to re:Invent it's like everyone just wants to take that week off, right. We got a week for everyone to recover and then it's in the hometown. >> You still have that look in your eyes when we interviewed you in Austin you came down, we both were pretty exhausted after re:Invent. >> Yeah, so we announced a bunch of things on Wednesday and Thursday and I had to turn it into a keynote by Tuesday and get everyone to agree. That's what was going on, that was very compressed. We have more time and all of the engineering teams that really want to be at an event like this, were right in the hometown for a lot. >> What's it like workin' at Amazon, I got to ask you it since you brought it up. I mean and you guys run hard at Amazon, you're releasing stuff with a pace that's unbelievable. I mean, I get blown away every year. Almost seems like, inhuman that that you guys can run at that pace. And earnings, obviously, the business results speak for themselves, what's it like there? I mean, you put your running shoes on, you run a marathon every day. >> It's lots of small teams working relatively independently and that scales and that's something other engineering organizations have trouble with. They build hierarchies that slow down. We have a really good engineering culture where every time you start a new team, it runs at its own speed. We've shown that as we add more and more resources, more teams, they are just executing. In fact, their accelerated, they're building on top of other things. We get to build higher and higher level abstractions to layer into. Just getting easier and easier to build things. We're accelerating our pace of innovation there's no slowing down. >> I was telling Jassy they're going to write a Harvard Business School case study on a lot of the management practices, but certainly the impact on the business side with the model that you guys do. But I got to ask you, on the momentum side, super impressed with SageMaker. I predicted on theCUBE at AWS Summit that that will be the fastest growing service. It will overtake Aurora, I think that is currently on stage, presented as the fastest growing service. SageMaker is really popular. Updates there, its role in the community. Obviously, Kubernete's a good fit for orchestrating things. We heard about CubeFlow, is an interesting model. What's going on with SageMaker how is it interplaying with Kubernetes? >> People that want to run, if you're running on-premise, cluster of GPU enabled machines then CubeFlow is a great way of doing that. You're on TensorFlow, that manages your cluster, you run CubeFlow on top. SageMaker is running at very low scale and like a lot of things we do at AWS, what you need to run an individual cluster for any one customer is different from running a multi-tenant service. SageMaker sits on top of ECS and it's now one of the largest generators of traffic to ECS which is Amazon's horizontally scaled, multi-tenant, cluster management system, which is now doing hundreds of millions of container launches a week. That is continuing to grow. We see Kubernetes as it's a more portable abstraction. It has some more, different layers of API's and a big community around it. But for the heavy lifting of running tens of thousands of containers in for a single application, we're still at the level where ECS does that every day and Kubernetes that's kind of the extreme case, where a few people are pushing it. It'll gradually grow scale. >> It's evolution. >> There's an evolution here. But the interesting things are, we're starting to get some convergence on some of the interfaces. Like the interfacing at CNA, CNA is the way you do networking on containers and there is one way of doing that, that is shared by everybody through CNA. EKS uses it, BCS uses it and Kubernetes uses it. >> And the impact of customers is what for that? What's the impact? >> It means the networking structures you want to set up will be the same. And the capabilities and the interfaces. But what happens on AWS is because it has a direct plug-in, you can hook it up to our accelerated networking infrastructure. So, AWS's instances right now, we've offloaded most of the network traffic processing. You're running 25 gigabits of traffic, that's quite a lot of work even for a big CPU, but it's handled by the the Nitro plug-in architecture we have, this in our latest instance type. So if you talked a bit about that at re:Invent but what you're getting is enormous, complete hypervisor offload at the core machine level. You get to use that accelerated networking. You're plugging into that interface. But that, if you want to have a huge number of containers on a machine and you're not really trying to drive very high throughput, then you can use Calico and we support that as well. So, multiple different ways but all through the same thing, the same plug-ins on both. >> System portability. You mentioned some stats, what's the numbers you mentioned? How many containers you're launching a week, hundreds of thousands? On ECS, our container platform that's been out for a few years, so hundreds of millions a week. It's really growing very fast. The containers are taking off everywhere. >> Microservices growth is, again that's the architecture. As architecture is a big part of the conversation what's your dialogue with customers? Because the modern software architecture in cloud, looks a lot different than what it was in the three layered approach that used to be the web stack. >> Yeah, and I think to add to that, you know we were just talking to folks about how in large enterprise organizations, you're still finding groups that do waterfall development. How are you working to kind of bring these customers and these developers into the future, per se? >> Yeah, that's actually, I spend about half my time managing the open source team and recruiting. The other half is talking to customers about this topic. I spend my time traveling around the world, talking at summits and events like this and meeting with customers. There's lots of different problems slowing people down. I think you see three phases of adoption of cloud, in general. One is just speed. I want to get something done quickly, I have a business need, I want to do it. I want machines in minutes instead of months, right, and that speeds everything up so you get something done quickly. The second phase is where you're starting to do stuff at scale and that's where you need cloud native. You really need to have elastic services, you can scale down as well as up, otherwise, you just end up with a lot of idle machines that cost you too much and it's not giving you the flexibility. The third phase we're getting into is complete data center shutdown. If you look at investing in a new data center or data center refresh or just opening an AWS account, it really doesn't make sense nowadays. We're seeing lots of large enterprises either considering it or well into it. Some are a long way into this. When you shut down the data center all of the backend core infrastructure starts coming out. So we're starting to see sort of mainframe replacement and the really critical business systems being replaced. Those are the interesting conversations, that's one of the areas that I'm particularly interested in right now and it's leading into this other buzzword, if you like, called chaos engineering. Which is sort of the, think of it as the availability model for cloud native and microservices. We're just starting a working group at CNCF around chaos engineering, is being started this week. So you can get a bit involved in how we can build some standards. >> That's going to be at Stanford? >> It's here, I mean it's a working group. >> Okay, online. >> The CNCF working group, they are wherever the people are, right. >> So, what is that conversation when you talk about that mainframe kind of conversation or shut down data centers to the cloud. What is the key thing that you promote, up front, that needs to get done by the by the customer? I mean, obviously you have the pillars, the key pillars, but you think about microservices it's a global platform, it's not a lift and shift situation, kind of is, it shut down, but I mean not at that scale. But, security, identity, authentication, there's no perimeter so you know microservices, potentially going to scale. What are the things that you promote upfront, that they have to do up front. What are the up front, table stake decisions? >> For management level, the real problem is people problems. And it's a technology problem somewhere down in the weeds. Really, if you don't get the people structures right then you'll spend forever going through these migrations. So if you sort of bite the bullet and do the reorganization that's needed first and get the right people in the right place, then you move much faster through it. I say a lot of the time, we're way upstream of picking a technology, it's much more about understanding the sort of DevOps, Agile and the organizational structures for these more cellular based organizations, you know, AWS is a great example of that. Netflix are another good example of that. Capital One is becoming a good example of that too. In banking, they're going much faster because they've already gone through that. >> So they're taking the Amazon model, small teams. Is that your general recommendation? What's your general recommendation? >> Well, this is the whole point of microservices, is that they're built by these small teams. It's called Conway's law, which says that the code will end up looking like the team, the org structure that built it. So, if you set up a lots of small teams, you will end up with microservices. That's just the way it works, right. If you try to take your existing siloed architecture with your long waterfall things, it's very hard not to build a monolith. Getting the org structure done first is right. Then we get into kind of the landing zone thing. You could spend years just debating what your architecture should be and some people have and then every year they come back, and it's changing faster than they can decide what to do. That's another kind of like analysis paralysis mode you see some larger enterprises in. I always think just do it. What's the standard best practice, layout my accounts like this, my networks like this, my structures we call it landing zone. We get somebody up to speed incredibly quickly and it's the beaten path. We're starting to build automation around these on boarding things, we're just getting stuff going. >> That's great. >> Yeah, and then going back to the sort of chaos engineering kind of idea, one of the first things I should think you should put into this infrastructure is the disaster recovery automation. Because if that gets there before the apps do, then the apps learn to live with the chaos monkeys and things like that. Really, one of the first apps we installed at Netflix was Chaos Monkey. It wasn't added later, it was there when you arrived. Your app had to survive the chaos that was in the system. So, think of that as, it used to be disaster recovery was incredibly expensive, hard to build, custom and very difficult to test. People very rarely run through their disaster recovery testing data center fail over, but if you build it in on day one, you can build it automated. I think Kubernetes is particularly interesting because the API's to do that automation are there. So we're looking at automating injecting failure at the Kubernetes level and also injecting into the underlying machines that are running Good Maze, like attacking the control plane to make sure that the control plane recovery works. I think there's a lot we can do there to automate it and make it into a low-cost, productized, safe, reliable thing, that you do a lot. Rather than being something that everyone's scared of doing that. >> Or they bolted on after they make decisions and the retrofit, pre-existing conditions into a disaster recovery. Which is chaotic in and of itself. >> So, get the org chart right and then actually get the disaster recovery patterns. If you need something highly available, do that first, before the apps turn up. >> Adrian, thanks for coming on, chaos engineering, congratulations and again, we know you know a little about Netflix, you know that environment, and been big Amazon customer. Congratulations on your success, looking forward to keeping in touch. Thanks for coming on and sharing the AWS perspective on theCUBE. I'm John Furrier, Lauren Cooney live in Denmark for KubeCon 2018 part of the CNC at the Cloud Native Compute Foundation. We'll back with more live coverage, stay with us. We'll be right back. (upbeat music)

(music playing) >> Narrator: Live, from downtown San Francisco It's the Cube. Covering IBM Chief Data Officer Startegy Summit 2018. Brought to you by: IBM >> Welcome back to San Francisco everybody we're at the Parc 55 in Union Square. My name is Dave Vellante, and you're watching the Cube. The leader in live tech coverage and this is our exclusive coverage of IBM's Chief Data Officer Strategy Summit. They hold these both in San Francisco and in Boston. It's an intimate event, about 150 Chief Data Officers really absorbing what IBM has done internally and IBM transferring knowledge to its clients. Steven Eluk is here. He is one of those internal practitioners at IBM. He's the Vice President of Deep Learning and the Global Chief Data Office at IBM. We just heard from him and some of his strategies and used cases. He's joined by Sumit Gupta, a Cube alum. Who is the Vice President of Machine Learning and deep learning within IBM's cognitive systems group. Sumit. >> Thank you. >> Good to see you, welcome back Steven, lets get into it. So, I was um paying close attention when Bob Picciano took over the cognitive systems group. I said, "Hmm, that's interesting". Recently a software guy, of course I know he's got some hardware expertise. But bringing in someone who's deep into software and machine learning, and deep learning, and AI, and cognitive systems into a systems organization. So you guys specifically set out to develop solutions to solve problems like Steven's trying to solve. Right, explain that. >> Yeah, so I think ugh there's a revolution going on in the market the computing market where we have all these new machine learning, and deep learning technologies that are having meaningful impact or promise of having meaningful impact. But these new technologies, are actually significantly I would say complex and they require very complex and high performance computing systems. You know I think Bob and I think in particular IBM saw the opportunity and realized that we really need to architect a new class of infrastructure. Both software and hardware to address what data scientist like Steve are trying to do in the space, right? The open source software that's out there: Denzoflo, Cafe, Torch - These things are truly game changing. But they also require GPU accelerators. They also require multiple systems like... In fact interestingly enough you know some of the super computers that we've been building for the scientific computing world, those same technologies are now coming into the AI world and the enterprise. >> So, the infrastructure for AI, if I can use that term? It's got to be flexible, Steven we were sort of talking about that elastic versus I'm even extending it to plastic. As Sumit you just said, it's got to have that tooling, got to have that modern tooling, you've got to accommodate alternative processor capabilities um, and so, that forms what you've used Steven to sort of create new capabilities new business capabilities within IBM. I wanted to, we didn't touch upon this before, but we touched upon your data strategy before but tie it back to the line of business. You essentially are a presume a liaison between the line of business and the chief data office >> Steven: Yeah. >> Officer office. How did that all work out, and shake out? Did you defining the business outcomes, the requirements, how did you go about that? >> Well, actually, surprisingly, we have very little new use cases that we're generating internally from my organization. Because there's so many to pick from already throughout the organization, right? There's all these business units coming to us and saying, "Hey, now the data is in the data lake and now we know there's more data, now we want to do this. How do we do it?" You know, so that's where we come in, that's where we start touching and massaging and enabling them. And that's the main efforts that we have. We do have some derivative works that have come out, that have been like new offerings that you'll see here. But mostly we already have so many use cases that from those businesses units that we're really trying to heighten and bring extra value to those domains first. >> So, a lot of organizations sounds like IBM was similar you created the data lake you know, things like "a doop" made a lower cost to just put stuff in the data lake. But then, it's like "okay, now what?" >> Steven: Yeah. >> So is that right? So you've got the data and this bog of data and you're trying to make more sense out of it but get more value out of it? >> Steven: Absolutely. >> That's what they were pushing you to do? >> Yeah, absolutely. And with that, with more data you need more computational power. And actually Sumit and I go pretty far back and I can tell you from my previous roles I heightened to him many years ago some of the deficiencies in the current architecture in X86 etc and I said, "If you hit these points, I will buy these products." And what they went back and they did is they, they addressed all of the issues that I had. Like there's certain issues... >> That's when you were, sorry to interrupt, that's when you were a customer, right? >> Steven: That's when I was... >> An external customer >> Outside. I'm still an internal customer, so I've always been a customer I guess in that role right? >> Yep, yep. >> But, I need to get data to the computational device as quickly as possible. And with certain older gen technologies, like PTI Gen3 and certain issues around um x86. I couldn't get that data there for like high fidelity imaging for autonomous vehicles for ya know, high fidelity image analysis. But, with certain technologies in power we have like envy link and directly to the CPU. And we also have PTI Gen4, right? So, so these are big enablers for me so that I can really keep the utilization of those very expensive compute devices higher. Because they're not starved for data. >> And you've also put a lot of emphasis on IO, right? I mean that's... >> Yeah, you know if I may break it down right there's actually I would say three different pieces to the puzzle here right? The highest level from Steve's perspective, from Steven's teams perspective or any data scientist perspective is they need to just do their data science and not worry about the infrastructure, right? They actually don't want to know that there's an infrastructure. They want to say, "launch job" - right? That's the level of grand clarity we want, right? In the background, they want our schedulers, our software, our hardware to just seamlessly use either one system or scale to 100 systems, right? To use one GPU or to use 1,000 GPUs, right? So that's where our offerings come in, right. We went and built this offering called Powder and Powder essentially is open source software like TensorFlow, like Efi, like Torch. But performace and capabilities add it to make it much easier to use. So for example, we have an extremely terrific scheduling software that manages jobs called Spectrum Conductor for Spark. So as the name suggests, it uses Apache Spark. But again the data scientist doesn't know that. They say, "launch job". And the software actually goes and scales that job across tens of servers or hundreds of servers. The IT team can determine how many servers their going to allocate for data scientist. They can have all kinds of user management, data management, model management software. We take the open source software, we package it. You know surprisingly ugh most people don't realize this, the open source software like TensorFlow has primarily been built on a (mumbles). And most of our enterprise clients, including Steven, are on Redhat. So we, we engineered Redhat to be able to manage TensorFlow. And you know I chose those words carefully, there was a little bit of engineering both on Redhat and on TensorFlow to make that whole thing work together. Sounds trivial, took several months and huge value proposition to the enterprise clients. And then the last piece I think that Steven was referencing too, is we also trying to go and make the eye more accessible for non data scientist or I would say even data engineers. So we for example, have a software called Powder Vision. This takes images and videos, and automatically creates a trained deep learning model for them, right. So we analyze the images, you of course have to tell us in these images, for these hundred images here are the most important things. For example, you've identified: here are people, here are cars, here are traffic signs. But if you give us some of that labeled data, we automatically do the work that a data scientist would have done, and create this pre trained AI model for you. This really enables many rapid prototyping for a lot of clients who either kind of fought to have data scientists or don't want to have data scientists. >> So just to summarize that, the three pieces: It's making it simpler for the data scientists, just run the job - Um, the backend piece which is the schedulers, the hardware, the software doing its thing - and then its making that data science capability more accessible. >> Right, right, right. >> Those are the three layers. >> So you know, I'll resay it in my words maybe >> Yeah please. >> Ease of use right, hardware software optimized for performance and capability, and point and click AI, right. AI for non data scientists, right. It's like the three levels that I think of when I'm engaging with data scientists and clients. >> And essentially it's embedded AI right? I've been making the point today that a lot of the AI is going to be purchased from companies like IBM, and I'm just going to apply it. I'm not going to try to go build my own, own AI right? I mean, is that... >> No absolutely. >> Is that the right way to think about it as a practitioner >> I think, I think we talked about it a little bit about it on the panel earlier but if we can, if we can leverage these pre built models and just apply a little bit of training data it makes it so much easier for the organizations and so much cheaper. They don't have to invest in a crazy amount of infrastructure, all the labeling of data, they don't have to do that. So, I think it's definitely steering that way. It's going to take a little bit of time, we have some of them there. But as we as we iterate, we are going to get more and more of these types of you know, commodity type models that people could utilize. >> I'll give you an example, so we have a software called Intelligent Analytics at IBM. It's very good at taking any surveillance data and for example recognizing anomalies or you know if people aren't suppose to be in a zone. Ugh and we had a client who wanted to do worker safety compliance. So they want to make sure workers are wearing their safety jackets and their helmets when they're in a construction site. So we use surveillance data created a new AI model using Powder AI vision. We were then able to plug into this IVA - Intelligence Analytic Software. So they have the nice gooey base software for the dashboards and the alerts, yet we were able to do incremental training on their specific use case, which by the way, with their specific you know equipment and jackets and stuff like that. And create a new AI model, very quickly. For them to be able to apply and make sure their workers are actually complaint to all of the safety requirements they have on the construction site. >> Hmm interesting. So when I, Sometimes it's like a new form of capture says identify "all the pictures with bridges", right that's the kind of thing you're capable to do with these video analytics. >> That's exactly right. You, every, clients will have all kinds of uses I was at a, talking to a client, who's a major car manufacturer in the world and he was saying it would be great if I could identify the make and model of what cars people are driving into my dealership. Because I bet I can draw a ugh corelation between what they drive into and what they going to drive out of, right. Marketing insights, right. And, ugh, so there's a lot of things that people want to do with which would really be spoke in their use cases. And build on top of existing AI models that we have already. >> And you mentioned, X86 before. And not to start a food fight but um >> Steven: And we use both internally too, right. >> So lets talk about that a little bit, I mean where do you use X86 where do you use IBM Cognitive and Power Systems? >> I have a mix of both, >> Why, how do you decide? >> There's certain of work loads. I will delegate that over to Power, just because ya know they're data starved and we are noticing a complication is being impacted by it. Um, but because we deal with so many different organizations certain organizations optimize for X86 and some of them optimize for power and I can't pick, I have to have everything. Just like I mentioned earlier, I also have to support cloud on prim, I can't pick just to be on prim right, it so. >> I imagine the big cloud providers are in the same boat which I know some are your customers. You're betting on data, you're betting on digital and it's a good bet. >> Steven: Yeah, 100 percent. >> We're betting on data and AI, right. So I think data, you got to do something with the data, right? And analytics and AI is what people are doing with that data we have an advantage both at the hardware level and at the software level in these two I would say workloads or segments - which is data and AI, right. And we fundamentally have invested in the processor architecture to improve the performance and capabilities, right. You could offer a much larger AI models on a power system that you use than you can on an X86 system that you use. Right, that's one advantage. You can train and AI model four times faster on a power system than you can on an Intel Based System. So the clients who have a lot of data, who care about how fast their training runs, are the ones who are committing to power systems today. >> Mmm.Hmm. >> Latency requirements, things like that, really really big deal. >> So what that means for you as a practitioner is you can do more with less or is it I mean >> I can definitely do more with less, but the real value is that I'm able to get an outcome quicker. Everyone says, "Okay, you can just roll our more GPU's more GPU's, but run more experiments run more experiments". No no that's not actually it. I want to reduce the time for a an experiment Get it done as quickly as possible so I get that insight. 'Cause then what I can do I can get possibly cancel out a bunch of those jobs that are already running cause I already have the insight, knowing that that model is not doing anything. Alright, so it's very important to get the time down. Jeff Dean said it a few years ago, he uses the same slide often. But, you know, when things are taking months you know that's what happened basically from the 80's up until you know 2010. >> Right >> We didn't have the computation we didn't have the data. Once we were able to get that experimentation time down, we're able to iterate very very quickly on this. >> And throwing GPU's at the problem doesn't solve it because it's too much complexity or? >> It it helps the problem, there's no question. But when my GPU utilization goes from 95% down to 60% ya know I'm getting only a two-thirds return on investment there. It's a really really big deal, yeah. >> Sumit: I mean the key here I think Steven, and I'll draw it out again is this time to insight. Because time to insight actually is time to dollars, right. People are using AI either to make more money, right by providing better customer products, better products to the customers, giving better recommendations. Or they're saving on their operational costs right, they're improving their efficiencies. Maybe their routing their trucks in the right way, their routing their inventory in the right place, they're reducing the amount of inventory that they need. So in all cases you can actually coordinate AI to a revenue outcome or a dollar outcome. So the faster you can do that, you know, I tell most people that I engage with the hardware and software they get from us pays for itself very quickly. Because they make that much more money or they save that much more money, using power systems. >> We, we even see this internally I've heard stories and all that, Sumit kind of commented on this but - There's actually sales people that take this software & hardware out and they're able to get an outcome sometimes in certain situations where they just take the clients data and they're sales people they're not data scientists they train it it's so simple to use then they present the client with the outcomes the next day and the client is just like blown away. This isn't just a one time occurrence, like sales people are actually using this right. So it's getting to the area that it's so simple to use you're able to get those outcomes that we're even seeing it you know deals close quicker. >> Yeah, that's powerful. And Sumit to your point, the business case is actually really easy to make. You can say, "Okay, this initiative that you're driving what's your forecast for how much revenue?" Now lets make an assumption for how much faster we're going to be able to deliver it. And if I can show them a one day turn around, on a corpus of data, okay lets say two months times whatever, my time to break. I can run the business case very easily and communicate to the CFO or whomever the line of business head so. >> That's right. I mean just, I was at a retailer, at a grocery store a local grocery store in the bay area recently and he was telling me how In California we've passed legislation that does not allow plastic bags anymore. You have to pay for it. So people are bringing their own bags. But that's actually increased theft for them. Because people bring their own bag, put stuff in it and walk out. And he didn't want to have an analytic system that can detect if someone puts something in a bag and then did not buy it at purchase. So it's, in many ways they want to use the existing camera systems they have but automatically be able to detect fraudulent behavior or you know anomalies. And it's actually quite easy to do with a lot of the software we have around Power AI Vision, around video analytics from IBM right. And that's what we were talking about right? Take existing trained AI models on vision and enhance them for your specific use case and the scenarios you're looking for. >> Excellent. Guys we got to go. Thanks Steven, thanks Sumit for coming back on and appreciate the insights. >> Thank you >> Glad to be here >> You're welcome. Alright, keep it right there buddy we'll be back with our next guest. You're watching "The Cube" at IBM's CDO Strategy Summit from San Francisco. We'll be right back. (music playing)

(upbeat electronic music) >> Hello, everyone. Welcome to the Cube Studios in Palo Alto, California. I'm John Furrier, the co-host of the Cube and co-founder of SiliconANGLE Media Inc. I'm here with George Gilbert for a Wikibon conversation on the state of the big data. George Gilbert is the analyst at Wikibon covering big data. George, great to see you. Looking good. (laughing) >> Good to see you, John. >> So George, you're obviously covering big data. Everyone knows you. You always ask the tough questions, you're always drilling down, going under the hood, and really inspecting all the trends, and also looking at the technology. What are you working on these days as the big data analyst? What's the hot thing that you're covering? >> OK, so, what's really interesting is we've got this emerging class of applications. The name that we've used so far is modern operational analytic applications. Operational in the sense that they help drive business operations, but analytical in the sense that the analytics either inform or drive transactions, or anticipate and inform interactions with people. That's the core of this class of apps. And then there are some sort of big challenges that customers are having in trying to build, and deploy, and operate these things. That's what I want to go through. >> George, you know, this is a great piece. I can't wait to (mumbling) some of these questions and ask you some pointed questions. But I would agree with you that to me, the number one thing I see customers either fumbling with or accelerating value with is how to operationalize some of the data in a way that they've never done it before. So you start to see disciplines come together. You're starting to see people with a notion of digital business being something that's not a department, it's not a marketing department. Data is everywhere, it's horizontally scalable, and the smart executives are really looking at new operational tactics to do that. With that, let me kick off the first question to you. People are trying to balance the cloud, On Premise, and The Edge, OK. And that's classic, you're seeing that now. I've got a data center, I have to go to the cloud, a hybrid cloud. And now the edge of the network. We were just taking about Block Chain today, there's this huge problem. They've got the balance that, but they've got to balance it versus leveraging specialized services. How do you respond to that? What is your reaction? What is your presentation? >> OK, so let's turn it into something really concrete that everyone can relate to, and then I'll generalize it. The concrete version is for a number of years, everyone associated Hadoop with big data. And Hadoop, you tried to stand up on a cluster on your own premises, for the most part. It was on had EMR, but sort of the big company activity outside, even including the big tech companies was stand up a Hadoop cluster as a pilot and start building a data lake. Then see what you could do with sort of huge amounts of data that you couldn't normally sort of collect and analyze. The operational challenges of standing up that sort of cluster was rather overwhelming, and I'll explain that later, so sort of park that thought. Because of that complexity, more and more customers, all but the most sophisticated, are saying we need a cloud strategy for that. But once you start taking Hadoop into the cloud, the components of this big data analytic system, you have tons more alternatives. So whereas in Cloudera's version of Hadoop you had Impala as your MPP sequel database. On Amazon, you've got Amazon Redshift, you've got Snowflake, you've got dozens up MPP sequel databases. And so the whole playing field shifts. And not only that, Amazon has instrumented their, in that particular case, their application, to be more of a more managed service, so there's a whole lot less for admins to do. And you take that on sort of, if you look at the slides, you take every step in that pipeline. And when you put it on a different cloud, it's got different competitors. And even if you take the same step in a pipeline, let's say Spark on HDFS to do your ETL, and your analysis, and your shaping of data, and even some of the machine learning, you put that on Azure and on Amazon, it's actually on different storage foundation. So even if you're using the same component, it's different. There's a lot of complexity and a lot of trade off that you got to make. >> Is that a problem for customers? >> Yes, because all of a sudden, they have to evaluate what those trade offs are. They have to evaluate the trade off between specialization. Do I use the best to breed thing on one platform. And if I do, it's not compatible with what I might be running on prem. >> That'll slow a lot of things down. I can tell you right now, people want to have the same code base on all environments, and then just have the same seamless operational role. OK, that's a great point, George. Thanks for sharing that. The second point here is harmonizing and simplifying management across hybrid clouds. Again, back to your point. You set that up beautifully. Great example, open source innovation hits a roadblock. And the roadblock is incompatible components in multiple clouds. That's a problem. It's a management nightmare. How do harmonization about hybrid cloud work? >> You couldn't have asked it better. Let me put it up in terms of an X Y chart where on the x-axis, you have the components of an analytic pipeline. Ingest, process, analyze, predict, serve. But then on the y-axis, this is for an admin, not a developer. These are just some of the tasks they have to worry about. Data governance, performance monitoring, scheduling and orchestration, availability and recovery, that whole list. Now, if you have a different product for each step in that pipeline, and each product has a different way of handling all those admin tasks, you're basically taking all the unique activities on the y-axis, multiplying it by all the unique products on the x-axis, and you have overwhelming complexity, even if these are managed services on the cloud. Here now you've got several trade offs. Do I use the specialized products that you would call best to breed? Do I try and do end to end integration so I get simplification across the pipeline? Or do I use products that I had on-prem, like you were saying, so that I have seamless compatibility? Or do I use the cloud vendors? That's a tough trade off. There's another similar one for developers. Again, on the y-axis, for all the things that a developer would have to deal with, not all of them, just a sample. The data model and the data itself, how to address it, the programing model, the persistence. So on that y-axis, you multiply all those different things you have to master for each product. And then on the x-axis, all the different products and the pipeline. And you have that same trade off, again. >> Complexity is off the charts. >> Right. And you can trade end to end integration to simplify the complexity, but we don't really have products that are fully fleshed out and mature that stretch from one end of the pipeline to the other, so that's a challenge. Alright. Let's talk about another way of looking at management. This was looking at the administrators and the developers. Now, we're getting better and better software for monitoring performance and operations, and trying to diagnose root cause when something goes wrong and then remediate it. There's two real approaches. One is you go really deep, but on a narrow part of your application and infrastructure landscape. And that narrow part might be, you know, your analytic pipeline, your big data. The broad approach is to get end to end visibility across Edge with your IOT devices, across on-prem, perhaps even across multiple clouds. That's the breadth approach, end to end visibility. Now, there's a trade off here too as in all technology choices. When you go deep, you have bounded visibility, but that bounded visibility allows you to understand exactly what is in that set of services, how they fit together, how they work. Because the vendor, knowing that they're only giving you management of your big data pipeline, they can train their models, their machine learning models, so that whenever something goes wrong, they know exactly what caused it and they can filter out all the false positives, the scattered errors that can confuse administrators. Whereas if you want breadth, you want to see end to end your entire landscape so that you can do capacity planning and see if there was an error way upstream, something might be triggered way downstream or a bunch of things downstream. So the best way to understand this is how much knowledge do you have of all the pieces work together, and how much knowledge you have of all the pieces, the software pieces fit together. >> This is actually an interesting point. So if I kind of connect the dots for you here is the bounded root cause analysis that we see a lot of machine learning, that's where the automation is. >> George: Yeah. >> The unbounded, the breadth, that's where the data volume is. But they can work together, that's what you're saying. >> Yes. And actually, I hadn't even got to that, so thanks for taking it out. >> John: Did I jump ahead on that one? (laughing) >> No, no, you teed it out. (laughing) Because ultimately-- >> Well a lot of people want to know where it's going to be automated away. All the undifferentiated labored and scale can be automated. >> Well, when you talk about them working together. So for the deep depth first, there's a small company called Unravel Data that sort of modeled eight million jobs or workloads of big data workloads from high tech companies, so they know how all that fits together and they can tell you when something goes wrong exactly what goes wrong and how to remediate it. So take something like Rocana or Splunk, they look end to end. The interesting thing that you brought up is at some point, that end to end product is going to be like a data warehouse and the depth products are going to sit on top of it. So you'll have all the contextual data of your end to end landscape, but you'll have the deep knowledge of how things work and what goes wrong sitting on it. >> So just before we jump to the machine learning question which I want to ask you, what you're saying is the industry is evolving to almost looking like a data warehouse model, but in a completely different way. >> Yeah. Think of it as, another cue. (laughing) >> John: That's what I do, George. I help you out with the cues. (laughing) No, but I mean the data warehouse, everyone knows what that was. A huge industry, created a lot of value, but then the world got rocked by unstructured data. And then their bounded, if you will, view has got democratized. So creative destruction happened which is another word for new entrants came in and incumbents got rattled. But now it's kind of going back to what looks like a data warheouse, but it's completely distributed around. >> Yes. And I was going to do one of my movie references, but-- >> No, don't do it. Save us the judge. >> If you look at this starting in the upper right, that's the data lake where you're collecting all the data and it's for search, it's exploratory. As you get more structure, you get to the descriptive place where you can build dashboards to monitor what's going on. And you get really deep, that's when you have the machine learning. >> Well, the machine learning is hitting the low hanging fruit, and that's where I want to get to next to move it along. Sourcing machine learning capability, let's discuss that. >> OK, alright. Just to set contacts before we get there, notice that when you do end to end visibility, you're really seeing across a broad landscape. And when I'm showing my public cloud big data, that would be depth first just for that component. But you would do breadth first, you could do like a Rocana or a Splunk that then sees across everything. The point I wanted to make was when you said we're reverting back to data warehouses and revisiting that dream again, the management applications started out as saying we know how to look inside machine data and tell you what's going on with your landscape. It turns out that machine data and business operations data, your application data, are really becoming one and the same. So what used to be a transaction, there was one transaction. And that, when you summarized them, that went into the data warehouse. Then we had with systems of engagement, you had about 100 interaction events that you tracked or sort of stored for everything business transaction. And then when we went out to the big data world, it's so resource intensive that we actually had 1,000 to 10,000 infrastructure events for every business transaction. So that's why the data volumes have grown so much and why we had to go back first to data lake, and then curate it to the warehouse. >> Classic innovation story, great. Machine learning. Sourcing machine learning capabilities 'cause that's where the rubber starts hitting the road. You're starting to see clear skies when it comes to where machine learning is starting fit in. Sourcing machine learning capabilities. >> You know, even though we sort of didn't really rehearse this, you're helping cue me on perfectly. Let me make the assertion that with machine learning, we have the same shortage of really trained data scientists that we had when we were trying to stand up Hadoop clusters and do big data analytics. We did not have enough administrators because these were open source components built from essentially different projects, and putting them all together required a huge amount of skills. Data science requires, really, knowledge of algorithms that even really sophisticated programmers will tell you, "Jeez, now I need a PhD "to really understand how this stuff works." So the shortage, that means we're not going to get a lot of hand-built machine learning applications for a while. >> John: In a lot of libraries out there right now, you see TensorFlow from Google. Big traction with that application. >> George: But for PhDs, for PhDs. My contention is-- >> John: Well developers too, you could argue developers, but I'm just putting it out there. >> George: I will get to that, actually. A slide just on that. Let me do this one first because my contention is the first big application, widespread application of machine learning, is going to be the depth first management because it comes with a model built in of how all the big data workloads, services, and infrastructure fit together and work together. And if you look at how the machine learning model operates, when it knows something goes wrong, let's say an analytic job takes 17 hours and then just falls over and crashes, the model can actually look at the data layout and say we have way too much on one node, and it can change the settings and change the layout or the data because it knows how all the stuff works. The point about this is the vendor. In this particular example, Unravel Data, they built into their model an understanding of how to keep a big data workload running as opposed to telling the customer, "You have to program it." So that fits into the question you were just asking which is where do you get this talent. When you were talking about like TensorFlow, and Cafe, and Torch, and MXnet, those are all like assembly language. Yes, those are the most powerful places you could go to program machine learning. But the number of people is inversely proportional to the power of those. >> John: Yeah, those are like really unique specialty people. High, you know, the top guys. >> George: Lab coats, rocket scientists. >> John: Well yeah, just high end tier one coders, tier one brains coding away, AI gurus. This is not your working developer. >> George: But if you go up two levels. So go up one level is Amazon machine learning, Spark machine learning. Go up another level, and I'm using Amazon as an example here. Amazon has a vision service called Recognition. They have a speech generation service, Natural Language. Those are developer ready. And when I say developer ready, I mean developer just uses an API, you know, passes in the data that comes out. He doesn't have to know how the model works. >> John: It's kind of like what DevOps was for cloud at the end of the day. This slide is completely accurate in my opinion. And we're at the early days and you're starting to see the platforms develop. It's the classic abstraction layer. Whoever can extract away the complexity as AI and machine learning grows is going to be the winning platform, no doubt about it. Amazon is showing some good moves there. >> George: And you know how they abstracted away. In traditional programming, it was just building higher and higher APIs, more accessible. In machine learning, you can't do that. You have to actually train the models which means you need data. So if you look at the big cloud vendors right now. So Google, Microsoft, Amazon, and IBM. Most of them, the first three, they have a lot of data from their B to C businesses. So you know, people talking to Echo, people talking to Google Assistant or Siri. That's where they get enough of their speech. >> John: So data equals power? >> George: Yes. >> By having data, you have the ingredients. And the more data that you have, the more data that you know about, the more data that has information around it, the more effective it can be to train machine learning algorithms. >> Yes. >> And the benefit comes back to the people who have the data. >> Yes. And so even though your capabilities get narrower, 'cause you could do anything on TensorFlow. >> John: Well, that's why Facebook is getting killed right now just to kind of change tangents. They have all this data and people are very unhappy, they just released that the Russians were targeting anti-semitic advertising, they enabled that. So it's hard to be a data platform and still provide user utility. This is what's going on. Whoever has the data has the power. It was a Frankenstein moment for Facebook. So there's that out there for everyone. How do companies do the right thing? >> And there's also the issue of customer intellectual property protection. As consumers, we're like you can take our voice, you can take all our speech to Siri or to Echo or whatever and get better at recognizing speech because we've given up control of that 'cause we want those services for free. >> Whoever can shift the data value to the users. >> George: To the developers. >> Or to the developers, or communities, better said, will win. >> OK. >> In my opinion, that's my opinion. >> For the most part, Amazon, Microsoft, and Google have similar data assets. For the most part, so far. IBM has something different which is they work closely with their industry customers and they build progressively. They're working with Mercedes, they're working with BMW. They'll work on the connected car, you know, the autonomous car, and they build out those models slowly. >> So George, this slide is really really interesting and I think this should be a roadmap for all customers to look at to try to peg where they are in the machine learning journey. But then the question comes in. They do the blocking and tackling, they have the foundational low level stuff done, they're building the models, they're understanding the mission, they have the right organizational mindset and personnel. Now, they want to orchestrate it and implement it into action. That's the final question. How do you orchestrate the distributed machine learning feedback and the data coherency? How do you get this thing scaling? How do these machines and the training happen so you have the breadth, and then you could bring the machine learning up the curve into the dashboard? >> OK. We've saved the best for last. It's not easy. When I show the chevrons, that's the analytic data pipeline. And imagine in the serve and predict at the very end, let's take an IOT app, a very sophisticated one. which would be an autonomous car. And it doesn't actually have to be an autonomous one, you could just be collected a lot of information off the car to do a better job insuring it, the insurance company. But the key then is you're collecting data on a fleet of cars, right? You're collecting data off each one, but you're also collecting then the fleet. And that, in the cloud, is where you keep improving your model of how the car works. You run simulations to figure out not just how to design better ones in the future, but how to tune and optimize the ones that are on the road now. That's number three. And then in four, you push that feedback back out to the cars on the road. And you have to manage, and this is tricky, you have to make sure that the models that you trained in step three are coherent, or the same, when you take out the fleet data and then you put the model for a particular instance of a car back out on the highway. >> George, this is a great example, and I think this slide really represents the modern analytical operational role in digital business. You can't look further than Tesla, this is essentially Tesla, and now all cars as a great example 'cause it's complex, it's an internet (mumbling) device, it's on the edge of the network, it's mobility, it's using 5G. It encapsulates everything that you are presenting, so I think this is example, is a great one, of the modern operational analytic applications that supports digital business. Thanks for joining this Wikibon conversaion. >> Thank you, John. >> George Gilbert, the analyst at Wikibon covering big data and the modern operational analytical system supporting digital business. It's data driven. The people with the data can train the machines that have the power. That's the mandate, that's the action item. I'm John Furrier with George Gilbert. Thanks for watching. (upbeat electronic music)

[Voiceover] - Live from Washington D.C. It's theCUBE covering dot next conference. Brought to you by Nutanix. >> Welcome back to the district everybody. This is Nutanix NEXTconf, hashtag NEXTconf. And this is theCUBE, the leader in live tech coverage. Stephanie Chiras is here. She's the Vice President of IBM Power Systems Offering Management, and she's joined by Raja Mukhopadhyay who is the VP of Product Management at Nutanix. Great to see you guys again. Thanks for coming on. >> Yeah thank you. Thanks for having us. >> So Stephanie, you're welcome, so Stephanie I'm excited about you guys getting into this whole hyper converged space. But I'm also excited about the cognitive systems group. It's kind of a new play on power. Give us the update on what's going on with you guys. >> Yeah so we've been through some interesting changes here. IBM Power Systems, while we still maintain that branding around our architecture, from a division standpoint we're now IBM Cognitive Systems. We've been through a change in leadership. We have now Senior Vice President Bob Picciano leading IBM Cognitive Systems, which is foundationally built upon the technology that's comes from Power Systems. So our portfolio remains IBM Power Systems, but really what it means is we've set our sights on how to take our technology into really those cognitive workloads. It's a focus on clients going to the cognitive era and driving their business into the cognitive era. It's changed everything we do from how we deliver and pull together our offerings. We have offerings like Power AI, which is an offering built upon a differentiated accelerated product with Power technology inside. It has NVIDIA GPU's, it has NVLink capability, and we have all the optimized frameworks. So you have Caffe, Torch, TensorFlow, Chainer, Theano. All of those are optimized for the server, downloadable right in a binary. So it's really about how do we bring ease of use for cognitive workloads and allow clients to work in machine learning and deep learning. >> So Raja, again, part of the reason I'm so excited is IBM has a $15 billion analytics business. You guys talk, you guys talked to the analysts this morning about one of the next waves of workloads is this sort of data oriented, AI, machine learning workloads. IBM obviously has a lot of experience in that space. How did this relationship come together, and let's talk about what it brings to customers. >> It was all like customer driven, right? So all our customers they told us that, look Nutanix we have used your software to bring really unprecedented levels of like agility and simplicity to our data center infrastructure. But, you know, they run at certain sets of workloads on, sort of, non IBM platforms. But a lot of mission critical applications, a lot of the, you know, the cognitive applications. They want to leverage IBM for that, and they said, look can we get the same Nutanix one click simplicity all across my data center. And that is a promise that we see, can we bring all of the AHV goodness that abstracts the underlying platform no matter whether you're running on x86, or your cognitive applications, or your mission critical applications on IBM power. You know, it's a fantastic thing for a joint customer. >> So Stephanie come on, couldn't you reach somewhere into the IBM portfolio and pull out a hyper converged, you know, solution? Why Nutanix? >> Clients love it. Look what the hyper converged market is doing. It's growing at incredible rates, and clients love Nutanix, right? We see incredible repurchases around Nutanix. Clients buy three, next they buy 10. Those repurchase is a real sign that clients like the experience. Now you can take that experience, and under the same simplicity and elegance right of the Prism platform for clients. You can pull in and choose the infrastructure that's best for your workload. So I look at a single Prism experience, if I'm running a database, I can pull that onto a Power based offering. If I'm running a BDI I can pull that onto an alternative. But I can now with the simplicity of action under Prism, right for clients who love that look and feel, pick the best infrastructure for the workloads you're running, simply. That's the beauty of it. >> Raja, you know, Nutanix is spread beyond the initial platform that you had. You have Supermicro inside, you've got a few OEMs. This one was a little different. Can you bring us inside a little bit? You know, what kind of engineering work had to happen here? And then I want to understand from a workload perspective, it used to be, okay what kind of general purpose? What do you want on Power, and what should you say isn't for power? >> Yeah, yeah, it's actually I think a power to, you know it speaks to the, you know, the power of our engineering teams that the level of abstraction that they were able to sort of imbue into our software. The transition from supporting x86 platforms to making the leap onto Power, it has not been a significant lift from an engineering standpoint. So because the right abstractions were put in from the get go. You know, literally within a matter of mere months, something like six to eight months, we were able to have our software put it onto the IBM power platform. And that is kind of the promise that our customers saw that look, for the first time as they are going through a re-platforming of their data center. They see the power in Nutanix as software to abstract all these different platforms. Now in terms of the applications that, you know, they are hoping to run. I think, you know, we're at the cusp of a big transition. If you look at enterprise applications, you could have framed them as systems of record, and systems of engagement. If you look forward the next 10 years, we'll see this big shift, and this new class of applications around systems of intelligence. And that is what a lot-- >> David: Say that again, systems of-- >> Systems of intelligence, right? And that is where a lot of like IBM Power platform, and the things that the Power architecture provides. You know, things around better GPU capabilities. It's going to drive those applications. So our customers are thinking of running both the classical mission critical applications that IBM is known for, but as well as the more sort of forward leaning cognitive and data analytics driven applications. >> So Stephanie, on one hand I look at this just as an extension of what IBM's done for years with Linux. But why is it more, what's it going to accelerate from your customers and what applications that they want to deploy? >> So first, one of the additional reasons Nutanix was key to us is they support the Acropolis platform, which is KVM based. Very much supports our focus on being open around our playing in the Linux space, playing in the KVM space, supporting open. So now as you've seen, throughout since we launched POWER8 back in early 2014 we went Little Endian. We've been very focused on getting a strategic set of ISV's ported to the platform. Right, Hortonworks, MongoDB, EnterpriseDB. Now it's about being able to take the value propositions that we have and, you know, we're pretty bullish on our value propositions. We have a two x price performance guarantee on MongoDB that runs better on Power than it runs on the alternative competition. So we're pretty bullish. Now for clients who have taken a stance that their data center will be a hyper converged data center because they like the simplicity of it. Now they can pull in that value in a seamless way. To me it's really all about compatibility. Pick the best architecture, and all compatible within your data center. >> So you talked about, six to eight months you were able to do the integration. Was that Open Power that allowed you to do that, was it Little Endian, you know, advancements? >> I think it was a combination of both, right? We have done a lot from our Linux side to be compatible within the broad Linux ecosystem particularly around KVM. That was critical for this integration into Acropolis. So we've done a lot from the bottoms up to be, you know, Linux is Linux is Linux. And just as Raja said, right, they've done a lot in their platform to be able to abstract from the underlying and provide a seamless experience that, you know, I think you guys used the term invisible infrastructure, right? The experience to the client is simple, right? And in a simple way, pick the best, right for the workload I run. >> You talked about systems of intelligence. Bob Picciano a lot of times would talk about the insight economy. And so we're, you're right we have the systems of records, systems of engagement. Systems of intelligence, let's talk about those workloads a little bit. I infer from that, that you're essentially basically affecting outcomes, while the transaction is occurring. Maybe it's bringing transactions in analytics together. And doing so in a fashion that maybe humans aren't as involved. Maybe they're not involved at all. What do you mean by systems of intelligence, and how do your joint solutions address those? >> Yeah so, you know, one way to look at it is, I mean, so far if you look at how, sort of decisions are made and insights are gathered. It's we look at data, and between a combination of mostly, you know we try to get structured data, and then we try to draw inferences from it. And mostly it's human beings drawing the inferences. If you look at the promise of technologies like machine learning and deep learning. It is precisely that you can throw unstructured data where no patterns are obvious, and software will find patterns there in. And what we mean by systems of intelligence is imagine you're going through your business, and literally hundreds of terabytes of your transactional data is flowing through a system. The software will be able to come up with insights that would be very hard for human beings to otherwise kind of, you know infer, right? So that's one dimension, and it speaks to kind of the fact that there needs to be a more real time aspect to that sort of system. >> Is part of your strategy to drive specific solutions, I mean integrating certain IBM software on Power, or are you sort of stepping back and say, okay customers do whatever you want. Maybe you can talk about that. >> No we're very keen to take this up to a solution value level, right? We have architected our ISV strategy. We have architected our software strategy for this space, right? It is all around the cognitive workloads that we're focused on. But it's about not just being a platform and an infrastructure platform, it's about being able to bring that solution level above and target it. So when a client runs that workload they know this is the infrastructure they should put it on. >> What's the impact on the go to market then for that offering? >> So from a solutions level or when the-- >> Just how you know it's more complicated than the traditional, okay here is your platform for infrastructure. You know, what channel, maybe it's a question for Raja, but yeah. >> Yeah sure, so clearly, you know, the product will be sold by, you know, the community of Nutanix's channel partners as well as IBM's channels partners, right? So, and, you know, we'll both make the appropriate investments to make sure that the, you know, the daughter channel community is enabled around how they essentially talk about the value proposition of the solution in front of our joint customers. >> Alright we have to leave there, Stephanie, Raja, thanks so much for coming back in theCUBE. It's great to see you guys. >> Raja: Thank you. >> Stephanie: Great to see you both, thank you. >> Alright keep it right there everybody we'll be back with our next guest we're live from D.C. Nutanix dot next, be right back. (electronic music)

>> Announcer: Live from San Jose, California, it's theCUBE, covering Big Data Silicon Valley 2017. (upbeat percussion music) >> Okay, welcome back everyone. Live here in Silicon Valley, San Jose, is the Big Data SV, Big Data Silicon Valley in conjunction with Strata Hadoop, this is theCUBE's exclusive coverage. Over the next two days, we've got wall-to-wall interviews with thought leaders, experts breaking down the future of big data, future of analytics, future of the cloud. I'm John Furrier with my co-host George Gilbert with Wikibon. Our next guest is Yuanhao Sun, who's the co-founder and CTO of Transwarp Technologies. Welcome to theCUBE. You were on, during the, 166 days ago, I noticed, on theCUBE, previously. But now you've got some news. So let's get the news out of the way. What are you guys announcing here, this week? >> Yes, so we are announcing 5.0, the latest version of Transwarp Hub. So in this version, we will call it probably revolutionary product, because the first one is we embedded communities in our product, so we will allow people to isolate different kind of workloads, using dock and containers, and we also provide a scheduler to better support mixed workloads. And the second is, we are building a set of tools allow people to build their warehouse. And then migrate from existing or traditional data warehouse to Hadoop. And we are also providing people capability to build a data mart, actually. It allow you to interactively query data. So we build a column store in memory and on SSD. And we totally write the whole SQL engine. That is a very tiny SQL engine, allow people to query data very quickly. And so today that tiny SQL engine is like about five to ten times faster than Spark 2.0. And we also allow people to build cubes on top of Hadoop. And then, once the cube is built, the SQL performance, like the TBCH performance, is about 100 times faster than existing database, or existing Spark 2.0. So it's super-fast. And in, actually we found a Paralect customer, so they replace their data with software, to build a data mart. And we already migrate, say 100 reports, from their data to our product. So the promise is very good. And the first one is we are providing tool for people to build the machine learning pipelines and we are leveraging TensorFlow, MXNet, and also Spark for people to visualize the pipeline and to build the data mining workflows. So this is kind of like Datasense tools, it's very easy for people to use. >> John: Okay, so take a minute to explain, 'cus that was great, you got the performance there, that's the news out of the way. Take a minute to explain Transwarp, your value proposition, and when people engage you as a customer. >> Yuanhao: Yeah so, people choose our product and the major reason is our compatibility to Oracle, DV2, and teradata SQL syntax, because you know, they have built a lot of applications onto those databases, so when they migrate to Hadoop, they don't want to rewrote whole program, so our compatibility, SQL compatibility is big advantage to them, so this is the first one. And we also support full ANCIT and distribute transactions onto Hadoop. So that a lot of applications can be migrate to our product, with few modification or without any changes. So this is the first our advantage. The second is because we are providing, even the best streaming engine, that is actually derived from Spark. So we apply this technology to IOT applications. You know the IOT pretty soon, they need a very low latency but they also need very complicated models on top of streams. So that's why we are providing full SQL support and machine learning support on top of streaming events. And we are also using event-driven technology to reduce the latency, to five to ten milliseconds. So this is second reason people choose our product. And then today we are announcing 5.0, and I think people will find more reason to choose our product. >> So you have the compatibility SQL, you have the tooling, and now you have the performance. So kind of the triple threat there. So what's the customer saying, when you go out and talk with your customers, what's the view of the current landscape for customers? What are they solving right now, what are the key challenges and pain points that customers have today? >> We have customers in more than 12 vertical segments, and in different verticals they have different pain points, actually so. Take one example: in financial services, the main pain point for them is to migrate existing legacy applications to Hadoop, you know they have accumulated a lot of data, and the performance is very bad using legacy database, so they need high performance Hadoop and Spark to speed up the performance, like reports. But in another vertical, like in logistic and transportation and IOT, the pain point is to find a very low latency streaming engine. At the same time, they need very complicated programming model to write their applications. And that example, like in public sector, they actually need very complicated and large scale search engine. They need to build analytical capability on top of search engine. They can search the results and analyze the result in the same time. >> George: Yuanhao, as always, whenever we get to interview you on theCube, you toss out these gems, sort of like you know diamonds, like big rocks that under millions of years, and incredible pressure, have been squeezed down into these incredibly valuable, kind of, you know, valuable, sort of minerals with lots of goodness in them, so I need you to unpack that diamond back into something that we can make sense out of, or I should say, that's more accessible. You've done something that none of the Hadoop Distro guys have managed to do, which is to build databases that are not just decision support, but can handle OLTP, can handle operational applications. You've done the streaming, you've done what even Databricks can't do without even trying any of the other stuff, which is getting the streaming down to event at a time. Let's step back from all these amazing things, and tell us what was the secret sauce that let you build a platform this advanced? >> So actually, we are driven by our customers, and we do see the trends people are looking for, better solutions, you know there are a lot of pain to set up a habitable class to use the Hadoop technology. So that's why we found it's very meaningful and also very necessary for us to build a SQL database on top of Hadoop. Quite a lot of customers in FS side, they ask us to provide asset until the transaction can be put on top of Hadoop, because they have to guarantee the consistency of their data. Otherwise they cannot use the technology. >> At the risk of interrupting, maybe you can tell us why others have built the analytic databases on top of Hadoop, to give the familiar SQL access, and obviously have a desire also to have transactions next to it, so you can inform a transaction decision with the analytics. One of the questions is, how did you combine the two capabilities? I mean it only took Oracle like 40 years. >> Right, so. Actually our transaction capability is only for analytics, you know, so this OLTP capability it is not for short term transactional applications, it's for data warehouse kind of workloads. >> George: Okay, so when you're ingesting. >> Yes, when you're ingesting, when you modify your data, in batch, you have to guarantee the consistency. So that's the OLTP capability. But we are also building another distributed storage, and distributed database, and that are providing that with OLTP capability. That means you can do concurrent transactions, on that database, but we are still developing that software right now. Today our product providing the digital transaction capability for people to actually build their warehouse. You know quite a lot of people believe data warehouse do not need transaction capability, but we found a lot of people modify their data in data warehouse, you know, they are loading their data continuously to data warehouse, like the CRM tables, customer information, they can be changed over time. So every day people need to update or change the data, that's why we have to provide transaction capability in data warehouse. >> George: Okay, and then so then well tell us also, 'cus the streaming problem is, you know, we're told that roughly two thirds of Spark deployments use streaming as a workload. And the biggest knock on Spark is that it can't process one event at a time, you got to do a little batch. Tell us some of the use cases that can take advantage of doing one event at a time, and how you solved that problem? >> Yuanhao: Yeah so the first use case we encounter is the anti-fraud, or fraud detection application in FSI, so whenever you swipe your credit card, the bank needs to tell you if the transaction is a fraud or not in a few milliseconds. But if you are using Spark streaming, it will usually take 500 milliseconds, so the latency is too high for such kind of application. And that's why we have to provide event per time, like means event-driven processing to detect the fraud, so that we can interrupt the transaction in a few milliseconds, so that's one kind of application. The other can come from IOT applications, so we already put our streaming framework in large manufacture factory. So they have to detect the main function of their equipments in a very short time, otherwise it may explode. So if you... So if you are using Spark streaming, probably when you submit your application, it will take you hundreds of milliseconds, and when you finish your detection, it usually takes a few seconds, so that will be too long for such kind of application. And that's why we need a low latency streaming engine, but you can see it is okay to use Storm or Flink, right? And problem is, we found it is: They need a very complicated programming model, that they are going to solve equation on the streaming events, they need to do the FFT transformation. And they are also asking to run some linear regression or some neural network on top of events, so that's why we have to provide a SQL interface and we are also embedding the CEP capability into our streaming engine, so that you can use pattern to match the events and to send alerts. >> George: So, SQL to get a set of events and maybe join some in the complex event processing, CEP, to say, does this fit a pattern I'm looking for? >> Yuanhao: Yes. >> Okay, and so, and then with the lightweight OLTP, that and any other new projects you're looking at, tell us perhaps the new use cases you'd be appropriated for. >> Yuanhao: Yeah so that's our official product actually, so we are going to solve the problem of large scale OLTP transaction problems like, so you know, a lot of... You know, in China, there is so many population, like in public sector or in banks, they need build a highly scalable transaction systems so that they can support a very high concurrent transactions at the same time, so that's why we are building such kind of technology. You know, in the past, people just divide transaction into multiple databases, like multiple Oracle instances or multiple mySQL instances. But the problem is: if the application is simple, you can very easily divide a transaction over the multiple instances of databases. But if the application is very complicated, especially when the ISV already wrote the applications based on Oracle or traditional database, they already depends on the transaction systems so that's why we have to build a same kind of transaction systems, so that we can support their legacy applications, but they can scale to hundreds of nodes, and they can scale to millions of transactions per second. >> George: On the transactional stuff? >> Yuanhao: Yes. >> Just correct me if I'm wrong, I know we're running out of time but I thought Oracle only scales out when you're doing decision support work, not when you're doing OLTP, not that it, that it can only, that it can maybe stretch to ten nodes or something like that, am I mistaken? >> Yuanhao: Yes, they can scale to 16 to all 32 nodes. >> George: For transactional work? >> For transaction works, but so that's the theoretical limit, but you know, like Google F1 and Google Spanner, they can scale to hundreds of nodes. But you know, the latency is higher than Oracle because you have to use distributed particle to communicate with multiple nodes, so the latency is higher. >> On Google? >> Yes. >> On Google. The latency is higher on the Google? >> 'Cus it has to go like all the way to Europe and back. >> Oracle or Google latency, you said? >> Google, because if you are using two phase commit protocol you have to talk to multiple nodes to broadcast your request to multiple nodes, and then wait for the feedback, so that mean you have a much higher latency, but it's necessary to maintain the consistency. So in a distributed OLTP databases, the latency is usually higher, but the concurrency is also much higher, and scalability is much better. >> George: So that's a problem you've stretched beyond what Oracle's done. >> Yuanhao: Yes, so because customer can tolerant the higher latency, but they need to scale to millions of transactions per second, so that's why we have to build a distributed database. >> George: Okay, for this reason we're going to have to have you back for like maybe five or ten consecutive segments, you know, maybe starting tomorrow. >> We're going to have to get you back for sure. Final question for you: What are you excited about, from a technology, in the landscape, as you look at open source, you're working with Spark, you mentioned Kubernetes, you have micro services, all the cloud. What are you most excited about right now in terms of new technology that's going to help simplify and scale, with low latency, the databases, the software. 'Cus you got IOT, you got autonomous vehicles, you have all this data, what are you excited about? >> So actually, so this technology we already solve these problems actually, but I think the most exciting thing is we found... There's two trends, the first trend is: We found it's very exciting to find more competition framework coming out, like the AI framework, like TensorFlow and MXNet, Torch, and tons of such machine learning frameworks are coming out, so they are solving different kinds of problems, like facial recognition from video and images, like human computer interactions using voice, using audio. So it's very exciting I think, but for... And also it's very, we found it's very exciting we are embedding these, we are combining these technologies together, so that's why we are using competitors you know. We didn't use YARN, because it cannot support TensorFlow or other framework, but you know, if you are using containers and if you have good scheduler, you can schedule any kind of competition frameworks. So we found it's very interesting to, to have these new frameworks, and we can combine together to solve different kinds of problems. >> John: Thanks so much for coming onto theCube, it's an operating system world we're living in now, it's a great time to be a technologist. Certainly the opportunities are out there, and we're breaking it down here inside theCube, live in Silicon Valley, with the best tech executives, best thought leaders and experts here inside theCube. I'm John Furrier with George Gilbert. We'll be right back with more after this short break. (upbeat percussive music)

>> [Narrator] Live from Boston Massachusetts. This is the Cube, covering Sparks Summit East 2017. Brought to you by Databricks. Now here are your hosts, Dave Alante and George Gilbert. >> Back to you Boston everybody. This is the Cube and we're here live at Spark Summit East, #SparkSummit. Ziya Ma is here. She's the Vice President of Big Data at Intel. Ziya, thanks for coming to the Cube. >> Thanks for having me. >> You're welcome. So software is our topic. Software at Intel. You know people don't necessarily associate Intel with always with software but what's the story there? >> So actually there are many things that we do for software. Since I manage the Big Data engineering organization so I'll just say a little bit more about what we do for Big Data. >> [Dave] Great. >> So you know Intel do all the processors, all the hardware. But when our customers are using the hardware, they like to get the best performance out of Intel hardware. So this is for the Big Data space. We optimize the Big Data solution stack, including Spark and Hadoop on top of Intel hardware. And make sure that we leverage the latest instructions set so that the customers get the most performance out of the newest released Intel hardware. And also we collaborated very extensively with the open source community for Big Data ecosystem advancement. For example we're a leading contributor to Apache Spark ecosystem. We're also a top contributor to Apache Hadoop ecosystem. And lately we're getting into the machine learning and deep learning and the AI space, especially integrating those capabilities into the Big Data eTcosystem. >> So I have to ask you a question to just sort of strategically, if we go back several years, you look at during the Unix days, you had a number of players developing hardware, microprocessors, there were risk-based systems, remember MIPS and of course IBM had one and Sun, et cetera, et cetera. Some of those live on but very, very small portion of the market. So Intel has dominated the general purpose market. So as Big Data became more mainstream, was there a discussion okay, we have to develop specialized processors, which I know Intel can do as well, or did you say, okay, we can actually optimize through software. Was that how you got here? Or am I understanding that? >> We believe definitely software optimization, optimizing through software is one thing that we do. That's why Intel actually have, you may not know this, Intel has one of the largest software divisions that focus on enabling and optimizing the solutions in Intel hardware. And of course we also have very aggressive product roadmap for advancing continuously our hardware products. And actually, you mentioned a general purpose computing. CPU today, in the Big Data market, still has more than 95% of the market. So that's still the biggest portion of the Big Data market. And will continue our advancement in that area. And obviously as the Ai and machine learning, deep learning use cases getting added into the Big Data domain and we are expanding our product portfolio into some other Silicon products. >> And of course that was kind of the big bet of, we want to bet on Intel. And I guess, I guess-- >> You should still do. >> And still do. And I guess, at the time, Seagate or other disk mounts. Now flash comes in. And of course now Spark with memory, it's really changing the game, isn't it? What does that mean for you and the software group? >> Right, so what do we... Actually, still we focus on the optimi-- Obviously at the hardware level, like Intel now, is not just offering the computing capability. We also offer very powerful network capability. We offer very good memory solutions, memory hardware. Like we keep talking about this non-volatile memory technologies. So for Big Data, we're trying to leverage all those newest hardware. And we're already working with many of our customers to help them, to improve their Big Data memory solution, the e-memory, analytics type of capability on Intel hardware, give them the most optimum performance and most secure result using Intel hardware. So that's definitely one thing that we continue to do. That's going to be our still our top priority. But we don't just limit our work to optimization. Because giving user the best experience, giving user the complete experience on Intel platform is our ultimate goal. So we work with our customers from financial services company. We work with folks from manufacturing. From transportation. And from other IOT internet of things segment. And to make sure that we give them the easiest Big Data analytics experience on Intel hardware. So when they are running those solutions they don't have to worry too much about how to make their application work with Intel hardware, and how to make it more performant with Intel hardware. Because that's the Intel software solution that's going to bridge the gap. We do that part of the job. And so that it will make our customers experience easier and more complete. >> You serve as the accelerant to the marketplace. Go ahead George. >> [Ziya] That's right. >> So Intel's big ML as the news product, as of the last month of so, open source solution. Tell us how there are other deep learning frameworks that aren't as fully integrated with Spark yet and where BigML fits in since we're at a Spark conference. How it backfills some functionality and how it really takes advantage of Intel hardware. >> George, just like you said, BigDL, we just open sourced a month ago. It's a deep learning framework that we organically built onto of Apache Spark. And it has quite some differences from the other mainstream deep learning frameworks like Caffe, Tensorflow, Torch and Tianu are you name it. The reason that we decide to work on this project was again, through our experience, working with our analytics, especially Big Data analytic customers, as they build their AI solutions or AI modules within their analytics application, it's funny, it's getting more and more difficult to build and integrate AI capability into their existing Big Data analytics ecosystem. They had to set up a different cluster and build a different set of AI capabilities using, let's say, one of the deep learning frameworks. And later they have to overcome a lot of challenges, for example, moving the model and data between the two different clusters and then make sure that AI result is getting integrated into the existing analytics platform or analytics application. So that was the primary driver. How do we make our customers experience easier? Do they have to leave their existing infrastructure and build a separate AI module? And can we do something organic on top of the existing Big Data platform, let's say Apache Spark? Can we just do something like that? So that the user can just leverage the existing infrastructure and make it a naturally integral part of the overall analytics ecosystem that they already have. So this was the primary driver. And also the other benefit that we see by integrating this BigDL framework naturally was the Big Data platform, is that it enables efficient scale-out and fault tolerance and elasticity and dynamic resource management. And those are the benefits that's on naturally brought by Big Data platform. And today, actually, just with this short period of time, we have already tested that BigDL can scale easily to tens or hundreds of nodes. So the scalability is also quite good. And another benefit with solution like BigDL, especially because it eliminates the need of setting a separate cluster and moving the model between different hardware clusters, you save your total cost of ownership. You can just leverage your existing infrastructure. There is no need to buy additional set of hardware and build another environment just for training the model. So that's another benefit that we see. And performance-wise, again we also tested BigDL with Caffe, Torch and TensorFlow. So the performance of BigDL on single node Xeon is orders of magnitude faster than out of box at open source Caffe, TensorFlow or Torch. So it definitely it's going to be very promising. >> Without the heavy lifting. >> And useful solution, yeah. >> Okay, can you talk about some of the use cases that you expect to see from your partners and your customers. >> Actually very good question. You know we already started a few engagement with some of the interested customers. The first customer is from Stuart Industry. Where improving the accuracy for steel-surface defect recognition is very important to it's quality control. So we worked with this customer in the last few months and built end-to-end image recognition pipeline using BigDL and Spark. And the customer just through phase one work, already improved it's defect recognition accuracy to 90%. And they're seeing a very yield improvement with steel production. >> And it used to by human? >> It used to be done by human, yes. >> And you said, what was the degree of improvement? >> 90, nine, zero. So now the accuracy is up to 90%. And another use case and financial services actually, is another use case, especially for fraud detection. So this customer, again I'm not at the customer's request, they're very sensitive the financial industry, they're very sensitive with releasing their name. So the customer, we're seeing is fraud risks were increasing tremendously. With it's wide range of products, services and customer interaction channels. So the implemented end-to-end deep learning solution using BigDL and Spark. And again, through phase one work, they are seeing the fraud detection rate improved 40 times, four, zero times. Through phase one work. We think there were more improvement that we can do because this is just a collaboration in the last few month. And we'll continue this collaboration with this customer. And we expect more use cases from other business segments. But that are the two that's already have BigDL running in production today. >> Well so the first, that's amazing. Essentially replacing the human, have to interact and be much more accurate. The fraud detection, is interesting because fraud detection has come a long way in the last 10 years as you know. Used to take six months, if they found fraud. And now it's minutes, seconds but there's a lot of false positives still. So do you see this technology helping address that problem? >> Yeah, we actually that's continuously improving the prediction accuracy is one of the goals. This is another reason why we need to bring AI and Big Data together. Because you need to train your model. You need to train your AI capabilities with more and more training data. So that you get much more improved training accuracy. Actually this is the biggest way of improving your training accuracy. So you need a huge infrastructure, a big data platform so that you can host and well manage your training data sets. And so that it can feed into your deep learning solution or module for continuously improving your training accuracy. So yes. >> This is a really key point it seems like. I would like to unpack that a little bit. So when we talk to customers and application vendors, it's that training feedback loop that gets the models smarter and smarter. So if you had one cluster for training that was with another framework, and then Spark was your... Rest of your analytics. How would training with feedback data work when you had two separate environments? >> You know that's one of the drivers why we're creating BigDL. Because, we tried to port, we did not come to BigDL at the very beginning. We tried to port the existing deep learning frameworks like Caffe and Tensorflow onto Spark. And you also probably saw some research papers folks. There's other teams that out there that's also trying to port Caffe, Tensorflow and other deep learning framework that's out there onto Spark. Because you have that need. You need to bring the two capabilities together. But the problem is that those systems were developed in a very traditional way. With Big Data, not yet in consideration, when those frameworks were created, were innovated. But now the need for converging the two becomes more and more clear, and more necessary. And that's we way, when we port it over, we said gosh, this is so difficult. First it's very challenging to integrate the two. And secondly the experience, after you've moved it over, is awkward. You're literally using Spark as a dispatcher. The integration is not coherent. It's like they're superficially integrated. So this is where we said, we got to do something different. We can not just superficially integrate two systems together. Can we do something organic on top of the Big Data platform, on top of Apache Spark? So that the integration between the training system, between the feature engineering, between data management can &be more consistent, can be more integrated. So that's exactly the driver for this work. >> That's huge. Seamless integration is one of the most overused phrases in the technology business. Superficial integration is maybe a better description for a lot of those so-called seamless integrations. You're claiming here that it's seamless integration. We're out of time but last word Intel and Spark Summit. What do you guys got going here? What's the vibe like? >> So actually tomorrow I have a keynote. I'm going to talk a little bit more about what we're doing with BigDL. Actually this is one of the big things that we're doing. And of course, in order for BigDL, system like BigDL or even other deep learning frameworks, to get optimum performance on Intel hardware, there's another item that we're highlighting at MKL, Intel optimized Math Kernel Library. It has a lot of common math routines. That's optimized for Intel processor using the latest instruction set. And that's already, today, integrated into the BigDL ecosystem.z6 So that's another thing that we're highlighting. And another thing is that those are just software. And at hardware level, during November, Intel's AI day, our executives from BK, Diane Bryant and Doug Fisher. They also highlighted the Nirvana product portfolio that's coming out. That will give you different hardware choices for AI. You can look at FPGA, Xeon Fi, Xeon and our new Nirvana based Silicon like Crestlake. And those are some good silicon products that you can expect in the future. Intel, taking us to Nirvana, touching every part of the ecosystem. Like you said, 95% share and in all parts of the business. Yeah, thanks very much for coming the Cube. >> Thank you, thank you for having me. >> You're welcome. Alright keep it right there. George and I will be back with our next guest. This is Spark Summit, #SparkSummit. We're the Cube. We'll be right back.