(bright upbeat music) >> Hello and welcome to theCUBE's coverage of International Women's Day. I'm your host, John Furrier here in Palo Alto, California Studio and remoting is a great guest CUBE alumni, co-founder, technical co-founder and she's also the VP of Product at Platform9 Systems. It's a company pioneering Kubernetes infrastructure, been doing it for a long, long time. Madhura Maskasky, thanks for coming on theCUBE. Appreciate you. Thanks for coming on. >> Thank you for having me. Always exciting. >> So I always... I love interviewing you for many reasons. One, you're super smart, but also you're a co-founder, a technical co-founder, so entrepreneur, VP of product. It's hard to do startups. (John laughs) Okay, so everyone who started a company knows how hard it is. It really is and the rewarding too when you're successful. So I want to get your thoughts on what's it like being an entrepreneur, women in tech, some things you've done along the way. Let's get started. How did you get into your career in tech and what made you want to start a company? >> Yeah, so , you know, I got into tech long, long before I decided to start a company. And back when I got in tech it was very clear to me as a direction for my career that I'm never going to start a business. I was very explicit about that because my father was an entrepreneur and I'd seen how rough the journey can be. And then my brother was also and is an entrepreneur. And I think with both of them I'd seen the ups and downs and I had decided to myself and shared with my family that I really want a very well-structured sort of job at a large company type of path for my career. I think the tech path, tech was interesting to me, not because I was interested in programming, et cetera at that time, to be honest. When I picked computer science as a major for myself, it was because most of what you would consider, I guess most of the cool students were picking that as a major, let's just say that. And it sounded very interesting and cool. A lot of people were doing it and that was sort of the top, top choice for people and I decided to follow along. But I did discover after I picked computer science as my major, I remember when I started learning C++ the first time when I got exposure to it, it was just like a light bulb clicking in my head. I just absolutely loved the language, the lower level nature, the power of it, and what you can do with it, the algorithms. So I think it ended up being a really good fit for me. >> Yeah, so it clicked for you. You tried it, it was all the cool kids were doing it. I mean, I can relate, I did the same thing. Next big thing is computer science, you got to be in there, got to be smart. And then you get hooked on it. >> Yeah, exactly. >> What was the next level? Did you find any blockers in your way? Obviously male dominated, it must have been a lot of... How many females were in your class? What was the ratio at that time? >> Yeah, so the ratio was was pretty, pretty, I would say bleak when it comes to women to men. I think computer science at that time was still probably better compared to some of the other majors like mechanical engineering where I remember I had one friend, she was the single girl in an entire class of about at least 120, 130 students or so. So ratio was better for us. I think there were maybe 20, 25 girls in our class. It was a large class and maybe the number of men were maybe three X or four X number of women. So relatively better. Yeah. >> How about the job when you got into the structured big company? How did that go? >> Yeah, so, you know, I think that was a pretty smooth path I would say after, you know, you graduated from undergrad to grad school and then when I got into Oracle first and VMware, I think both companies had the ratios were still, you know, pretty off. And I think they still are to a very large extent in this industry, but I think this industry in my experience does a fantastic job of, you know, bringing everybody and kind of embracing them and treating them at the same level. That was definitely my experience. And so that makes it very easy for self-confidence, for setting up a path for yourself to thrive. So that was it. >> Okay, so you got an undergraduate degree, okay, in computer science and a master's from Stanford in databases and distributed systems. >> That's right. >> So two degrees. Was that part of your pathway or you just decided, "I want to go right into school?" Did it go right after each other? How did that work out? >> Yeah, so when I went into school, undergrad there was no special major and I didn't quite know if I liked a particular subject or set of subjects or not. Even through grad school, first year it wasn't clear to me, but I think in second year I did start realizing that in general I was a fan of backend systems. I was never a front-end person. The backend distributed systems really were of interest to me because there's a lot of complex problems to solve, and especially databases and large scale distributed systems design in the context of database systems, you know, really started becoming a topic of interest for me. And I think luckily enough at Stanford there were just fantastic professors like Mendel Rosenblum who offered operating system class there, then started VMware and later on I was able to join the company and I took his class while at school and it was one of the most fantastic classes I've ever taken. So they really had and probably I think still do a fantastic curriculum when it comes to distributor systems. And I think that probably helped stoke that interest. >> How do you talk to the younger girls out there in elementary school and through? What's the advice as they start to get into computer science, which is changing and still evolving? There's backend, there's front-end, there's AI, there's data science, there's no code, low code, there's cloud. What's your advice when they say what's the playbook? >> Yeah, so I think two things I always say, and I share this with anybody who's looking to get into computer science or engineering for that matter, right? I think one is that it's, you know, it's important to not worry about what that end specialization's going to be, whether it's AI or databases or backend or front-end. It does naturally evolve and you lend yourself to a path where you will understand, you know, which systems, which aspect you like better. But it's very critical to start with getting the fundamentals well, right? Meaning all of the key coursework around algorithm, systems design, architecture, networking, operating system. I think it is just so crucial to understand those well, even though at times you make question is this ever going to be relevant and useful to me later on in my career? It really does end up helping in ways beyond, you know, you can describe. It makes you a much better engineer. So I think that is the most important aspect of, you know, I would think any engineering stream, but definitely true for computer science. Because there's also been a trend more recently, I think, which I'm not a big fan of, of sort of limited scoped learning, which is you decide early on that you're going to be, let's say a front-end engineer, which is fine, you know. Understanding that is great, but if you... I don't think is ideal to let that limit the scope of your learning when you are an undergrad phrase or grad school. Because later on it comes back to sort of bite you in terms of you not being able to completely understand how the systems work. >> It's a systems kind of thinking. You got to have that mindset of, especially now with cloud, you got distributed systems paradigm going to the edge. You got 5G, Mobile World Congress recently happened, you got now all kinds of IOT devices out there, IP of devices at the edge. Distributed computing is only getting more distributed. >> That's right. Yeah, that's exactly right. But the other thing is also happens... That happens in computer science is that the abstraction layers keep raising things up and up and up. Where even if you're operating at a language like Java, which you know, during some of my times of programming there was a period when it was popular, it already abstracts you so far away from the underlying system. So it can become very easier if you're doing, you know, Java script or UI programming that you really have no understanding of what's happening behind the scenes. And I think that can be pretty difficult. >> Yeah. It's easy to lean in and rely too heavily on the abstractions. I want to get your thoughts on blockers. In your career, have you had situations where it's like, "Oh, you're a woman, okay seat at the table, sit on the side." Or maybe people misunderstood your role. How did you deal with that? Did you have any of that? >> Yeah. So, you know, I think... So there's something really kind of personal to me, which I like to share a few times, which I think I believe in pretty strongly. And which is for me, sort of my personal growth began at a very early phase because my dad and he passed away in 2012, but throughout the time when I was growing up, I was his special little girl. And every little thing that I did could be a simple test. You know, not very meaningful but the genuine pride and pleasure that he felt out of me getting great scores in those tests sort of et cetera, and that I could see that in him, and then I wanted to please him. And through him, I think I build that confidence in myself that I am good at things and I can do good. And I think that just set the building blocks for me for the rest of my life, right? So, I believe very strongly that, you know, yes, there are occasions of unfair treatment and et cetera, but for the most part, it comes from within. And if you are able to be a confident person who is kind of leveled and understands and believes in your capabilities, then for the most part, the right things happen around you. So, I believe very strongly in that kind of grounding and in finding a source to get that for yourself. And I think that many women suffer from the biggest challenge, which is not having enough self-confidence. And I've even, you know, with everything that I said, I've myself felt that, experienced that a few times. And then there's a methodical way to get around it. There's processes to, you know, explain to yourself that that's actually not true. That's a fake feeling. So, you know, I think that is the most important aspect for women. >> I love that. Get the confidence. Find the source for the confidence. We've also been hearing about curiosity and building, you mentioned engineering earlier, love that term. Engineering something, like building something. Curiosity, engineering, confidence. This brings me to my next question for you. What do you think the key skills and qualities are needed to succeed in a technical role? And how do you develop to maintain those skills over time? >> Yeah, so I think that it is so critical that you love that technology that you are part of. It is just so important. I mean, I remember as an example, at one point with one of my buddies before we started Platform9, one of my buddies, he's also a fantastic computer scientists from VMware and he loves video games. And so he said, "Hey, why don't we try to, you know, hack up a video game and see if we can take it somewhere?" And so, it sounded cool to me. And then so we started doing things, but you know, something I realized very quickly is that I as a person, I absolutely hate video games. I've never liked them. I don't think that's ever going to change. And so I was miserable. You know, I was trying to understand what's going on, how to build these systems, but I was not enjoying it. So, I'm glad that I decided to not pursue that. So it is just so important that you enjoy whatever aspect of technology that you decide to associate yourself with. I think that takes away 80, 90% of the work. And then I think it's important to inculcate a level of discipline that you are not going to get sort of... You're not going to get jaded or, you know, continue with happy path when doing the same things over and over again, but you're not necessarily challenging yourself, or pushing yourself, or putting yourself in uncomfortable situation. I think a combination of those typically I think works pretty well in any technical career. >> That's a great advice there. I think trying things when you're younger, or even just for play to understand whether you abandon that path is just as important as finding a good path because at least you know that skews the value in favor of the choices. Kind of like math probability. So, great call out there. So I have to ask you the next question, which is, how do you keep up to date given all the changes? You're in the middle of a world where you've seen personal change in the past 10 years from OpenStack to now. Remember those days when I first interviewed you at OpenStack, I think it was 2012 or something like that. Maybe 10 years ago. So much changed. How do you keep up with technologies in your field and resources that you rely on for personal development? >> Yeah, so I think when it comes to, you know, the field and what we are doing for example, I think one of the most important aspect and you know I am product manager and this is something I insist that all the other product managers in our team also do, is that you have to spend 50% of your time talking to prospects, customers, leads, and through those conversations they do a huge favor to you in that they make you aware of the other things that they're keeping an eye on as long as you're doing the right job of asking the right questions and not just, you know, listening in. So I think that to me ends up being one of the biggest sources where you get tidbits of information, new things, et cetera, and then you pursue. To me, that has worked to be a very effective source. And then the second is, you know, reading and keeping up with all of the publications. You guys, you know, create a lot of great material, you interview a lot of people, making sure you are watching those for us you know, and see there's a ton of activities, new projects keeps coming along every few months. So keeping up with that, listening to podcasts around those topics, all of that helps. But I think the first one I think goes in a big way in terms of being aware of what matters to your customers. >> Awesome. Let me ask you a question. What's the most rewarding aspect of your job right now? >> So, I think there are many. So I think I love... I've come to realize that I love, you know, the high that you get out of being an entrepreneur independent of, you know, there's... In terms of success and failure, there's always ups and downs as an entrepreneur, right? But there is this... There's something really alluring about being able to, you know, define, you know, path of your products and in a way that can potentially impact, you know, a number of companies that'll consume your products, employees that work with you. So that is, I think to me, always been the most satisfying path, is what kept me going. I think that is probably first and foremost. And then the projects. You know, there's always new exciting things that we are working on. Even just today, there are certain projects we are working on that I'm super excited about. So I think it's those two things. >> So now we didn't get into how you started. You said you didn't want to do a startup and you got the big company. Your dad, your brother were entrepreneurs. How did you get into it? >> Yeah, so, you know, it was kind of surprising to me as well, but I think I reached a point of VMware after spending about eight years or so where I definitely packed hold and I could have pushed myself by switching to a completely different company or a different organization within VMware. And I was trying all of those paths, interviewed at different companies, et cetera, but nothing felt different enough. And then I think I was very, very fortunate in that my co-founders, Sirish Raghuram, Roopak Parikh, you know, Bich, you've met them, they were kind of all at the same journey in their careers independently at the same time. And so we would all eat lunch together at VMware 'cause we were on the same team and then we just started brainstorming on different ideas during lunchtime. And that's kind of how... And we did that almost for a year. So by the time that the year long period went by, at the end it felt like the most logical, natural next step to leave our job and to, you know, to start off something together. But I think I wouldn't have done that had it not been for my co-founders. >> So you had comfort with the team as you knew each other at VMware, but you were kind of a little early, (laughing) you had a vision. It's kind of playing out now. How do you feel right now as the wave is hitting? Distributed computing, microservices, Kubernetes, I mean, stuff you guys did and were doing. I mean, it didn't play out exactly, but directionally you were right on the line there. How do you feel? >> Yeah. You know, I think that's kind of the challenge and the fun part with the startup journey, right? Which is you can never predict how things are going to go. When we kicked off we thought that OpenStack is going to really take over infrastructure management space and things kind of went differently, but things are going that way now with Kubernetes and distributed infrastructure. And so I think it's been interesting and in every path that you take that does end up not being successful teaches you so much more, right? So I think it's been a very interesting journey. >> Yeah, and I think the cloud, certainly AWS hit that growth right at 2013 through '17, kind of sucked all the oxygen out. But now as it reverts back to this abstraction layer essentially makes things look like private clouds, but they're just essentially DevOps. It's cloud operations, kind of the same thing. >> Yeah, absolutely. And then with the edge things are becoming way more distributed where having a single large cloud provider is becoming even less relevant in that space and having kind of the central SaaS based management model, which is what we pioneered, like you said, we were ahead of the game at that time, is becoming sort of the most obvious choice now. >> Now you look back at your role at Stanford, distributed systems, again, they have world class program there, neural networks, you name it. It's really, really awesome. As well as Cal Berkeley, there was in debates with each other, who's better? But that's a separate interview. Now you got the edge, what are some of the distributed computing challenges right now with now the distributed edge coming online, industrial 5G, data? What do you see as some of the key areas to solve from a problem statement standpoint with edge and as cloud goes on-premises to essentially data center at the edge, apps coming over the top AI enabled. What's your take on that? >> Yeah, so I think... And there's different flavors of edge and the one that we focus on is, you know, what we call thick edge, which is you have this problem of managing thousands of as we call it micro data centers, rather than managing maybe few tens or hundreds of large data centers where the problem just completely shifts on its head, right? And I think it is still an unsolved problem today where whether you are a retailer or a telecommunications vendor, et cetera, managing your footprints of tens of thousands of stores as a retailer is solved in a very archaic way today because the tool set, the traditional management tooling that's designed to manage, let's say your data centers is not quite, you know, it gets retrofitted to manage these environments and it's kind of (indistinct), you know, round hole kind of situation. So I think the top most challenges are being able to manage this large footprint of micro data centers in the most effective way, right? Where you have latency solved, you have the issue of a small footprint of resources at thousands of locations, and how do you fit in your containerized or virtualized or other workloads in the most effective way? To have that solved, you know, you need to have the security aspects around these environments. So there's a number of challenges that kind of go hand-in-hand, like what is the most effective storage which, you know, can still be deployed in that compact environment? And then cost becomes a related point. >> Costs are huge 'cause if you move data, you're going to have cost. If you move compute, it's not as much. If you have an operating system concept, is the data and state or stateless? These are huge problems. This is an operating system, don't you think? >> Yeah, yeah, absolutely. It's a distributed operating system where it's multiple layers, you know, of ways of solving that problem just in the context of data like you said having an intermediate caching layer so that you know, you still do just in time processing at those edge locations and then send some data back and that's where you can incorporate some AI or other technologies, et cetera. So, you know, just data itself is a multi-layer problem there. >> Well, it's great to have you on this program. Advice final question for you, for the folks watching technical degrees, most people are finding out in elementary school, in middle school, a lot more robotics programs, a lot more tech exposure, you know, not just in Silicon Valley, but all around, you're starting to see that. What's your advice for young girls and people who are getting either coming into the workforce re-skilled as they get enter, it's easy to enter now as they stay in and how do they stay in? What's your advice? >> Yeah, so, you know, I think it's the same goal. I have two little daughters and it's the same principle I try to follow with them, which is I want to give them as much exposure as possible without me having any predefined ideas about what you know, they should pursue. But it's I think that exposure that you need to find for yourself one way or the other, because you really never know. Like, you know, my husband landed into computer science through a very, very meandering path, and then he discovered later in his career that it's the absolute calling for him. It's something he's very good at, right? But so... You know, it's... You know, the reason why he thinks he didn't pick that path early is because he didn't quite have that exposure. So it's that exposure to various things, even things you think that you may not be interested in is the most important aspect. And then things just naturally lend themselves. >> Find your calling, superpower, strengths. Know what you don't want to do. (John chuckles) >> Yeah, exactly. >> Great advice. Thank you so much for coming on and contributing to our program for International Women's Day. Great to see you in this context. We'll see you on theCUBE. We'll talk more about Platform9 when we go KubeCon or some other time. But thank you for sharing your personal perspective and experiences for our audience. Thank you. >> Fantastic. Thanks for having me, John. Always great. >> This is theCUBE's coverage of International Women's Day, I'm John Furrier. We're talking to the leaders in the industry, from developers to the boardroom and everything in between and getting the stories out there making an impact. Thanks for watching. (bright upbeat music)

>> Announcer: From the CUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Welcome to this special CUBE conversation. I'm John Furrier here in Palo Alto, California. In our remote studio, we have a quarantine crew here during this COVID-19 crisis. Here talking about the crisis and the impact to business and overall work. Joined by a great guest Dustin Kirkland, CUBE alumni, who's now the chief product officer at Apex Clearing. This COVID-19 has really demonstrated to the mainstream world stage, not just inside the industry that we've been covering for many, many years, that the idea of at-scale means something completely different, and certainly DevOps and Agile is going mainstream to survive, and people are realizing that now. No better guest than have Dustin join us, who's had experiences in open source. He's worked across the industry from Ubuntu, Open Stack, Kubernetes, Google, Canonical. Dustin, welcome back to the CUBE here remotely. Looking good. >> Yeah, yeah, thanks, John. Last time we talked, I was in the studio, and here we are talking over the internet. This is a lot of fun. >> Well, I really appreciate it. I know you've been in your new role since September. A lot's changed, but one of the things why I wanted to talk with you is because you and I have talked many times around DevOps. This has been the industry conversation. We've been inside the ropes. Now you're starting to see, with this new scale of work-at-home forcing all kinds of new pressure points, giving people the realization that the entire life with digital and with technology can be different, doesn't have to be augmented with their existing life. It's a full-on technology driven impact, and I think a lot of people are learning that, and certainly, healthcare and finance are two areas, in particular, that are impacted heavily. Obviously, people are worried about the economy, and we're worried about people's lives. These are two major areas, but even outside that, there's new entrepreneurs right now that I know who are working on new ventures. You're seeing people working on new solutions. This is kind of bringing the DevOps concept to areas that quite frankly weren't there. I want to get your thoughts and reaction to that. >> Yeah, without a doubt, I mean, the whole world has changed in 30 short days. We knew something was amiss in China. We knew that there was a lot of danger for people. The danger for business, though, didn't become apparent until vast swathes of the work force got sent home. And there's a number of businesses and industries that are coping relatively well with this. Certainly those who have previously adopted, or have experienced, doing work remotely, doing business by video, teleconference, having resources in the cloud, having people and expertise who are able to continue working at nearly 100% capacity in 100% remote environments. There's a lot of technology behind that, and there are some industries, and in particular, some firms, some organizations, that were really adept and were able to make that shift almost overnight. Maybe there were a couple bumps along the way, some VPN settings needed to be tweaked, and Zoom settings needed to be changed a little bit, but for many, this was a relatively smooth transition, and we may be doing this for a very long time. >> Yeah, I want to get your thoughts, before we get into some of the product stuff that you guys are working on and some other things. What's your general reaction to people in your circles, inside industry and tech industry, and outside, what are you seeing a reaction to this new scale, work from home, social distancing, isolation, what are your observations? >> Yeah, you know, I think we're in for a long haul. This is going to be the new normal for quite some time. I think it's super important to check on the people you care about, and before we get into dev and tech, check on the people you care about, especially people who either aren't yet respecting the social distancing norms and impress upon them the importance that, hey, this is about you, this is about the people you care about, it's about people you don't even know, because there are plenty of people who can carry this and not even know. So definitely check on the people that you care about. And reach out to those people and stay in touch. We all need one another more than ever, right? I manage a team, and it's super important, I think, to understand how much stress everyone is under. I've got over a dozen people that report to me. Most of them have kids and families. We start out our weekly staff meeting now, and we bring the kids in. They're curious, they want to know what's going on. First five, 10 minutes of our meeting is meet the family. And that demystifies some of what we're doing, and actually keeps the other 50 minutes of the meeting pretty quiet in our experience. But it's really humanized an aspect of work from home that's always been a bit taboo. We laugh about the reporter in Korea whose kid and his wife came in during the middle of a live on-air interview. There's certainly, I've worked from home for almost 12 years, like, those are really uncomfortable situations. Until about a month ago, when that just became the norm. And from that perspective, I think there's a humanization that we're far more understanding of people who work from home now than ever before. >> It's funny, I've heard people say, you know, my wife didn't know what I did until I started working at home. And comments to seeing people's family, and saying, wow, that's awesome, and just bringing a personal connection, not just this software mechanism that connects people for some meeting, and we've all been on those meetings. They go long, and you're sitting there, and you're turning the camera off so you can sneeze. All those things are happening. But when you start to think about, beyond it being a software mechanism, that it's a social equation right now. People have shared experiences. It's been an interesting time. >> Yeah, and just sharing those experiences. We do a think internal on our Slack channel every day. We try to post a picture. We call it hashtag recess, and at recess we take a picture of walking the dogs, or playing with the kids, or gardening, or whatever it is, going for a run. Again, just trying to make the best of this, take advantage of, you know, it's hard working from home, but trying to take advantage of some of those once in a lifetime opportunities we have here. And my team has started pub quiz on Fridays, so we're mostly spread across, in the U.S., so we're able to do this at a reasonable hour, but the last couple of Fridays, we've jumped on a Zoom, downloaded a pub trivia game, most of us a crack a beer, or glass of wine, or a cocktail, and you know, it's just, it actually puts a punctuate mark on the end of the week, puts a period on the end of the week. Because that's the other thing about this, man, if you don't have some boundaries, it's easy to go from an eight or nine hour normal day to 10, 12, 14, 16 hour days, Saturday bleeds into Sunday bleeds into Monday, and then the rat race takes over. >> You got to get the exercise. You have a routine. That's my experience. What's your advice for people who are working at home for the first time? Do you have any best practices? >> I actually had a blog post on this about two weeks ago and put up almost a shopping list of some of the things that I've assembled here in the work from home environment. It's something I've been doing since 2008, so it's been there for a good long while. It's a little bit hard to accumulate all the technology that you need, but I would say, most important, have a space, some kind of space. Some people have more room or less, but even just a corner in a master bedroom with a standup desk, some space that is your own, that the family understands and respects. The other best practice is set some time boundaries. I like to start my day early. I'll try to break more a little bit for that recess, see the family some, and then knock off at a reasonable hour, so establish those boundaries. Yeah, I've got a bunch of tips in that blog post I can shoot you after this, but it's the sort of thing that, be a bit understanding, too, of other people in this situation for the first time, perhaps. So you know, offer whatever help and assistance you can, and be understanding that, man, things just aren't like they used to be. >> That's great advice. Thanks for the insights. Want to get to something that I see happening, and this always kind of happens when you see these waves where there's a downturn, or there's some sort of an event. In this case it's catastrophic in the way it vectored in like this and the impact that we just discussed. But what comes out of it is creativity around entrepreneurial activity, and certainly reinvention, businesses reforming, retrenching, resetting, whatever word, pivot, digital transformation, there's plenty of words for it. But this is the time where people can actually get a lot done. I always comment, in my last interview I did, you know, Shakespeare wrote Macbeth when he was sheltering in place, and Isaac Newton invented calculus, so you can actually get some work done. And you're starting to see people look at the new technology and start disrupting old incumbent markets, because now more than ever, things are exposed. The opportunity of recognition becomes clearer. So I wanted to get your thoughts on this. You're a product person, you've got a lot of product management skills, and you're currently taking this DevOps to financial market with fintech and your business, so you're applying known principles and software and tech and disrupting an existing industry. I think this is going to be a common trend for the next five years. >> Yeah, so on that first note, I think you're exactly right. There will be a reckoning, and there will be a ton of opportunities that come out of this for the already or the rapidly transformed digital native, digital focused business. There will be some that survive and thrive here. I think you're seeing a lot of this with the popularity of Zoom that has spiked recently. I think you're going to see technologies like DocuSign being used in places that, some of those places that still require wet signatures, but you just can't get to the notary and sign a, I don't know, a refi on your mortgage or something like that. And so I think you're going to see a bunch of those. The biggest opportunities are really around our education system. I've got two kids at home, and I'm in a pretty forward thinking school district in Austin, Texas, you know, but that's not the norm where our teachers are conducting classes and assignments over Zoom. I've got a kindergartener and a second grader. There's somewhat limits to what they can do with technology. I think you're going to see a lot of entrepreneurial solutions that develop in that space, and that's going to go from K through 12, and then into college. You think about how universities have had to shift and cancel classes, and what's happening with graduation. I've got a six and an eight year old, and I've been told I need to save $200,000 apiece for each of them to go to college, which is just an astounding number, especially to someone like me, who went to an inexpensive public university on a scholarship. Saving that kind of money for college, and just thinking about how much more efficient our education system might be with a lot more digital, a lot more digital education, digital testing and classes, while still maintaining the college experience, what that's going to look like in 10 years. I think we're going to see a lot of changes over these next 18 months to our educational system. >> Dustin, talk about the event dynamics. Physical events don't exist currently. Certainly, when they do come back, they should, and they will, the role of the virtual space is going to be highlighted and new opportunities will emerge. You mentioned education. People learn, not just for school, whether they're kids, whether they're professionals, learning and collaboration, work tools are going to reshape. What's your take on that marketplace, because we got to do virtual events. You can't just replicate a physical event and move it to digital. It's a complex system. >> Yeah, you're talking about an entire industry. We saw the Google Events, Google Next, Google IO, the Microsoft Events, just across the, I'm here in Austin, Texas, all of South by Southwest was canceled, which is just, it's breathtaking. When does that come back, and what does it look like? Is it a year or two or more from now? Events is where I spend my time, and when I get on a plane, and I fly somewhere, I'm usually going to a conference or trade show. Think about the sports industry. People who get on a plane, they go to an NFL game. John, I don't have all the answers, man, but I'm telling you, that entire industry is rapidly, rapidly going to evolve. I hope and pray that one day we're back to a, I can go back to a college football game again. I hope I can sit in a CUBE studio at a CUBE Con or an Open Stack or some other conference again. >> Hey, we should do a rerun, because I was watching the Patriots game last night, Tom Brady beating the Chiefs, October from last year. It was one of the best games of the season, went down to the wire, and I watched it, and I'm like, okay, that's Tom Brady, he's still in the Patriot uniform on the TV. Do we do reruns? This is the question. Right now, there's a big void for the next three months. What do we do? Do we replay the highlights from the CUBE? Do we have physical get togethers with Zoom? What's your take on how people should think about these events? >> Yeah, you know, the reruns only go so far, right? I'm a Texas Aggie, man. I could watch Johnny Football in his prime anytime. But I know what happened, and those games are just not as exciting as something that's a surprise. I'm actually curious about e-sports for the first time. What would it look like to watch a couple of kids who are really good at Madden Football on a Playstation go at it? What would other games that I've never seen look like? In our space, it's a lot more about, I think, podcasts and live content and staying connected and apprised of what's going on, making-- Oh, we locked up there for a second. It's, I think it's going to be really interesting. I'm still following you guys. I certainly see you active on social media. I'm sort of more addicted than ever to the live news, and in fact, I'm ready to start seeing some stuff that doesn't involve COVID-19, so from that perspective, man, keep churning out good content, and good content that's pertinent to the rest of our industry. >> That's great stuff. Well, Dustin, take a minute to explain what you're doing at Apex Clearing, your mission, and what are you guys excited about. >> Yeah, so Apex Clearing, we're a fintech. We're a very forward-focused, digitally-focused fintech. We are well positioned to continue servicing the needs of our clients in this environment. We went fully remote the first week of March, long before it was mandatory, and our business shifted pretty seamlessly. We worked through a couple of hiccups, provisioning extra VPN IP addresses, and upgrading a couple of service plans on some of the softwares, the service we buy, but besides that, our team has done just a marvelous job transitioning to remote. We are in the broker, dealer, and registered advisor space, so we provide the clearing services, which handles stock trades, equity trades, in the back end, and the custodial services. We actually hold, safeguard, the equities that our correspondents, we call our clients correspondents, their retail customers end up holding. So we've been around in our current form since about 2012. This was a retread of a previous company that was bought and retooled as Apex Clearing in 2012. Very shortly after that, we helped Robinhood, Wealthfront, Betterment, a whole bunch of really forward-looking companies reinvent what it meant to buy and sell and trade securities online, and to hold assets in a robo advisor like Betterment. Today, we are definitely well-known, well-respected for how quickly and seamlessly our APIs can be used by our correspondents in building really modern e-banking and e-brokerage experiences. >> So you guys-- >> So that went-- >> Are you guys like a DevOps platform-- >> We're more like software as a service for fintech and brokerage. So our products are largely APIs that our correspondents use their own credentials to interact with, and then using our APIs, they can open accounts, which means get an account number from the systems that allows them to then fund that account, connect via ACH and other bank connectivity platforms, transfer cash into those accounts, and then start conducting trades. Some of our correspondents have that down to a 60-second experience in a mobile app. From a mobile app, you can register for that account, if you need to, take a picture of an IED, have all of that imported, add your tax information, have that account number associated with your banking account, move a couple hundred dollars into that banking account, and then if the stock market's open, start buying and selling stock in that same window. >> Great, well, I wanted to talk about this, because to the earlier bigger picture, I think people are going to be applying DevOps principles, younger entrepreneurs, but also, reborn, if you will, professionals who are old school IT or whatever, moving faster. And you wrote a blog post I want to get your thoughts on. You wrote it on April second. How we've adapted Ubuntu's time-based release cycles to fintech and software as a service. What is that all about? What's the meaning behind this post? You guys are doing something new, unique, or-- >> To this industry and to many of the people around me, even our clients and customers around me, this is a whole new world. They've never seen anything like it. To those of us who have been around Linux, open source, certainly Ubuntu, Open Stack, Kubernetes, it's just standard operating procedures. There's nothing surprising about it, necessarily. But either it's some combination of the financial services world, just the nature of proprietary software, but also the concept of software as a service, SaaS, which is very different than Ubuntu or Kubernetes or Open Stack, which is released software, right. We ship software at the end of an Ubuntu cycle or a Kubernetes cycle. It's very different when you're a software as a service platform, and it's a matter of rolling out to production some changes, and those changes then going live. So, I wrote a post mainly to give some transparency, largely to our clients, our correspondents. We've got a couple hundred customers that use the Apex platform. I've met with many of them in a sort of one-on-many, one-to-one, one-on-many basis, where I'll show up and deliver the product road map, a couple of product managers will come and do a deep dive. Part of what we communicate to those customers is around, now, around our release cycles, and to many of them, it's a foreign concept that they've just never seen or heard before, and so I put together the blog post. We shared it internally, and educated the teams, and it was well-received. We shared it externally privately with a number of customers, and it was well-received, and a couple of them, actually a couple of the Silicon Valley based customers said, hey, why don't you just put this out there on Medium or on your blog or under an Apex banner, because this actually would be really well-received by others in the family, other partners in the family. So I'm happy to kind of dive into a couple of the key principles here, and we can sort of talk through it if you're interested, John. >> Well, I think the main point is you guys have a release cycle that is the speed of open source to SaaS, and fintech, which again, proprietary stuff is slower, monolithic. >> Yeah, the key principle is that we've taken this, and we've made it predictable and transparent, and we commit to these cycles. You know, most people maybe familiar with Ubuntu releasing twice a year, right, April and October, Ubuntu has released every April and October since 2004. I was involved with Ubuntu between 2008 and 2018 as an engineer, an engineering manager, and then a product manager, and eventually a VP of product at Canonical, and that was very much my life for 10 years, oriented around that. In that time, I spent a lot of time around Open Stack, which adopted a very similar model. Open Stack's released every six months, just after the Ubuntu release. A number of the members of the technical team and the committee that formed Open Stack came out of either Ubuntu or Canonical or both, and really helped influence that community. It's actually quite similar in Kubernetes, which developed independent, generally, of Ubuntu. Kubernetes releases on a quarterly basis, about every three months, and again, it's the sort of thing where it's just a cycle. It happens like clockwork every three months. So when I joined Apex and took a look at a number of the needs that we had, our correspondents had, our relationship managers, our sales team, the client-facing people in the organization, one of the biggest items that bubbled straight to the top is our customers wanted more transparency into our road maps, tighter commitments on when we're going to deliver things, and the ability to influence those. And you know what, that's not dissimilar from any product managers plight anywhere in the industry. But what I was able to do is take some of those principles that are common around Ubuntu and Kubernetes and Open Stack, which by the way, are quite familiar. We use a lot of Ubuntu and Kubernetes inside of Apex, and many of our correspondents are quite familiar with those cycles, but they'd never really seen or heard of a software as a service, a SaaS vendor, using something like that. So that's what's new. >> You've got some cycles going now. You've got schedules, so just looking here, just to get this out there, 'cause I think it's data. You did it last year in October, November, mid-cycle in January of this year. You've got a couple summits coming up? >> Yeah, that's right, we've broken it down into three cycles per year, three 16-week cycles per year. So it's a little bit more frequent than the twice a year Ubuntu, not quite as frenetic as the quarterly Kubernetes cycles. 16 weeks time three is 48. That leaves us four weeks of slack, really to handle Thanksgiving and Christmas and end of year holidays, Chinese New Year, whatever might come up. I'll tell you from experience, that's always been a struggle in the Ubuntu and Open Stack and Kubernetes world, it's hard to plan around those cycles, so what we've done here is we've actually just allocated four weeks of a slush fund to take care of that. We're at three 16-week cycles per year. We version them according to the year and then an iterator. So 20A, 20B, 20C are our three cycles in 2020, and we'll do 21A, B, and C next year. Each of those cycles has three summits. So to your point about we get together, back in the before everyone stopped traveling, we very much enjoyed twice a year getting together for CUBE con. We very much enjoyed the Open Stack summits and the various Ubuntu summits. Inside of a small company like ours, these were physical. We'd get together in Dallas or New York or Chicago or Portland, which is the four places we have offices. We were doing that basically every six weeks or so for one of these summits. Now they're all virtual. We handle them over Zoom. When they were physical, we'd do the summit in about three days of packed agendas, Tuesday, Wednesday, Thursday. Now that we've gone to virtual, we've actually spread it a little bit thinner across the week, and so we've done, we've poked some holes in the day, which has been an interesting learning experience, and I think we're all much happier with the most recent summit we did, spreading it over the course of the week, accounting for time zones, giving ourself, everyone, lunch breaks and stuff. >> Well, we'll have to keep checking in. I want to certainly collaborate with you on the virtual digital, check your progress. We're all learning, and iterating, if you will, on the value that you can do with these digital ones. Try to get that success with physical, not always easy. Appreciate, and you're looking good, looking good and safe. Stay safe, and great to check in with you, and congratulations on the new opportunity. >> Yeah, thanks, John. >> Appreciate it. Dustin Kirkland, chief product officer at Apex Clearing. I'm John Furrier with the CUBE, checking in with a remote interview during this time when we are getting all the information of best practices on how to deal with this new at-scale, the new shift that is digital, that is impacting, and opportunities are there, certainly a lot of challenges, and hopefully, the healthcare, the finance, and the business models of these companies can continue and get back to work soon. But certainly, the people are still sheltered in place, working hard, being creative, be the coverage here in the CUBE. I'm John Furrier, thanks for watching. (bright electronic music)

>> from our studios in the heart of Silicon Valley, Palo Alto, California It is a cute conversation >> lover on Welcome to this cube conversation here in Palo Alto, California. The Cube Studio. I'm John for the co host of the Cube Weird Sylvan shop. Who's the head of solution Architecture and open systems securing Esti win of right of other cloud to point out like capabilities. Very successful. 20 plus years. Operation Civil was the one of the first folks are coming over to the US to expand their operation from Europe into New York. Now here in Silicon Valley. Welcome to the Cube conversation. Thank you. So instituting trivia. You were part of the original team of three to move to the U. S. From Switzerland. You guys had phenomenal success in Europe. You've come to the U. S. Having phenomenal success in the US Now you moving west out here to California on that team, you're opening things up at the market. >> It's been a chance, Mikey. Things can presented themselves step by step, and I jumped on the trains and it's been a good right. >> Awesome. You guys have had great success. We interviewed your CEO a variety of your top people. One of the things that's interesting story is that you guys have been around for a long time. Been there, done that, riding this next next wave of digital transformation. What we call a cloud two point. Oh, but really is about enterprise. Full cloud scale, securing it. You have a lot of organic growth with customers, great word of mouth. So that's not a lot of big marketing budgets, riel. Real success there. You guys now are in the US doing the same thing here. What's been the key to success for open systems wide such good customers? Why the success formula is it you guys are on the right wave. What is it? The product? All the above. What's the What's the secret formula? >> So multiple things I say. And we started as a privately owned company like broad banks to, um, to the Internet email into one back in the nineties. And, um, yeah, we started to grow organically, as he said were by mouth, and Indiana is we put heavy focus on operations, so we wanted to make our customers happy and successful, and, um, yeah, that's how we got there like it was slow organic growth. But we always kind of kept the core and we tried to be unconventional, tried to do things differently than others do. And that's what brought us to where we are today and now capabilities Being here in the Valley, um, opens up a lot of more doors. >> It's got a nice office and we would see I saw the video so props for that. Congratulations. But the real to me, the meat on the bone and story is, is that and I've been really ranting on this whole SD win is changing. SD Win used to be around for a long, long time. It's been known industries known market. It's got a total addressable market, but really, what has really talks to is the the cloud. The cloud is a wide area network. Why do we never used to be locked down? He had the old way permitted based security. Now everything is a wide area. That multi cloud in hybrid club. This is essentially networking. It's a networking paradigms. It's not lately rocket science technically, but the cloud 2.0 shift is about, you know, data. It's about applications, different architectures you have everything kind of coming together, which creates a security problem, an opportunity for new people to come in. That's what you guys? One of them. This is the big wave. What? It explain the new s t win with, you know, the old way and the new way. What is the what? What should people know about the new S D win marketplace? >> Yeah. So let me start. Where do Owen has come from and how digital transformation has impacted that. So typically corporate wider networks were centered around the Clear Data Center where all applications were hosted, storage and everything and all traffic was back holding to the data center. Typically, one single provider that Broady, Mpls links on dhe. It was all good. You had a central location where you could manage it. You had always ability security stack was there. So you had full control. Now new requirements from natural transformation broad as users are on the road, they're on their phones ipads on the in, restaurants in ah, hotels, Starbucks. Wherever we have applications moved to the cloud. So their access directly You wanna have or be as close as possible Unify Communications. I OT It's all things deposed. Different requirements now in the network and the traditional architecture didn't were wasn't able to respond to that. It's just that the links they were filled up. You couldn't invest enough thio blow up your Nampula slings to handle the band with You lost visibility because users were under road. You lost control, and that's where new architectures had to be found. That's where Ston step them and say, Hey, look now we're not centered around the headquarter anymore were sent around where the applications are, your scent around, where the data is, and we need to find means to connected a data as quickly as possible. And so you can use the Internet. Internet has become a commodity. It's become more performance more stable, so we can leverage that we can route traffic according to our policies. We can include the cloud, and that's where Ston actually benefits from the clown. As much as the club benefits from SD went because they go hand in hand and that's also what we really drive to say, Hey, look, now the cloud can be directly brought into your network, no matter where, where data and where applications. >> Yeah, and this is the thing. You know, Although you've been critical of S t when I still see it as the path of the future because it's networking. And the end of the day networking is networking. You moving packets from point A to point B and you're moving somebody story you moving from point A to store the point C. It's hard. And you brought this up about Mpls. It's hard to, like rip and replace You can't just do a wholesale change on the network has the networks are running businesses. So this is where the trick is, in my opinion. So I want to get your thoughts on how companies were dealing with this because, I mean, if you want to move, change something in the network, it takes a huge task. How did you guys discover this new opportunity? How did you implement it? What was the and how should customers think about not disrupting their operations at the same time bringing in the new capabilities of this SD win two point? Oh, >> yeah, that's it's a perfect sweet spot, because in the end is, um, nobody starts at a green field. If you could start with a green field. It's easy. You just take on the new technology and you're happy. But, um, customers that we look up large enterprises, they have a brownfield. They haven't existing that work. They have business critical applications running 24 7 And if you look at what options large enterprises have to implement and manage a nasty when is typically three approaches, they either do it themselves, meaning they need a major investment in on boarding people having the talent validating technology and making the project work already. Look at a conventional managers provider. In the end, that is just the same as doing yourself. It's just done by somebody else, and you have the the challenge that those providers typically, um, have a lot of portfolio that they manage. And they do not have enough expertise in Nasty Wen. And so you just end up with the same problems and a lot of service, Janey. So even then you do not get the expertise that you need. >> I think what's interesting about what you guys have done? I want to get your reaction to this is that the manage service piece of it makes it easier to get in without a lot of tinkering with existing infrastructure. Exact. And that's been one of that tail winds for you guys and success wise. Talk about that dynamic of why they managed service is a good approach because you put your toe in the water, so to speak, and you can kind of get involved, get as much as you need to go and go further. Talk about that dynamic and why that's important. >> Yeah, technology Jane is very quickly. So you need people that are able to manage that and open systems as a pure play provider. We build purposely build our platform for us, he went. So we integrated feature sets. We we know how to monitor it, how to configure it, how to manage it. Lifecycle management, technology, risk technology management. All this is purposely purposely built into it, so we strongly believe that to be successful, you need people that are experts in what they do to help you so that you and your I t people can focus in enabling the business. And that's kind of our sweet spot where we don't say we have experts. Our experts operating the network for you as a customer and therefore our experts are your experts. And that's kind of where we believe that a manage service on the right way ends up in Yeah, the best customer. >> And I think the human capital pieces interesting people can level up faster when you when you're not just deploying here. Here's the software load. It is the collaborations important. They're good. They're all right. While you're on this topic, I want to get your thoughts. Since you're an expert, we've been really evaluating this cloud 2.0, for lack of a better description. Cloud 2.0, implying that the cloud 1.0 was Amazon miss on The success of Amazon Web service is really shows Dev Ops in Action Agility The Lean startup Although all that stuff we read reading about for the past 10 plus years great compute storage at scale, amazing use of data like you, said Greenfield. Why not use the cloud? Great. Now all the talk about hybrid cloud even going back to 2013 We were of'em world at that time start 10th year their hybrid cloud was just introduced. Now it's mainstream now multi cloud is around the corner. This teases out cloud 2.0, Enterprise Cloud Enterprise Scale Enterprise Security Cloud Security monitoring 2.0, is observe ability. Got Cooper All these new things air coming on. This is the new clout to point out what is your definition of cloud two point? Oh, if you had to describe it to a customer or a friend, >> it is really ah, some of hybrid cloud or multi cloud, as you want to name it, because in the end, probably nobody can say I just select one cloud, and that's going to make me successful because in the end, cloud is it's not everywhere, as we kind of used to believe in the beginning, but in the end, it's somebody else's computer in a somebody else's data center. So the cloud is you selectively pick the location where you want to for your cloud instances and asked if Cloud Service providers opened up more locations that are closer to your users in the or data you actually can leverage more possibilities. So what we see emerging now is that while for a long time everything has moved to the cloud, the cloud is again coming back to us at the sietch. So a lot of compute stuff is done close to where data is generated. Um, it's where the users are. I mean, Data's generated with with us. Yeah, phones and touch and feel and vision and everything. So we can leverage these technologies to really compute closer to the data. But everything controlled out of central cloud instances. >> So this brings up a good point. You essentially kind of agreeing with cloud one detto being moved to the cloud. But now you mentioned something that's really interesting around cloud to point out, which is moving having cloud, certainly public clouds. Great. But now moving technology to the edge edge being a data center edge being, you know, industrial I ot other things wind farms, whatever users running around remotely you mentioned. So the edges now becomes a critical component of this cloud. Two point. Oh, okay. So I gotta ask the question, How does the networking and what's the complexity? And I'm just imagining massive complexity from this. What are some of the complexities and challenges and opportunities will arise out of this new dynamic of club two point. Oh, >> So the traditional approaches does just don't work anymore. So we need new ways to not only on the networking side, but obviously also the security side. So we need to make sure that not on Lee the network follows in the footsteps of the business of what it needs. But actually, the network can drive business innovation and that the network is ready to handle those new leaps and technologies. And that's what we see is kind of being able to tightly integrate whatever pops up, being able to quickly connect to a sass provider, quickly integrate a new cloud location into your network and have the strong security posture there. Directly integrated is what you need because if you always have to think about weight, if I add this, it's gonna break something else, and I have to. To change is here. Then you lose all the speed that your business needs. >> I mean, the ripple effect of it's like throwing a stone in the lake and seeing the ripple effect with cloud to point. You mentioned a few of them. Network and Security won't get to that in a second, but doesn't change every aspect of computing categories. Backup monitoring. I mean all the sectors that were traditional siloed on premise that moves with the cloud are now being disrupted again for the third time. Yeah, you agree with that? >> It's true. And I mean your club 0.1 point. Oh, you say a lot of things will be seen his lift and shift and that still works like there is a lot of work loads where it's not worth it to re factor everything. But then, for your core applications, the business where the business makes money, you want a leverage, the latest instead of technologies to really drive, drive your business there. >> I got to get your take on this because you're the head of architecture solutions at Open Systems. Um, is a marketing tagline that I saw that you guys promote, which I live. I want to get your thoughts on. It says, Stop treating your network like a network little marketing. I love it, but it's kind of like stop trying your network like a network implying that the networks changing may be inadequate. Antiquated needs to modernize. I'm kind of feeling the vibe there on that. What do you mean by that? Slow Stop treating your network like a network. What's what's the purpose >> behind that? But yeah, in the end, it to be a little flaw provoking. But I mean, even est even in its pure forms, where you have a softer controller that steers your traffic along different path. Already. For me, as an engineer, I'm gonna lose my mind because I want to know where routing is going. I want deterministic. Lee defined my policy, so I always have things under control. But now it's a softer agent that takes care. Furred takes care of it for me so that already I lose control in favor off. Yeah, more capabilities. And I think that's cloud just kind of accelerate. >> So you guys really put security kind of in between the network and application? Is that the way you're thinking about it? It used to be Network was at the bottom. You built the application, had security. Now you're thinking differently. Explain that the the architectural thinking around this because this is a modern approach you guys were taking, and I want to get this on the record. Applications have serving users and machines network delivers packets, and then you're saying security's wrapping up between them explain. >> So when we go back again to the traditional model Central Data Center, you had a security stack full rack of appliances that the care of your security was easy to manage. Now, if you wanna go ask you when connect every brand side to the Internet, you cannot replicate such an infrastructure to every branch. Location just doesn't skill. So what do you do? Why do you say I cannot benefit of this where I use new methods? And that's where we say we integrate security directly into our networking stack. So to be able to not rely on the service training but have everything compiled into one platform and be able to leverage that data is passing through our network. You've eyes. But then why not apply the same security functions that we used to do in our headquarter directly at the edge and therefore every branch benefits of the same security posture that I typically were traditionally only had in my data center? >> You guys so but also weighing as a strategic infrastructure critical infrastructure opponent. I would agree with that. That's obvious, but as we get into hybrid cloud and multi cloud infrastructures of service support. Seamless integration is critical. This has become a topic, will certainly be talking about for the rest of the year Of'em world and reinvented other conferences like Marcel that night as well. This is the big challenge for customers. Do I invest in Azure A. W as Google in another cloud? Who knows how many clouds coming be another cloud potentially around the corner? I don't want to fork my development team. I want to do one of the great different code bases. This has become kind of like the challenge. How do you see this playing out? Because again, the applications want to run on the best cloud possible. I'm a big believer in that. I think that the cloud should dictate the AP should dictate which cloud runs. That's why I'm a believer in the single cloud for the workload, not a single cloud for all workloads. So your thoughts, >> I think, from an application point of view. As you say, the application guys have to determine more cloud is best for them, I think from a networking point of view, as a network architect, we need to we can't work against this but enable them and be able to find ways that the network can seamlessly connect to whatever cloud the business wants to use. And there's plenty of opportunity to do that today and to integrate or partner with other providers that actually have partnered with dozens of cloud providers. And as we now can architect, we have solutions to directly bring you as a customer within milliseconds, to each cloud, premise is a huge advantage. It takes a few clicks in a portal. You have a new clouds instance up and running, and now you're connected. And the good thing is, we have different ways to do that. Either. We spin up our virtual instance virtual esti one appliance in cloud environments so we can leverage the Internet to go. They're still all secured, all encrypted, ordering me again. Use different cloud connect interconnections to access the clouds. Depending on the business requirements, >> you guys have been very successful. A lot of comfort from financial service is the U. N. With NGOs, variety of industries. So I want to get your thoughts on this. I've been we've been covering the Department of Defense is joining and Chet I joint and the presentation of defense initiative where the debate was soul single purpose Cloud. Now the reality is and we've covered this on silicon angle that D O D is going multi cloud as an organization because they're gonna have Microsoft Cloud for collaboration and other contracts. They're gonna win $8,000,000,000. So that a Friday cloud opportunities, but for the particular workload for the military, they have unique requirements. Their workload has chosen one cloud. That was the controversy. Want to get your thoughts on this? Should the workloads dictate the cloud? And is that okay? And certainly multi cloud is preferred Narada instances. But is it okay to have a single cloud for a workload? >> Yeah, again, from if the business is okay with that, that's fine from our side of you. We see a lot of lot of business that have global presence, so they're spread across the globe. So for them, it's beneficial to done distribute workloads again across different regions, and it could still be the same provider, but across different regions. And then already, question is How do you now we're out traffic between those workloads? Do we? Do you love right? Your esteem and infrastructure or do you actually use, for example, the backbone that the cloud provider provides you in case of Microsoft? They guarantee you the traffic between regions stay in their backbone. So gifts, asshole, new opportunities to leverage large providers. Backbone. >> And this is an interesting nuance point because multi cloud doesn't have to be. That's workload. Spreading the workload across three different clouds. It's this workload works on saving Amazon. This workload works on Azure. This workload works on another cloud that's multi cloud from a reality standpoint today, so that implies that most every country will be multi cloud for sure. But workloads might have a single cloud for either the routing and the transit security with the data stored. And that's okay, too. >> Yeah, yeah, and keep in mind, Cloud is not only infrastructure or platform is the service. It's also software as a service. So as soon as we have sales forests, work day office 3 65 dropbox or box, then we are multiplied. >> So basically the clouds are fighting it out by the applications that they support and the infrastructure behind. Exactly. All right, well, what's next for you? You're on the road. You guys doing a lot of customer activity. What's the coolest thing that you're seeing in the customer base from open system standpoint that you like to share with the audience? >> Um, so again, it's just cool to see that customers realized that there's plenty of opportunities. And just to see how we go through that evolution with our customers, were they initially or little concerned? But then eventually we see that actually, the network change drives new business project and customers air happy that they launched or collaborate with us. That's what that's what makes me happy and makes me and a continuing down that path >> and securing it is a key. Yeah, he wins in this market Having security? >> Absolutely. Yeah, Sylvia saying mind and not wake up at 2 a.m. Full sweat, because here >> we'll manage. Service is a preferred for my people like to consume and procure product in So congratulations and congressional on your Silicon Valley office looking for chatting more. I'm John for here in the keep studios for cute conversation. Thanks for watching

(lively funk music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBEConversation. >> Hi, welcome to the CUBE Studio for another CUBEConversation where we go in-depth with the thought leaders driving outcomes with technology. I'm your host, Peter Burris. One of the biggest challenges that enterprises face is how to appropriately apply artificial intelligence. Now, let's be clear, the basic precepts and concepts and approaches to artificial intelligence have been around for a long time. One might argue decades. It's happening now because the technology can perform it. And one of the technologies that's especially important, and is absolutely essential to determining success or failure in AI, is storage. So what we're gonna do now is have a conversation with David Floyer, the CTO and co-founder of Wikibon, about that crucial relationship between AI and storage. David, welcome to the conversation. >> Thanks very much, indeed, Peter. Interesting subject. >> Oh, very interesting subject, so let's get right into it, David. >> Sure. >> What is it about AI and storage that makes the two of them so essential to the co-evolution of each? >> Absolutely, so first of all, you've got different parts of AI. So you've got the part where you're developing all of the models themselves, where you've got a large amount of data. You're trying to capture that data. You're trying to find out what's important in that data. And then you're developing models which you're going to use to do something. Either automate something or give information to somebody about the business process. >> All right, so that's the first one. What's the second one? >> So the second one, they're both concerned with inferencing. There's inferencing close to that data, the overall data, and there's inferencing right at the Edge itself. And they both important and driven in different ways. The inferencing close to the applications, the centralized applications-- >> So inferencing in the data center, so to speak. >> In the data center itself. Those are going to be, essentially, most of them, real-time decisions that are being made. For example, if I am trying to find out what sort of customer you are, what sort of price that I'm gonna give you, what sort of delivery, what sort of terms I'm gonna give you, that's information that I'm gonna have to get from a whole number of different sources, push them all together, and give that information to my systems of record. They are gonna make those decisions and they're gonna push them down to maybe an Edge or Apple-type device to give you the answer to that. That's going on in real-time and has to be extremely rapidly done. >> And now we've got inferencing at the Edge. >> And then you've got inferencing at the Edge. Now here's all of the data coming in, whether it be a mobile Edge or a stationery Edge, huge amounts of data coming in to cameras to other senses of one sort or another. >> Or being generated right there where the-- >> Absolutely, generated, that's the first time that status has ever existed. And what you want to do with that is put the inference there and take what's important from that data. Because 99% or 99.9% of that data is absolutely free of value. So you're trying to extract that 0.01% of data and do actions locally with that and also pass those up the line. So you're actually getting rid of a huge amount of data at the Edge. >> All right, so that's an overall AI taxonomy. >> Yeah. >> How does storage influence what happens at the modeling and development level? What's the relationship between AI modeling and storage? >> So AI modeling is about lots and lots of data. Lots and lots of small files. Imagine thousands of millions of pictures going through millions of any sort of artificial intelligence you're trying to generate on that. So, that's one thing is, it's large amounts of data and you don't do modeling just once. You reuse the data. You run it again. You check it against something else. You're constantly looking for new types of data, new data, large amounts of data, lot of large-scale processing of that data to create models of one sort or another. >> You're not gonna do that on disk. >> You're not gonna to that on disk. That has to be flash. Has to be fast flash. And what you want, if you can, is to integrate the processing and the data, all as one, so that it fits in, it can be viewed as a system for the data scientists, which it sits there and does what they want to do and then can be managed from a storage point-of-view by the professionals. >> So in the center, it's gonna be very fast, very high-performance, very scalable, and flash. >> Yes. >> What about at the Edge? >> So, well, (laughs) >> What about at the activity Edge, let's call it? >> Yeah, activity, that again, is here you've got real-time processing. So again, the emphasis is on flash most of the time. And you've, in fact, got other technologies like, for example, envidems, which are coming in and increasing. So you've got a hierarchy there which you want to be able to use the right sort of storage for that job. But a lot of that's gonna be extremely rapid. And you want to be able to take your current systems of record, squeeze those down to allow space for all this inference work to be added in so that everything is real-time. So that's really, it's much faster. Of course, it doesn't mean you get rid of all of the things like data services and all of the things which you've collected. >> Well, on the contrary, doesn't it mean that those types of things become more important? >> Become more important. >> Well, so here's a hypothesis that I've had for a while and we've talked about, that the traditional storage notion of data, which was size, class, format-- >> Latency. >> IOPs. >> Yeah. >> Those types of things-- >> Bandwidth. >> Means nothing to the data scientists. >> Correct. >> AI is a business problem driving business observations so data services, in many respects, are a way of mediating the performance and other realities at the device level with the business and tool chain requirements at the AI level, right? >> Absolutely, absolutely, and you've gotta have those services. And, indeed, with hybrid computing, you want to move that processing to where the data is created, as much as you can. So if it's created in the Cloud, you go to the Cloud. If it's created-- >> Created or used? >> If you can, you want to do it where the data is created. The less data you move around, the better. So it's much better to send a request to that data where it's created, as close as possible to that. >> Okay, subject to the realities of latency. >> Absolutely. >> So, in many respects, it's still gonna be you want the data where it's gonna be used, but if you don't have to move it to where it's used, because the latency envelope is large enough, then keep it where it's created. >> Keep it where it's created. >> Got it. >> Absolutely, yes. And now, if we go to the Edge, there you really want to avoid having to store data at all. There's 99% of that data is useless. 99.9% of that data is useless. You wanna get rid of that. You want to use the inferencing to store only what is necessary. Now, to begin with, when you're still in the data modeling stage of AI, you may want to send some of that back, quite a lot of it back. But once you get into a normal running of it, you want to get rid of as much of that possible data as you can, take the core of that data, what it matters, the exceptions, etc. Send that up and get rid of it. Just destroy it. >> Well, this is one area where you and I, we generally agree. You say 99%, maybe it's 95%, maybe it's 90% of the data gets, you know, gotten rid of. Because there's always gonna be derivative opportunities to use data in valuable ways. But that's something we're gonna discover over the next few years. >> Sure. >> But we're not gonna go through that process if we don't have storage that can handle these workloads. >> Absolutely. >> All right. >> Yep. >> David Floyer, talking about the relationship between AI and storage. Thanks again for being on the CUBE. >> You're welcome. >> And thanks for joining us for another CUBEConversation. I'm Peter Burris. See you next time. (lively funk music)

>> Hi, I'm Peter Burris and once again, welcome to a Wikibon Action Item. (lively electronic music) We are again broadcasting from the beautiful theCUBE Studios here in Palo Alto, California, and we're joined today by a relatively larger group. So, let me take everybody through who's here in the studio with us. David Floyer, George Gilbert, once again, we've been joined by John Furrier, who's one of the key CUBE hosts, and on the remote system is Jim Kobielus, Neil Raden, and another CUBE host, Dave Vellante. Hey guys. >> Hi there. >> Good to be here. >> Hey. >> So, one of the things we're, one of the reasons why we have a little bit larger group here is because we're going to be talking about a community gathering that's taking place in the big data universe in a couple of weeks. Large numbers of big data professionals are going to be descending upon Strata for the purposes of better understanding what's going on within the big data universe. Now we have run a CUBE show next to that event, in which we get the best thought leaders that are possible at Strata, bring them in onto theCUBE, and really to help separate the signal from the noise that Strata has historically represented. We want to use this show to preview what we think that signal's going to be, so that we can help the community better understand what to look for, where to go, what kinds of things to be talking about with each other so that it can get more out of that important event. Now, George, with that in mind, what are kind of the top level thing? If it was one thing that we'd identify as something that was different two years ago or a year ago, and it's going to be different from this show, what would we say it would be? >> Well, I think the big realization that's here is that we're starting with the end in mind. We know the modern operational analytic applications that we want to build, that anticipate or influence a user interaction or inform or automate a business transaction. And for several years, we were experimenting with big data infrastructure, but that was, it wasn't solution-centric, it was technology-centric. And we kind of realized that the do it yourself, assemble your own kit, opensource big data infrastructure created too big a burden on admins. Now we're at the point where we're beginning to see a more converged set of offerings take place. And by converged, I mean an end to end analytic pipeline that is uniform for developers, uniform for admins, and because it's pre-integrated, is lower latency. It helps you put more data through one single analytic latency budget. That's what we think people should look for. Right now, though, the hottest new tech-centric activity is around Machine Learning, and I think the big thing we have to do is recognize that we're sort of at the same maturity level as we were with big data several years ago. And people should, if they're going to work with it, start with the knowledge, for the most part, that they're going to be experimenting, 'cause the tooling isn't quite mature enough, we don't have enough data scientists for people to be building all these pipelines bespoke. And the third-party applications, we don't have a high volume of them where this is embedded yet. >> So if I can kind of summarize what you're saying, we're seeing bifurcation occur within the ecosystem associated with big data that's driving toward simplification on the infrastructure side, which increasingly is being associated with the term big data, and new technologies that can apply that infrastructure and that data to new applications, including things like AI, ML, DL, where we think about modeling and services, and a new way of building value. Now that suggests that one or the other is more or less hot, but Neil Raden, I think the practical reality is that here in Silicon Valley, we got to be careful about getting too far out in front of our skis. At the end of the day, there's still a lot of work to be done inside how you simply do things like move data from one place to the other in a lot of big enterprises. Would you agree with that? >> Oh absolutely. I've been talking to a lot clients this week and, you know, we don't talk about the fact that they're still running their business on what we would call legacy systems, and they don't know how to, you know, get out of them or transform from them. So they're still starting to plan for this, but the problem is, you know, it's like talking about the 27 rocket engines on the whatever it was that he launched into space, launching a Tesla into space. But you can talk about the engineering of those engines and that's great, but what about all the other things you're going to have to do to get that (laughs) car into space? And it's the same thing. A year ago, we were talking about Hadoop and big data and, to a certain extent, Machine Learning, maybe more data science. But now people are really starting to say, How do we actually do this, how do we secure it, how do we govern it, how do we get some sort of metadata or semantics on the data we're working with so people know what they're using. I think that's where we are in a lot of companies. >> Great, so that's great feedback, Neil. So as we look forward, Jim Kobielus, the challenges associated with what it means to better improve the facilities of your infrastructure, but also use that as a basis for increasing your capability on some of the new applications services, what are we looking for, what should folks be looking for as they explore the show in the next couple of weeks on the ML side? What new technologies, what new approaches? Going back to what George said, we're in experimentation mode. What are going to be the experiments that are going to generate greatest results over the course of the next year? >> Yeah, for the data scientists, who flock to Strata and similar conferences, automation of the Machine Learning pipeline is super hot in terms of investments by the solution providers. Everybody from Google to IBM to AWS, and others, are investing very heavily in automation of, not just the data engine, that problem's been had a long time ago. It's automation of more of the feature engineering and the trending. These very manual, often labor intensive, jobs have to be sped up and automated to a great degree to enable the magic of productivity by the data scientists in the new generation of app developers. So look for automation of Machine Learning to be a super hot focus. Related to that is, look for a new generation of development suites that focus on DevOps, speeding the Machine Learning in DL and AI from modeling through training and evaluation deployment in iteration. We've seen a fair upswing in the number of such toolkits on the market from a variety of startup vendors, like the DataRobots of the world. But also coming to say, AWS with SageMaker, for example, that's hot. Also, look for development toolkits that automate more of the cogeneration, you know, a low-code tools, but the new generation of low-code tools, as highlighted in a recent Wikibons study, use ML to drive more of the actual production of fairly decent, good enough code, as a first rough prototype for a broad range of applications. And finally we're seeing a fair amount of ML-generated code generation inside of things like robotic process automation, RPA, which I believe will probably be a super hot theme at Strata and other shows this year going forward. So there's a, you mentioned the idea of better tooling for DevOps and the relationship between big data and ML, and what not, and DevOps. One of the key things that we've been seeing over the course of the last few years, and it's consistent with the trends that we're talking about, is increasing specialization in a lot of the perspectives associated with changes within this marketplace, so we've seen other shows that have emerged that have been very, very important, that we, for example, are participating in. Places like Splunk, for example, that is the vanguard, in many respects, of a lot of these trends in big data and how big data can applied to business problems. Dave Vellante, I know you've been associated with a number of, participating in these shows, how does this notion of specialization inform what's going to happen in San Jose, and what kind of advice and counsel should we tell people to continue to explore beyond just what's going to happen in San Jose in a couple weeks? >> Well, you mentioned Splunk as an example, a very sort of narrow and specialized company that solves a particular problem and has a very enthusiastic ecosystem and customer base around that problem. LAN files to solve security problems, for example. I would say Tableau is another example, you know, heavily focused on Viz. So what you're seeing is these specialized skillsets that go deep within a particular domain. I think the thing to think about, especially when we're in San Jose next week, is as we talk about digital disruption, what are the skillsets required beyond just the domain expertise. So you're sort of seeing this bifurcated skillsets really coming into vogue, where if somebody understands, for example, traditional marketing, but they also need to understand digital marketing in great depth, and the skills that go around it, so there's sort of a two-tool player. We talk about five-tool player in baseball. At least a multidimensional skillset in digital. >> And that's likely to occur not just in a place like marketing, but across the board. David Floyer, as folks go to the show and start to look more specifically about this notion of convergence, are there particular things that they should think about that, to come back to the notion of, well, you know, hardware is going to make things more or less difficult for what the software can do, and software is going to be created that will fill up the capabilities of hardware. What are some of the underlying hardware realities that folks going to the show need to keep in mind as they evaluate, especially the infrastructure side, these different infrastructure technologies that are getting more specialized? >> Well, if we look historically at the big data area, the solution has been to put in very low cost equipment as nodes, lots of different nodes, and move the data to those nodes so that you get a parallelization of the, of the data handling. That is not the only way of doing it. There are good ways now where you can, in fact, have a single version of that data in one place in very high speed storage, on flash storage, for example, and where you can allow very fast communication from all of the nodes directly to that data. And that makes things a lot simpler from an operational point of view. So using current Batch Automation techniques that are in existence, and looking at those from a new perspective, which is I do IUs apply these to big data, how do I automate these things, can make a huge difference in just the practicality in the elapsed time for some of these large training things, for example. >> Yeah, I was going to say that to many respects, what you're talking about is bringing things like training under a more traditional >> David: Operational, yeah. >> approach and operational set of disciplines. >> David: Yes, that's right. >> Very, very important. So John Furrier, I want to come back to you, or I want to come to you, and say that there are some other technologies that, while they're the bright shiny objects and people think that they're going to be the new kind of Harry Potter technologies of magic everywhere, Blockchain is certainly going to become folded into this big data concept, because Blockchain describes how contracts, ownership, authority ultimately get distributed. What should folks look for as the, as Blockchain starts to become part of these conversations? >> That's a good point, Peter. My summary of the preview for BigData SV Silicon Valley, which includes the Strata show, is two things: Blockchain points to the future and GDPR points to the present. GDPR is probably the most, one of the most fundamental impacts to the big data market in a long time. People have been working on it for a year. It is a nightmare. The technical underpinnings of what companies have to do to comply with GDPR is a moving train, and it's complete BS. There's no real solutions out there, so if I was going to tell everyone to think about that and what to look for: What is happening with GDPR, what's the impact of the databases, what's the impact of the architectures? Everyone is faking it 'til they make it. No one really has anything, in my opinion from what I can see, so it's a technical nightmare. Where was that database? So it's going to impact how you store the data, and the sovereignty issue is another issue. So the Blockchain then points to the sovereignty issue of the data, both in terms of the company, the country, and the user. These things are going to impact software development, application development, and, ultimately, cloud choice and the IoT. So to me, GDPR is not just a one and done thing and Blockchain is kind of a future thing to look at. So I would look out of those two lenses and say, Do you have a direction or a narrative that supports me today with what GDPR will impact throughout the organization. And then, what's going on with this new decentralized infrastructure and the role of data, and the sovereignty of that data, with respect to company, country, and user. So to me, that's the big issue. >> So George Gilbert, if we think about this question of these fundamental technologies that are going to become increasingly important here, database managers are not dead as a technology. We've seen a relative explosion over the last few years in at least invention, even if it hasn't been followed with, as Neil talked about, very practical ways of bringing new types of disciplines into a lot of enterprises. What's going to happen with the database world, and what should people be looking for in a couple of weeks to better understand how some of these data management technologies are going to converge and, or involve? >> It's a topic that will be of intense interest and relevance to IT professionals, because it's become the common foundation of all modern apps. But I think what we can do is we can see, for instance, a leading indicator of what's going to happen with the legacy vendors, where we have in-memory technologies from both transaction processing and analytics, and we have more advanced analytics embedded in the database engine, including Machine Learning, the model training, as well as model serving. But the, what happened in the big data community is that we disassembled the DBMS into the data manipulation language, which is an analytic language, like, could be Spark, could be Flink, even Hive. We had the Catalog, which I think Jim has talked about or will be talking about, where we're not looking, it's not just a dictionary of what's in one DBMS, but it's a whole way of tracking and governing data across many stores. And then there's the Storage Manager, could be the file system, an object store, could be just something like Kudu, which is a MPP way of, in parallel, performing a bunch of operations on data that's stored. The reason I bring all this up is, following on David's comment about the evolution of hardware, databases are fundamentally meant to expose capabilities in the hardware and to mediate access to data, using these hardware capabilities. And now that we have this, what's emerging as this unigrid, with memory-intensive architectures and super low latency to get from any point or node on that cluster to any other node, like with only a five microsecond lag, relative to previous architectures. We can now build databases that scale up with the same knowledge base that we built databases... I'm sorry, that scale out, that we used to build databases that scale up. In other words, it democratizes the ability to build databases of enormous scale, and that means that we can have analytics and the transactions working together at very low latency. >> Without binding them. Alright, so I think it's time for the action items. We got a lot to do, so guys, keep it really tight, really simple. David Floyer, let me start with you. Action item. >> So action item on big data should be focus on technologies that are going to reduce the elapse time of solutions in the data center, and those are many and many of them, but it's a production problem, it's becoming a production problem, treat it as a production problem, and put it in the fundamental procedures and technologies to succeed. >> And look for vendors >> Who can do that, yes. >> that do that. George Gilbert, action item. >> So I talked about convergence before. The converged platform now is shifting, it's center of gravity is shifting to continuous processing, where the data lake is a reference data repository that helps inform the creation of models, but then you run the models against the streaming continuous data for the freshest insights-- >> Okay, Jim Kobielus, action item. >> Yeah, focus on developer productivity in this new era of big data analytics. Specifically focus on the next generation of developers, who are data scientists, and specifically focus on automating most of what they do, so they can focus on solving problems and sifting through data. Put all the grunt work or training, and all that stuff, take and carry it by the infrastructure, the tooling. >> Peter: Neil Raden, action item. >> Well, one thing I learned this week is that everything we're talking about is about the analytical problem, which is how do you make better decisions and take action? But companies still run on transactions, and it seems like we're running on two different tracks and no one's talking about the transactions anymore. We're like the tail wagging the dog. >> Okay, John Furrier, action item. >> Action item is dig into GDPR. It is a really big issue. If you're not proactive, it could be a nightmare. It's going to have implications that are going to be far-reaching in the technical infrastructure, and it's the Sarbanes-Oxley, what they did for public companies, this is going to be a nightmare. And evaluate the impact of Blockchains. Two things. >> David Vellante, action item. >> So we often say that digital is data, and just because your industry hasn't been upended by digital transformations, don't think it's not coming. So it's maybe comfortable to sit back and say, Well, we're going to wait and see. Don't sit back and wait and see. All industries are susceptible to digital transformation. >> Alright, so I'll give the action item for the team. We've talked a lot about what to look for in the community gathering that's taking place next week in Silicon Valley around strata. Our observations as the community, it descends upon us, and what to look for is, number one, we're seeing a bifurcation in the marketplace, in the thought leadership, and in the tooling. One set of group, one group is going more after the infrastructure, where it's focused more on simplification, convergence; another group is going more after the developer, AI, ML, where it's focused more on how to create models, training those models, and building applications with the services associated with those models. Look for that. Don't, you know, be careful about vendors who say that they do it all. Be careful about vendors that say that they don't have to participate in a converged approach to doing this. The second thing I think we need to look for, very importantly, is that the role of data is evolving, and data is becoming an asset. And the tooling for driving velocity of data through systems and applications is going to become increasingly important, and the discipline that is necessary to ensure that the business can successfully do that with a high degree of predictability, bringing new production systems are also very important. A third area that we take a look at is that, ultimately, the impact of this notion of data as an asset is going to really come home to roost in 2018 through things like GDPR. As you scan the show, ask a simple question: Who here is going to help me get up to compliance and sustain compliance, as the understanding of privacy, ownership, etc. of data, in a big data context, starts to evolve, because there's going to be a lot of specialization over the next few years. And there's a final one that we might add: When you go to the show, do not just focus on your favorite brands. There's a lot of new technology out there, including things like Blockchain. They're going to have an enormous impact, ultimately, on how this marketplace unfolds. The kind of miasma that's occurred in big data is starting to specialize, it's starting to break down, and that's creating new niches and new opportunities for new sources of technology, while at the same time, reducing the focus that we currently have on things like Hadoop as a centerpiece. A lot of convergence is going to create a lot of new niches, and that's going to require new partnerships, new practices, new business models. Once again, guys, I want to thank you very much for joining me on Action Item today. This is Peter Burris from our beautiful Palo Alto theCUBE Studio. This has been Action Item. (lively electronic music)

(electronic music) >> Hi, I'm Peter Burris and welcome once again to Wikibon's weekly research meeting from the CUBE studios in Palo Alto, California. This week we're going to build upon a conversation we had last week about the idea of different data shapes or data tiers. For those of you who watched last week's meeting, we discussed the idea that data across very complex distributed systems featuring significant amounts of work associated with the edge are going to fall into three classifications or tiers. At the primary tier, this is where the sensor data that's providing direct and specific experience about the things that the sensors are indicating, that data will then signal work or expectations or decisions to a secondary tier that aggregates it. So what is the sensor saying? And then the gateways will provide a modeling capacity, a decision making capacity, but also a signal to tertiary tiers that increasingly look across a system wide perspective on how the overall aggregate system's performing. So very, very local to the edge, gateway at the level of multiple edge devices inside a single business event, and then up to a system wide perspective on how all those business events aggregate and come together. Now what we want to do this week is we want to translate that into what it means for some of the new technologies, new analytics technologies that are going to provide much of the intelligence against each of this data. As you can imagine, the characteristics of the data is going to have an impact on the characteristics of the machine intelligence that we can expect to employ. So that's what we want to talk about this week. So Jim Kobielus, with that as a backdrop, why don't you start us off? What are we actually thinking about when we think about machine intelligence at the edge? >> Yeah, Peter, we at the edge, the edge of body, the device be in the primary tier that acquires fresh environmental data through its sensors, what happens at the edge? In the extreme model, we think about autonomous engines, let me just go there just very briefly, basically, it's a number of workloads that take place at the edge, the data workloads. The data is (mumbles) or ingested, it may be persisted locally, and that data then drives local inferences that might be using deep layer machine learning chipsets that are embedded in that device. It might also trigger various tools called actuations. Things, actions are taken at the edge. If it's the self-driving vehicle for example, an action may be to steer the car or brake the car or turn on the air conditioning or whatever it might be. And then last but not least, there might be some degree of adaptive learning or training of those algorithms at the edge, or the training might be handled more often up at the second or tertiary tier. The tertiary tier at the cloud level, which has visibility usually across a broad range of edge devices and is ingesting data that is originated from all of the many different edge devices and is the focus of modeling, of training, of the whole DevOps process, where teams of skilled professionals make sure that the models are trained to a point where they are highly effective for their intended purposes. Then those models are sent right back down to the secondary and the primary tiers, where act out inferences are made, you know, 24 by seven, based on those latest and greatest models. That's the broad framework in terms of the workloads that take place in this fabric. >> So Neil, let me talk to you, because we want to make sure that we don't confuse the nature of the data and the nature of the devices, which may be driven by economics or physics or even preferences inside of business. There is a distinction that we have to always keep track of, that some of this may go up to the Cloud, some of it may stay local. What are some of the elements that are going to indicate what types of actual physical architectures or physical infrastructures will be built out as we start to find ways to take advantage of this very worthwhile and valuable data that's going to be created across all of these different tiers? >> Well first of all, we have a long way to go with sensor technology and capability. So when we talk about sensors, we really have to define classes of sensors and what they do. However, I really believe that we'll begin to think in a way that approximates human intelligence, about the same time as airplanes start to flap their wings. (Peter laughs) So, I think, let's have our expectations and our models reflect that, so that they're useful, instead of being, you know hypothetical. >> That's a great point Neil. In fact, I'm glad you said that, because I strongly agree with you. But having said that, the sensors are going to go a long ways, when we... but there is a distinction that needs to be made. I mean, it may be that that some point in time, a lot of data moves up to a gateway, or a lot of data moves up to the Cloud. It may be that a given application demands it. It may be that the data that's being generated at the edge may have a lot of other useful applications we haven't anticipated. So we don't want to presume that there's going to be some hard wiring of infrastructure today. We do want to presume that we better understand the characteristics of the data that's being created and operated on, today. Does that make sense to you? >> Well, there's a lot of data, and we're just going to have to find a way to not touch it or handle it any more times than we have to. We can't be shifting it around from place to place, because it's too much. But I think the market is going to define a lot of that for us. >> So George, if we think about the natural place where the data may reside, the processes may reside, give us a sense of what kinds of machine learning technologies or machine intelligence technologies are likely to be especially attractive at the edge, dealing with this primary information. Okay, I think that's actually a softball which is, we've talked before about bandwidth and latency limitations, meaning we're going to have to do automated decisioning at the edge, because it's got to be fast, low latency. We can't move all the data up to the Cloud for bandwidth limitations. But, by contrast, so that's data intensive and it's fast, but up in the cloud, where we enhance our models, either continual learning of the existing ones or rethinking them entirely, that's actually augmented decisions, and augmented means it's augmenting a human in the process, where, most likely, a human is adding additional contextual data, performing simulations, and optimizing the model for different outcomes or enriching the model. >> It may in fact be a crucial element or crucial feature of the training by in fact, validating that the action taken by the system was appropriate. >> Yes, and I would add to that, actually, that you might, you used an analogy, people are going from two extremes where they say, some people say, "Okay, so all the analytics has to be done in the cloud," Wikibon and David Floyer, and Jim Kovielus have been pioneering the notion that we have to do a lot more at the client. But you might look back at client server computing where the client was focused on presentation, the server was focused on data integrity. Similarly, here, the edge or client is going to be focused on fast inferencing and the server is going to do many of the things that were associated with a DBMS and data integrity in terms of reproducibility, of decisions in the model for auditing, security, versioning, orchestration in terms of distributing updated models. So we're going to see the roles of the edge and the cloud rhyme with what we saw in server. Neither one goes away, they augment each other. >> So, Jim Kovielus, one of the key issues there is going to be the gateway, and the role that the gateway plays, and specifically here, we talked about the nature of again, the machine intelligence that's going to be operating more on the gateway. What are some of the characteristics of the work that's going to be performed at the gateway that kind of has oversight of groupings or collections of sensor and actuator devices? >> Right, good question. So the perfect example that everybody's familiar with now about a gateway in this environment, a smart home hub. A smart home hub, just for the sake of discussion, has visibility across two or more edge devices. It could be a smart speaker, could be the HVAC system is sensor equipped and so forth, what it does, the pool it performs, a smart hub of any sort, is that it acquires data from the edge devices, the edge devices might report all of their data directly to the hub, or the sensor devices might also do inferences and then pass on the results of the inferences it has given to the hub, regardless. What the hub does is A, it aggregates the data across those different edge devices over which it has this ability and control, B, it may perform it's own inferences based on models that look out across an entire home in terms of patterns of activity. Then it might take the hub, various actions autonomous by itself, without consulting an end user or anything else. It might take action in terms of beef up the security, adjust the HVAC, it adjusts the light in the house or whatever it might be, based on all that information streaming in real time. Possibly, its algorithms will allow you to determine what of that data shows an anomalous condition that deviates from historical patterns. Those kinds of determinations, whether it's anomalous or a usual pattern, are often taken at the hub level, 'cause it's maintaining sort of a homeostatic environment, as it were, within its own domain, and that hub might also communicate up the stream, to a tertiary tier that has oversight, let's say, of a smart city environment, where everybody in that city or whatever, might have a connection into some broader system that say, regulates utility usage across the entire region to avoid brownouts and that kind of thing. So that gives you an idea of what the role of a hub is in this kind of environment. It's really a controller. >> So, Neil, if we think about some of the issues that people really have to consider as they start to architect what some of these systems are going to look like, we need to factor both what is the data doing now, but also ensure that we build into the entire system enough of a buffer so that we can anticipate and take advantage of future ways of using that data. Where do we draw that fine line between we only need this data for this purpose now and geez, let's ensure that we keep our options open so that we can use as much data as we want at some point in time in the future? >> Well, that's a hard question, Peter, but I would say that if it turns out that this detailed data coming from sensors, that the historical aspect of it isn't really that important. If the things you might be using that data for are more current, then you probably don't need to capture all that. On the other hand, there have been many, many occasions historically, where data has been used other than its original purpose. My favorite example was scanners in grocery stores, where it was meant to improve the checkout process, not have to put price stickers on everything, manage inventory and so forth. It turned out that some smart people like IRI and some other companies said, "We'll buy that data from you, "and we're going to sell it to advertisers," and all sorts of things. We don't know the value of this data yet, it's too new. So I would err on the side of being conservative and capturing and saving as much as I could. >> So what we need to do is, we need to marry or we need to do an optimization of some form about how much is it going to cost to transmit the data versus what kind of future value or what kinds of options of future value might there be on that data. That is, as you said, a hard problem, but we can start to conceive of an approach to characterizing that ratio, can't we? >> I hope so. I know that, personally, when I download 10 gigabytes of data, I pay for 10 gigabytes of data, and it doesn't matter if it came from a mile away or 10,000 miles away. So there has to be adjustments for that. There's also ways of compressing data because this sensor data I'm sure is going to be fairly sparse, can be compressed, is redundant, you can do things like RLL encoding, which takes all the zeroes out and that sort of thing. There are going to be a million practices that we'll figure out. >> So as we imagine ourselves in this schemata of edge, hub, tertiary or primary, secondary and tertiary data and we start to envision the role that data's going to play and how we conduct or how we build these architectures and these infrastructures, it does raise an interesting question, and that is, from an economic standpoint, what do we anticipate is going to be the classes of devices that are going to exploit this data? David Foyer who's not here today, hope you're feeling better David, has argued pretty forcibly, that over the next few years we'll see a lot of advances made in microprocessor technology. Jim, I know you've been thinking about this a fair amount. What types of function >> Jim: Right. >> might we actually see being embedded in some of these chips that software developers are going to utilize to actually build some of these more complex and interesting systems? >> Yeah, first of all, one of the trends we're seeing in the chipset market for deep learning, just to be there for a moment, is that deep learning chipsets traditionally, when I say traditionally, the last several years the market has been dominated by GP's graphic processing unit. Invidia of course, is the primary provider of those. Of course, Invidia has been along around for a long time as a gaming solution provider. Now, what's happening with GPU technology, in fact, the latest generation of Invidia's architecture shows where it's going. The thing that is more deep learning optimized capabilities at the chipset level. They're called tensor processing, and I don't want to bore you with all the technical details, but the whole notion of-- >> Peter: Oh, no, Jim, do bore us. What is it? (Jim laughs) >> Basically deep learning is based on doing high speed, fast matrix map. So fundamentally, tensor cores do high velocity fast matrix math, and the industry as a whole is moving toward embedding more tensor cores directly into the chipset, higher density of tensor core. Invidia in its latest generation of chip has done that. They haven't totally taken out the gaming oriented GPU capabilities, but there are competitors and they have a growing list, more than a dozen competitors on the chipset side now. We're all going down a road of embedding far more technical processing units into every chip. Google is well known for something called GPU tensor processing units, their chip architecture. But they're one of many vendors that are going down that road. The bottom line is the chipset itself is becoming authenticated and being optimized for the core function that CPU and really GPU technology and even ASIX and FPGAs were not traditionally geared to do, which is just deep learning at a high speed, many cores, to do things like face recognition and video and voice recognition freakishly fast, and really, that's where the market is going in terms of enabling underlying chipset technology. What we're seeing is that, what's likely to happen in the chipsets of the year 2020 and beyond, they'll be predominantly tensor core processing units, But they'll be systemed on a chip that, and I'm just talking about future, not saying it's here now, systems on a chip that include some, a CPU, to managing real time OS, like a real time Linux or what not, and with highly dense tensor core processing unit. And in this capability, these'll be low power chips, and low cost commodity chips that'll be embedded in everything. Everything from your smart phone, to your smart appliances in your home, to your smart cars and so forth. Everything will have these commodity chips. 'Cause suddenly every edge device, everything will be an edge device, and will be able to provide more than augmentation, automation, all these things we've been talking about, in ways that are not necessarily autonomous, but can operate with a great degree of autonomy to help us human beings to live our lives in an environmentally contextual way at all points in time. >> Alright, Jim, let me cut you off there, because you said something interesting, a lot more autonomy. George, what does it mean, that we're going to dramatically expand the number of devices that we're using, but not expand the number of people that are going to be in place to manage those devices. When we think about applying software technologies to these different classes of data, we also have to figure out how we're going to manage those devices and that data. What are we looking at from an overall IT operations management approach to handling a geometrically greater increase in the number of devices and the amount of data that's being generated? (Jim starts speaking) >> Peter: Hold on, hold on, George? >> There's a couple dimensions to that. Let me start at the modeling side, which is, we need to make data scientists more productive or we need to push out to a greater, we need to democratize the ability to build models, and again, going back to the notion of simulation, there's this merging of machine learning and simulation where machine learning tells you correlations in factors that influence an answer. Whereas, the simulation actually lets you play around with those correlations, to find the causations, and by merging them, we make it much, much more productive to find the models that are both accurate and to optimize them for different outcomes. >> So that's the modeling issue. >> Yes. >> When we think about after we, which is great. Now as we think about some of the data management elements, what are we looking at from a data management standpoint? >> Well, and this is something Jim has talked about, but, you know we had DevOps for joining the, essentially merging the skills of the developers with the operations folks, so that there's joint responsibility of keeping stuff live. >> Well what about things like digital twins, automated processes, we've talked a little it about breadth versus depth, ITOM, What do you think? Are we going to build out, are all these devices going to reveal themselves, or are we going to have to put in place a capacity for handling all of these things in some consistent, coherent way? >> Oh, okay, in terms of managing. >> In terms of managing. >> Okay. So, digital twins were interesting because they pioneered or they made well known a concept called essentially, a symmetric network, or a knowledge graph, which is just a way of abstracting what is a whole bunch of data models and machine learning models that represents the structure and behavior of a device. In IIoT terminology, it was like an industrial device, like a jet engine. But that same construct, the knowledge graph and the digital twin, can be used to describe the application software and the infrastructure, both middleware and hardware, that makes up this increasingly sophisticated network of learning and inferencing applications. And the reason this is important, it sounds arcane, the reason it's important is we're building now vastly more sophisticated applications over great distances, and the only way we can manage them is to make the administrators far more productive. The state of the art today is, alerts on the performance of the applications, and alerts on the, essentially, the resource intensity of the infrastructure. By combining that type of monitoring with the digital twin, we can get a, essentially much higher fidelity reading on when something goes wrong. We don't get false positives. In other words, you don't have, if something goes wrong, it's like the fairy tale of the pea underneath the mattress, all the way up, 10 mattresses, you know it's uncomfortable. Here, it'll pinpoint exactly what gets wrong, rather than cascading all sorts of alerts, and that is the key to productivity in managing this new infrastructure. >> Alright guys, so let's go into the action item around here. What I'd like to do now is ask each of you for the action item that you think users are going to have to apply or employ to actually get some value, and start down this path of utilizing machine intelligence across these different tiers of data to build more complex, manageable application infrastructures. So, Jim, I'd like to start with you, what's your action item? >> My action item is related what George just said, modeled centrally, deployed in a decentralized fashion, machine learning, and use digital twin technology to do your modeling against device classes, in a more coherent way. There's not one model that won't fit all of the devices. Use digital twin technology to structure the modeling process to be able to tune a model to each class of device out there. >> George, action item. >> Okay, recognize that there's a big difference between edge and cloud, as Jim said. But I would elaborate, edge is automated, low latency decision making, extremely data intensive. Recognize that the cloud is not just where you trickle up a little bit of data, this is where you're going to use simulations, with a human in the loop, to augment-- >> System wide, system wide. >> System wide, with a human in the loop to augment how you evaluate new models. >> Excellent. Neil, action item. >> I would have people start on the right side of the diagram and start to think about what their strategy is and where they fit into these technologies. Be realistic about what they think they can accomplish and do the homework. >> Alright, great. So let me summarize our meeting this week. This week we talked about the role that the three tiers of data that we've described will play in the use of machine intelligence technologies as we build increasingly complex and sophisticated applications. We've talked about the difference between primary, secondary, and tertiary data. Primary data being the immediate experience of sensors. Analog being translated into digital, about a particular thing or set of things. Secondary being the data that is then aggregated off of those sensors for business event purposes, so that we can make a business decision, often automatically down at an edge scenario, as a consequence of signals that we're getting from multiple sensors. And then finally, tertiary data, that looks at a range of gateways and a range of systems, and is considering things at a system wide level, for modeling, simulation and integration purposes. Now, what's important about this is that it's not just better understanding the data and not just understanding the classes of technologies that we used, that will remain important. For example, we'll see increasingly powerful low cost device specific arm like processors pushed into the edge. And a lot of competition at the gateway, or at the secondary data tier. It's also important, however to think about the nature of the allocations and where the work is going to be performed across those different classifications. Especially as we think about machine learning, machine etiologies and deep learning. Our expectation is that we will see machine learning being used on all three levels, Where machine etiology is being used on against all forms of data to perform a variety of different work, but that the work that will be performed will be a... Will be naturally associated and related to the characteristics of the data that's being aggregated at that point. In other words, we won't see simulations, which are characteristics of tertiary data, George, at the edge itself. We will however, see edge devices often reduce significant amounts of data from a perhaps a video camera or something else to make relatively simple decisions that may involve complex technologies to allow a person into a building, for example. So our expectation is that over the next five years we're going to see significant new approaches to applying increasingly complex machine etiologies technologies across all different classes of data, but we're going to see them applied in ways that fit the patterns associated with that data, because it's the patterns that drive the applications. So our overall action item, it's absolutely essential that businesses that considering and conceptualizing what machine intelligence can do, but be careful about drawing huge generalizations about what the future machine intelligence is. The first step is to parse out the characteristics of the data driven by the devices that are going to generate it and the applications that are going to use it, and understand the relationship between the characteristics of that data and the types of machine intelligence work that can be performed. What is likely, is that an impedance mismatch between data and expectations of machine intelligence will generate a significant number of failures that often will put businesses back years in taking full advantage of some of these rich technologies. So, once again we want to thank you this week for joining us here on the Wikibon weekly research meeting. I want to thank George Gilbert who is here CUBE Studio in Palo Alto, and Jim Kobielus and Neil Raden who were both on the phone. And we want to thank you very much for joining us here today, and we look forward to talking to you again in the future. So this is Peter Burris, from the CUBE's Palo Alto Studio. Thanks again for watching Wikibon's weekly research meeting. (electronic music)

>> Announcer: Live from Silicon Valley, it's theCube, covering Mobile World Congress 2017, brought to you by Intel. >> Okay, welcome back, everyone. Here, live, in Palo Alto, California, the Silicon Angle Studio for the Silicon Valley coverage of Mobile World Congress 2017. I'm John Furrier. We're in theCube. We're here with Cube alumni and one of our favorite guests, Alan Cohen, the Chief Commercial Officer of Illumio, hot security startup, coming in to share his commentary on Mobile World Congress. Alan's a veteran in the industry. Great to have you. Been in the Silicon Valley Friday Show a few weeks ago. Great to see you. >> Thrilled to be back. Beautiful environment. You know, party. >> It was great to see you on the Silicon Valley Friday Show because after our segment the New York Times ran that story Friedman had that the cross where they took our content. >> We're going to Freeport next. >> Exactly. (laughing) And great content, we're serving it up. So I want to say thank you, it was great coverage. Thanks to the New York Times for picking up our content, taking it to the next level. Always great to have a conversation. You've got a good way to put the finger on the pulse. Mobile World Congress, two days of coverage for us. I'll just give you a quick Reader's Digest summary of what we're seeing. It's a bipolar show. It's a device show and a telco trying-to-figure-things-out show. Then in the middle is a lot of money to be had by whoever can help sort out the counseling of the telco business. Intel certainly is a big player in that with 5G. And there's a lot of under the covers stuff. SDN, NFV, new networks and new paradigms of how to configure these architectures. Not much mention of security, but that's essentially what's going on. You've got everyone's working out the devices, the new LG, the Yahweh, all this stuff's going on. Then you get the telcos well speeds and feeds and build out and business models. So what's your assessment? >> I've been to the Mobile World Congress 10 times. We never talked about this, but I actually worked the cellular carrier in the 90s. To me the show is the same every year. It's drones, clones, and phones. That's what people really focus on, right? So the 11,000 versions of the Android phone, even though Apple's still taking 89% of the profit at the industry so it actually only one phone you have to pay attention to on one side. Then more bits, less money side of being on the carrier, because what is being an ISP, wireless ISP or a wired ISP. Every year I give you more bits and I make less money. I'm going to make it up in volume. And I keep pouring all this capital into this. So to me, they haven't really yet completely broken out of that paradigm. The key thing is that the mobile network is the primary network. So all the profitability in telco is in the mobile network. Nobody says hey, I'm going to get up and build a wired network and pull some more copper to your house, right? So that is the principle way that people are using it and we have now an entire generation that don't know you can actually plug a phone into a wall or an ethernet connection. I think that's the kind of competitive dynamics that people go with. >> And that's under pressure though, because now the carrier's always in the operating, always controlled the relationship to the user via the contract. Did you buy an iPhone lately? There's no more relationship. You just buy whatever device you want. The subsidy ended ... I'm not talking about subsidy. I'm talking about like I have a contract with AT and T, I can certainly change it to Verizon, so I can certainly swap. But for the most part the carrier views me as a subscriber. Pretty much that's it. They bill me, I'm not really getting anything extra from AT and T. Maybe I'll get some hotspots. But I mean come on, what value? >> You are just our poo. >> Where does it go from here? We had the guys from Datatron on who had an interesting proposition. They had a ton of data. So there really has been this struggle institutionally, as you know, I mean core competency has been provisioning, truck roll, and billing. So what else can they do? What's your thoughts, okay let's change the mental, here's the exercise. We get elected to be the CEO of the biggest telco. >> You're Verizon, I'm A T and T. >> We own the telcos, and what do we do? Do we fire everybody? Do we do what Donald Trump does and just fire everyone and run it the way we want to run it? Or do we build it? What would we do seriously, what would we do if we were telcos and we want to put our business hat on? >> I think you have to kind of deconstruct the value chain of that. So what telcos do is they offer up content, for the most part. These devices, I've had to teach my kids that you can make a call with it. But aside from a call mostly what people do is use some form of internet application. They don't get any other money for the internet application. They don't get any money for hosting it, they don't get any money for managing it. They don't get very much money for making it perform. So to me, the biggest challenge of the telcos is actually Amazon because if you think about it, Amazon is now becoming the supply chain for so much internet delivery content. If the telco wants to be something other than the last mile and the wires connecting that last mile, it takes a lot of wires to build a wireless network, people forget that. They're going to have to start to figure out can I, whether it's cash and data center, can I turn profitable services to the people who are all competing at the edge of that universe and applications. I don't think they really have done that. I mean they are some of the largest data center operators in the world, but they haven't really thought it through. I was in a studio in L.A. a couple weeks ago and it's one of the large national studios. It's an Illumio customer and they've now moved all their content distribution into Amazon. So they don't send the content from their network to the affiliates. They put it in Amazon, and Amazon delivers it. How much longer is it going to before there's actually studio that works out of Amazon? >> Yeah, I mean the head end's dead. This cable is kind of changing. That's the media piece, but also you have all these new use cases, the fantasy autonomous driving cars which you can say it's a data center on wheels, yes I could buy that. Is it going to be uploading data every half mile? Where's the wire? So you have this new construction. Smart cities is another one, smart homes is an echo in there. >> I made my living out of making data centers more secure. But the data center is going to completely evolve. The share perfusion of data that's going to come out of these devices, and a lot of people have talked about the edge architecture, is going to blow up the idea of back hauling it to a centralized server. Process it in a bunch of ways and spit it back out. For me, if I wanted to write a smart or autonomous car management system, let's say I was the city of Palo Alto and I'm responsible for now instead of just the traffic lights, I'm also responsible for how autonomous cars go through Palo Alto, I'm not sending something back to some data center in Virginia for Amazon. I'm going to have to figure out how to process all that data closest to where those cars are. Make intelligent decisions about them while at local, and then send back out instructions. What I think you're going to do is you're going to see a shift from this central model to a much more distributed model and I'm going to have to have mini data centers. So instead of having 10 mega data centers I might have 1,000 mini mega data centers that's going to make all of these things happen. I don't think a lot of people have paid attention to that architectural shift. If you're in the process of, business of selling server networks you're still thinking client-server back haul it into the giant data center next to the nuclear power plant. But it's all going to have to move a lot closer to where something, because I only care about that decision right now with the 50 cars coming down middle field and the streets that feed into it. >> But there's a bigger architecture thing that the Mobile World Congress is trying to point at, which is an ecosystem. Let me take a step back. Is Mobile Congress a relevant show, or is it becoming a CES sideshow, Biz Dev show? I mean Cy Gerli was on yesterday saying look, it's where everyone goes, who's who goes there. It's essentially a Biz Dev show that happens to have a trade show running with it. >> It's the agora, right? The Greek term for marketplace. You go there to do business with people. It's like RSA two weeks ago, right? You guys were up at RSA. It's like is it really fun to walk through 14,000 vendor booths, or is it like everybody who make decisions on buying and selling security stuff happened to be in the same two-square miles of San Francisco. I don't think that part goes away, but I do think ... >> It's a super important part. >> Yeah, but I think the architecture of who plays is going to change. The the question you've got to ask is who's going to be the Amazon of the mobile world and disrupt the network model? The network is now just something glued together with software. I mean years ago they had the same thing, it didn't really work out, that they called the cloud where I would rent my access point in London to people and I'd use their wifi. The stuff that glues it together is always much more important than the infrastructure itself. So if Mobile World Congress can be important there's going to be a track on the people actually glue all of that stuff all together. >> All right, so I've got to get your take on the business conversation, the marketplace that runs there. What are some of the conversations that you could imagine that was happening at Mobile World Congress? I know we're not there, I mean we've been seeing and hearing some of the hallway conversations. Obviously 5G's the big story. What are some of the marketplace hallway conversations or business meetings that are going on in your mind's eye if you had to make a guess on what's happening? >> What are the most important content that people like to use today? Pop quiz, do you know this? >> Yeah, video. >> Video, right? So to me, one of the conversation Netflix was having and Amazon Prime was having because they're not just waiting for you to be in your TV, to consume, right? People are consuming increasing amounts of video content on mobile devices. So I think there's the Hollywood influence or the studio or what is it? The National Association of Programming Executives, NAPE right? What you're doing, if you're a content producer you're looking for eyeballs and people to pay for it. There's nothing more ubiquitous than that piece of glass we're all carrying in front of our nose 17 hours a day. I think that's a big set of business discussions. Your partner was talking about this, is okay, is there just a dramatically different way to build this network? 5G is going to give you the promise, more is a lot of work. The physics are I'm getting a lot more bandwidth. What am I going to do with it? Well people are going to fill it up. >> There's different use cases. There's the mobility and then with dense areas. Then things that are moving at a hundred miles an hour, 50 miles and hour, planes, trains. >> I think there's an element of that. I think there's the internet of things discussion. I still think five years will take the internet whatever things, right? I call the IOWT, right, because it's like nobody's, it's not really about connecting your lightbulb to the network, but there are a lot of things in motion that people want to better manage. >> We just introduced a research agenda this morning with Peter Burroughs, IOT, IOT people. Things and people. >> Have you gone back to the Furrier family and counted up how many IP addresses you have as a family? The Cohen family has 111 IP addresses. >> John: IPV6 for you. (laughing) >> Yeah, we need a gateway man for the network router that comes into the house. But that is actually ... >> We just bought the new Google access points, the ones that have that little mesh instrument. >> But yes, I'm just kidding you. So there are a lot of things. The other thing is that there is the interaction of the mobile, actually I think Google is a great example. If you think about Google produces the wifi at Starbucks and a lot of retail. They're interested in what's going on. Today we think about the mobile network as a mobile network and we think about the broadband fixed network as a different network. And like the interplay between those two, it's like there's a lot more than Foursquare and Facebook. >> Sure fibers of the home is very capital intensive. We knew it would cost us to do a truck roll, the trench, and connect to the home which we did. Overlay wireless, fixed wireless would be fantastic there. >> So you have the overlay and then when I know that you're coming by, right, because the fixed network is now actually a wifi network, I mean it has wires. So you have the mobile network, you have the wifi network, and you have people moving in and out of those environments. I think I'm seeing a lot of companies getting funded. People actually trying to say how do we monetize that experience? This is obviously was Foursquare and those other location guys started years ago. I mean, look at something like Wayce. Wayce went from a GPS app with social interaction to a car sharing, ride sharing going after Uber, this Google company. >> Well we had an NTD Delcomo VC, Chris McCoo, talk about mapping as a huge app for these telcos. >> Mapping is the killer app. Almost everything on your phone local works off a map which, by the way, is paid for by us as taxpayers. The GPS comes from the United States government. It's free. The most powerful utility in mobility is location, and GPS is free. >> All right, final question. Bumper sticker from Mobile World Congress from your perspective this year. Yawner, golf clap, or standing ovation? >> I say golf clap because more bandwidth is good and I think there's an insatiable demand. We're a long way from ending the bandwidth drought, and there is a bandwidth drought. I think the other thing is there aren't camps anymore. I think people will coalesce very quickly on 5G. So good time to be in that business. One hand clap maybe. >> Yeah, not a hole in one. Certainly more golf analogies coming on theCube. Alan Cohen here, Chief Commercial Officer, Illumio. We didn't get to security, but we'll do that next time. I'm John Furrier, I'll be right back with more Mobile World Congress coverage after this short break. (upbeat instrumental music)