>> Narrator: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> As you know by now, Jeff Bezos, CEO of Amazon, is stepping aside from his CEO role and AWS CEO, Andy Jassy, is being promoted to head all of Amazon. Bezos, of course, is going to remain executive chairman. Now, 15 years ago, next month, Amazon launched it's simple storage service, which was the first modern cloud offering. And the man who wrote the business plan for AWS, was Andy Jassy, and he's navigated the meteoric rise and disruption that has seen AWS grow into a $45 billion company that draws off the vast majority of Amazon's operating profits. No one in the media has covered Jassy more intimately and closely than John Furrier, the founder of SiliconANGLE. And John joins us today to help us understand on theCUBE this move and what we can expect from Jassy in his new role, and importantly what it means for AWS. John, thanks for taking the time to speak with us. >> Hey, great day. Great to see you as always, we've done a lot of interviews together over the years and we're on our 11th year with theCUBE and SiliconANGLE. But I got to be excited too, that we're simulcasters on Clubhouse, which is kind of cool. Love Clubhouse but not since the, in December. It's awesome. It's like Cube radio. It's like, so this is a Cube talk. So we opened up a Clubhouse room while we're filming this. We'll do more live hits in studio and syndicate the Clubhouse and then take questions after. This is a huge digital transformation moment. I'm part of the digital transformation club on Clubhouse which has almost 5,000 followers at the moment and also has like 500 members. So if you're not on Clubhouse, yet, if you have an iPhone go check it out and join the digital transformation club. Android users you'll have to wait until that app is done but it's really a great club. And Jeremiah Owyang is also doing a lot of stuff on digital transformation. >> Or you can just buy an iPhone and get in. >> Yeah, that's what people are doing. I can see all the influences are on there but to me, the digital transformation, it's always been kind of a cliche, the consumerization of IT, information technology. This has been the boring world of the enterprise over the past, 20 years ago. Enterprise right now is super hot because there's no distinction between enterprise and society. And that's clearly the, because of the rise of cloud computing and the rise of Amazon Web Services which was a side project at AWS, at Amazon that Andy Jassy did. And it wasn't really pleasant at the beginning. It was failed. It failed a lot and it wasn't as successful as people thought in the early days. And I have a lot of stories with Andy that he told me a lot of the inside baseball and we'll share that here today. But we started covering Amazon since the beginning. I was as an entrepreneur. I used it when it came out and a huge fan of them as a company because they just got a superior product and they have always had been but it was very misunderstood from the beginning. And now everyone's calling it the most important thing. And Andy now is becoming Andy Jassy, the most important executive in the world. >> So let's get it to the, I mean, look at, you said to me over holidays, you thought this might have something like this could happen. And you said, Jassy is probably in line to get this. So, tell us, what can you tell us about Jassy? Why is he qualified for this job? What do you think he brings to the table? >> Well, the thing that I know about Amazon everyone's been following the Amazon news is, Jeff Bezos has a lot of personal turmoil. They had his marriage fail. They had some issues with the smear campaigns and all this stuff going on, the run-ins with Donald Trump, he bought the Washington post. He's got a lot of other endeavors outside of Amazon cause he's the second richest man in the world competing with Elon Musk at Space X versus Blue Origin. So the guy's a billionaire. So Amazon is his baby and he's been running it as best he could. He's got an executive team committee they called the S team. He's been grooming people in the company and that's just been his mode. And the rise of AWS and the business performance that we've been documenting on SiliconANGLE and theCUBE, it's just been absolutely changing the game on Amazon as a company. So clearly Amazon Web Services become a driving force of the new Amazon that's emerging. And obviously they've got all their retail business and they got the gaming challenges and they got the studios and the other diversified stuff. So Jassy is just, he's just one of those guys. He's just been an Amazonian from day one. He came out of Harvard business school, drove across the country, very similar story to Jeff Bezos. He did that in 1997 and him and Jeff had been collaborating and Jeff tapped him to be his shadow, they call it, which is basically technical assistance and an heir apparent and groomed him. And then that's how it is. Jassy is not a climber as they call it in corporate America. He's not a person who is looking for a political gain. He's not a territory taker, but he's a micromanager. He loves details and he likes to create customer value. And that's his focus. So he's not a grandstander. In fact, he's been very low profile. Early days when we started meeting with him, he wouldn't meet with press regularly because they weren't writing the right stories. And everyone is, he didn't know he was misunderstood. So that's classic Amazon. >> So, he gave us the time, I think it was 2014 or 15 and he told us a story back then, John, you might want to share it as to how AWS got started. Why, what was the main spring Amazon's tech wasn't working that great? And Bezos said to Jassy, going to go figure out why and maybe explain how AWS was born. >> Yeah, we had, in fact, we were the first ones to get access to do his first public profile. If you go to the Google and search Andy Jassy, the trillion dollar baby, we had a post, we put out the story of AWS, Andy Jassy's trillion dollar baby. This was in early, this was January 2015, six years ago. And, we back then, we posited that this would be a trillion dollar total addressable market. Okay, people thought we were crazy but we wrote a story and he gave us a very intimate access. We did a full drill down on him and the person, the story of Amazon and that laid out essentially the beginning of the rise of AWS and Andy Jassy. So that's a good story to check out but really the key here is, is that he's always been relentless and competitive on creating value in what they call raising the bar outside Amazon. That's a term that they use. They also have another leadership principle called working backwards, which is like, go to the customer and work backwards from the customer in a very Steve Job's kind of way. And that's been kind of Jobs mentality as well at Apple that made them successful work backwards from the customer and make things easier. And that was Apple. Amazon, their philosophy was work backwards from the customer and Jassy specifically would say it many times and eliminate the undifferentiated heavy lifting. That was a key principle of what they were doing. So that was a key thesis of their entire business model. And that's the Amazonian way. Faster, cheaper, ship it faster, make it less expensive and higher value. While when you apply the Amazon shipping concept to cloud computing, it was completely disrupted. They were shipping code and services faster and that became their innovation strategy. More announcements every year, they out announced their competition by huge margin. They introduced new services faster and they're less expensive some say, but in the aggregate, they make more money but that's kind of a key thing. >> Well, when you, I was been listening to the TV today and there was a debate on whether or not, this support tends that they'll actually split the company into two. To me, I think it's just the opposite. I think it's less likely. I mean, if you think about Amazon getting into grocery or healthcare, eventually financial services or other industries and the IOT opportunity to me, what they do, John, is they bring in together the cloud, data and AI and they go attack these new industries. I would think Jassy of all people would want to keep this thing together now whether or not the government allows them to do that. But what are your thoughts? I mean, you've asked Andy this before in your personal interviews about splitting the company. What are your thoughts? >> Well, Jon Fortt at CNBC always asked the same question every year. It's almost like the standard question. I kind of laugh and I ask it now too because I liked Jon Fortt. I think he's an awesome dude. And I'll, it's just a tongue in cheek, Jassy. He won't answer the question. Amazon, Bezos and Jassy have one thing in common. They're really good at not answering questions. So if you ask the same question. They'll just say, nothing's ever, never say never, that's his classic answer to everything. Never say never. And he's always said that to you. (chuckles) Some say, he's, flip-flopped on things but he's really customer driven. For example, he said at one point, no one should ever build a data center. Okay, that was a principle. And then they come out and they have now a hybrid strategy. And I called them out on that and said, hey, what, are you flip-flopping? You said at some point, no one should have a data center. He's like, well, we looked at it differently and what we meant was is that, it should all be cloud native. Okay. So that's kind of revision, but he's cool with that. He says, hey, we'll revise based on what customers are doing. VMware working with Amazon that no one ever thought that would happen. Okay. So, VMware has some techies, Raghu, for instance, over there, super top notch. He worked with Jassy, directly in his team Sanjay Poonen when they went to business school together, they cut a deal. And now Amazon essentially saved VMware, in my opinion. And Pat Gelsinger drove that deal. Now, Pat Gelsinger, CEO, Intel, and Pat told me that directly in candid conversation off theCUBE, he said, hey, we have to make a decision either we're going to be in cloud or we're not going to be in cloud, we will partner. And I'll see, he was Intel. He understood the Intel inside mentality. So that's good for VMware. So Jassy does these kinds of deals. He's not afraid he's got a good stomach for business and a relentless competitor. >> So, how do you think as you mentioned Jassy is a micromanager. He gets deep into the technology. Anybody who's seen his two hour, three hour keynotes. No, he has a really fine grasp of the technology across the entire stack. How do you think John, he will approach things like antitrust, the big tech lash of the unionization of the workforce at Amazon? How do you think Jassy will approach that? >> Well, I think one of the things that emerges Jassy, first of all, he's a huge sports fan. And many people don't know that but he's also progressive person. He's very progressive politically. He's been on the record and off the record saying things like, obviously, literacy has been big on, he's been on basically unrepresented minorities, pushing for that, and certainly cloud computing in tech, women in tech, he's been a big proponent. He's been a big supporter of Teresa Carlson. Who's been rising star at Amazon. People don't know who Teresa Carlson is and they should check out her. She's become one of the biggest leaders inside Amazon she's turned around public sector from the beginning. She ran that business, she's a global star. He's been a great leader and he's been getting, forget he's a micromanager, he's on top of the details. I mean, the word is, and nothing gets approved without Andy, Andy seeing it. But he's been progressive. He's been an Amazon original as they call it internally. He's progressive, he's got the business acumen but he's perfect for this pragmatic conversation that needs to happen. And again, because he's so technically strong having a CEO that's that proficient is going to give Amazon an advantage when they have to go in and change how DC works, for instance, or how the government geopolitical landscape works, because Amazon is now a global company with regions all over the place. So, I think he's pragmatic, he's open to listening and changing. I think that's a huge quality >> Well, when you think of this, just to set the context here for those who may not know, I mean, Amazon started as I said back in 2006 in March with simple storage service that later that year they announced EC2 which is their compute platform. And that was the majority of their business, is still a very large portion of their business but Amazon, our estimates are that in 2020, Amazon did 45 billion, 45.4 billion in revenue. That's actually an Amazon reported number. And just to give you a context, Azure about 26 billion GCP, Google about 6 billion. So you're talking about an industry that Amazon created. That's now $78 billion and Amazon at 45 billion. John they're growing at 30% annually. So it's just a massive growth engine. And then another story Jassy told us, is they, he and Jeff and the team talked early on about whether or not they should just sort of do an experiment, do a little POC, dip their toe in and they decided to go for it. Let's go big or go home as Michael Dell has said to us many times, I mean, pretty astounding. >> Yeah. One of the things about Jassy that people should know about, I think there's some compelling relative to the newest ascension to the CEO of Amazon, is that he's not afraid to do new things. For instance, I'll give you an example. The Amazon Web Services re-invent their annual conference grew to being thousands and thousands of people. And they would have a traditional after party. They called a replay, they'd have a band like every tech conference and their conference became so big that essentially, it was like setting up a live concert. So they were spending millions of dollars to set up basically a one night concert and they'd bring in great, great artists. So he said, hey, what's been all this cash? Why don't we just have a festival? So they did a thing called Intersect. They got LA involved from creatives and they basically built a weekend festival in the back end of re-invent. This was when real life was, before COVID and they turned into an opportunity because that's the way they think. They like to look at the resources, hey, we're already all in on this, why don't we just keep it for the weekend and charge some tickets and have a good time. He's not afraid to take chances on the product side. He'll go in and take a chance on a new market. That comes from directly from Bezos. They try stuff. They don't mind failing but they put a tight leash on measurement. They work backwards from the customer and they are not afraid to take chances. So, that's going to board well for him as he tries to figure out how Amazon navigates the contention on the political side when they get challenged for their dominance. And I think he's going to have to apply that pragmatic experimentation to new business models. >> So John I want you to take on AWS. I mean, despite the large numbers, I talked about 30% growth, Azure is growing at over 50% a year, GCP at 83%. So despite the large numbers and big growth the growth rates are slowing. Everybody knows that, we've reported it extensively. So the incoming CEO of Amazon Web Services has a TAM expansion challenge. And at some point they've got to decide, okay, how do we keep this growth engine? So, do you have any thoughts as to who might be the next CEO and what are some of their challenges as you see it? >> Well, Amazon is a real product centric company. So it's going to be very interesting to see who they go with here. Obviously they've been grooming a lot of people. There's been some turnover. You had some really strong executives recently leave, Jeff Wilkes, who was the CEO of the retail business. He retired a couple of months ago, formerly announced I think recently, he was probably in line. You had Mike Clayville, is now the chief revenue officer of Stripe. He ran all commercial business, Teresa Carlson stepped up to his role as well as running public sector. Again, she got more power. You have Matt Garman who ran the EC2 business, Stanford grad, great guy, super strong on the product side. He's now running all commercial sales and marketing. And he's also on the, was on Bezos' S team, that's the executive kind of team. Peter DeSantis is also on that S team. He runs all infrastructure. He took over for James Hamilton, who was the genius behind all the data center work that they've done and all the chip design stuff that they've innovated on. So there's so much technical innovation going on. I think you still going to see a leadership probably come from, I would say Matt Garman, in my opinion is the lead dog at this point, he's the lead horse. You could have an outside person come in depending upon how, who might be available. And that would probably come from an Andy Jassy network because he's a real fierce competitor but he's also a loyalist and he likes trust. So if someone comes in from the outside, it's going to be someone maybe he trusts. And then the other wildcards are like Teresa Carlson. Like I said, she is a great woman in tech who's done amazing work. I've profiled her many times. We've interviewed her many times. She took that public sector business with Amazon and changed the game completely. Outside the Jedi contract, she was in competitive for, had the big Trump showdown with the Jedi, with the department of defense. Had the CIA cloud. Amazon set the standard on public sector and that's directly the result of Teresa Carlson. But she's in the field, she's not a product person, she's kind of running that group. So Amazon has that product field kind of structure. So we'll see how they handle that. But those are the top three I think are going to be in line. >> So the obvious question that people always ask and it is a big change like this is, okay, in this case, what is Jassy going to bring in? And what's going to change? Maybe the flip side question is somewhat more interesting. What's not going to change in your view? Jassy has been there since nearly the beginning. What are some of the fundamental tenets that he's, that are fossilized, that won't change, do you think? >> I think he's, I think what's not going to change is Amazon, is going to continue to grow and develop their platform business and enable more SaaS players. That's a little bit different than what Microsoft's doing. They're more SaaS oriented, Office 365 is becoming their biggest application in terms of revenue on Microsoft side. So Amazon is going to still have to compete and enable more ecosystem partners. I think what's not going to change is that Bezos is still going to be in charge because executive chairman is just a code word for "not an active CEO." So in the corporate governance world when you have an executive chairman, that's essentially the person still in charge. And so he'll be in charge, will still be the boss of Andy Jassy and Jassy will be running all of Amazon. So I think that's going to be a little bit the same, but Jassy is going to be more in charge. I think you'll see a team change over, whether you're going to see some new management come in, Andy's management team will expand, I think Amazon will stay the same, Amazon Web Services. >> So John, last night, I was just making some notes about notable transitions in the history of the tech business, Gerstner to Palmisano, Gates to Ballmer, and then Ballmer to Nadella. One that you were close to, David Packard to John Young and then John Young to Lew Platt at the old company. Ellison to Safra and Mark, Jobs to Cook. We talked about Larry Page to Sundar Pichai. So how do you see this? And you've talked to, I remember when you interviewed John Chambers, he said, there is no rite of passage, East coast mini-computer companies, Edson de Castro, Ken Olsen, An Wang. These were executives who wouldn't let go. So it's of interesting to juxtapose that with the modern day executive. How do you see this fitting in to some of those epic transitions that I just mentioned? >> I think a lot of people are surprised at Jeff Bezos', even stepping down. I think he's just been such the face of Amazon. I think some of the poll numbers that people are doing on Twitter, people don't think it's going to make a big difference because he's kind of been that, leader hand on the wheel, but it's been its own ship now, kind of. And so depending on who's at the helm, it will be different. I think the Amazon choice of Andy wasn't obvious. And I think a lot of people were asking the question who was Andy Jassy and that's why we're doing this. And we're going to be doing more features on the Andy Jassy. We got a tons, tons of content that we've we've had shipped, original content with them. We'll share more of those key soundbites and who he is. I think a lot of people scratching their head like, why Andy Jassy? It's not obvious to the outsiders who don't know cloud computing. If you're in the competing business, in the digital transformation side, everyone knows about Amazon Web Services. Has been the most successful company, in my opinion, since I could remember at many levels just the way they've completely dominated the business and how they change others to be dominant. So, I mean, they've made Microsoft change, it made Google change and even then he's a leader that accepts conversations. Other companies, their CEOs hide behind their PR wall and they don't talk to people. They won't come on Clubhouse. They won't talk to the press. They hide behind their PR and they feed them, the media. Jassy is not afraid to talk to reporters. He's not afraid to talk to people, but he doesn't like people who don't know what they're talking about. So he doesn't suffer fools. So, you got to have your shit together to talk to Jassy. That's really the way it is. And that's, and he'll give you mind share, like he'll answer any question except for the ones that are too tough for him to answer. Like, are you, is facial recognition bad or good? Are you going to spin out AWS? I mean these are the hard questions and he's got a great team. He's got Jay Carney, former Obama press secretary working for him. He's been a great leader. So I'm really bullish on, is a good choice. >> We're going to jump into the Clubhouse here and open it up shortly. John, the last question for you is competition. Amazon as a company and even Jassy specifically I always talk about how they don't really focus on the competition, they focus on the customer but we know that just observing these folks Bezos is very competitive individual. Jassy, I mean, you know him better than I, very competitive individual. So, and he's, Jassy has been known to call out Oracle. Of course it was in response to Larry Ellison's jabs at Amazon regarding database. But, but how do you see that? Do you see that changing at all? I mean, will Amazon get more publicly competitive or they stick to their knitting, you think? >> You know this is going to sound kind of a weird analogy. And I know there's a lot of hero worshiping on Elon Musk but Elon Musk and Andy Jassy have a lot of similarities in the sense of their brilliance. They got both a brilliant people, different kinds of backgrounds. Obviously, they're running different things. They both are builders, right? If you were listening to Elon Musk on Clubhouse the other night, what was really striking was not only the magic of how it was all orchestrated and what he did and how he interviewed Robin Hood. He basically is about building stuff. And he was asked questions like, what advice do you give startups? He's like, if you need advice you shouldn't be doing startups. That's the kind of mentality that Jassy has, which is, it's not easy. It's not for the faint of heart, but Elon Musk is a builder. Jassy builds, he likes to build stuff, right? And so you look at all the things that he's done with AWS, it's been about enabling people to be successful with the tools that they need, adding more services, creating things that are lower price point. If you're an entrepreneur and you're over the age of 30, you know about AWS because you know what, it's cheaper to start a business on Amazon Web Services than buying servers and everyone knows that. If you're under the age of 25, you might not know 50 grand to a hundred thousand just to start something. Today you get your credit card down, you're up and running and you can get Clubhouses up and running all day long. So the next Clubhouse will be on Amazon or a cloud technology. And that's because of Andy Jassy right? So this is a significant executive and he continue, will bring that mindset of building. So, I think the digital transformation, we're in the digital engine club, we're going to see a complete revolution of a new generation. And I think having a new leader like Andy Jassy will enable in my opinion next generation talent, whether that's media and technology convergence, media technology and art convergence and the fact that he digs music, he digs sports, he digs tech, he digs media, it's going to be very interesting to see, I think he's well-poised to be, and he's soft-spoken, he doesn't want the glamorous press. He doesn't want the puff pieces. He just wants to do what he does and he puts his game do the talking. >> Talking about advice at startups. Just a quick aside. I remember, John, you and I when we were interviewing Scott McNealy former CEO of Sun Microsystems. And you asked him advice for startups. He said, move out of California. It's kind of tongue in cheek. I heard this morning that there's a proposal to tax the multi-billionaires of 1% annually not just the one-time tax. And so Jeff Bezos of course, has a ranch in Texas, no tax there, but places all over. >> You see I don't know. >> But I don't see Amazon leaving Seattle anytime soon, nor Jassy. >> Jeremiah Owyang did a Clubhouse on California. And the basic sentiment is that, it's California is not going away. I mean, come on. People got to just get real. I think it's a fad. Yeah. This has benefits with remote working, no doubt, but people will stay here in California, the network affects beautiful. I think Silicon Valley is going to continue to be relevant. It's just going to syndicate differently. And I think other hubs like Seattle and around the world will be integrated through remote work and I think it's going to be much more of a democratizing effect, not a win lose. So that to me is a huge shift. And look at Amazon, look at Amazon and Microsoft. It's the cloud cities, so people call Seattle. You've got Google down here and they're making waves but still, all good stuff. >> Well John, thanks so much. Let's let's wrap and let's jump into the Clubhouse and hear from others. Thanks so much for coming on, back on theCUBE. And many times we, you and I've done this really. It was a pleasure having you. Thanks for your perspectives. And thank you for watching everybody, this is Dave Vellante for theCUBE. We'll see you next time. (soft ambient music)

>> Live from San Francisco. It's theCUBE. Covering DevNet Create 2017. Brought to you by Cisco. >> Welcome back everyone. We're here live in San Francisco for theCUBE's exclusive two days of coverage for Cisco Systems' inaugural event called DevNet Create extension. DevNet their classic developer program, for the Cisco install base of network routers. Now going to the cloud, native, going to the developer where dev-ops and the enterprise are connecting. I'm John Furrier, my cohost Peter Burris. Next is Dan Kohn, who is the Executive Director of the Cloud Native Compute Foundation, CNCF. Formerly known as Kubecon. Which is the event, Kubecon.io. Dan, great to see you. Executive Director, how's business, is going good? >> Fantastic! (John laughs) Yeah, six months ago we chatted at our last event in Seattle. And it's just amazing to see the progress since then. Projects members. >> It's been a whirlwind. Even I can't keep track. You guys are announcing all these new projects. What's the current count of projects that you guys have under the Cloud Native Compute Foundation? >> So we're up to 10. I should definitely start with the fact that Kubernetes is the anchor 10 in our original project. In a lot of ways, foundation was setup around that. And that project is just continuing to do incredibly well. Where it's one of the highest velocity projects in the history of open source. In terms of number of authors, number of commits, poll requests, issues. But now we have a constellation of other projects that are in support of that one. It can be used in a lot of different ways. >> John: Yeah. >> That we've been adding in. >> We had Craig McLuckie on earlier. Now he's with Heptio. Again, when he was doing that work, at Google, back in the days with what's his name from Microsoft now. >> Peter: Brendan Burns. >> Brendan Burns, yeah. >> Now it's an interesting question, where you say, oh, wait a minute, the three sort of key people behind Kubernetes, Craig McLuckie, Joe Beda, who's his co-founder at Heptio, then Brendan Burns, they all left Google. Is this a bad sign for the project and the technology? >> John: No, I don't think so. >> And we would say it's a spectacularly good sign. Now, if they had left and said, ah you know, containers, I'm going to do virtual machines. But in fact they said, there's such an enormous market for this. And to have Microsoft and Azure step in and say, we really want to invest in this space and we want to bring on one of the co-founders, Brendan. And for the other two co-founders, say, hey Google is making a huge investment. But we also think there's an opportunity for independent venture funded startup. >> Craig is completely passionate about this because there is an interoperability ethos that's always been around the open web. >> Dan: Umhmm. >> And certainly open source has the same ethos. Cloud Native brings an interesting thing, and it's clear now to people that there's not going to be one cloud winning them all. >> It's a multi-could world. >> Dan: Right. >> How is the Cloud Native Foundation floating in the open source world? Is it gravitating towards more infrastructure, more edge, software edge? Are you guys kind of in the middle? Are you guys the glue layer? How do you view that? >> Sure. So one way of looking at what we're doing is, helping to build a stack of software. That allows you to run your applications either on bare metal in your own data center or on any of the public clouds. Or hybrid solution where you're mixing back and forth. But the key idea is that all the core parts of that are open source. They're supported by multiple different vendors. And what that means is, you get to avoid lock-it. So today, Amazon web services has some of the most extraordinary engineering. They have all these great services that make it very easy to go onboard. But if you build your whole architecture around that, then you're stuck with AWS forever. And when time goes up, time to renegotiate your contract in a year or two, you're back again and don't have a lot of leverage. Where we think AWS is fantastic platform to run Kubernetes, to run our other projects on top of. But we don't think you want to lock-in to those services to such a degree. >> Okay, when I'm on, first of all, pretend I'm Amazon, I'm a competitive strategist, lock-in, I got to get you locked-in. I'm just going to run Kubernetes on Amazon. Why don't I just do that? >> We think that's a great solution. >> John: You do? >> Heptio and lots other folks make it very easy to run Kubernetes on Amazon. But we also think you should at least look at Kubernetes on Bluemix, on Google, on Azure. And know that in the future when you're negotiation comes up, even if you never leave, you at least threaten to leave. That you're not locked into that one vendor forever. >> So if you think about how the cloud industry structure is starting to layout, you knew we were going to have IAAS. >> Dan: Umhmm. >> SAS has been around for quite sometime. >> Dan: Right. >> The big question is what happens with that platform as a service. >> The developer world. >> Dan: Yeah. Some people think it's going to end up in the IAS element. >> Dan: Umhmm. Some people end up in the SAS. If it ends up in the IAS, you got the lock-in. Do you see a world going forward where developers have their own place, where they go and build and create software independent of either target but then add it to the various platforms. Is that a direction that you think this is all going to end up in? >> I do. Our view is that Heroku, which really invented this platform as a service concept or popularized it. You do, get push Heroku and magically your application's up. And then Cloud Foundry which came along and created a open source version of that. Those were two building blocks. But the Cloud Native essentially taking that scenario and saying, hey, that continuous integration, continuous deployment pipeline, that ability to deploy your software dozens of times per day, that's an absolute table ante for being a modern company. Not just a software company but arguably every company today needs to be doing software development like that. And then Cloud Native is a whole set of infrastructure around that to allow you to, not just have that environment in development but also to push it into production. >> So compare and contrast, based on your vision >> Dan: Umhmm. >> of how things are going to play out. A developer spends her time today doing this, and in three years, she's going to spend her time doing that. Kind of give us a sense of how >> Dan: Sure. >> you think it's going to play out. >> The simplest way to say it is that, Docker came along a few years ago, and was incredibly transformative technology for software development. It solved this really basic problem that, you hire a new employee and does it take her an entire day or entire week to get her environment together. Or can she just copy over the document container and be ready to go. And so I would argue it had the fastest uptake of any developer technology in history. But now when you have all those pieces running, okay, that's great in development, how do you get it in production? And my goal is that in a few years, hopefully much sooner, that those developers that are getting the container, they're getting the different pieces of microservices working. And then it's this tiny little YAML file that just says, here's the requirements for my application, here's what kind of redundancy it needs, what is backend databases, other sorts of things. And they're deploying it up. For most developers they can get out of that business of dev-ops. Of having to worry about all those issues. Your dev-ops team can be so much more efficient cuz Kubernetes and the related platform really enables that. >> I got to ask you, I just Tweeted cuz I had, make sure I captured it. I'm blown away by your success on the sponsorship participation. And usually it's a sign of opportunity. Because there's money making to be made, having the big vendors in there. But the growth of Kubernetes as you mentioned, all the success, we're well aware of that. But you got a lot going on. You're like got the tiger by the tail, your hair's blown back, you're running as hard as you can. Why are you guys successful? What is your gut? As executive director, you got to have the 20 mile stare but you also implement the here and now. >> Dan: Sure. >> How are you rationalizing the success? >> The most important point is, there's not some sort of magic formula, that CNCF has done or the Linux Foundation. And we're just so much better promoting or marketing it. At the end of the day, it really comes down to the developers behind Kubernetes. They've built a tool that tons and tons of people want to use. And that leverages 15 years of work that Google has done on containerization. Work that IBM and Docker and all of our other member companies, RedHat, have put together. And now, I think tiger by the tail is the right analogy. That we just happen to be, luckily, do have the technology and the constellation technology that a lot of folks want to do. The biggest thing we're trying to deal with is, some of the challenges around scaling. There's over 17 hundred authors. Individual developers contributed to Kubernetes in the last 12 months. Trying to figure out how can we get good reviews of all their codes, better documentation. >> There is a secret formula if you look at it. In away, relevance is one of them. >> Dan: Umhmm. >> Being relevant and being an awesome technology. But what I want get your thoughts in is, I looked at Kubernetes right out of the gate and said, hmm, will this be a MapReduced moment for Google. >> Dan: Yeah. >> And interesting enough, they didn't pull the same move. They didn't just let Cloud Air, walk away with or someone. >> Dan: Right, exactly. >> They made sure that if they preserved it. Google kind of let MapReduced >> Dan: Yeah, I think-- >> on the side of the road. >> Dan: No, no, I think this-- >> Cloud Air ran with it. >> Google had something that they replaced it with. I mean the -- >> SPAN is pretty damn good. >> And that's an interesting thing because in a world of strategy, across technology, and this is related to this, is that it used to be, you define a process, and then let's call it the end level process, and then you would go off and you make it obsolete because you had something that was more efficient, more effective. And then you license the old technology. And that way, the industry built capacity around the old technology and you had the new, more efficient technology that drove your business forward. And I think that, I'm not saying that's exactly, I'm not saying that Google did that, that's the tremendous >> Google knew. >> effect it will have. >> John: I have sources that tell me that. I investigated this story three years ago or maybe four, maybe three years ago. Google had conversations going up to the Eric Schmidt level, and Larry Page level, do we keep Kubernetes, do we open source it? And it went all the way to the top. And they almost wanted, they were afraid of MapReduced. Because MapReduced was a lost opportunity. Now they made it up but-- >> Now I would argue that there's a slightly subtler decision they had to make, where they have this internal system board, that is just tons of engineering and analysis and improvement has gone into it. They wrote Kubernetes as essentially next generation version of that. I think they kind of had four paths. Craig McLuckie was one of the key people behind that. Where they could have made it a proprietary service that if you're a customer of Google cloud, you get access to it. That's essentially what Amazon and Elastic Container Services today. Or they could have said, hey, we're going to open source it but we're still keep control of it. Essentially that's the path they went with the Go language. Where lots of people use it, lots of people contribute to it, but it's Google who decides at the end of the day, which direction it goes. Or they could have gone and created a Kubernetes Foundation. And if they'd gone to the Linux Foundation and said, we want to create a Kubernetes Foundation, they absolutely could have and that would have been a home for it. But when you look at all the complementary technologies that have come in, they would never have gone into a Kubernetes Foundation. So instead, they really chose the most open path of saying, no we want to have a Cloud Native Computing Foundation. Have Kubernetes be the anchor tenant for it. But then have a place that companies like Mesophere with Mesos and Docker with Docker Swarm and other partners can come in and agree on something. So today, we're really pleased to announce the container network interface, just got accepted as our 10th project. And that's used by those and also by Cloud Foundry. And then they can disagree on others, about the orchestration- >> So it's a liberating move, really, if you think about it. Because at the time this happened, there was a lot of land grab talk going on. >> Dan: Umhmm. >> Until Amazon was winning big the hockey stick was going up. >> Dan: Right. You saw the numbers, and financial performance. But there was a fear of lock-in. To your point. >> Dan: Right, exactly. >> Then Kubernetes provides a nice layer. And you guys as a group, are looking holistically and saying, choice and multi-cloud. Is that the vision? >> Definitely. But, I mean you can see, strategically why Google decided to do it. Because if you pick an open source platform, and say, hey, this is the best of breed approach. Now, you're actually willing to evaluate the cloud on what the prices are, the supplementary services, et cetera. Where before that, you might have just said, ah, AWS is the safe service, I'm going just go with that. >> But Kubernetes is an invasive technology. And I don't mean that in a bad way. (Dan laughs) >> When you decide to move with Kubernetes, you are foreclosing other options at your disposal. And so, I think what you're saying is that, Google wanted to ensure that it remained a consistent coherent thing. While at the same time, making it obvious to all those around them that also wanted to invest in it, that their investments were going to be safe and sound going forward. >> I think that's fair but on the other hand, I do want to say that very few companies have moved their entire business and all of their IT over to Kubernetes. >> Peter: Oh, I'm not saying that they would. >> We do recommend that they start with a stable service. >> Peter: But Meso and some of those other companies are now investing in Kubernetes as a platform. Or making a bet on Kubernetes, want to make sure that their bets are as good as their company is. >> Sure. But there are other orchestration plateforms still. So Kubernetes has plenty of competition. And our biggest competition of course is Enertia. Of folks not changing into anything. >> I got to ask you a question. So Leonard, our producer is just telling me, Kubernetes is boring per Craig McLuckie. So Craig said earlier in theCUBE today, Kubernetes needs to be boring. He said his biggest problem with Kubernetes is it's too exciting right now. >> Dan: That's great. Now what he means by that is, he's kind of making a play on words but his point is, it should be obstracted away. >> Dan: Yeah. In terms of Kubernetes. But that's a problem you have. It's too exciting. >> Dan: Umhmm. What's your reaction to his comment that Kubernetes needs to be boring. >> He and I did a little Google trends comparison of Kubernetes and TensorFlow, which is another open source project out of Google. TensorFlow is something like three or four acts. And artificial intelligence is just so much more interesting and exciting. And yeah, I certainly would love to see a situation. We have this metaphor for Linux, with the Linux Foundation. That we describe it as plumbing. Where it's so intrinsic to almost every piece of technology in existence. And like plumbing, you'll get very upset when if it stops working. And you'll know it and you'll complain. But there's a huge piece of what we're trying to do which is the infrastructure to make things work. >> Here's an idea. Marketing idea. Just call it AI for containers. >> Dan: That's good. >> It'll be the hottest thing on the planet. >> Dan, great to-- >> Peter: Probably be more be more exciting. >> Dan, great to see you. Congratulations on your success. >> Yeah. So I do want to just make a quick mention December sixth through eighth is CloudNativeCon and KubeCon. It's our biggest annual conference. We're looking to actually triple in size from Seattle to three thousand people or more. We have every expert coming in. Michelle Noorali and Kelsey Hightower are the co-chairs and are going to be speaking there. We would love to see a lot of you guys. >> John: In Austin. >> In Austin. >> We hope you'll be there. >> TheCUBE will be there. >> We'll definitely be there. >> Dan: As well to ah, >> We've been to the inaugural >> Dan: Exactly. >> show for KubeCon and Cloud Native conference. We'll defintely be there. December sixth through the eighth, in December, in Austin. Great time of the year to be in Texas. Congratulations on all your success. And as Kubernetes and nine other projects continue to get traction. Still exciting times. And as they say, we live in interesting times. (Dan laughs) This is theCUBE with more interesting, exciting, not boring stuff coming back from the inaugural event here at Cisco DevNet Create. I'm John Ferrier, Peter Burris. Stay with us.

>> Narrator: Live from Silicon Valley, it's theCUBE, covering Google Cloud, Next 17. >> Hey, welcome back everyone. We're here live in the Palo Alto Studios, SiliconANGLE Media, is theCUBE's new 4400 square foot studio, here in our studio, this is our sports center. I'm here with Stu Miniman, analyst at Wikibon on the team. I was at the event all day today, drove down to Palo Alto to give us the latest in-person updates, as well as, for the past two days, Stu has been at the Analyst Summit, which is Google's first analyst summit, Google Cloud. And Stu, we're going to break down day one in the books. Certainly, people starting to get onto there. After-meetups, parties, dinners, and festivities. 10,000 people came to the Google Annual Cloud Next Conference. A lot of customer conversations, not a lot of technology announcements, Stu. But we got another day tomorrow. >> John, first of all, congrats on the studio here. I mean, it's really exciting. I remember the first time I met you in Palo Alto, there was the corner in ColoSpace-- >> Cloud Air. >> A couple towards down for fries, at the (mumbles) And look at this space. Gorgeous studio. Excited to be here. Happy to do a couple videos. And I'll be in here all day tomorrow, helping to break down. >> Well, Stu, first allows us to, one, do a lot more coverage. Obviously, Google Next, you saw, was literally a blockbuster, as Diane Greene said. People were around the block, lines to get in, mass hysteria, chaos. They really couldn't scale the event, which is Google's scale, they nailed the scale software, but scaling event, no room for theCUBE. But we're pumping out videos. We did, what? 13 today. We'll do a lot more tomorrow, and get more now. So you're going to be coming in as well. But also, we had on-the-ground, cause we had phone call-ins from Akash Agarwal from SAP. We had an exclusive video with Sam Yen, who was breaking down the SAP strategic announcement with Google Cloud. And of course, we have a post going on siliconangle.com. A lot of videos up on youtube.com/siliconangle. Great commentary. And really the goal was to continue our coverage, at SiliconANGLE, theCUBE, Wikibon, in the Cloud. Obviously, we've been covering the Cloud since it's really been around. I've been covering Google since it was founded. So we have a lot history, a lot of inside baseball, certainly here in Palo Alto, where Larry Page lives in the neighborhood, friends at Google Earth. So the utmost respect for Google. But really, I mean, come on. The story, you can't put lipstick on a pig. Amazon is crushing them. And there's just no debate about that. And people trying to put that out there, wrote a post this morning, to actually try to illustrate that point. You really can't compare Google Cloud to AWS, because it's just two different animals, Stu. And my point was, "Okay, you want to compare them? "Let's compare them." And we're well briefed on the Cloud players, and you guys have the studies coming out of Wikibon. So there it is. And my post pretty much sums up the truth, which is, Google's really serious about the enterprise. Their making steps, there's some holes, there's some potential fatal flaws in how they allow customers to park their data. They have some architectural differences. But Stu, it's really a different animal. I mean, it's apples and oranges in the Cloud. I don't think it's worthy complaining, because certainly Amazon has the lead. But you have Microsoft, you have Google, you have Oracle, IBM, SAP, they're all kind of in the cluster of this, I call "NASCAR Formation", where they're all kind of jocking around, some go ahead. And it really is a race to get the table stake features done. And really, truly be serious contender for the enterprise. So you can be serious about the enterprise, and say, "Hey, I'm serious about the enterprise." But to be serious winner and leader, are two different ball games. >> And a lot to kind of break down here, John. Because first of all, some of the (mumbles) challenges, absolutely, they scaled that event really big. And kudos to them, 10,000 people, a lot of these things came together last minute. They treated the press and analysts really well. We got to sit up front. They had some good sessions. You just tweeted out, Diane Greene, in the analyst session, and in the Q&A after, absolutely nailed it. I mean, she is an icon in the industry. She's brilliant, really impressive. And she's been pulling together a great team of people that understand the enterprise. But who is Google going after, and how do they compete against so of the other guys, is really interesting to parse. Because some people were saying in the keynote, "We heard more about G Suite "than we heard about some of the Cloud features." Some of that is because they're going to do the announcements tomorrow. And you keep hearing all this G Suite stuff, and it makes me think of Microsoft, not Amazon. It makes me think of Office 365. And we've been hearing out of Amazon recently, they're trying to go after some of those business productivity applications. They're trying to go there where Microsoft is embedded. We know everybody wants to go after companies like IBM and Oracle, and their applications. Because Google has some applications, but really, their strength is been on the data. The machine the AI stuff was really interesting. Dr. Fei-Fei Li from Stanford, really good piece in the keynote there, when they hired her not that long ago. The community really perked up, and is really interesting. And everybody seems to think that this could be the secret weapon for Google. I actually asked them like, in some of the one-on-ones, "Is this the entry point? "Are most people coming for this piece, "when it's around these data challenges in the analytics, "and coming to Google." And they're like, "Well, it's part of it. "But no, we have broad play." Everything from devices through G Suite. And last year, when they did the show, it was all the Cloud. And this year, it's kind of the full enterprise suite, that they're pulling in. So there's some of that sorting out the messaging, and how do you pull all of these pieces together? As you know, when you've got a portfolio, it's like, "Oh well, I got to have a customer for G Suite." And then when the customer's up there talking about G Suite for a while, it's like, "Wait, it's--" >> Wait a minute. Is this a software? >> "What's going on?" >> Is this a sash show? Is this a workplace productivity show? Or is this a Cloud show? Again, this is what my issue is. First of all, the insight is very clear. When you start seeing G Suite, that means that they've got something else that they are either hiding or waiting to announce. But the key though, that is the head customers. That was one important thing. I pointed out in my blog post. To me, when I'm looking for it's competitive wins, and I want to parse out the G Suite, because it's easy just to lay that on, Microsoft does it with 365 of Office, Oracle does it with their stuff. And it does kind of make the numbers fuzzy a little bit. But ultimately, where's the beef on infrastructure as a service, and platform as a service? >> And John, good customers out there, Disney, Colgate, SAP as a partner, HSBC, eBay, Home Depot, which was a big announcement with Pivotal, last year, and Verizon were there. So these are companies, we all know them. Dan Greene was joking, "Disney is going to bring their magic onto our magic. "And make that work." So real enterprise use cases. They seem to have some good push-around developers. They just acquired Kaggle, which is working in some of that space. >> Apogee. >> Yeah, Apogee-- >> I think Apogee's an API company, come on. What does that relate to? It has nothing to do with the enterprise. It's an API management solution. Okay, yes. I guess it fits the stack for Cloud-Native, and for developers. I get that. But this show has to nail the enterprise, Stu. >> And John, you remember back four years ago, when we went to the re:Invent show for the first time, and it was like, they're talking to all the developers, and they haven't gotten to the enterprise. And then they over-pivoted to enterprise. And I listen to the customers that were talking and keynote today, and I said, "You know, they're talking digital transformation, "but it's not like GE and Nike getting up on stage, "being like, "'We're going to be a software company, "'and we're hiring lots--'" >> John: Moving our data center over. >> They were pulling all of over stuff, and it's like, "Oh yeah, Google's a good partner. "And we're using them--" >> But to be fair, Stu. Let's be fair, for a second. First of all, let's break down the keynotes. And then we'll get to some of the things about being fair. And I think, one, people should be fair to Diane Greene, because I think that the press and the coverage of it, looking at the media coverage, is weak. And I'll tell you why it's weak. Cause everyone has the same story as, "Oh, Google's finally serious about Cloud. "That's old news. "Diane Greene from day one says "we're serious with the Cloud." That's not the story. The story is, can they be a serious contender? That's number one. On the keynote, one, customer traction, I saw that, the slide up there. Yeah, the G Suite in there, but at least they're talking customers. Number two, the SAP news was strategic for Google. SAP now has Google Cloud platform, I mean, Google Cloud support for HANA, and also the SAP Cloud platform. And three, the Chief Data Science from AIG pointed. To me, those were the three highlights of the keynote. Each one, thematically, represents at least a positive direction for Google, big time, which is, one, customer adoption, the customer focus. Two, partnerships with SAP, and they had Disney up there. And then three, the real game changer, which is, can they change the AI machine learning, TensorFlow has a ton of traction. Intel Xeon chips now are optimized with TensorFlow. This is Google. >> TensorFlow, Kubernetes, it's really interesting. And it's interesting, John, I think if the media listened to Eric Schmidt at the end, he was talking straight to them. He's like, "Look, bullet one. "17 years ago, I told Google that "this is where we need to go. "Bullet two, 30 billion dollars "I'm investing in infrastructure. "And yes, it's real, "cause I had to sign off on all of this money. And we've been all saying for a while, "Is this another beta from Google. "Is it serious? "There's no ad revenue, what is this?" And Diane Greene, in the Q&A afterwards, somebody talked about, "Perpetual beta seems to be Google." And she's like, "Look, I want to differentiate. "We are not the consumer business. "The consumer business might kill something. "They might change something. "We're positioning, "this a Cloud that the enterprise can build on. "We will not deprecate something. "We'll support today. "We'll support the old version. "We will support you going forward." Big push for channel, go-to-market service and support, because they understand that that-- >> Yeah, but that's weak. >> For those of us that used Google for years, understand that-- >> There's no support. >> "Where do I call for Google?" Come on, no. >> Yeah, but they're very weak on that. And we broke that down with Tom Kemp earlier, from Centrify, where Google's play is very weak on the sales and marketing side. Yeah, I get the service piece. But go to Diane Greene for a second, she is an incredible, savvy enterprise executive. She knows Cloud. She moved from server to virtualization. And now she can move virtualization to Cloud. That is her playbook. And I think she's well suited to do that. And I think anyone who rushes to judgment on her keynote, given the fail of the teleprompter, I think is a little bit overstepping their bounds on that. I think it's fair to say that, she knows what she's doing. But she can only go as fast as they can go. And that is, you can't like hope that you're further along. The reality is, it takes time. Security and data are the key points. On your point you just mentioned, that's interesting. Because now the war goes on. Okay, Kubernetes, the microservices, some of the things going on in the applications side, as trends like Serverless come on, Stu, where you're looking at the containerization trend that's now gone to Kubernetes. This is the battleground. This is the ground that we've been at Dockercon, we've been at Linux, CNCF has got huge traction, the Cloud Native Compute Foundation. This is key. Now, that being said. The marketplace never panned out, Stu. And I wanted to get your analysis on this, cause you cover this. Few years ago, the world was like, "Oh, I want to be like Facebook." We've heard, "the Uber of this, and the Airbnb of that." Here's the thing. Name one company that is the Facebook of their company. It's not happening. There is no other Facebook, and there is no other Google. So run like Google, is just a good idea in principle, horizontally scalable, having all the software. But no one is like Google. No one is like Facebook, in the enterprise. So I think that Google's got to downclock their messaging. I won't say dumb down, maybe I'll just say, slow it down a little bit for the enterprise, because they care about different things. They care more about SLA than pricing. They care more about data sovereignty than the most epic architecture for data. What's your analysis? >> John, some really good points there. So there's a lot of technology, where like, "This is really cool." And Google is the biggest of it. Remember that software-defined networking we spent years talking about? Well, the first big company we heard about was Google, and they got up of stage, "We're the largest SDN deployer in the world on that." And it's like, "Great. "So if you're the enterprise, "don't deploy SDN, go to somebody else "that can deliver it for you. "If that's Google, that's great." Dockercon, the first year they had, 2014, Google got up there, talked about how they were using containers, and containers, and they spin up and spin down. Two billion containers in a week. Now, nobody else needs to spin up two billion containers a week, and do that down. But they learned from that. They build Kubernetes-- >> Well, I think that's a good leadership position. But it's leadership position to show that you got the mojo, which again, this is again, what I like about Google's strategy is, they're going to play the technology card. I think that's a good card to play. But there are some just table stakes they got to nail. One is the certifications, the security, the data. But also, the sales motions. Going into the enterprise takes time. And our advice to Diane Greene was, "Don't screw the gold Google culture. "Keep that technology leadership. "And buy somebody, "buy a company that's got a full blown sales force." >> But John, one of the critiques of Google has always been, everything they create, they create like for Google, and it's too Googley. I talked to a couple of friends, that know about AWS for a while, and when they're trying to do Google, they're like, "Boy, this is a lot tougher. "It's not as easy as what we're doing." Google says that they want to do a lot of simplicity. You touched on pricing, it's like, "Oh, we're going to make pricing "so much easier than what Amazon's doing." Amazon Reserved Instances is something that I hear a lot of negative feedback in the community on, and Google's like, "It's much simpler." But when I've talked to some people that have been using it, it's like, "Well, generally it should be cheaper, "and it should be easier. "But it's not as predictable. "And therefore, it's not speaking to what "the CFO needs to have. "I can't be getting a rebate sometime down the road. "Based on some advanced math, "I need to know what I'm going to be getting, "and how I'm going to be using it." >> And that's a good point, Stu. And this comes down to the consumability of the Cloud. I think what Amazon has done well, and this came out of many interviews today, but it was highlighted by Val Bercovici, who pointed out that, Amazon has made their service consumable by the enterprise. I think that's important. Google needs to start thinking about how enterprises want to consume Cloud, and hit those points. The other thing that Val and I teased at, was kind of some new ground, and he coined the term, or used the term, maybe he coined it, I'm not sure, empathy. Enterprise empathy. Google has developer empathy, they understand the developer community. They're rock solid on open source. Obviously, their mojo's phenomenal on technology, AI, et cetera, TensorFlow, all that stuff's great. Empathy for the enterprise, not there. And I think that's something that they're going to have to work on. And again, that's just evolution. You mentioned Amazon, our first event, developer, developer, developer. Me and Pat Gelsinger once called it the developer Cloud. Now they're truly the enterprise Cloud. It took three years for Amazon to do that. So you just can't jump to a trajectory. There's a huge amount of diseconomies of scale, Stu, to try and just be an enterprise player overnight, because, "We're Google." That's just not going to fly. And whether it's sales motions, pricing and support, security, this is hard. >> And sorting out that go-to-market, is going to take years. You see a lot of the big SIs are there. PwC, everywhere at the show. Accenture, big push at the show. We saw that a year or two ago, at the Amazon show. I talked to some friends in the channel, and they're like, "Yeah, Google's still got work to do. "They're not there." Look, Amazon has work to do on the go-to-market, and Google is still a couple-- >> I mean, Amazon's not spring chicken here. They're quietly, slowly, ramming up. But they're not in a good position with their sales force, needs to be where they want to be. Let's talk about technology now. So tomorrow we're expecting to see a bunch of stuff. And one area that I'm super excited about with Google, is if they can have their identity identified, and solidified with the mind of the enterprise, make their product consumable, change or adjust or buy a sales force, that could go out and actually sell to the enterprise, that's going to be key. But you're going to hear some cool trends that I like. And if you look at the TensorFlow, and the relationship, Intel, we're going to see Intel on stage tomorrow, coming out during one of the keynotes. And you're going to start to see the Xeon chip come out. And now you're starting to see now, the silicon piece. And this has been a data center nuisance, Stu. As we talked about with James Hamilton at Amazon, which having a hardware being optimized for software, really is the key. And what Intel's doing with Xeon, and we talked to some other people today about it, is that the Cloud is like an operating system, it's a global computer, if you want look at that. It's a mainframe, the software mainframe, as it's been called. You want a diversity of chipsets, from two cores Atom to 72 cores Xeon. And have them being used in certain cases, whether it's programmable silicon, or whether it's GPUs, having these things in use case scenarios, where the chips can accelerate the software evolution, to me is going to be the key, state of the art innovation. I think if Intel continues to get that right, companies like Google are going to crush it. Now, Amazon, they do their own. So this is going to another interesting dynamic. >> Yeah, it was actually one of the differentiating points Google's saying, is like, "Hey, you can get the Intel Skylake chip, "on Google Cloud, "probably six months before you're going to be able to "just call up your favorite OEM of choice, "and get that in there." And it's an interesting move. Because we've been covering for years, John, Google does a ton of servers. And they don't just do Intel, they've been heavily involved in the openPOWER movement, they're looking at alternatives, they're looking at low power, they're looking at from their device standpoint. They understand how to develop to all these pieces. They actually gave to the influencers, the press, the analysts, just like at Amazon, we all walked home with Echo Dot, everybody's walking home with the Google Homes. >> John: Did you get one? >> I did get one, disclaimer. Yeah, I got one. I'll be playing with it home. I figured I could have Alexa and Google talking to each other. >> Is it an evaluation unit? You have to give it back, or do you get to keep? >> No, I'm pretty sure they just let us keep that. >> John: Tainted. >> But what I'm interested to see, John, is we talk like Serverless, so I saw a ton of companies that were playing with Alexa at re:Invent, and they've been creating tons of skills. Lambda currently has the leadership out there. Google leverages Serverless in a lot of their architecture, it's what drives a lot of their analytics on the inside. Coming into the show, Google Cloud Functions is alpha. So we expect them to move that forward, but we will see with the announcements come tomorrow. But you would think if they're, try to stay that leadership though there, I actually got a statement from one of the guys that work on the Serverless, and Google believes that for functions, that whole Serverless, to really go where it needs to be, it needs to be open. Google isn't open sourcing anything this week, as far as I know. But they want to be able to move forward-- >> And they're doing great at open source. And I think one of the things, that not to rush to judgment on Google, and no one should, by the way. I mean, certainly, we put out our analysis, and we stick by that, because we know the enterprise pretty well, very well actually. So the thing that I like is that there are new use cases coming out. And we had someone who came on theCUBE here, Tarun Thakur, who's with Datos, datos.io. They're reimagining data backup and recovery in the Cloud. And when you factor in IoT, this is a paradigm shift. So I think we're going to see use cases, and this is a Google opportunity, where they can actually move the goal post a bit on the market, by enabling these no-use cases, whether it's something as, what might seem pedestrian, like backup and recovery, reimagining that is huge. That's going to take impact as the data domains of the world, and what not, that (mumbles). These new uses cases are going to evolve. And so I'm excited by that. But the key thing that came out of this, Stu, and this is where I want to get your reaction on is, Multicloud. Clearly the messaging in the industry, over the course of events that we've been covering, and highlighted today on Google Next is, Multicloud is the world we are living in. Now, you can argue that we're all in Amazon's world, but as we start developing, you're starting to see the emergence of Cloud services providers. Cloud services providers are going to have some tiering, certainly the big ones, and then you're going to have secondary partner like service providers. And Google putting G Suite in the mix, and Office 365 from Microsoft, and Oracle put in their apps in their Clouds stuff, highlights that the SaaS market is going to be very relevant. If that's the case, then why aren't we putting Salesforce in there, Adobe? They all got Clouds too. So if you believe that there's going to be specialism around Clouds, that opens up the notion that there'll be a series of Multicloud architectures. So, Stu-- >> Stu: Yeah so, I mean, John, first of all-- >> BS? Real? I mean what's going on? >> Cloud is this big broad term. From Wikibon's research standpoint, SaaS, today, is two-thirds of the public Cloud market. We spend a lot of time talking-- >> In revenue? >> In revenue. Revenue standpoint. So, absolutely, Salesforce, Oracle, Infor, Microsoft, all up there, big dollars. If we look at the much smaller part of the world, that infrastructures a service, that's where we're spending a lot of time-- >> And platforms a service, which Gartner kind of bundles in, that's how Gartner looks at it. >> It's interesting. This year, we're saying PaaS as a category goes away. It's either SaaS plus, I'm sorry, it's SaaS minus, or infrastructure plus. So look at what Salesforce did with Heroku. Look at what company service now are doing. Yes, there are solutions-- >> Why is PaaS going away? What's the thesis? What's the premise of that for Wikibon research? >> If we look at what PaaS, the idea was it tied to languages, things like portability. There are other tools and solutions that are going to be able to help there. Look at, Docker came out of a PaaS company, DockCloud. There's a really good article from one of the Docker guys talking about the history of this, and you and I are going to be at Dockercon. John, from what I hear, we're going to spending a lot of time talking about Kubernetes, at Dockercon. OpenStack Summit is going to be talking a lot about-- >> By the way, Kubernetes originated at Google. Another cool thing from Google. >> All right, so the PaaS as a market, even if you talk to the Cloud Foundry people, the OpenShift people. The term we got, had a year ago was PaaS is Passe, the nice piffy line. So it really feeds into, because, just some of these categorizations are what we, as industry watchers have a put in there, when you talk to Google, it's like, "Well, why are they talking about G Suite, "and Google Cloud, and even some of their pieces?" They're like, "Well, this is our bundle "that we put together." When you talk to Microsoft, and talk about Cloud, it's like, "Oh, well." They're including Skype in that. They're including Office 365. I'm like, "Well, that's our productivity. "That's a part of our overall solutions." Amazon, even when you talk to Amazon, it's not like that there are two separate companies. There's not AWS and Amazon, it's one company-- >> Are we living in a world of alternative facts, Stu? I mean, Larry Ellison coined the term "Fake Cloud", talking about Salesforce. I'm not going to say Google's a fake Cloud, cause certainly it's not. But when you start blending in these numbers, it's kind of shifting the narrative to having alternative facts, certainly skewing the revenue numbers. To your point, if PaaS goes away because the SaaS minuses that lower down the stack. Cause if you have microservices and orchestration, it kind of thins that out. So one, is that the case? And then I saw your tweet with Sam Ramji, he formally ran Cloud Foundry, he's now at Google, knows his stuff, ex-Microsoft guy, very strong dude. What's he take? What's his take on this? Did you get a chance to chat with Sam at all? >> Yeah, I mean, it was interesting, because Sam, right, coming from Cloud Foundry said, what Cloud Foundry was one of the things they were trying to do, was to really standardize across the clouds. And of course, little bias that he works at Google now. But he's like, "We couldn't do that with Google, "cause Google had really cool features. And of course, when you put an abstraction layer on, can I actually do all the stuff? And he's like, "We couldn't do that." Sure, if you talked to Amazon, they'll be like, "Come on. "Thousand features we announced last year, "look at all the things we have. "It's not like you can just take all of our pieces, "and use it there." Yes, at the VM, or container, or application microservices layer, we can sit on a lot of different Clouds, public or private. But as we said today, the Cloud is not a utility. John, you've been in this discussion for years. So we've talked about, "Oh, I'm just going "to have a Cloud broker, "and go out in a service." It's like, this is not, I'm not buying from Domino's and Pizza Hut, and it's pepperoni pizza's a pepperoni pizza. >> Well, Multicloud, and moving workloads across Clouds, is a different challenge. Certainly, I might have to some stuff here, maybe put some data and edge my bets on leveraging other services. But this brings up the total cost of ownership problem. If you look at the trajectory, say OpenStack, just as a random example. OpenStack, at one point, had a great promise. Now it's kind of niched down into infrastructural service. I know you're going to be covering that summit in Boston. And it's going to be interesting to see how that is. But the word in the community is, that OpenStack is struggling because of the employment challenges involved with it. So to me, Google has an opportunity to avoid that OpenStack kind of concept. Because, talking about Sam Ramji, open source is the wildcard in all of this. So if you look at a open source, and you believe that that PaaS layer's thinning down, to infrastructure and SaaS, then you got to look at the open source community, and that's going to be a key area, that we're certainly watching, and we've identified, and we've mentioned it before. But here's my point. If you look at the total cost of ownership. If I'm a customer, Stu, I'm like, "Okay, if I'm just going to move to the Cloud, "I need to rely and lean on my partner, "my vendor, my supplier, "Amazon, or Google, or Microsoft, whoever, "to provide really excellent manageability. "Really excellent security. "Because if I don't, I have to build it myself." So it's becoming the shark fin, the tip of the iceberg, that you don't see the hidden cost, because I would much rather have more confidence in manageability that I can control. But I don't want to have to spend resources building manageability software, if the stuff doesn't work. So there's the issue about Multicloud that I'm watching. Your thoughts? Or is that too nuance? >> No, no. First of all, one of the things is that if I look at what I was doing on premises, before versus public Cloud, yes, there are some hidden costs, but in general I think we understand them a little bit better in public Cloud. And public Cloud gives us a chance to do a do-over for this like security, which most of us understand that security is good in public Cloud. Now, security overall, lots of work to do, challenges, not security isn't the same across all of them. We've talked to plenty of companies that are helping to give security across Clouds. But this Multicloud discussion is still something that is sorting out. Portability is not simple, but it's where we're going. Today, most companies, if I'm not really small, have some on-prem pieces. And they're leveraging at least one Cloud. They're usually using many SaaS providers. And there's this whole giant ecosystem, John, around the Cloud management platforms. Because managing across lots of environment, is definitely a challenge. There's so many companies that are trying to solve them. And there's just dozens and dozens of these companies, attacking everything from licensing, to the data management, to everything else. So there's a lot of challenges there, especially the larger you get as a company, the more things you need to worry about. >> So Stu, just to wrap up our segment. Great day. Wanted to just get some color on the day. And highlighting some parody from the web is always great. Just got a tweet from fake Andy Jassy, which we know really isn't Andy Jassy. But Cloud Opinion was very active to the hashtag, that Twitter handle Cloud Opinion. But he had a medium post, and he said, "Eric Schmidt was boring. "Diane Greene was horrible. "Unfortunately, day one keynote were missed opportunity, "that left several gaps, "failed to portray Google's vision for Google Cloud. "They could've done the following, A, "explain the vision for the Cloud, "where do they see Google Cloud going. "Identify customer use cases that show samples "and customer adoption." They kind of did that. So discount that. My favorite line is this one, "Differentiate from other Cloud providers. "'We're Google damn it,' isn't working so well. "Neither is indirect shots as S3 downtime, "didn't work either as well as either. "Where is the customer's journey going? "And what's the most compelling thing for customers?" This phrase, "We're Google damn it," has kind of speaks to the arrogance of Google. And we've seen this before, and always say, Google doesn't have a bad arrogance. I like the Google mojo. I think the technology, they run hard. But they can sometimes, like, "Customer support, self-service." You can't really get someone on the phone. It's hard to replies from Google. >> "Check out YouTube video. "We own that too, don't you know that?" >> So this is a perception of Google. This could fly in the face, and that arrogance might blow up in the enterprise, cause the enterprises aren't that sophisticated to kind of recognize the mojo from Google. And they, "Hey, I want support. "I want SLAs. "I want security. "I want data flexibility." What's your thoughts? >> So Cloud Opinion wrote, I thought a really thoughtful piece leading up to it, that I didn't think was satire. Some of what he's putting in there, is definitely satire-- >> John: Some of it's kind of true though. >> From the keynote. So I did not get a sense in the meetings I've been in, or watching the keynote, that they were arrogant. They're growing. They're learning. They're working with the community. They're reaching out. They're doing all the things we think they need to do. They're listening really well. So, yes, I think the keynote was a missed opportunity overall. >> John: But we've got to give, point out that was a teleprompter fail. >> That was a piece of it. But even, we felt with a little bit of polish, some of the interactions would've been a little bit smoother. I thought Eric Schmidt's piece was really good at end. As I said before, the AI discussion was enlightening, and really solid. So I don't give it a glowing rating, but I'm not ready to trash it. And tomorrow is when they're going to have the announcements. And overall, there's good buzz going at the show. There's lots going on. >> Give 'em a letter. Letter grade. >> For the keynote? Or the show in general? >> So far, your experience as an analyst, cause you had the, again, to give them credit, I agree with you. First analyst conference. They are listening. And the slideshow, you see what they're doing. They're being humble. They didn't take any real direct shots at its competitors. They were really humble. >> And that is something that I think they could've helped to focus one something that differentiated a little bit. Something we had to pry out of them in some of the one-on-ones, is like, "Come on, what are you doing?" And they're like, "We're winning 50, 60% of our competitive deals." And I'm like, "Explain to us why. "Because we're not hearing it. "You're not articulating it as well." It's not like we expect them, it's like, "Oh wait, they told us we're arrogant. "Maybe we should be super humble now." It's kind of-- >> I don't think they're thinking that way. I think my impression of Google, knowing the companies history, and the people involved there, and Diane Greene in particular, as you know from the Vmware days. She's kind of humble, but she's not. She's tough. And she's good. And she's smart. >> And she's bringing in really good people. And by the way, John, I want to give them kudos, really supported International Women's Day, I love the, Fei-Fei got up, and she talked about her, one of her compatriots, another badass woman up there, that got like one of the big moments of the keynote there. >> John: Did they have a woman in tech panel? >> Not at this event. Because Diane was there, Fei-Fei was there. They had some women just participating in it. I know they had some other events going on throughout the show. >> I agree, and I think it's awesome. I think one of the things that I like about Google, and again, I'll reiterate, is that apples and oranges relative to the other Cloud guys. But remember, just because Amazon's lead is so far ahead, that you still have this jocking of position between the other players. And they're all taking the same pattern. Again, this is the same thing we talked about at our other analysis, is that, certainly at re:Invent, we talked about the same thing. Microsoft, Oracle, IBM, and now Google, are differentiating with their apps. And I think that's smart. I don't think that's a bad move at all. It does telegraph a little bit, that maybe they got, they could add more to show, we'll see tomorrow. But I don't think that's a bad thing. Again, it does make the numbers a little messy, in terms of what's what. But I think it's totally cool for a company to differentiate on their offering. >> Yeah, definitely. And John, as you said, Google is playing their game. They're not trying to play Amazon's game. They're not, Oracle's thing was what? You kind of get a little bit of the lead, and kind of just make sure how you attack and stay ahead of what they're doing, going to the boating analogy there. But Google knows where they're going, moving themselves forward. That they've made some really good progress. The amount of people, the amount of news they have. Are they moving fast enough to really try to close a little bit on the Amazon's world, is something I want to come out of the show with. Where are customers going? >> And it's a turbulent time too. As Peter Burris, our own Peter Buriss at Wikibon, would say, is a turbulent time. And it's going to really put everyone on notice. There's a lot to cover, if you're an analyst. I mean, you have compute, network storage, services. I mean, there's a slew of stuff that's being rolled out, either in table stakes for existing enterprises, plus new stuff. I mean, I didn't hear a lot of IoT today. Did you hear much IoT? Is there IoT coming to you at the briefing? >> Come on. I'm sure there's some service coming out from Google, that'll help us be able to process all this stuff much faster. They'll just replace this with-- >> So you're in the analyst meeting. I know you're under NDA, but is there IoT coming tomorrow? >> IoT was a term that I heard this week, yes. >> So all right, that's a good confirmation. Stu cannot confirm or deny that IoT will be there tomorrow. Okay, well, that's going to end day one of coverage, here in our studio. As you know, we got a new studio. We have folks on the ground. You're going to start to see a new CUBE formula, where we have in-studio coverage, and out in the field, like our normal CUBE, our "game day", as we say. Getting all the signal, extracting it from that noise out there, for you. Again, in-studio allows us to get more content. We bring our friends in. We want to get the content. We're going to get the summaries, and share that with you. I'm John Furrier, Stu Miniman, day one coverage. We'll see you tomorrow for another full day of special coverage, sponsored by Intel, two days of coverage. I want to thank Intel for supporting our editorial mission. We love the enterprise, we love Cloud, we love big data, love Smart Cities, autonomous vehicles, and the changing landscape in tech. We'll be back tomorrow, thanks for watching.

(upbeat music) >> Narrator: Live, from Silicon Valley. It's the Cube. Covering Google Cloud X17. >> Okay welcome back, everyone. We are live in Palo Alto for two days of coverage of Google Next 2017. I'm John Furrier, we're here with Tom Kemp, CEO of Centrify. No longer a startup, they're scaling up. You guys do it very well. Tom, great to see you. Welcome to the Cube. >> Great to be here. >> Saw you at RSA, you guys had an exceptional event. One Presence to show, obviously a security show, you're in the security business. But also mobile world congress will try to get you on again security's hot, front and center at mobile world congress. >> Yeah. >> Security is front and center at Google Cloud Next. Security is front and center at blank event. It's happening everywhere right? So give us the update. What is Centrify, obviously the "No Breach" is your tagline. What's up with Centrify? Give us a quick update on what you're up to. >> Yeah, absolutely. So we're a security company focused, as you said, on identity. And we really address the issue of too many passwords and too much privilege. The fundamental issue that's happening within security, is like 75 billion dollars is being spent on it, it's one of the fastest growing market segments, but it's failing because the breaches are far outnumbering, and growing at a faster rate, than the amount of money being spent on that. And so, we're trying to rethink security by looking at where are the breaches are coming from, and they're coming in from, like in the case of Podesta, stealing usernames and passwords. And Verizon said two thirds of breaches involve stolen credentials. And Forrester just recently said that 80 percent of breaches involve the compromise of privileged accounts, the rude accounts for the infrastructure etc. So if two thirds, to 80 percent of breaches involve identity, we fundamentally believe you need to focus a lot more on that, and that's what we're all about. Focusing on identity. >> And what is this? Is this a new revelation, or is this something that you guys have felt was happening for a long time, or has it just been the matter of fact, that's what's happening? >> You know it's, we have some great investors, and we have Excel, Mayfield, Index, Sigma now called Jex, and Square Adventures. And one of the board members told me, the markets come to you, because we've been doing this for over 10 years. And focusing on identity, and people are like, "Oh okay, that's interesting." But now, if you look at just the massive number of breaches that are occurring, and the focus that identity is the leading attack vector, and then you couple it with the whole move to the Cloud, I know we're going to be talking about what Google is doing in the Cloud, etc. It actually makes the problem even worse. And so we feel that we've been plugging along, doing and focusing on identity, and now kind of the market has come to us, because of the move to Cloud, and the hackers are going after identities. >> Yeah it's interesting, I saw a Facebook friend, I won't say his name for privacy, because I don't have the right to talk about it, he's in bitcoin, so obviously that world is an underbelly in itself. Yeah but, interesting thing is that he had two factor authentication on his phone, and someone hacked his phone and they sent the password back to his phone, all his bank accounts are gone. >> Oh my goodness. >> So this is an example of that privileged identity. So that even two factor authentication, in that case, didn't work. So you starting to see this, right? So what's the answer, and how does it relate to cloud? There's no perimeter in the cloud. Is it federated identity, is it some blocked chain thing, is there new model? What's your view on this, and how you guys attacking it specifically? >> Yeah, I mean in a world in which we're increasingly moving to the cloud, what can you secure? Like if I'm at a Starbucks in Palo Alto, on my Ipad, talking to Google apps, talking to sales force etc., I don't have any Anti Virus, I'm not using any next gen firewall, or VPN etc. So the focus needs to shift to securing the user. And you really need to start integrating, and leveraging, from a multi factor authentication biometrics as well. Use that phone, use the touch ID, to actually ensure that. And then also, in the cloud, start analyzing user behavior. And actually determine, well wait a minute, this person normally doesn't login from China, but now he's accessing the sales force, or Facebook etc. So, it's becoming, evolving more to utilizing mobile device as part of your identity, and it's also leveraging machine learning to understand what normal behavior is, and blocking abnormal behavior. >> And also using big data techniques, because your point about China is interesting. Anyone who travels might have had this situation, we go to Vegas a lot for the Cube, but like I'm in Vegas then I pull out an ATM withdrawal, next I go to use my other credit card, and it says "woah fraud alert." >> Tom: Yes. >> Well, wait a minute, I made a cell phone call, I took money out of the bank, and yet the credit card didn't know that I'm in Vegas. Now that's interesting, so conversely, China's accessing my accounts, and I'm making phone calls in Palo Alto, that should be obvious. >> Yeah. >> That just seems like it's just so disfragmented data sets. >> So historically, the definition of identity was a username, and a password. But, in a Cloud world, identity should be redefined in terms of your applications, your device, your location, and your activity. So, if you are trying to access an app from China, it should ask you for four or five additional bits of information, instead of two factors, it should be multi-factor, and it should include biometrics as well. So, machine learning is this going to become even more critical to reduce fraud, and the compromise of credentials. >> So, let's talk about google next. Because one of the things that, I mean really we know Google, we're living in Palo Alto, they're all around us, they're in Mountain View, Larry Page lives in the hood here. Google has always been a technology innovator, and it's clear that that's the lead for their Cloud. But the enterprise, which they're by the way serious, Dian Green is very serious with enterprise, they're just starting to move down that road. You've been there for awhile, on mobile, and in the enterprise, what is some of the things that people should know about on how hard it is in the enterprise? Specifically with Cloud, what is some of the things that you see as table stakes? >> Yeah, it's actually having meat eating sales reps out in the field. Not relying on some person who's-- >> John: Some bot. >> Yeah some bot, or some 20 year old calling from Austin, or Mountain View, but it's actually having someone there, with a technical architect, that can hop on a subway, or be there within a half hour to spend some quality time. >> John: And strategic selling too right? >> Exactly. Because they have a challenge, which is they're competing with both Microsoft and Amazon. And obviously Microsoft has the enterprise people, and Amazon is really ramping up in that area. And I think that, so you can throw the technology, but enterprise accounts want to be able to have a conversation face to face, more so than executive coming out and having a dinner with someone. >> Take me through a sales motion, because this is important. You and I have talked about this in the past, and Dave Loth and I always talk about it on the Cube. And it used to be well known in the VC circles, that sales forces are expensive because the sales motions are different. What is the typical sales motion for an enterprise like Sell. Because it's not as simple as saying, "self service, Cloud, put your credit card down," and get you know, Cooper and Eddy's support, terminal access, static IP's, virtual servers, oh by the way I got a support DB2 as well. A non Oracle database, or Oracle. >> Well, look I mean, it's very easy to have that bite over the web for when you start a developer for a new application. And Amazon's done a great job at that, Microsoft's getting there as well. So if you really want the existing applications to move to the Cloud, you have to sit down and have conversations about a hybrid Cloud environment. Because people will have on premise active directory, they'll have a set of security policies, etc. And so the conversation needs to be had, is like how do you bridge on premise, with the Cloud as well, and make that heterogeneous environment look and feel and smell like it's homogeneous from authentication, authorization, audit perspective, compliance perspective, etc. So you certainly need to first and foremost be able to put architects out there, have that conversation, etc. And you just can't rely too much on partners. And I think from there service level agreements, and then also showing that your Cloud platform is incredibly secure as well. >> Yeah I would agree, I would just say one, on the meat eating sales rep, basically what that means people understand the domain, with an architect technically that's going to SC, and then you have to really kind of have an understanding that there's a multiple stakeholder role. One's a recommender, one's an influencer, one's a decision maker, and it is a campaign. It's a multi pronged campaign. >> Yeah you have to think-- >> John: Know their problems, give them a solution, value creation. >> Absolutely. >> John: Value selling. >> Because there's just a level of complexity. And again I'm not saying that Google for new projects, with the current sales motion, can't bring on an app, and maybe that app leverages their machine learning, which seems to be world class right? >> TensorFlow's getting great traction, Intel's building chips for that as well. >> Yeah. >> Google owns a great developer mind share, and I think they've really cracked the code on open source, and they have great empathy with the developers, we were talking about with Val earlier. But with operationally I just see a disconnect. And Amazon's quietly ramping up too, they're no spring chicken either when it comes to direct selling, but they're been working more years on that. >> And I think you seen the word Hybrid Cloud, and I know you spent time with the folks at Vmware, talking about the relationship with Ama... That's all about the Hybrid Cloud, which people need, the enterprises need a bridge and on ramp. And I think, from our perspective- >> Vmware is very solid with Gelsinger and their sales force. They're very, >> Yeah absolutely. >> Very strong with enterprise selling. >> And that's what we focus, cause we initially started on premise, we tied things in to active directory for example, but now we have a Cloud platform, and we advertise and promote ourselves as addressing identity for the Hybrid environment, and providing the bridge between the two, and I think that's critical. >> Now do you guys have an enterprise sales force, right? >> Absolutely. >> So you've invested in that, over ten years? >> Oh yeah, absolutely. So we have over 60 percent of the Fortune 50, and 80 percent of our sales comes from the Global 2000. We've grown, we're over 100 million in sales, so we're in there having that conversation with enterprises all the time. >> So Tom, so we know Diane Green lives in the neighborhood, so let's pretend she calls us up, "Hey Tom, John, come over. "We'll have a cocktail, and dinner. "I need your advice on how to ramp up my enterprise, "operational empathy, and strategy." What would we advise her? What would you advise her, I have my own opinion. But go to you first. >> I really think and focus on, obviously use the machine learning as a key wedge for new applications, but really focus on the concept of Hybrid. And she mastered going from physical to virtual. Now, everyone's virtualized, and so she needs to figure out how I can get virtual to Cloud, V to C, right? And have the people, and have the conversation, and provide bridging technologies as well. So I think that is going to require, not just purely Cloud based stuff, but it's going to probably provide, she's going to need, either through partnerships, or developer stuff. >> Or M and A. >> Or M and A, she's going to have to build connectors, to help facilitate the bridging, because she can go after definitely the 20 percent of the new stuff, but if you want to attack the 80 percent of the existing stuff, and she did a masterful job of going physical to virtual-- >> At VMware. >> At VMware, and now her challenge is to go V to C. Virtual to Cloud. >> So my advice, Diane if you're watching, is the following: One, don't screw up the Google formula. And I know she's transforming Google, and that's a good thing, they need that right now. But I think, what I like about what I'm seeing at Google Next right now is that they have great technology chops. In kind of the Google, pat themselves on the back kind of way, which is they got mojo, they've always had great technology mojo, and that comes down from the founder. So the machine learning stuff, the AI, the stuff that they're doing in their portfolio has, I call the coolness-relevant factor on the tech. What I would do, is I would specifically nurture that, cause she's also a good knack for doing innovative things, and she's very innovative manager, and I've seen that at Vmware, and other places that she's been advising. So she's got a knack for, "Ahh that's cool, look we should do that cool technology "that's going to have legs in the future." So she's got a good sage picking out the technology. I would do an M and A. I would just stop expanding the existing Google culture relative to that sales motions and the enterpriseness, and just go buy somebody. Spend the billion dollars, or more, take someone out whose got full global, regional sales force, why not? Because then those guys already have the relationships, so the buy, build, to the sales force might take too long. I'm not sure that they could get there. I mean, what do you think about that? >> Yeah I think it's, I think they've been public about it. I think they have to invest in their own, but I do think that M and A, I mean they're number three, and they got to do something. Clearly the machine learning AI stuff is going to be huge. We're actually very impressed, I got emails from the folks at the show, about this whole video stuff, in terms of their ability to use the machine learning, and AI to interpret video, which is pretty impressive. But again that's going to be more for a vertical. Or a specific type of application. And so I think they're going to need to do a combination . >> Here's the thing that I'm seeing though. There's a speed of Google, and there's a speed of enterprise. They might have to throttle down, I don't want to say dumb down, that's particularly not the issue, it's more of throttle down the cadence of what enterprises are comfortable with. For example, SLA's, their SLA's are a little bit gray area, but they're awesome on, "hey it only costs X dollars, "import this great data and crunch all this stuff." So they've got great pricing. >> They need to master, Diane did a masterful job of like, overnight she had a utility that could go P to V, and you flipped it up, and everything just magically worked. And they need to prove that they can forklift the applications, with minimal to no changes, and things magically work. And that requires a bunch of software partnership technology, that it's like flipping a switch to go the Cloud. And if you don't like it, then you can roll it back as well. >> What's their security in position in your mind? You've done an audit, you been keeping track of it, or they're secure. Or what's the needs of the enterprise that they should be addressing for security? Well you guys have a relationship with any other booth at the event. >> Yeah absolutely, and we integrate at multiple levels as well. I think they're doing a pretty good job, I think that other vendors like Microsoft are really more heavily investing in areas that we're in, such as identity, so Microsoft has basically replicated the playbook with active directory, and they have something called Azure AD, and so Google doesn't have anything that's equivalent. That's good for us, that actually leads to opportunities, but they could do more in the areas of identity. I think if you look at what Amazon's doing in terms of web application firewalls, and protecting applications that are being spun up in the cloud. I think those are areas that can be improved. Encryption, key management, etc. So if you look at the slide that they have where they say insecurity, I think they list three items, but then if you were to compare it to say Microsoft, or Amazon, they've got five, six, seven items right there as well. I think that there's definitely going to be needs and requirements that need to be met and addressed there. So it's good, for us. >> Well to me it's just a matter of their evolution, they can only go as fast as they can go. That's what the people that I tend to talk to don't get. They can be critical of Google, but at the end of the day they can only go so fast. >> Yeah, and also another bit of advice, is they do have a very good install base with Gsuite, formerly Google apps, but they got to do a better job of leveraging that when people try to move to infrastructure as a server-- >> I think they're taken that advice because it was clear that they're at this event, was they're showcasing a lot of the stats on Gsuite, they're also talking about the apps. And that's consistent with IBM, Oracle, and Microsoft. They're throwing in their Sass layers as part of the stack as well. That's how they can differentiate from Google. What else do they have right? >> Really it's almost like a startup company that's been around for a few years. They have their initial product, and they come out with their second product and the board members will say, "Well what's the adoption of cross selling "the new product with the existing?" And so it should be interesting to see if they can get people that bought in to the Gsuite vision, to say, "Oh okay, now I'm going to start firing up servers "on the Google Cloud platform." >> Well you bring up a good point about their Gsuite, and I mentioned Microsoft using Office 365 as an example. Oracle throws their apps into the blender, if you will. On the numbers and everything. It's interesting Wikibon research is showing that the past layers squeezing, that's a big debate in our own research team, but Gartner research that I just recently looked at from February. Basically there's a new talk about Sass, so if you start including Sass, then you got to open up the conversation to Salesforce, Adobe, and on and on and on. Because there's a Cloud service provider model out there. Linkedin's a service provider. So what is Sass, I always look at it like what's the Sass equation look like. I mean, what does Cloud really look like? >> I look at the statistics, because we address both infrastructure as a service, and software as a service as well, with our identity solutions. Clearly infrastructure as a service is a much bigger market, Sass is pretty significant, but if you add up Sass, infrastructure, and Pass, it's about 24 billionish right there. But guess what, Amazon already has over 10 of it last year. Amazon has 40 percent of the Cloud market as well. And they've proven that you don't have to have a Sass capability to be incredibly successful in the Cloud. >> Well they have their one Sass that was called Amazon.com, but they broke that out. Alright, Tom what's next for you guys at Centrify. What's on your, anything coming up, things you're working on, share some quick plug for Centrify, and the progress you're making in status? >> We've been doing this for 10 years, and we feel really good about providing basically a platform for identity. And one theme and trend that we're seeing a lot of in the security market is that buyers have security fatigue, they're so sick of dealing with point solutions, and I think that's working to our advantage, that people are looking at a vendor such as us, that can address, not only single sign up, but multi-factor authentication, privilege account management as well. So we're very much focused these days on providing a set of solutions that are all built on a platform, and just kind of filling in-- >> When you say fatigue, you mean sprawl and applications they're buying just another platform, because they do try to try everything, why wouldn't they? They're getting tired of that? >> In security you just have a lack of security knowledge. There's a huge skills gap when it comes to security. And if you have to buy a point solution to address every little bit of security, you just can't hire people, right? And then you find that you have air gaps that actually makes you less secure. And so we've over time built this platform up, and now we're really seeing that people are like, I don't have to get a standalone EMM, a standalone SSO, a standalone MFA solution, a standalone password vault solution etc. So we're very much focused on selling our platform to customers and with this whole mindset of customers wanting to consolidate vendors. Historically vendor consolidation was about buyers wanting that, but now IT people want that. And so we're really just focusing on, internally articulating how we can actually address a lot of problems that people have with too much privilege, and too many passwords. >> And you guys are expanding your sales force team? >> Oh absolutely. We've definitely hit the critical mass. We're over a hundred million sales, we're growing fast, we're cash flow positive as well. >> John: Alright, congratulations. The VC's happy. Time to go public, so what's your evaluation? Unicorn. >> No comment on that, rule 40 and all that fun stuff. We got a lot of checkboxes right there. >> I think your VC partner is right, your investor, the world is spinning towards you because if you look at the identity, and nearly everything in the digital world, whether it's Cloud, data, or packets or people. It's going to be a persona based focus. Not like, what company you work for. >> We had this huge trend of consumerization of IT, so it's really about the user. So focus on securing the user, not focusing on securing the network, because the network's gone. >> Finally, 30 years later, it's coming back to the user. It's been talked about, the passports, the digital wallet. >> Exactly. >> John: Tom Kemp, CEO of Centrify, a hot startup growing over 100 million in sales. Heard here on the Cube. Very successful company. Really have a nice approach, world's spinning towards them. Really hopefully a great solution for our security and our liberties so we don't get hacked over and over again. It's the Cube, bringing you all the coverage of Google Next, here in the studio I'm John Furrier. Be right back with more, after this short break. (resonant techno music)

>> Announcer: Live, from Silicon Valley, it's the Cube. Covering Google Cloud Next 17. (ambient music) >> Okay, welcome back everyone. We are here live in Palo Alto for a special two days of coverage of Google Next 2017 events in San Francisco. Sold out, 10,000 plus people. Yeah, really, an amazing turn of events. Amazon Web Services Reinvent had 36,000, Google's nipping at their heels, although different, we're going to break down the differences with Google versus Amazon because they're really two different things and again, this is Cube coverage here in Palo Alto studio, getting reaction. Sponsored by Intel, thanks, Intel, for allowing us to continue the wall-to-wall coverage of the key events in the tech industry. Our next guest is Val Bercovici who's the boardmember of the Cloud Native Compute Foundation, boardmember. >> That's right. >> Welcome back, you were here last week from Mobile World Congress, great to see you. Silicon contributor, what your reaction to the Google keynote, Google news? Not a lot of news, we saw the SAP, that was the biggest news and the rest were showcasing customers, most of the customers were G Suite customers. >> Yeah, exactly. So, I would say my first reaction is bit of a rough keynote, you know, there's definitely not as quit as much polish as Microsoft had in their heyday and of course, Amazon nowadays in the Cloud era. But what's interesting to me is there's the whole battle around empathy right now. So, the next gen developers and the Clouderati talk about user empathy and that means understanding the workflow of the user and getting the user to consume more of your stuff, you know, Snapchat gets user empathy for the millennial generation but anybody else. Facebook as well. So, you see Google, we emphasize, even the Google Twitter account, it emphasizes developer productivity and they have pretty strong developer empathy. But what AWS has, Amazon with AWS is enterprise empathy, right, they really understand how to package themselves and make themselves more consumable right now for a lot of mainstream enterprises, they've been doing this for three, four years at their Reinvent events now. Whereas Google is just catching up. They've got great developer empathy but they're just catching up on enterprise empathy. Those are the main differences I see. >> Yeah, I think that's an important point, Val, great, great point, I think Amazon certainly has, and I wrote this in my blog post this morning, getting a lot of reaction from that, actually, and some things I want to drill down on the network and security side. Some Google folks DMing me we're going to do that. But really, Amazon's lead is way out front on this. But the rest, you know, call 'em IBM, not in any particular, IBM, Oracle, Google, SAP, others, put Salesforces, we're talking Sass and Adobe, they're all in this kind of pack. It's like a NASCAR, you know, pack and you don't know who's going to slimshot around and get out there. But they all have their own unique use cases, they're using their own products to differentiate. We're hearing Google and again, this is a red flag for me because it kind of smells like they're hiding the ball. G Suite, I get the workplace productivity is a Cloud app, but that's not pure Cloud conversations, if you look at the Gartner, Gartner's recent, last report which I had a chance to get a peek at, there's no mention of Sassifications, Google G Suite's not in there, so the way Cloud is strictly defined doesn't even include Sass. >> Yeah. >> If you're going to include Sass, then you got to include Salesforce in that conversation or Adobe or others. >> Exactly. >> So, this is kind of an optical illusion in my mind. And I think that's something that points to Google's lack of traction on customers in the enterprise. >> This is where behind the scenes, Kubernetes, is so important and why I'm involved with the the CNCF. If anything, the first wave of Clouded option particularly by enterprise was centered around the VM model. And you know, infrastructure's a service based on VMs, Amazon, AWS is the king of that. What we're seeing right now is developers in particular that are developing the next generation of apps, most of them are already on our phones and our tablets and our houses and stuff, which is, you know, all these Echo-style devices. That is a container-based architecture that these next gen applications are based on. And so, Kubernetes, in my mind, is really nothing more than Google's attempt to create as much of a container-based ecosystem at scale so that the natural home for container-based apps will be GCP as opposed to AWS. That's the real long term play in why Google's investing so heavily in Kubernetes. >> Is that counterintuitive? Is that a good thing? I mean, it sounds like they're trying to change the goalpost, if you will, to change the game because we had Joe Arnold on, the founder of Swiftstack and you know, ultimately, you know, Clouds are Clouds and inter-Clouding and multi-Cloud is important. Does Kubernete actually help the industry? Or is that more Google specific in your mind? >> I think it will help the industry but the industry itself is moving so rapidly, we're seeing server-less right now and functions of service, and so, I think the landscape is shifting away from what we would think of as either VM or container-based infrastructure service towards having the right abstractions. What I'm seeing is that, really, even the most innovative enterprises today don't really care about their per minute or per hour cost for a cycle of computer, a byte of, you know, network transferred or stored. They care about big table, big quarry, the natural language processing, visual search, and a whole category of these AI based applications that they want to base their own new revenue-generating products and services based on. So, it's abstraction now as a new battlefield. AWS brings that cult of modularity to it, they're delivering a lot of cool services that are very high level Lambda centered based on really cool modularity, whereas Google's doing it, which is very, very elegant abstraction. It's at the developer level, at the technical level, that's what the landscape is at right now. >> Are you happy with Google's approach because I think Google actually doesn't want to be compared to AWS in a way. I mean, from what I can see from the keynote... >> Only by revenue. (laughs) >> Well, certainly, they're going to win that by throwing G Suite on it but, I mean, this is, again, a philosophy game, right? I mean, Andy Jassy is very customer focused, but they don't have their own Sass app, except for Amazon which they don't count on the Cloud. So, their success is all about customers, building on Amazon. Google actually has its own customer and they actually include that in, as does Microsoft with Office 365. >> Yeah, that's the irony, is if we go back to enterprise empathy I think it's Microsoft has that legacy of understanding the enterprise better than all the others. And they're beginning to leverage that, we're definitely seeing, as you're sliding comfortably to a number two position behind AWS, but it really does come back to, you know, are you going to lead with a propeller head lead in technology which Google clearly has, they've got some of the most superior technology, we were rattling off some the speeds and feeds that one of their product managers shared with you this morning. They've had amazing technology, that's unquestioned. But they do have also is this reputation of almost flying in rarefied air when it comes to enterprises. >> What do you mean by that? >> What I mean by that is that most enterprise IT organizations, even the progressive ones, have a hard time relating to Google technology. It's too far out there, it's too advanced, in some cases, they just can't understand it. They've never been trained in college courses on it or even post-grad courses on it. MBA is older than three years old, don't even reference the Cloud. So, there's a lot of training, a lot of knowledge that has to be, you know, conducted on the enterprise side. AWS is packaged, that technology there is the modularity in such a way that's more consumable. Not perfect, but more consumable than any other Cloud render and that's why, with an early head start, they've got the biggest enterprise traction today. >> Yeah, I mean, and I'm really bullish on Google, I love the company, I've been following them since '98, a lot of friends here at Palo Alto, a lot of Googlers living in my neighborhood, they're all around us. Larry Page, seen him around town. Great, great company and very, always been kind of like an academic, speed of academic. Very strong, technically, and that is, clearly, they're playing that card, "We have the technology." So, I would just say that, to counter that argument would be if Google, I'm Google, I'm on the team, the guy in green and you know, lookit, what I want to do is, we want to be the intel for the Cloud. So, the hard and top is we don't really care if people are trained, should be so easy to use, training doesn't matter. So, I mean, that's really more of an arrogant approach, but I don't think Google's being arrogant in the Cloud. I think that ship has sailed, I think Google has kind of been humbled in the sense, in recognizing that the enterprise is hard, they're checking the boxes. They have a partner program. >> Yeah, you're right, I mean, if you take a look at their customers today, you've got Spotify, and Snap, and Evernote, and you know, Pokemon Go and Niantic, all of the leading edge technology companies that have gone mainstream that are, you know, startup oriented Snap, of course. They're on Google Cloud. But that's not enough, you know, the enterprise, I did a seminar just last week promoting Container World with Jim Forge from ADP. The enterprise is not homogeneous, the enterprise is complicated. The L word legacy is all over, what they have to budget and plan for. So, the enterprise is just a lot more complicated than Google will acknowledge right now. And I believe if they were to humanize some of their advanced technology and package it and price it in such a way that AWS, you know, where they're seeing success, they'll accelerate their inevitable sort of leap to being one of those top three contenders. >> So, I'm just reading some of my, I'm putting together because for the Google folks, I'm going to interview them, just prepping for this, but just networking alone, isolating Cloud resources. That's hard, right? So, you know, virtual network in the Cloud, Google's got the virtual network. You get multiple IP addresses, for instance, ability to move network interfaces and IPs between instances, and AS networking support. Network traffic logging, virtual network peering, manage NAT gateways, subnet level filtering, IP V stick support, use any CIDR including RC 1918. Multiple network interface instances, I mean, this is complicated! (laughs) It's not easy so, you know, I think the strategy's going to be interesting to see how, does Google go into the point to point solution set, or they just say, "This is what we got, take it or leave it," and try to change the game? >> That's where they've been up until now and I don't think it's working because they have very formidable competitors that are not standing still. So, I think they're going to have to keep upping their game, again, not in terms of better technology but in terms of better packaging, better accessibility to their technology. Better trust, if you will, overseas. Cloud is a global game, it's not US only. And trust is so critical, there's a lot of skepticism in Europe today with the latest Wikileaks announcements, or Asia Today around. Any American based Cloud provider truly being able to isolate and protect my citizen's data, you know, within my borders. >> I think Google Cloud has one fatal flaw that I, looking at all the data, is that and the analysis that we've been looking at with Bookie Bontine and our research is that there's one thing that jumps out at me. I mean, the rest are all, I look at as, you know, Google's got such great technologies, they can move up fast, they can scale up to code. But the one thing that's interesting is their architecture, the way they handle their architecture is they can't let customers dictate data where data's stored. That is a huge issue for them. And if, to your point, if a user in Germany is using an app and it's got to stay in Germany. >> This is back to the empathy disconnect, right? As an abstraction layer for a developer, what I want is exactly what Google offers. I don't want to care as a developer where the bits and bytes are stored, I want this consistent, uniform API, I want to do cool stuff with the data. The operation side, particularly within legal parameters, regulatory parameters, you know, all sorts of other costs and quality assurance parameters, they really care about where that data is stored, and that's where having more enterprise empathy, and their thinking, and their offerings, and their pricing, and their packaging will leapfrog Google to where they want to be today. >> Val Bercovici, great analysis, I mean, I would totally agree just to lock that in, their developer empathy is so strong. And their operational one needs to be, they got a blind spot there where they got to work on that. And this is interesting because people who don't know Google are very strong operations, it's not like they don't have any ops chops. (Val laughs) They're absolutely in the five nines, they are awesome operations. But they've been operations for themselves. >> Exactly. >> So, that's the distinction you're getting at, right? >> Absolutely. >> Okay, so the next question I got to ask you is back to the developer empathy, 'cause I think it's a really big opportunity for Google. So, pointing out the fatal flaw in my opinions in the data locality thing. But I think the opportunity for Google to change the game, using the developer community opportunity because you mentioned the Kubernetes. There is a huge, open source, I don't want to say transformation but an evolution to the next generation, you're starting to see machine learning and AI start to tease out the leverage of not just data now. Data's become so massive now, you have data sets. That can be addressable and be treated like software programs. So, data as code becomes a new dynamic with AI. So, with AI, with open source, you're seeing a lot of activity, CNCF, the Cloud Native Compute Foundation, folks should check that out, that's an amazing group, analytics foundation. This is an awesome opportunity for Google to use Kubernetes as saying, "Hey, we will make orchestration of application workloads." >> Absolutely. >> This is something, Amazon's been great with open source, but they don't get a lot of love... >> Amazon has a blind spot on containers, let's not, you know, let's not call, you know, let's call it the speed of speed, let's not, you know, beat around the bush, they do have a blind spot around containers. It is something they strategically have to get a hold of, they've got some really interesting proprietary offerings. But it's not a natural home for a Docker workflow, it's not a natural home for a Kubernetes workflow yet. And it's something they have to work on and AI as a use case could not be more pertinent to business today because it's that quote, you know, "The future is here "but unevenly distributed." That's exactly where AI is today, the businesses that are figuring it out are really leaping ahead of their competitors. >> We're getting some great tweets, my phone's blowing up. Val, you've got great commentary. I want to bring up, so, I've been kind of over the top with the comment that I've been making. It's maybe mischaracterized but I'll say it again. There seems to be a Cold War going on inside the communities between, as Kubernetes have done, we've seen doc, or we've seen Docker Containers be so successful in this service list, server list vision, which is absolutely where Cloud Native needs to be in that notion of, you know, separating out fiscal gear and addressability, making it completely transparent, full dev ops, if you will. To who's going to own the orchestration and where does it sit on the stack? And with Kubernetes, to me, is interesting is that it tugs at some sacred cows in the container world. >> Yes. >> And it opens up the notion of multi-Cloud. I mean, assume latency can be solved at some point, but... >> It's actually core religion, what impressed me about he whole Kubernetes community, and community is its greatest strength, by the way, is the fact that they had a religion on multi-Cloud from day one. It wasn't about, "We'll add it later "'cause we know it's important," it's about portability and you know, even Docker lent that to the community. Portability is just a number one priority and now portability, at scale, across multiple Clouds, dynamically orchestrated, not through, you know, potential for human error, human interventions we saw last week. That the secret sauce there to stay. >> I think not only is, a Cold War is a negative connotation, but I think it's an opportunity to be sitting in the sun, if you will, on the beach with a pina colada because if you take the Kubernetes trend that's got developer empathy with portability, that speaks to what developers want, I want to have the ability to write code, ship it up to the network, and have it integrate in nicely and seamlessly so, you know, things can self-work and do all that. And AI can help in all those things. Connecting with operational challenges. So, what is, in your mind, that intersection? Because let's just say that Kubernetes is going to develop a nice trajectory which it has now and continues to be a nice way to galvanize a community around orchestration, portability, etc. Where does that intersect with some of the challenges and needs for operational effectiveness and efficiency? >> So, the dirtiest secret in that world is data gravity, rigtht? It's all well and fine to have workload portability across, you know, multiple instances and a cluster across multiple Clouds, so to speak. But data has weight, data has mass and gravity, and it's very hard to move particularly at scale. Kubernetes only in the last few releases with a furious pace in evolution, one four, one five, has a notion of provisioning persistent volumes, this thing they affectionately called pet sets that are not a stateful sets, I love that name. >> Cattle. >> Exactly. (laughs) So, Google is waking up and Kubernetes, I should say, in particular is waking up to the whole notion of managing data is really that last mile problem of Cloud portability and operational maturity. And planning around data gravity and overcoming where you can data gravity through meta-operational procedures is where this thing is going to really take off. >> I think that's where Google, I like Google's messaging, I like their posture on machine learning AI, I think that's key. But Amazon has been doing AI, they've got machine learning as a service, they've had Kineses for a while. In fact, Redshift and Kineses were their fastest growing services before Aurora became the big thing that they had. So, I think, you know, they're interested in the jets, with the trucks, and the snowmobile stuff. So I think certainly, Amazon's been doing that data and then rolling in as some sort of AI. >> And they've been humanizing it better, right? I can relate to some of Amazon's offering and sometimes I have it in the house. You know, so, the packaging and just the consumerability of these Amazon services today is ahead of where Google is and Google arguably has the superior technology. >> Yeah, and I think, you know, I was laying out my analysis of Google versus Amazon but I think it's not fair to try to compare them too much because Google is just making their opening moves on the chessboard. Because they had Diane Green, got to give her credit, she's really starting behind. And that's been talked about but they are serious, they're going to get there. The question is what does an enterprise need to do? So, your advice to enterprise would be what? Stick with the use cases that are either Google specific apps or Cloud Native, where do you go, how do you...? >> I would say to remember the lock-in days of the Linux vendors and even Microsoft in their heyday and definitely think multi-Cloud, you know, Cloud first is fine. But think, we need data first in a Cloud before I think a particular Cloud first. Always keep your options open, seek the highest levels of abstraction, particularly as you're innovating early on and fast failing in the Cloud. Don't go low right away, go low later on when you're operationalizing and scaled and looking to squeeze efficiencies out of a new product or service. >> Don't go low, you mean don't go low in the stack? >> Don't go low in the stack, exactly. Start very high in the stack. >> What would be an example? >> Lambda, you know, taking advantage of, if we bring in Kineses, IOT workflows, all sorts of sensor data coming in from the Edge. Don't code that for efficiency day one and switch to Kafka or something else that's more sophisticated, but keep it really high level as events triggering off, whether it's the IOTICK in the sensor inputs or whether it's S3 events, Dynamo, DB events. Write your functions that are very, very high level. >> Yeah. >> Get the workflows right. Pay a bit more money up front, pay premium for the fast... >> Well, there's also Bootstraps and the Training Channel Digimation, so, with Google, pick some things that are known out there. But you mentioned IOT and one of the things I was kind of disappointed in the keynote today, there wasn't much talk about IOT. You're not seeing IOT in the Google story. >> That may come up in tomorrow's keynote, it may come up tomorrow in a more technical context. But you're right, it's an area both Agar and AWS have a monster of a lead right now, as they've had really good SDKs out there to be able to create workflows without even being an expert in some of the devices that you know, you might own and maintain. >> Google's got some differentiation, they've got something, I'll highlight one that I like that I think is really compelling. Tensor flow. Tensor flow as got a lot of great traction and then Intel is writing chips with their Skylake product that actually runs much faster silicon... >> What was that, Nvidia? You know, it's a GPU game as much as a CPU game when it comes to machine learning. And it's just... >> What does that mean for you? I mean, that's exciting, you smile on that, I get geeked out on that because if you think about that, if you can have a relationship between the silicon and software, what does it mean from an impact standpoint? Do you think that's going to be a good accelerant for the game? >> Massive accelerant, you know, and this is where we get into sort of more rarefied air with Elon Musk's quote around the fact we'll need universal income for society. There a lot of static tasks that are automated today. There's more and more dynamic tasks now that these AI algorithms, through machine learning, can be trained to conduct in a very intelligent manner. So, more and more task based work all over the world, including in a robotic context but also call centers, stock brokerage, for example, it's been demonstrated that AI ML algorithms are superior to humans nine times out of ten in terms of recommending stocks. So, there's a lot of white collars, while it's blue collared work that just going to be augmented and then eliminated with these technologies and the fact that you have major players, economies at scales such as Intel and Nvidia and so forth accelerating that, making it affordable, fast, low power in certain edge context. That's, you know, really good for the industry. >> So, day one of two days of coverage here with Google, just thoughts real quick on what Google needs to do to really conquer the enterprise and really be credible, viable, successful, number two, or leader in the enterprise? >> I'm a big fan, you know, I've had personal experiences with fast following as opposed to leading and innovating sometimes in terms of getting market traction. I think they should unabashedly, unashamedly examine what Microsoft or what Amazon are doing right in the Cloud. Because you know, simple things like conducting a bit more of a smooth keynote, Google doesn't seem to have mastered it yet, right now in the Cloud space. And it's not rocket science, but shamelessly copying what works, shamelessly copying the packaging and the humanization that some of the advanced technologies that Amazon and Microsoft have done in particular. And then applying their technical superiority, you know, their uptime availability advantages, their faster networks, their strong consistency which is a big deal for developers across their regions. Emphasizing their strengths after they package and make their technology more consumable. As opposed to leading where the tech specs. >> And you have a lot of experience in the enterprise, table stakes out there that are pretty obvious that they need to check the boxes on, and would be what? >> A very good question, I would say, first and foremost, you really have to focus on more, you know, transparent pricing. Think something that is a whole black art in terms of optimizing your AWS usage in this industry that's formed around that. I think Google has and they enact blogs advertising a lot of advantages they have in the granularity, in the efficiency of their auto scaling up and down. But businesses don't really map that, they don't think of that first even though it can save them millions of dollars as they do move to Cloud first approaches. >> Yeah and I think Google got to shake that academic arrogance, in a way, that they've had a reputation for. Not that that's a bad thing, I'll give you an example, I love the fact that Google leads a lot of price performance on many levels in the Cloud, yet their SLAs are kind of wonky here and there. So, it's like, okay, enterprises like SLAs. You got to nail that. And then maybe keep their price a little high here, it can make more money, but... So, you were saying, is that enterprise might not get the fact that it's such a good deal. >> It's like enterprise sales 101, you talk about, you know, the operational benefits but you also talk about financial benefits and business benefits. Catching into those three contexts in terms of their technical superiority would do them a world of good as they seek more and more enterprise opportunities. >> Alright, Val Bercovici, CTO, also CTO, and also on the board of the Cloud Native Compute Foundation known as CNCF, a newly formed organization, part of the Linux Foundation. Really looking at the orchestration, looking at the containers, looking at Kubernetes, looking at a whole new world of app enablement. Val, thanks for the company, great to see you. Turning out to be guest contributor here on the Cube studio, appreciate his time. This is the Cube, two days of live coverage. Hope to have someone from Google on the security and network side coming in and calling in, we're going to try to set that up, a lot of conversations happening around that. Lot of great stuff happening at Google Next, we've got all the wall-to-wall coverage, reporters on the ground in San Francisco as well as analysts. And of course, in studio reaction here in Palo Alto. We'll be right back. (ambient music)

>> Narrator: Live from New York, it's the Cube, covering big data New York City 2016. Brought to you by headline sponsors, CISCO, IBM, NVIDIA, and our ecosystem sponsors, now here's your host, Dave Valante. >> Welcome back to New York City, everybody, this is the Cube, the worldwide leader in live tech coverage, and this is a cube first, we've got a nine person, actually eight person panel of experts, data scientists, all alike. I'm here with my co-host, James Cubelis, who has helped organize this panel of experts. James, welcome. >> Thank you very much, Dave, it's great to be here, and we have some really excellent brain power up there, so I'm going to let them talk. >> Okay, well thank you again-- >> And I'll interject my thoughts now and then, but I want to hear them. >> Okay, great, we know you well, Jim, we know you'll do that, so thank you for that, and appreciate you organizing this. Okay, so what I'm going to do to our panelists is ask you to introduce yourself. I'll introduce you, but tell us a little bit about yourself, and talk a little bit about what data science means to you. A number of you started in the field a long time ago, perhaps data warehouse experts before the term data science was coined. Some of you started probably after Hal Varian said it was the sexiest job in the world. (laughs) So think about how data science has changed and or what it means to you. We're going to start with Greg Piateski, who's from Boston. A Ph.D., KDnuggets, Greg, tell us about yourself and what data science means to you. >> Okay, well thank you Dave and thank you Jim for the invitation. Data science in a sense is the second oldest profession. I think people have this built-in need to find patterns and whatever we find we want to organize the data, but we do it well on a small scale, but we don't do it well on a large scale, so really, data science takes our need and helps us organize what we find, the patterns that we find that are really valid and useful and not just random, I think this is a big challenge of data science. I've actually started in this field before the term Data Science existed. I started as a researcher and organized the first few workshops on data mining and knowledge discovery, and the term data mining became less fashionable, became predictive analytics, now it's data science and it will be something else in a few years. >> Okay, thank you, Eves Mulkearns, Eves, I of course know you from Twitter. A lot of people know you as well. Tell us about your experiences and what data scientist means to you. >> Well, data science to me is if you take the two words, the data and the science, the science it holds a lot of expertise and skills there, it's statistics, it's mathematics, it's understanding the business and putting that together with the digitization of what we have. It's not only the structured data or the unstructured data what you store in the database try to get out and try to understand what is in there, but even video what is coming on and then trying to find, like George already said, the patterns in there and bringing value to the business but looking from a technical perspective, but still linking that to the business insights and you can do that on a technical level, but then you don't know yet what you need to find, or what you're looking for. >> Okay great, thank you. Craig Brown, Cube alum. How many people have been on the Cube actually before? >> I have. >> Okay, good. I always like to ask that question. So Craig, tell us a little bit about your background and, you know, data science, how has it changed, what's it all mean to you? >> Sure, so I'm Craig Brown, I've been in IT for almost 28 years, and that was obviously before the term data science, but I've evolved from, I started out as a developer. And evolved through the data ranks, as I called it, working with data structures, working with data systems, data technologies, and now we're working with data pure and simple. Data science to me is an individual or team of individuals that dissect the data, understand the data, help folks look at the data differently than just the information that, you know, we usually use in reports, and get more insights on, how to utilize it and better leverage it as an asset within an organization. >> Great, thank you Craig, okay, Jennifer Shin? Math is obviously part of being a data scientist. You're good at math I understand. Tell us about yourself. >> Yeah, so I'm a senior principle data scientist at the Nielsen Company. I'm also the founder of 8 Path Solutions, which is a data science, analytics, and technology company, and I'm also on the faculty in the Master of Information and Data Science program at UC Berkeley. So math is part of the IT statistics for data science actually this semester, and I think for me, I consider myself a scientist primarily, and data science is a nice day job to have, right? Something where there's industry need for people with my skill set in the sciences, and data gives us a great way of being able to communicate sort of what we know in science in a way that can be used out there in the real world. I think the best benefit for me is that now that I'm a data scientist, people know what my job is, whereas before, maybe five ten years ago, no one understood what I did. Now, people don't necessarily understand what I do now, but at least they understand kind of what I do, so it's still an improvement. >> Excellent. Thank you Jennifer. Joe Caserta, you're somebody who started in the data warehouse business, and saw that snake swallow a basketball and grow into what we now know as big data, so tell us about yourself. >> So I've been doing data for 30 years now, and I wrote the Data Warehouse ETL Toolkit with Ralph Timbal, which is the best selling book in the industry on preparing data for analytics, and with the big paradigm shift that's happened, you know for me the past seven years has been, instead of preparing data for people to analyze data to make decisions, now we're preparing data for machines to make the decisions, and I think that's the big shift from data analysis to data analytics and data science. >> Great, thank you. Miriam, Miriam Fridell, welcome. >> Thank you. I'm Miriam Fridell, I work for Elder Research, we are a data science consultancy, and I came to data science, sort of through a very circuitous route. I started off as a physicist, went to work as a consultant and software engineer, then became a research analyst, and finally came to data science. And I think one of the most interesting things to me about data science is that it's not simply about building an interesting model and doing some interesting mathematics, or maybe wrangling the data, all of which I love to do, but it's really the entire analytics lifecycle, and a value that you can actually extract from data at the end, and that's one of the things that I enjoy most is seeing a client's eyes light up or a wow, I didn't really know we could look at data that way, that's really interesting. I can actually do something with that, so I think that, to me, is one of the most interesting things about it. >> Great, thank you. Justin Sadeen, welcome. >> Absolutely, than you, thank you. So my name is Justin Sadeen, I work for Morph EDU, an artificial intelligence company in Atlanta, Georgia, and we develop learning platforms for non-profit and private educational institutions. So I'm a Marine Corp veteran turned data enthusiast, and so what I think about data science is the intersection of information, intelligence, and analysis, and I'm really excited about the transition from big data into smart data, and that's what I see data science as. >> Great, and last but not least, Dez Blanchfield, welcome mate. >> Good day. Yeah, I'm the one with the funny accent. So data science for me is probably the funniest job I've ever to describe to my mom. I've had quite a few different jobs, and she's never understood any of them, and this one she understands the least. I think a fun way to describe what we're trying to do in the world of data science and analytics now is it's the equivalent of high altitude mountain climbing. It's like the extreme sport version of the computer science world, because we have to be this magical unicorn of a human that can understand plain english problems from C-suite down and then translate it into code, either as soles or as teams of developers. And so there's this black art that we're expected to be able to transmogrify from something that we just in plain english say I would like to know X, and we have to go and figure it out, so there's this neat extreme sport view I have of rushing down the side of a mountain on a mountain bike and just dodging rocks and trees and things occasionally, because invariably, we do have things that go wrong, and they don't quite give us the answers we want. But I think we're at an interesting point in time now with the explosion in the types of technology that are at our fingertips, and the scale at which we can do things now, once upon a time we would sit at a terminal and write code and just look at data and watch it in columns, and then we ended up with spreadsheet technologies at our fingertips. Nowadays it's quite normal to instantiate a small high performance distributed cluster of computers, effectively a super computer in a public cloud, and throw some data at it and see what comes back. And we can do that on a credit card. So I think we're at a really interesting tipping point now where this coinage of data science needs to be slightly better defined, so that we can help organizations who have weird and strange questions that they want to ask, tell them solutions to those questions, and deliver on them in, I guess, a commodity deliverable. I want to know xyz and I want to know it in this time frame and I want to spend this much amount of money to do it, and I don't really care how you're going to do it. And there's so many tools we can choose from and there's so many platforms we can choose from, it's this little black art of computing, if you'd like, we're effectively making it up as we go in many ways, so I think it's one of the most exciting challenges that I've had, and I think I'm pretty sure I speak for most of us in that we're lucky that we get paid to do this amazing job. That we get make up on a daily basis in some cases. >> Excellent, well okay. So we'll just get right into it. I'm going to go off script-- >> Do they have unicorns down under? I think they have some strange species right? >> Well we put the pointy bit on the back. You guys have in on the front. >> So I was at an IBM event on Friday. It was a chief data officer summit, and I attended what was called the Data Divas' breakfast. It was a women in tech thing, and one of the CDOs, she said that 25% of chief data officers are women, which is much higher than you would normally see in the profile of IT. We happen to have 25% of our panelists are women. Is that common? Miriam and Jennifer, is that common for the data science field? Or is this a higher percentage than you would normally see-- >> James: Or a lower percentage? >> I think certainly for us, we have hired a number of additional women in the last year, and they are phenomenal data scientists. I don't know that I would say, I mean I think it's certainly typical that this is still a male-dominated field, but I think like many male-dominated fields, physics, mathematics, computer science, I think that that is slowly changing and evolving, and I think certainly, that's something that we've noticed in our firm over the years at our consultancy, as we're hiring new people. So I don't know if I would say 25% is the right number, but hopefully we can get it closer to 50. Jennifer, I don't know if you have... >> Yeah, so I know at Nielsen we have actually more than 25% of our team is women, at least the team I work with, so there seems to be a lot of women who are going into the field. Which isn't too surprising, because with a lot of the issues that come up in STEM, one of the reasons why a lot of women drop out is because they want real world jobs and they feel like they want to be in the workforce, and so I think this is a great opportunity with data science being so popular for these women to actually have a job where they can still maintain that engineering and science view background that they learned in school. >> Great, well Hillary Mason, I think, was the first data scientist that I ever interviewed, and I asked her what are the sort of skills required and the first question that we wanted to ask, I just threw other women in tech in there, 'cause we love women in tech, is about this notion of the unicorn data scientist, right? It's been put forth that there's the skill sets required to be a date scientist are so numerous that it's virtually impossible to have a data scientist with all those skills. >> And I love Dez's extreme sports analogy, because that plays into the whole notion of data science, we like to talk about the theme now of data science as a team sport. Must it be an extreme sport is what I'm wondering, you know. The unicorns of the world seem to be... Is that realistic now in this new era? >> I mean when automobiles first came out, they were concerned that there wouldn't be enough chauffeurs to drive all the people around. Is there an analogy with data, to be a data-driven company. Do I need a data scientist, and does that data scientist, you know, need to have these unbelievable mixture of skills? Or are we doomed to always have a skill shortage? Open it up. >> I'd like to have a crack at that, so it's interesting, when automobiles were a thing, when they first bought cars out, and before they, sort of, were modernized by the likes of Ford's Model T, when we got away from the horse and carriage, they actually had human beings walking down the street with a flag warning the public that the horseless carriage was coming, and I think data scientists are very much like that. That we're kind of expected to go ahead of the organization and try and take the challenges we're faced with today and see what's going to come around the corner. And so we're like the little flag-bearers, if you'd like, in many ways of this is where we're at today, tell me where I'm going to be tomorrow, and try and predict the day after as well. It is very much becoming a team sport though. But I think the concept of data science being a unicorn has come about because the coinage hasn't been very well defined, you know, if you were to ask 10 people what a data scientist were, you'd get 11 answers, and I think this is a really challenging issue for hiring managers and C-suites when the generants say I was data science, I want big data, I want an analyst. They don't actually really know what they're asking for. Generally, if you ask for a database administrator, it's a well-described job spec, and you can just advertise it and some 20 people will turn up and you interview to decide whether you like the look and feel and smell of 'em. When you ask for a data scientist, there's 20 different definitions of what that one data science role could be. So we don't initially know what the job is, we don't know what the deliverable is, and we're still trying to figure that out, so yeah. >> Craig what about you? >> So from my experience, when we talk about data science, we're really talking about a collection of experiences with multiple people I've yet to find, at least from my experience, a data science effort with a lone wolf. So you're talking about a combination of skills, and so you don't have, no one individual needs to have all that makes a data scientist a data scientist, but you definitely have to have the right combination of skills amongst a team in order to accomplish the goals of data science team. So from my experiences and from the clients that I've worked with, we refer to the data science effort as a data science team. And I believe that's very appropriate to the team sport analogy. >> For us, we look at a data scientist as a full stack web developer, a jack of all trades, I mean they need to have a multitude of background coming from a programmer from an analyst. You can't find one subject matter expert, it's very difficult. And if you're able to find a subject matter expert, you know, through the lifecycle of product development, you're going to require that individual to interact with a number of other members from your team who are analysts and then you just end up well training this person to be, again, a jack of all trades, so it comes full circle. >> I own a business that does nothing but data solutions, and we've been in business 15 years, and it's been, the transition over time has been going from being a conventional wisdom run company with a bunch of experts at the top to becoming more of a data-driven company using data warehousing and BI, but now the trend is absolutely analytics driven. So if you're not becoming an analytics-driven company, you are going to be behind the curve very very soon, and it's interesting that IBM is now coining the phrase of a cognitive business. I think that is absolutely the future. If you're not a cognitive business from a technology perspective, and an analytics-driven perspective, you're going to be left behind, that's for sure. So in order to stay competitive, you know, you need to really think about data science think about how you're using your data, and I also see that what's considered the data expert has evolved over time too where it used to be just someone really good at writing SQL, or someone really good at writing queries in any language, but now it's becoming more of a interdisciplinary action where you need soft skills and you also need the hard skills, and that's why I think there's more females in the industry now than ever. Because you really need to have a really broad width of experiences that really wasn't required in the past. >> Greg Piateski, you have a comment? >> So there are not too many unicorns in nature or as data scientists, so I think organizations that want to hire data scientists have to look for teams, and there are a few unicorns like Hillary Mason or maybe Osama Faiat, but they generally tend to start companies and very hard to retain them as data scientists. What I see is in other evolution, automation, and you know, steps like IBM, Watson, the first platform is eventually a great advance for data scientists in the short term, but probably what's likely to happen in the longer term kind of more and more of those skills becoming subsumed by machine unique layer within the software. How long will it take, I don't know, but I have a feeling that the paradise for data scientists may not be very long lived. >> Greg, I have a follow up question to what I just heard you say. When a data scientist, let's say a unicorn data scientist starts a company, as you've phrased it, and the company's product is built on data science, do they give up becoming a data scientist in the process? It would seem that they become a data scientist of a higher order if they've built a product based on that knowledge. What is your thoughts on that? >> Well, I know a few people like that, so I think maybe they remain data scientists at heart, but they don't really have the time to do the analysis and they really have to focus more on strategic things. For example, today actually is the birthday of Google, 18 years ago, so Larry Page and Sergey Brin wrote a very influential paper back in the '90s About page rank. Have they remained data scientist, perhaps a very very small part, but that's not really what they do, so I think those unicorn data scientists could quickly evolve to have to look for really teams to capture those skills. >> Clearly they come to a point in their career where they build a company based on teams of data scientists and data engineers and so forth, which relates to the topic of team data science. What is the right division of roles and responsibilities for team data science? >> Before we go, Jennifer, did you have a comment on that? >> Yeah, so I guess I would say for me, when data science came out and there was, you know, the Venn Diagram that came out about all the skills you were supposed to have? I took a very different approach than all of the people who I knew who were going into data science. Most people started interviewing immediately, they were like this is great, I'm going to get a job. I went and learned how to develop applications, and learned computer science, 'cause I had never taken a computer science course in college, and made sure I trued up that one part where I didn't know these things or had the skills from school, so I went headfirst and just learned it, and then now I have actually a lot of technology patents as a result of that. So to answer Jim's question, actually. I started my company about five years ago. And originally started out as a consulting firm slash data science company, then it evolved, and one of the reasons I went back in the industry and now I'm at Nielsen is because you really can't do the same sort of data science work when you're actually doing product development. It's a very very different sort of world. You know, when you're developing a product you're developing a core feature or functionality that you're going to offer clients and customers, so I think definitely you really don't get to have that wide range of sort of looking at 8 million models and testing things out. That flexibility really isn't there as your product starts getting developed. >> Before we go into the team sport, the hard skills that you have, are you all good at math? Are you all computer science types? How about math? Are you all math? >> What were your GPAs? (laughs) >> David: Anybody not math oriented? Anybody not love math? You don't love math? >> I love math, I think it's required. >> David: So math yes, check. >> You dream in equations, right? You dream. >> Computer science? Do I have to have computer science skills? At least the basic knowledge? >> I don't know that you need to have formal classes in any of these things, but I think certainly as Jennifer was saying, if you have no skills in programming whatsoever and you have no interest in learning how to write SQL queries or RR Python, you're probably going to struggle a little bit. >> James: It would be a challenge. >> So I think yes, I have a Ph.D. in physics, I did a lot of math, it's my love language, but I think you don't necessarily need to have formal training in all of these things, but I think you need to have a curiosity and a love of learning, and so if you don't have that, you still want to learn and however you gain that knowledge I think, but yeah, if you have no technical interests whatsoever, and don't want to write a line of code, maybe data science is not the field for you. Even if you don't do it everyday. >> And statistics as well? You would put that in that same general category? How about data hacking? You got to love data hacking, is that fair? Eaves, you have a comment? >> Yeah, I think so, while we've been discussing that for me, the most important part is that you have a logical mind and you have the capability to absorb new things and the curiosity you need to dive into that. While I don't have an education in IT or whatever, I have a background in chemistry and those things that I learned there, I apply to information technology as well, and from a part that you say, okay, I'm a tech-savvy guy, I'm interested in the tech part of it, you need to speak that business language and if you can do that crossover and understand what other skill sets or parts of the roles are telling you I think the communication in that aspect is very important. >> I'd like throw just something really quickly, and I think there's an interesting thing that happens in IT, particularly around technology. We tend to forget that we've actually solved a lot of these problems in the past. If we look in history, if we look around the second World War, and Bletchley Park in the UK, where you had a very similar experience as humans that we're having currently around the whole issue of data science, so there was an interesting challenge with the enigma in the shark code, right? And there was a bunch of men put in a room and told, you're mathematicians and you come from universities, and you can crack codes, but they couldn't. And so what they ended up doing was running these ads, and putting challenges, they actually put, I think it was crossword puzzles in the newspaper, and this deluge of women came out of all kinds of different roles without math degrees, without science degrees, but could solve problems, and they were thrown at the challenge of cracking codes, and invariably, they did the heavy lifting. On a daily basis for converting messages from one format to another, so that this very small team at the end could actually get in play with the sexy piece of it. And I think we're going through a similar shift now with what we're refer to as data science in the technology and business world. Where the people who are doing the heavy lifting aren't necessarily what we'd think of as the traditional data scientists, and so, there have been some unicorns and we've championed them, and they're great. But I think the shift's going to be to accountants, actuaries, and statisticians who understand the business, and come from an MBA star background that can learn the relevant pieces of math and models that we need to to apply to get the data science outcome. I think we've already been here, we've solved this problem, we've just got to learn not to try and reinvent the wheel, 'cause the media hypes this whole thing of data science is exciting and new, but we've been here a couple times before, and there's a lot to be learned from that, my view. >> I think we had Joe next. >> Yeah, so I was going to say that, data science is a funny thing. To use the word science is kind of a misnomer, because there is definitely a level of art to it, and I like to use the analogy, when Michelangelo would look at a block of marble, everyone else looked at the block of marble to see a block of marble. He looks at a block of marble and he sees a finished sculpture, and then he figures out what tools do I need to actually make my vision? And I think data science is a lot like that. We hear a problem, we see the solution, and then we just need the right tools to do it, and I think part of consulting and data science in particular. It's not so much what we know out of the gate, but it's how quickly we learn. And I think everyone here, what makes them brilliant, is how quickly they could learn any tool that they need to see their vision get accomplished. >> David: Justin? >> Yeah, I think you make a really great point, for me, I'm a Marine Corp veteran, and the reason I mentioned that is 'cause I work with two veterans who are problem solvers. And I think that's what data scientists really are, in the long run are problem solvers, and you mentioned a great point that, yeah, I think just problem solving is the key. You don't have to be a subject matter expert, just be able to take the tools and intelligently use them. >> Now when you look at the whole notion of team data science, what is the right mix of roles, like role definitions within a high-quality or a high-preforming data science teams now IBM, with, of course, our announcement of project, data works and so forth. We're splitting the role division, in terms of data scientist versus data engineers versus application developer versus business analyst, is that the right breakdown of roles? Or what would the panelists recommend in terms of understanding what kind of roles make sense within, like I said, a high performing team that's looking for trying to develop applications that depend on data, machine learning, and so forth? Anybody want to? >> I'll tackle that. So the teams that I have created over the years made up these data science teams that I brought into customer sites have a combination of developer capabilities and some of them are IT developers, but some of them were developers of things other than applications. They designed buildings, they did other things with their technical expertise besides building technology. The other piece besides the developer is the analytics, and analytics can be taught as long as they understand how algorithms work and the code behind the analytics, in other words, how are we analyzing things, and from a data science perspective, we are leveraging technology to do the analyzing through the tool sets, so ultimately as long as they understand how tool sets work, then we can train them on the tools. Having that analytic background is an important piece. >> Craig, is it easier to, I'll go to you in a moment Joe, is it easier to cross train a data scientist to be an app developer, than to cross train an app developer to be a data scientist or does it not matter? >> Yes. (laughs) And not the other way around. It depends on the-- >> It's easier to cross train a data scientist to be an app developer than-- >> Yes. >> The other way around. Why is that? >> Developing code can be as difficult as the tool set one uses to develop code. Today's tool sets are very user friendly. where developing code is very difficult to teach a person to think along the lines of developing code when they don't have any idea of the aspects of code, of building something. >> I think it was Joe, or you next, or Jennifer, who was it? >> I would say that one of the reasons for that is data scientists will probably know if the answer's right after you process data, whereas data engineer might be able to manipulate the data but may not know if the answer's correct. So I think that is one of the reasons why having a data scientist learn the application development skills might be a easier time than the other way around. >> I think Miriam, had a comment? Sorry. >> I think that what we're advising our clients to do is to not think, before data science and before analytics became so required by companies to stay competitive, it was more of a waterfall, you have a data engineer build a solution, you know, then you throw it over the fence and the business analyst would have at it, where now, it must be agile, and you must have a scrum team where you have the data scientist and the data engineer and the project manager and the product owner and someone from the chief data office all at the table at the same time and all accomplishing the same goal. Because all of these skills are required, collectively in order to solve this problem, and it can't be done daisy chained anymore it has to be a collaboration. And that's why I think spark is so awesome, because you know, spark is a single interface that a data engineer can use, a data analyst can use, and a data scientist can use. And now with what we've learned today, having a data catalog on top so that the chief data office can actually manage it, I think is really going to take spark to the next level. >> James: Miriam? >> I wanted to comment on your question to Craig about is it harder to teach a data scientist to build an application or vice versa, and one of the things that we have worked on a lot in our data science team is incorporating a lot of best practices from software development, agile, scrum, that sort of thing, and I think particularly with a focus on deploying models that we don't just want to build an interesting data science model, we want to deploy it, and get some value. You need to really incorporate these processes from someone who might know how to build applications and that, I think for some data scientists can be a challenge, because one of the fun things about data science is you get to get into the data, and you get your hands dirty, and you build a model, and you get to try all these cool things, but then when the time comes for you to actually deploy something, you need deployment-grade code in order to make sure it can go into production at your client side and be useful for instance, so I think that there's an interesting challenge on both ends, but one of the things I've definitely noticed with some of our data scientists is it's very hard to get them to think in that mindset, which is why you have a team of people, because everyone has different skills and you can mitigate that. >> Dev-ops for data science? >> Yeah, exactly. We call it insight ops, but yeah, I hear what you're saying. Data science is becoming increasingly an operational function as opposed to strictly exploratory or developmental. Did some one else have a, Dez? >> One of the things I was going to mention, one of the things I like to do when someone gives me a new problem is take all the laptops and phones away. And we just end up in a room with a whiteboard. And developers find that challenging sometimes, so I had this one line where I said to them don't write the first line of code until you actually understand the problem you're trying to solve right? And I think where the data science focus has changed the game for organizations who are trying to get some systematic repeatable process that they can throw data at and just keep getting answers and things, no matter what the industry might be is that developers will come with a particular mindset on how they're going to codify something without necessarily getting the full spectrum and understanding the problem first place. What I'm finding is the people that come at data science tend to have more of a hacker ethic. They want to hack the problem, they want to understand the challenge, and they want to be able to get it down to plain English simple phrases, and then apply some algorithms and then build models, and then codify it, and so most of the time we sit in a room with whiteboard markers just trying to build a model in a graphical sense and make sure it's going to work and that it's going to flow, and once we can do that, we can codify it. I think when you come at it from the other angle from the developer ethic, and you're like I'm just going to codify this from day one, I'm going to write code. I'm going to hack this thing out and it's just going to run and compile. Often, you don't truly understand what he's trying to get to at the end point, and you can just spend days writing code and I think someone made the comment that sometimes you don't actually know whether the output is actually accurate in the first place. So I think there's a lot of value being provided from the data science practice. Over understanding the problem in plain english at a team level, so what am I trying to do from the business consulting point of view? What are the requirements? How do I build this model? How do I test the model? How do I run a sample set through it? Train the thing and then make sure what I'm going to codify actually makes sense in the first place, because otherwise, what are you trying to solve in the first place? >> Wasn't that Einstein who said if I had an hour to solve a problem, I'd spend 55 minutes understanding the problem and five minutes on the solution, right? It's exactly what you're talking about. >> Well I think, I will say, getting back to the question, the thing with building these teams, I think a lot of times people don't talk about is that engineers are actually very very important for data science projects and data science problems. For instance, if you were just trying to prototype something or just come up with a model, then data science teams are great, however, if you need to actually put that into production, that code that the data scientist has written may not be optimal, so as we scale out, it may be actually very inefficient. At that point, you kind of want an engineer to step in and actually optimize that code, so I think it depends on what you're building and that kind of dictates what kind of division you want among your teammates, but I do think that a lot of times, the engineering component is really undervalued out there. >> Jennifer, it seems that the data engineering function, data discovery and preparation and so forth is becoming automated to a greater degree, but if I'm listening to you, I don't hear that data engineering as a discipline is becoming extinct in terms of a role that people can be hired into. You're saying that there's a strong ongoing need for data engineers to optimize the entire pipeline to deliver the fruits of data science in production applications, is that correct? So they play that very much operational role as the backbone for... >> So I think a lot of times businesses will go to data scientist to build a better model to build a predictive model, but that model may not be something that you really want to implement out there when there's like a million users coming to your website, 'cause it may not be efficient, it may take a very long time, so I think in that sense, it is important to have good engineers, and your whole product may fail, you may build the best model it may have the best output, but if you can't actually implement it, then really what good is it? >> What about calibrating these models? How do you go about doing that and sort of testing that in the real world? Has that changed overtime? Or is it... >> So one of the things that I think can happen, and we found with one of our clients is when you build a model, you do it with the data that you have, and you try to use a very robust cross-validation process to make sure that it's robust and it's sturdy, but one thing that can sometimes happen is after you put your model into production, there can be external factors that, societal or whatever, things that have nothing to do with the data that you have or the quality of the data or the quality of the model, which can actually erode the model's performance over time. So as an example, we think about cell phone contracts right? Those have changed a lot over the years, so maybe five years ago, the type of data plan you had might not be the same that it is today, because a totally different type of plan is offered, so if you're building a model on that to say predict who's going to leave and go to a different cell phone carrier, the validity of your model overtime is going to completely degrade based on nothing that you have, that you put into the model or the data that was available, so I think you need to have this sort of model management and monitoring process to take this factors into account and then know when it's time to do a refresh. >> Cross-validation, even at one point in time, for example, there was an article in the New York Times recently that they gave the same data set to five different data scientists, this is survey data for the presidential election that's upcoming, and five different data scientists came to five different predictions. They were all high quality data scientists, the cross-validation showed a wide variation about who was on top, whether it was Hillary or whether it was Trump so that shows you that even at any point in time, cross-validation is essential to understand how robust the predictions might be. Does somebody else have a comment? Joe? >> I just want to say that this even drives home the fact that having the scrum team for each project and having the engineer and the data scientist, data engineer and data scientist working side by side because it is important that whatever we're building we assume will eventually go into production, and we used to have in the data warehousing world, you'd get the data out of the systems, out of your applications, you do analysis on your data, and the nirvana was maybe that data would go back to the system, but typically it didn't. Nowadays, the applications are dependent on the insight coming from the data science team. With the behavior of the application and the personalization and individual experience for a customer is highly dependent, so it has to be, you said is data science part of the dev-ops team, absolutely now, it has to be. >> Whose job is it to figure out the way in which the data is presented to the business? Where's the sort of presentation, the visualization plan, is that the data scientist role? Does that depend on whether or not you have that gene? Do you need a UI person on your team? Where does that fit? >> Wow, good question. >> Well usually that's the output, I mean, once you get to the point where you're visualizing the data, you've created an algorithm or some sort of code that produces that to be visualized, so at the end of the day that the customers can see what all the fuss is about from a data science perspective. But it's usually post the data science component. >> So do you run into situations where you can see it and it's blatantly obvious, but it doesn't necessarily translate to the business? >> Well there's an interesting challenge with data, and we throw the word data around a lot, and I've got this fun line I like throwing out there. If you torture data long enough, it will talk. So the challenge then is to figure out when to stop torturing it, right? And it's the same with models, and so I think in many other parts of organizations, we'll take something, if someone's doing a financial report on performance of the organization and they're doing it in a spreadsheet, they'll get two or three peers to review it, and validate that they've come up with a working model and the answer actually makes sense. And I think we're rushing so quickly at doing analysis on data that comes to us in various formats and high velocity that I think it's very important for us to actually stop and do peer reviews, of the models and the data and the output as well, because otherwise we start making decisions very quickly about things that may or may not be true. It's very easy to get the data to paint any picture you want, and you gave the example of the five different attempts at that thing, and I had this shoot out thing as well where I'll take in a team, I'll get two different people to do exactly the same thing in completely different rooms, and come back and challenge each other, and it's quite amazing to see the looks on their faces when they're like, oh, I didn't see that, and then go back and do it again until, and then just keep iterating until we get to the point where they both get the same outcome, in fact there's a really interesting anecdote about when the UNIX operation system was being written, and a couple of the authors went away and wrote the same program without realizing that each other were doing it, and when they came back, they actually had line for line, the same piece of C code, 'cause they'd actually gotten to a truth. A perfect version of that program, and I think we need to often look at, when we're building models and playing with data, if we can't come at it from different angles, and get the same answer, then maybe the answer isn't quite true yet, so there's a lot of risk in that. And it's the same with presentation, you know, you can paint any picture you want with the dashboard, but who's actually validating when the dashboard's painting the correct picture? >> James: Go ahead, please. >> There is a science actually, behind data visualization, you know if you're doing trending, it's a line graph, if you're doing comparative analysis, it's bar graph, if you're doing percentages, it's a pie chart, like there is a certain science to it, it's not that much of a mystery as the novice thinks there is, but what makes it challenging is that you also, just like any presentation, you have to consider your audience. And your audience, whenever we're delivering a solution, either insight, or just data in a grid, we really have to consider who is the consumer of this data, and actually cater the visual to that person or to that particular audience. And that is part of the art, and that is what makes a great data scientist. >> The consumer may in fact be the source of the data itself, like in a mobile app, so you're tuning their visualization and then their behavior is changing as a result, and then the data on their changed behavior comes back, so it can be a circular process. >> So Jim, at a recent conference, you were tweeting about the citizen data scientist, and you got emasculated by-- >> I spoke there too. >> Okay. >> TWI on that same topic, I got-- >> Kirk Borne I hear came after you. >> Kirk meant-- >> Called foul, flag on the play. >> Kirk meant well. I love Claudia Emahoff too, but yeah, it's a controversial topic. >> So I wonder what our panel thinks of that notion, citizen data scientist. >> Can I respond about citizen data scientists? >> David: Yeah, please. >> I think this term was introduced by Gartner analyst in 2015, and I think it's a very dangerous and misleading term. I think definitely we want to democratize the data and have access to more people, not just data scientists, but managers, BI analysts, but when there is already a term for such people, we can call the business analysts, because it implies some training, some understanding of the data. If you use the term citizen data scientist, it implies that without any training you take some data and then you find something there, and they think as Dev's mentioned, we've seen many examples, very easy to find completely spurious random correlations in data. So we don't want citizen dentists to treat our teeth or citizen pilots to fly planes, and if data's important, having citizen data scientists is equally dangerous, so I'm hoping that, I think actually Gartner did not use the term citizen data scientist in their 2016 hype course, so hopefully they will put this term to rest. >> So Gregory, you apparently are defining citizen to mean incompetent as opposed to simply self-starting. >> Well self-starting is very different, but that's not what I think what was the intention. I think what we see in terms of data democratization, there is a big trend over automation. There are many tools, for example there are many companies like Data Robot, probably IBM, has interesting machine learning capability towards automation, so I think I recently started a page on KDnuggets for automated data science solutions, and there are already 20 different forums that provide different levels of automation. So one can deliver in full automation maybe some expertise, but it's very dangerous to have part of an automated tool and at some point then ask citizen data scientists to try to take the wheels. >> I want to chime in on that. >> David: Yeah, pile on. >> I totally agree with all of that. I think the comment I just want to quickly put out there is that the space we're in is a very young, and rapidly changing world, and so what we haven't had yet is this time to stop and take a deep breath and actually define ourselves, so if you look at computer science in general, a lot of the traditional roles have sort of had 10 or 20 years of history, and so thorough the hiring process, and the development of those spaces, we've actually had time to breath and define what those jobs are, so we know what a systems programmer is, and we know what a database administrator is, but we haven't yet had a chance as a community to stop and breath and say, well what do we think these roles are, and so to fill that void, the media creates coinages, and I think this is the risk we've got now that the concept of a data scientist was just a term that was coined to fill a void, because no one quite knew what to call somebody who didn't come from a data science background if they were tinkering around data science, and I think that's something that we need to sort of sit up and pay attention to, because if we don't own that and drive it ourselves, then somebody else is going to fill the void and they'll create these very frustrating concepts like data scientist, which drives us all crazy. >> James: Miriam's next. >> So I wanted to comment, I agree with both of the previous comments, but in terms of a citizen data scientist, and I think whether or not you're citizen data scientist or an actual data scientist whatever that means, I think one of the most important things you can have is a sense of skepticism, right? Because you can get spurious correlations and it's like wow, my predictive model is so excellent, you know? And being aware of things like leaks from the future, right? This actually isn't predictive at all, it's a result of the thing I'm trying to predict, and so I think one thing I know that we try and do is if something really looks too good, we need to go back in and make sure, did we not look at the data correctly? Is something missing? Did we have a problem with the ETL? And so I think that a healthy sense of skepticism is important to make sure that you're not taking a spurious correlation and trying to derive some significant meaning from it. >> I think there's a Dilbert cartoon that I saw that described that very well. Joe, did you have a comment? >> I think that in order for citizen data scientists to really exist, I think we do need to have more maturity in the tools that they would use. My vision is that the BI tools of today are all going to be replaced with natural language processing and searching, you know, just be able to open up a search bar and say give me sales by region, and to take that one step into the future even further, it should actually say what are my sales going to be next year? And it should trigger a simple linear regression or be able to say which features of the televisions are actually affecting sales and do a clustering algorithm, you know I think hopefully that will be the future, but I don't see anything of that today, and I think in order to have a true citizen data scientist, you would need to have that, and that is pretty sophisticated stuff. >> I think for me, the idea of citizen data scientist I can relate to that, for instance, when I was in graduate school, I started doing some research on FDA data. It was an open source data set about 4.2 million data points. Technically when I graduated, the paper was still not published, and so in some sense, you could think of me as a citizen data scientist, right? I wasn't getting funding, I wasn't doing it for school, but I was still continuing my research, so I'd like to hope that with all the new data sources out there that there might be scientists or people who are maybe kept out of a field people who wanted to be in STEM and for whatever life circumstance couldn't be in it. That they might be encouraged to actually go and look into the data and maybe build better models or validate information that's out there. >> So Justin, I'm sorry you had one comment? >> It seems data science was termed before academia adopted formalized training for data science. But yeah, you can make, like Dez said, you can make data work for whatever problem you're trying to solve, whatever answer you see, you want data to work around it, you can make it happen. And I kind of consider that like in project management, like data creep, so you're so hyper focused on a solution you're trying to find the answer that you create an answer that works for that solution, but it may not be the correct answer, and I think the crossover discussion works well for that case. >> So but the term comes up 'cause there's a frustration I guess, right? That data science skills are not plentiful, and it's potentially a bottleneck in an organization. Supposedly 80% of your time is spent on cleaning data, is that right? Is that fair? So there's a problem. How much of that can be automated and when? >> I'll have a shot at that. So I think there's a shift that's going to come about where we're going to move from centralized data sets to data at the edge of the network, and this is something that's happening very quickly now where we can't just hold everything back to a central spot. When the internet of things actually wakes up. Things like the Boeing Dreamliner 787, that things got 6,000 sensors in it, produces half a terabyte of data per flight. There are 87,400 flights per day in domestic airspace in the U.S. That's 43.5 petabytes of raw data, now that's about three years worth of disk manufacturing in total, right? We're never going to copy that across one place, we can't process, so I think the challenge we've got ahead of us is looking at how we're going to move the intelligence and the analytics to the edge of the network and pre-cook the data in different tiers, so have a look at the raw material we get, and boil it down to a slightly smaller data set, bring a meta data version of that back, and eventually get to the point where we've only got the very minimum data set and data points we need to make key decisions. Without that, we're already at the point where we have too much data, and we can't munch it fast enough, and we can't spin off enough tin even if we witch the cloud on, and that's just this never ending deluge of noise, right? And you've got that signal versus noise problem so then we're now seeing a shift where people looking at how do we move the intelligence back to the edge of network which we actually solved some time ago in the securities space. You know, spam filtering, if an emails hits Google on the west coast of the U.S. and they create a check some for that spam email, it immediately goes into a database, and nothing gets on the opposite side of the coast, because they already know it's spam. They recognize that email coming in, that's evil, stop it. So we've already fixed its insecurity with intrusion detection, we've fixed it in spam, so we now need to take that learning, and bring it into business analytics, if you like, and see where we're finding patterns and behavior, and brew that out to the edge of the network, so if I'm seeing a demand over here for tickets on a new sale of a show, I need to be able to see where else I'm going to see that demand and start responding to that before the demand comes about. I think that's a shift that we're going to see quickly, because we'll never keep up with the data munching challenge and the volume's just going to explode. >> David: We just have a couple minutes. >> That does sound like a great topic for a future Cube panel which is data science on the edge of the fog. >> I got a hundred questions around that. So we're wrapping up here. Just got a couple minutes. Final thoughts on this conversation or any other pieces that you want to punctuate. >> I think one thing that's been really interesting for me being on this panel is hearing all of my co-panelists talking about common themes and things that we are also experiencing which isn't a surprise, but it's interesting to hear about how ubiquitous some of the challenges are, and also at the announcement earlier today, some of the things that they're talking about and thinking about, we're also talking about and thinking about. So I think it's great to hear we're all in different countries and different places, but we're experiencing a lot of the same challenges, and I think that's been really interesting for me to hear about. >> David: Great, anybody else, final thoughts? >> To echo Dez's thoughts, it's about we're never going to catch up with the amount of data that's produced, so it's about transforming big data into smart data. >> I could just say that with the shift from normal data, small data, to big data, the answer is automate, automate, automate, and we've been talking about advanced algorithms and machine learning for the science for changing the business, but there also needs to be machine learning and advanced algorithms for the backroom where we're actually getting smarter about how we ingestate and how we fix data as it comes in. Because we can actually train the machines to understand data anomalies and what we want to do with them over time. And I think the further upstream we get of data correction, the less work there will be downstream. And I also think that the concept of being able to fix data at the source is gone, that's behind us. Right now the data that we're using to analyze to change the business, typically we have no control over. Like Dez said, they're coming from censors and machines and internet of things and if it's wrong, it's always going to be wrong, so we have to figure out how to do that in our laboratory. >> Eaves, final thoughts? >> I think it's a mind shift being a data scientist if you look back at the time why did you start developing or writing code? Because you like to code, whatever, just for the sake of building a nice algorithm or a piece of software, or whatever, and now I think with the spirit of a data scientist, you're looking at a problem and say this is where I want to go, so you have more the top down approach than the bottom up approach. And have the big picture and that is what you really need as a data scientist, just look across technologies, look across departments, look across everything, and then on top of that, try to apply as much skills as you have available, and that's kind of unicorn that they're trying to look for, because it's pretty hard to find people with that wide vision on everything that is happening within the company, so you need to be aware of technology, you need to be aware of how a business is run, and how it fits within a cultural environment, you have to work with people and all those things together to my belief to make it very difficult to find those good data scientists. >> Jim? Your final thought? >> My final thoughts is this is an awesome panel, and I'm so glad that you've come to New York, and I'm hoping that you all stay, of course, for the the IBM Data First launch event that will take place this evening about a block over at Hudson Mercantile, so that's pretty much it. Thank you, I really learned a lot. >> I want to second Jim's thanks, really, great panel. Awesome expertise, really appreciate you taking the time, and thanks to the folks at IBM for putting this together. >> And I'm big fans of most of you, all of you, on this session here, so it's great just to meet you in person, thank you. >> Okay, and I want to thank Jeff Frick for being a human curtain there with the sun setting here in New York City. Well thanks very much for watching, we are going to be across the street at the IBM announcement, we're going to be on the ground. We open up again tomorrow at 9:30 at Big Data NYC, Big Data Week, Strata plus the Hadoop World, thanks for watching everybody, that's a wrap from here. This is the Cube, we're out. (techno music)