(bright upbeat music) >> Greetings, brilliant community and thank you so much for tuning in to theCUBE here for the last three days where we've been live from Detroit, Michigan. I've had the pleasure of spending this week with Lisa Martin and John Furrier. Thank you both so much for hanging out, for inviting me into the CUBE family. It's our first show together, it's been wonderful. >> Thank you. >> You nailed it. >> Oh thanks, sweetheart. >> Great job. Great job team, well done. Free wall to wall coverage, it's what we do. We stay till everyone else-- >> Savannah: 100 percent. >> Everyone else leaves, till they pull the plug. >> Lisa: Till they turn the lights out. We're still there. >> Literally. >> Literally last night. >> Still broadcasting. >> Whatever takes to get the stories and get 'em out there at scale. >> Yeah. >> Great time. >> 33. 33 different segments too. Very impressive. John, I'm curious, you're a trend watcher and you've been at every single KubeCon. >> Yep. >> What are the trends this year? Give us the breakdown. >> I think CNCF does this, it's a hard job to balance all the stakeholders. So one, congratulations to the CNCF for another great KubeCon and CloudNativeCon. It is really hard to balance bringing in the experts who, as time goes by, seven years we've been all of, as you said, you get experts, you get seniority, and people who can be mentors, 60% new people. You have vendors who are sponsoring and there's always people complaining and bitching and moaning. They want this, they want that. It's always hard and they always do a good job of balancing it. We're lucky that we get to scale the stories with CUBE and that's been great. We had some great stories here, but it's a great community and again, they're inclusive. As I've said before, we've talked about it. This year though is an inflection point in my opinion, because you're seeing the developer ecosystem growing so fast. It's global. You're seeing events pop up, you're seeing derivative events. CNCF is at the center point and they have to maintain the culture of developer experts, maintainers, while balancing the newbies. And that's going to be >> Savannah: Mm-hmm. really hard. And they've done a great job. We had a great conversation with them. So great job. And I think it's going to continue. I think the attendance metric is a little bit of a false positive. There's a lot of online people who didn't come to Detroit this year. And I think maybe the combination of the venue, the city, or just Covid preferences may not look good on paper, on the numbers 'cause it's not a major step up in attendance. It's still bigger, but the community, I think, is going to continue to grow. I'm bullish on it. >> Yeah, I mean at least we did see double the number of people that we had in Los Angeles. Very curious. I think Amsterdam, where we'll be next with CNCF in the spring, in April. I think that's actually going to be a better pulse check. We'll be in Europe, we'll see what's going on. >> John: Totally. >> I mean, who doesn't like Amsterdam in the springtime? Lisa, what have been some of your observations? >> Oh, so many observations. The evolution of the conference, the hallway track conversations really shifting towards adjusting to the enterprise. The enterprise momentum that we saw here as well. We had on the show, Ford. >> Savannah: Yes. We had MassMutual, we had ING, that was today. Home Depot is here. We are seeing all these big companies that we know and love, become software companies right before our eyes. >> Yeah. Well, and I think we forget that software powers our entire world. And so of course they're going to have to be here. So much running on Kubernetes. It's on-prem, it's at the edge, it's everywhere. It's exciting. Woo, I'm excited. John, what do you think is the number one story? This is your question. I love asking you this question. What is the number one story out KubeCon? >> Well, I think the top story is a combination of two things. One is the evolution of Cloud Native. We're starting to see web assembly. That's a big hyped up area. It got a lot of attention. >> Savannah: Yeah. That's kind of teething out the future. >> Savannah: Rightfully so. The future of this kind of lightweight. You got the heavy duty VMs, you got Kubernetes and containers, and now this web assembly, shows a trajectory of apps, server-like environment. And then the big story is security. Software supply chain is, to me, was the number one consistent theme. At almost all the interviews, in the containers, and the workflows, >> Savannah: Very hot. software supply chain is real. The CD Foundation mentioned >> Savannah: Mm-hmm. >> they had 16,000 vulnerabilities identified in their code base. They were going to automate that. So again, >> Savannah: That was wild. >> That's the top story. The growth of open source exposes potential vulnerabilities with security. So software supply chain gets my vote. >> Did you hear anything that surprised you? You guys did this great preview of what you thought we were going to hear and see and feel and touch at KubeCon, CloudNativeCon 2022. You talked about, for example, the, you know, healthcare financial services being early adopters of this. Anything surprise either one of you in terms of what you predicted versus what we saw? Savannah, let's start with you. >> You know what really surprised me, and this is ironic, so I'm a community gal by trade. But I was really just impressed by the energy that everyone brought here and the desire to help. The thing about the open source community that always strikes me is, I mean 187 different countries participating. You've got, I believe it's something like 175,000 people contributing to the 140 projects plus that CNCF is working on. But that culture of collaboration extends far beyond just the CNCF projects. Everyone here is keen to help each other. We had the conversation just before about the teaching and the learnings that are going on here. They brought in Detroit's students to come and learn, which is just the most heartwarming story out of this entire thing. And I think it's just the authenticity of everyone in this community and their passion. Even though I know it's here, it still surprises me to see it in the flesh. Especially in a place like Detroit. >> It's nice. >> Yeah. >> It's so nice to see it. And you bring up a good point. It's very authentic. >> Savannah: It's super authentic. >> I mean, what surprised me is one, the Wasm, or web assembly. I didn't see that coming at the scale of the conversation. It sucked a lot of options out of the room in my opinion, still hyped up. But this looks like it's got a good trajectory. I like that. The other thing that surprised me that was a learning was my interview with Solo.io, Idit, and Brian Gracely, because he's a CUBE alumni and former host of theCUBE, and analyst at Wikibon, was how their go-to-market was an example of a modern company in Covid with a clean sheet of paper and smart people, they're just doing things different. They're in Slack with their customers. And I walked away with, "Wow that's like a playbook that's not, was never, in the go-to-market VC-backed company playbook." I thought that was, for me, a personal walk away saying that's important. I like how they did that. And there's a lot of companies I think could learn from that. Especially as the recession comes where partnering with customers has always been a top priority. And how they did that was very clever, very effective, very efficient. So I walked away with that saying, "I think that's going to be a standard." So that was a pleasant surprise. >> That was a great surprise. Also, that's a female-founded company, which is obviously not super common. And the growth that they've experienced, to your point, really being catalyzed by Covid, is incredibly impressive. I mean they have some massive brand name customers, Amex, BMW for example. >> Savannah: Yeah. >> Great point. >> And I interviewed her years ago and I remember saying to myself, "Wow, she's impressive." I liked her. She's a player. A player for sure. And she's got confidence. Even on the interview she said, "We're just better, we have better product." And I just like the point of view. Very customer-focused but confident. And I just took, that's again, a great company. And again, I'm not surprised that Brian Gracely left Red Hat to go work there. So yeah, great, great call there. And of course other things that weren't surprising that I predicted, Red Hat continued to invest. They continue to bring people on theCUBE, they support theCUBE but more importantly they have a good strategy. They're in that multicloud positioning. They're going to have an opportunity to get a bite at the apple. And I what I call the supercloud. As enterprises try to go and be mainstream, Cloud Native, they're going to need some help. And Red Hat is always has the large enterprise customers. >> Savannah: What surprised you, Lisa? >> Oh my gosh, so many things. I think some of the memorable conversations that we had. I love talking with some of the enterprises that we mentioned, ING Bank for example. You know, or institutions that have been around for 100 plus years. >> Savannah: Oh, yeah. To see not only how much they've innovated and stayed relevant to meet the demands of the consumer, which are only increasing, but they're doing so while fostering a culture of innovation and a culture that allows these technology leaders to really grow within the organization. That was a really refreshing conversation that I think we had. 'Cause you can kind of >> Savannah: Absolutely. think about these old stodgy companies. Nah, of course they're going to digitize. >> Thinking about working for the bank, I think it's boring. >> Right? >> Yeah. And they were talking about, in fact, those great t-shirts that they had on, >> Yeah, yeah, yeah, yeah. were all about getting more people to understand how fun it is to work in tech for ING Bank in different industries. You don't just have to work for the big tech companies to be doing really cool stuff in technology. >> What I really liked about this show is we had two female hosts. >> Savannah: Yeah. >> How about that? Come on. >> Hey, well done, well done on your recruitment there, champ. >> Yes, thank you boss. (John laughs) >> And not to mention we have a really all-star production team. I do just want to give them a little shout out. To all the wonderful folks behind the lines here. (people clapping) >> John: Brendan. Good job. >> Yeah. Without Brendan, Anderson, Noah, and Andrew, we would be-- >> Of course Frank Faye holding it back there too. >> Yeah, >> Of course, Frank. >> I mean, without the business development wheels on the ship we'd really be in an unfortunate spot. I almost just swore on television. We're not going to do that. >> It's okay. No one's regulating. >> Yeah. (all laugh) >> Elon Musk just took over Twitter. >> It was a close call. >> That's right! >> It's going to be a hellscape. >> Yeah, I mean it's, shit's on fire. So we'll just see what happens next. I do, I really want to talk about this because I think it's really special. It's an ethos and some magic has happened here. Let's talk about Detroit. Let's talk about what it means to be here. We saw so many, and I can't stress this enough, but I think it really matters. There was a commitment to celebrating place here. Lisa, did you notice this too? >> Absolutely. And it surprised me because we just don't see that at conferences. >> Yeah. We're so used to going to the same places. >> Right. >> Vegas. Vegas, Vegas. More Vegas. >> Your tone-- >> San Francisco >> (both laugh) sums up my feelings. Yes. >> Right? >> Yeah. And, well, it's almost robotic but, and the fact that we're like, oh Detroit, really? But there was so much love for this city and recognizing and supporting its residents that we just don't see at conferences. You uncovered a lot of that with your swag-savvy segments, >> Savannah: Yeah. >> And you got more of that to talk about today. >> Don't worry, it's coming. Yeah. (laughs) >> What about you? Have you enjoyed Detroit? I know you hadn't been here in a long time, when we did our intro session. >> I think it's a bold move for the CNCF to come here and celebrate. What they did, from teaching the kids in the city some tech, they had a session. I thought that was good. >> Savannah: Loved that. I think it was a risky move because a lot of people, like, weren't sure if they were going to fly to Detroit. So some say it might impact the attendance. I thought they did a good job. Their theme, Road Ahead. Nice tie in. >> Savannah: Yeah. And so I think I enjoyed Detroit. The weather was great. It didn't rain. Nice breeze outside. >> Yeah. >> The weather was great, the restaurants are phenomenal. So Detroit's a good city. I missed some hockey games. I'd love to see the Red Wings play. Missed that game. But we always come back. >> I think it's really special. I mean, every time I talked to a company about their swag, that had sourced it locally, there was a real reason for this story. I mean even with Kasten in that last segment when I noticed that they had done Carhartt beanies, Carhartt being a Michigan company. They said, "I'm so glad you noticed. That's why we did it." And I think that type of, the community commitment to place, it all comes back to community. One of the bigger themes of the show. But that passion and that support, we need more of that. >> Lisa: Yeah. >> And the thing about the guests we've had this past three days have been phenomenal. We had a diverse set of companies, individuals come on theCUBE, you know, from Scott Johnston at Docker. A really one on one. We had a great intense conversation. >> Savannah: Great way to kick it off. >> We shared a lot of inside baseball, about Docker, super important company. You know, impressed with companies like Platform9 it's been around since the OpenStack days who are now in a relevant position. Rafi Systems, hot startup, they don't have a lot of resources, a lot of guerilla marketing going on. So I love to see the mix of startups really contributing. The big players are here. So it's a real great mix of companies. And I thought the interviews were phenomenal, like you said, Ford. We had, Kubia launched on theCUBE. >> Savannah: Yes. >> That's-- >> We snooped the location for KubeCon North America. >> You did? >> Chicago, everyone. In case you missed it, Bianca was nice enough to share that with us. >> We had Sarbjeet Johal, CUBE analyst came on, Keith Townsend, yesterday with you guys. >> We had like analyst speed dating last night. (all laugh) >> How'd that go? (laughs) >> It was actually great. One of the things that they-- >> Did they hug and kiss at the end? >> Here's the funny thing is that they were debating the size of the CNC app. One thinks it's too big, one thinks it's too small. And I thought, is John Goldilocks? (John laughs) >> Savannah: Yeah. >> What is John going to think about that? >> Well I loved that segment. I thought, 'cause Keith and Sarbjeet argue with each other on Twitter all the time. And I heard Keith say before, he went, "Yeah let's have it out on theCUBE." So that was fun to watch. >> Thank you for creating this forum for us to have that kind of discourse. >> Lisa: Yes, thank you. >> Well, it wouldn't be possible without the sponsors. Want to thank the CNCF. >> Absolutely. >> And all the ecosystem partners and sponsors that make theCUBE possible. We love doing this. We love getting the stories. No story's too small for theCUBE. We'll go with it. Do whatever it takes. And if it wasn't for the sponsors, the community wouldn't get all the great knowledge. So, and thank you guys. >> Hey. Yeah, we're, we're happy to be here. Speaking of sponsors and vendors, should we talk a little swag? >> Yeah. >> What do you guys think? All right. Okay. So now this is becoming a tradition on theCUBE so I'm very delighted, the savvy swag segment. I do think it's interesting though. I mean, it's not, this isn't just me shouting out folks and showing off t-shirts and socks. It's about standing out from the noise. There's a lot of players in this space. We got a lot of CNCF projects and one of the ways to catch the attention of people walking the show floor is to have interesting swag. So we looked for the most unique swag on Wednesday and I hadn't found this yet, but I do just want to bring it up. Oops, I think I might have just dropped it. This is cute. Is, most random swag of the entire show goes to this toothbrush. I don't really have more in terms of the pitch there because this is just random. (Lisa laughs) >> But so, everyone needs that. >> John: So what's their tagline? >> And you forget these. >> Yeah, so the idea was to brush your cloud bills. So I think they're reducing the cost of-- >> Kind of a hygiene angle. >> Yeah, yeah. Very much a hygiene angle, which I found a little ironic in this crowd to be completely honest with you. >> John: Don't leave the lights on theCUBE. That's what they say. >> Yeah. >> I mean we are theCUBE so it would be unjust of me not to show you a Rubik's cube. This is actually one of those speed cubes. I'm not going to be able to solve this for you with one hand on camera, but apparently someone did it in 17 seconds at the booth. Knowing this audience, not surprising to me at all. Today we are, and yesterday, was the t-shirt contest. Best t-shirt contest. Today we really dove into the socks. So this is, I noticed this trend at KubeCon in Los Angeles last year. Lots of different socks, clouds obviously a theme for the cloud. I'm just going to lay these out. Lots of gamers in the house. Not surprising. Here on this one. >> John: Level up. >> Got to level up. I love these 'cause they say, "It's not a bug." And anyone who's coded has obviously had to deal with that. We've got, so Star Wars is a huge theme here. There's Lego sets. >> John: I think it's Star Trek. But. >> That's Star Trek? >> John: That's okay. >> Could be both. (Lisa laughs) >> John: Nevermind, I don't want to. >> You can flex your nerd and geek with us anytime you want, John. I don't mind getting corrected. I'm all about, I'm all about the truth. >> Star Trek. Star Wars. Okay, we're all the same. Okay, go ahead. >> Yeah, no, no, this is great. Slim.ai was nice enough to host us for dinner on Tuesday night. These are their lovely cloud socks. You can see Cloud Native, obviously Cloud Native Foundation, cloud socks, whole theme here. But if we're going to narrow it down to some champions, I love these little bee elephants from Raft. And when I went up to these guys, I actually probably would've called these my personal winner. They said, again, so community focused and humble here at CNCF, they said that Wiz was actually the champion according to the community. These unicorn socks are pretty excellent. And I have to say the branding is flawless. So we'll go ahead and give Wiz the win on the best sock contest. >> John: For the win. >> Yeah, Wiz for the win. However, the thing that I am probably going to use the most is this really dope Detroit snapback from Kasten. So I'm going to be rocking this from now on for the rest of the segment as well. And I feel great about this snapback. >> Looks great. Looks good on you. >> Yeah. >> Thanks John. (John laughs) >> So what are we expecting between now and KubeCon in Amsterdam? >> Well, I think it's going to be great to see how they, the European side, it's a chill show. It's great. Brings in the European audience from the global perspective. I always love the EU shows because one, it's a great destination. Amsterdam's going to be a great location. >> Savannah: I'm pumped. >> The American crowd loves going over there. All the event cities that they choose are always awesome. I missed Valencia cause I got Covid. I'm really bummed about that. But I love the European shows. It's just a little bit, it's high intensity, but it's the European chill. They got a little bit more of that siesta vibe going on. >> Yeah. >> And it's just awesome. >> Yeah, >> And I think that the mojo that carried throughout this week, it's really challenging to not only have a show that's five days, >> but to go through all week, >> Savannah: Seriously. >> to a Friday at 4:00 PM Eastern Time, and still have the people here, the energy and all the collaboration. >> Savannah: Yeah. >> The conversations that are still happening. I think we're going to see a lot more innovation come spring 2023. >> Savannah: Mm-hmm. >> Yeah. >> So should we do a bet, somebody's got to buy dinner? Who, well, I guess the folks who lose this will buy dinner for the other one. How many attendees do you think we'll see in Amsterdam? So we had 4,000, >> Oh, I'm going to lose this one. >> roughly in Los Angeles. Priyanka was nice enough to share with us, there was 8,000 here in Detroit. And I'm talking in person, we're not going to meddle this with the online. >> 6500. >> Lisa: I was going to say six, six K. >> I'm going 12,000. >> Ooh! >> I'm going to go ahead and go big I'm going to go opposite Price Is Right. >> One dollar. >> Yeah. (all laugh) That's exactly where I was driving with it. I'm going, I'm going absolutely all in. I think the momentum here is building. I think if we look at the numbers from-- >> John: You could go Family Feud >> Yeah, yeah, exactly. And they mentioned that they had 11,000 people who have taken their Kubernetes course in that first year. If that's a benchmark and an indicator, we've got the veteran players here. But I do think that, I personally think that the hype of Kubernetes has actually preceded adoption. If you look at the data and now we're finally tipping over. I think the last two years we were on the fringe and right now we're there. It's great. (voice blares loudly on loudspeaker) >> Well, on that note (all laugh) On that note, actually, on that note, as we are talking, so I got to give cred to my cohosts. We deal with a lot of background noise here on theCUBE. It is a live show floor. There's literally someone on an e-scooter behind me. There's been Pong going on in the background. The sound will haunt the three of us for the rest of our lives, as well as the production crew. (Lisa laughs) And, and just as we're sitting here doing this segment last night, they turned the lights off on us, today they're letting everyone know that the event is over. So on that note, I just want to say, Lisa, thank you so much. Such a warm welcome to the team. >> Thank you. >> John, what would we do without you? >> You did an amazing job. First CUBE, three days. It's a big show. You got staying power, I got to say. >> Lisa: Absolutely. >> Look at that. Not bad. >> You said it on camera now. >> Not bad. >> So you all are stuck with me. (all laugh) >> A plus. Great job to the team. Again, we do so much flow here. Brandon, Team, Andrew, Noah, Anderson, Frank. >> They're doing our hair, they're touching up makeup. They're helping me clean my teeth, staying hydrated. >> We look good because of you. >> And the guests. Thanks for coming on and spending time with us. And of course the sponsors, again, we can't do it without the sponsors. If you're watching this and you're a sponsor, support theCUBE, it helps people get what they need. And also we're do a lot more segments around community and a lot more educational stuff. >> Savannah: Yeah. So we're going to do a lot more in the EU and beyond. So thank you. >> Yeah, thank you. And thank you to everyone. Thank you to the community, thank you to theCUBE community and thank you for tuning in, making it possible for us to have somebody to talk to on the other side of the camera. My name is Savannah Peterson for the last time in Detroit, Michigan. Thanks for tuning into theCUBE. >> Okay, we're done. (bright upbeat music)

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle, everybody to Cuban cloud. My name is Dave Volonte, and I'll be here throughout the day with my co host, John Ferrier, who was quarantined in an undisclosed location in California. He's all good. Don't worry. Just precautionary. John, how are you doing? >>Hey, great to see you. John. Quarantine. My youngest daughter had covitz, so contact tracing. I was negative in quarantine at a friend's location. All good. >>Well, we wish you the best. Yeah, well, right. I mean, you know what's it like, John? I mean, you're away from your family. Your basically shut in, right? I mean, you go out for a walk, but you're really not in any contact with anybody. >>Correct? Yeah. I mean, basically just isolation, Um, pretty much what everyone's been kind of living on, kind of suffering through, but hopefully the vaccines are being distributed. You know, one of the things we talked about it reinvent the Amazon's cloud conference. Was the vaccine on, but just the whole workflow around that it's gonna get better. It's kind of really sucky. Here in the California area, they haven't done a good job, a lot of criticism around, how that's rolling out. And, you know, Amazon is now offering to help now that there's a new regime in the U. S. Government S o. You know, something to talk about, But certainly this has been a terrible time for Cove it and everyone in the deaths involved. But it's it's essentially pulled back the covers, if you will, on technology and you're seeing everything. Society. In fact, um, well, that's big tech MIT disinformation campaigns. All these vulnerabilities and cyber, um, accelerated digital transformation. We'll talk about a lot today, but yeah, it's totally changed the world. And I think we're in a new generation. I think this is a real inflection point, Dave. You know, modern society and the geo political impact of this is significant. You know, one of the benefits of being quarantined you'd be hanging out on these clubhouse APS, uh, late at night, listening to experts talk about what's going on, and it's interesting what's happening with with things like water and, you know, the island of Taiwan and China and U. S. Sovereignty, data, sovereignty, misinformation. So much going on to talk about. And, uh, meanwhile, companies like Mark injuries in BC firm starting a media company. What's going on? Hell freezing over. So >>we're gonna be talking about a lot of that stuff today. I mean, Cuba on cloud. It's our very first virtual editorial event we're trying to do is bring together our community. It's a it's an open forum and we're we're running the day on our 3 65 software platform. So we got a great lineup. We got CEO Seo's data Practitioners. We got a hard core technologies coming in, cloud experts, investors. We got some analysts coming in and we're creating this day long Siri's. And we've got a number of sessions that we've developed and we're gonna unpack. The future of Cloud computing in the coming decade is, John said, we're gonna talk about some of the public policy new administration. What does that mean for tech and for big tech in General? John, what can you add to that? >>Well, I think one of the things that we talked about Cove in this personal impact to me but other people as well. One of the things that people are craving right now is information factual information, truth texture that we call it. But hear this event for us, Davis, our first inaugural editorial event. Robbo, Kristen, Nicole, the entire Cube team Silicon angle, really trying to put together Morva cadence we're gonna doom or of these events where we can put out feature the best people in our community that have great fresh voices. You know, we do interview the big names Andy Jassy, Michael Dell, the billionaires with people making things happen. But it's often the people under there that are the rial newsmakers amid savory, for instance, that Google one of the most impressive technical people, he's gotta talk. He's gonna present democratization of software development in many Mawr riel people making things happen. And I think there's a communal element. We're going to do more of these. Obviously, we have, uh, no events to go to with the Cube. So we have the cube virtual software that we have been building and over years and now perfecting and we're gonna introduce that we're gonna put it to work, their dog footing it. We're gonna put that software toe work. We're gonna do a lot mawr virtual events like this Cuban cloud Cuban startup Cuban raising money. Cuban healthcare, Cuban venture capital. Always think we could do anything. Question is, what's the right story? What's the most important stories? Who's telling it and increase the aperture of the lens of the industry that we have and and expose that and fastest possible. That's what this software, you'll see more of it. So it's super exciting. We're gonna add new features like pulling people up on stage, Um, kind of bring on the clubhouse vibe and more of a community interaction with people to meet each other, and we'll roll those out. But the goal here is to just showcase it's cloud story in a way from people that are living it and providing value. So enjoy the day is gonna be chock full of presentations. We're gonna have moderated chat in these sessions, so it's an all day event so people can come in, drop out, and also that's everything's on demand immediately after the time slot. But you >>want to >>participate, come into the time slot into the cube room or breakout session. Whatever you wanna call it, it's a cube room, and the people in there chatting and having a watch party. So >>when you're in that home page when you're watching, there's a hero video there. Beneath that, there's a calendar, and you'll see that red line is that red horizontal line of vertical line is rather, it's a linear clock that will show you where we are in the day. If you click on any one of those sessions that will take you into the chat, we'll take you through those in a moment and share with you some of the guests that we have upcoming and and take you through the day what I wanted to do. John is trying to set the stage for the conversations that folks are gonna here today. And to do that, I wanna ask the guys to bring up a graphic. And I want to talk to you, John, about the progression of cloud over time and maybe go back to the beginning and review the evolution of cloud and then really talk a little bit about where we think it Z headed. So, guys, if you bring up that graphic when a W S announced s three, it was March of 2000 and six. And as you recall, John you know, nobody really. In the vendor and user community. They didn't really pay too much attention to that. And then later that year, in August, it announced E C two people really started. They started to think about a new model of computing, but they were largely, you know, chicken tires. And it was kind of bleeding edge developers that really leaned in. Um what? What were you thinking at the time? When when you saw, uh, s three e c to this retail company coming into the tech world? >>I mean, I thought it was totally crap. I'm like, this is terrible. But then at that time, I was thinking working on I was in between kind of start ups and I didn't have a lot of seed funding. And then I realized the C two was freaking awesome. But I'm like, Holy shit, this is really great because I don't need to pay a lot of cash, the Provisional Data center, or get a server. Or, you know, at that time, state of the art startup move was to buy a super micro box or some sort of power server. Um, it was well past the whole proprietary thing. But you have to assemble probably anyone with 5 to 8 grand box and go in, and we'll put a couple ghetto rack, which is basically, uh, you know, you put it into some coasting location. It's like with everybody else in the tech ghetto of hosting, still paying monthly fees and then maintaining it and provisioning that's just to get started. And then Amazon was just really easy. And then from there you just It was just awesome. I just knew Amazon would be great. They had a lot of things that they had to fix. You know, custom domains and user interface Council got better and better, but it was awesome. >>Well, what we really saw the cloud take hold from my perspective anyway, was the financial crisis in, you know, 709 It put cloud on the radar of a number of CFOs and, of course, shadow I T departments. They wanted to get stuff done and and take I t in in in, ah, pecs, bite sized chunks. So it really was. There's cloud awakening and we came out of that financial crisis, and this we're now in this 10 year plus boom um, you know, notwithstanding obviously the economic crisis with cove it. But much of it was powered by the cloud in the decade. I would say it was really about I t transformation. And it kind of ironic, if you will, because the pandemic it hits at the beginning of this decade, >>and it >>creates this mandate to go digital. So you've you've said a lot. John has pulled forward. It's accelerated this industry transformation. Everybody talks about that, but and we've highlighted it here in this graphic. It probably would have taken several more years to mature. But overnight you had this forced march to digital. And if you weren't a digital business, you were kind of out of business. And and so it's sort of here to stay. How do you see >>You >>know what this evolution and what we can expect in the coming decades? E think it's safe to say the last 10 years defined by you know, I t transformation. That's not gonna be the same in the coming years. How do you see it? >>It's interesting. I think the big tech companies are on, but I think this past election, the United States shows um, the power that technology has. And if you look at some of the main trends in the enterprise specifically around what clouds accelerating, I call the second wave of innovations coming where, um, it's different. It's not what people expect. Its edge edge computing, for instance, has talked about a lot. But industrial i o t. Is really where we've had a lot of problems lately in terms of hacks and malware and just just overall vulnerabilities, whether it's supply chain vulnerabilities, toe actual disinformation, you know, you know, vulnerabilities inside these networks s I think this network effects, it's gonna be a huge thing. I think the impact that tech will have on society and global society geopolitical things gonna be also another one. Um, I think the modern application development of how applications were written with data, you know, we always been saying this day from the beginning of the Cube data is his integral part of the development process. And I think more than ever, when you think about cloud and edge and this distributed computing paradigm, that cloud is now going next level with is the software and how it's written will be different. You gotta handle things like, where's the compute component? Is it gonna be at the edge with all the server chips, innovations that Amazon apple intel of doing, you're gonna have compute right at the edge, industrial and kind of human edge. How does that work? What's Leighton see to that? It's it really is an edge game. So to me, software has to be written holistically in a system's impact on the way. Now that's not necessarily nude in the computer science and in the tech field, it's just gonna be deployed differently. So that's a complete rewrite, in my opinion of the software applications. Which is why you're seeing Amazon Google VM Ware really pushing Cooper Netease and these service messes in the micro Services because super critical of this technology become smarter, automated, autonomous. And that's completely different paradigm in the old full stack developer, you know, kind of model. You know, the full stack developer, his ancient. There's no such thing as a full stack developer anymore, in my opinion, because it's a half a stack because the cloud takes up the other half. But no one wants to be called the half stack developer because it doesn't sound as good as Full Stack, but really Cloud has eliminated the technology complexity of what a full stack developer used to dio. Now you can manage it and do things with it, so you know, there's some work to done, but the heavy lifting but taking care of it's the top of the stack that I think is gonna be a really critical component. >>Yeah, and that that sort of automation and machine intelligence layer is really at the top of the stack. This this thing becomes ubiquitous, and we now start to build businesses and new processes on top of it. I wanna I wanna take a look at the Big Three and guys, Can we bring up the other The next graphic, which is an estimate of what the revenue looks like for the for the Big three. And John, this is I asked and past spend for the Big Three Cloud players. And it's It's an estimate that we're gonna update after earning seasons, and I wanna point a couple things out here. First is if you look at the combined revenue production of the Big Three last year, it's almost 80 billion in infrastructure spend. I mean, think about that. That Z was that incremental spend? No. It really has caused a lot of consolidation in the on Prem data center business for guys like Dell. And, you know, um, see, now, part of the LHP split up IBM Oracle. I mean, it's etcetera. They've all felt this sea change, and they had to respond to it. I think the second thing is you can see on this data. Um, it's true that azure and G C P they seem to be growing faster than a W s. We don't know the exact numbers >>because >>A W S is the only company that really provides a clean view of i s and pass. Whereas Microsoft and Google, they kind of hide the ball in their numbers. I mean, I don't blame them because they're behind, but they do leave breadcrumbs and clues about growth rates and so forth. And so we have other means of estimating, but it's it's undeniable that azure is catching up. I mean, it's still quite distance the third thing, and before I want to get your input here, John is this is nuanced. But despite the fact that Azure and Google the growing faster than a W s. You can see those growth rates. A W s I'll call this out is the only company by our estimates that grew its business sequentially last quarter. Now, in and of itself, that's not significant. But what is significant is because AWS is so large there $45 billion last year, even if the slower growth rates it's able to grow mawr and absolute terms than its competitors, who are basically flat to down sequentially by our estimates. Eso So that's something that I think is important to point out. Everybody focuses on the growth rates, but it's you gotta look at also the absolute dollars and, well, nonetheless, Microsoft in particular, they're they're closing the gap steadily, and and we should talk more about the competitive dynamics. But I'd love to get your take on on all this, John. >>Well, I mean, the clouds are gonna win right now. Big time with the one the political climate is gonna be favoring Big check. But more importantly, with just talking about covert impact and celebrating the digital transformation is gonna create a massive rising tide. It's already happening. It's happening it's happening. And again, this shift in programming, uh, models are gonna really kinda accelerating, create new great growth. So there's no doubt in my mind of all three you're gonna win big, uh, in the future, they're just different, You know, the way they're going to market position themselves, they have to be. Google has to be a little bit different than Amazon because they're smaller and they also have different capabilities, then trying to catch up. So if you're Google or Microsoft, you have to have a competitive strategy to decide. How do I wanna ride the tide If you will put the rising tide? Well, if I'm Amazon, I mean, if I'm Microsoft and Google, I'm not going to try to go frontal and try to copy Amazon because Amazon is just pounding lead of features and scale and they're different. They were, I would say, take advantage of the first mover of pure public cloud. They really awesome. It passed and I, as they've integrated in Gardner, now reports and integrated I as and passed components. So Gardner finally got their act together and said, Hey, this is really one thing. SAS is completely different animal now Microsoft Super Smart because they I think they played the right card. They have a huge installed base converted to keep office 3 65 and move sequel server and all their core jewels into the cloud as fast as possible, clarified while filling in the gaps on the product side to be cloud. So you know, as you're doing trends job, they're just it's just pedal as fast as you can. But Microsoft is really in. The strategy is just go faster trying. Keep pedaling fast, get the features, feature velocity and try to make it high quality. Google is a little bit different. They have a little power base in terms of their network of strong, and they have a lot of other big data capabilities, so they have to use those to their advantage. So there is. There is there is competitive strategy game application happening with these companies. It's not like apples, the apples, In my opinion, it never has been, and I think that's funny that people talk about it that way. >>Well, you're bringing up some great points. I want guys bring up the next graphic because a lot of things that John just said are really relevant here. And what we're showing is that's a survey. Data from E. T. R R Data partners, like 1400 plus CEOs and I T buyers and on the vertical axis is this thing called Net score, which is a measure of spending momentum. And the horizontal axis is is what's called market share. It's a measure of the pervasiveness or, you know, number of mentions in the data set. There's a couple of key points I wanna I wanna pick up on relative to what John just said. So you see A W S and Microsoft? They stand alone. I mean, they're the hyper scale er's. They're far ahead of the pack and frankly, they have fall down, toe, lose their lead. They spend a lot on Capex. They got the flywheel effects going. They got both spending velocity and large market shares, and so, but they're taking a different approach. John, you're right there living off of their SAS, the state, their software state, Andi, they're they're building that in to their cloud. So they got their sort of a captive base of Microsoft customers. So they've got that advantage. They also as we'll hear from from Microsoft today. They they're building mawr abstraction layers. Andy Jassy has said We don't wanna be in that abstraction layer business. We wanna have access to those, you know, fine grain primitives and eso at an AP level. So so we can move fast with the market. But but But so those air sort of different philosophies, John? >>Yeah. I mean, you know, people who know me know that I love Amazon. I think their product is superior at many levels on in its way that that has advantages again. They have a great sass and ecosystem. They don't really have their own SAS play, although they're trying to add some stuff on. I've been kind of critical of Microsoft in the past, but one thing I'm not critical of Microsoft, and people can get this wrong in the marketplace. Actually, in the journalism world and also in just some other analysts, Microsoft has always had large scale eso to say that Microsoft never had scale on that Amazon owned the monopoly on our franchise on scales wrong. Microsoft had scale from day one. Their business was always large scale global. They've always had infrastructure with MSN and their search and the distributive how they distribute browsers and multiple countries. Remember they had the lock on the operating system and the browser for until the government stepped in in 1997. And since 1997 Microsoft never ever not invested in infrastructure and scale. So that whole premise that they don't compete well there is wrong. And I think that chart demonstrates that there, in there in the hyper scale leadership category, hands down the question that I have. Is that there not as good and making that scale integrate in because they have that legacy cards. This is the classic innovator's dilemma. Clay Christensen, right? So I think they're doing a good job. I think their strategy sound. They're moving as fast as they can. But then you know they're not gonna come out and say We don't have the best cloud. Um, that's not a marketing strategy. Have to kind of hide in this and get better and then double down on where they're winning, which is. Clients are converting from their legacy at the speed of Microsoft, and they have a huge client base, So that's why they're stopping so high That's why they're so good. >>Well, I'm gonna I'm gonna give you a little preview. I talked to gear up your f Who's gonna come on today and you'll see I I asked him because the criticism of Microsoft is they're, you know, they're just good enough. And so I asked him, Are you better than good enough? You know, those are fighting words if you're inside of Microsoft, but so you'll you'll have to wait to see his answer. Now, if you guys, if you could bring that that graphic back up I wanted to get into the hybrid zone. You know where the field is. Always got >>some questions coming in on chat, Dave. So we'll get to those >>great Awesome. So just just real quick Here you see this hybrid zone, this the field is bunched up, and the other companies who have a large on Prem presence and have been forced to initiate some kind of coherent cloud strategy included. There is Michael Michael, multi Cloud, and Google's there, too, because they're far behind and they got to take a different approach than a W s. But as you can see, so there's some real progress here. VM ware cloud on AWS stands out, as does red hat open shift. You got VM Ware Cloud, which is a VCF Cloud Foundation, even Dell's cloud. And you'd expect HP with Green Lake to be picking up momentum in the future quarters. And you've got IBM and Oracle, which there you go with the innovator's dilemma. But there, at least in the cloud game, and we can talk about that. But so, John, you know, to your point, you've gotta have different strategies. You're you're not going to take out the big too. So you gotta play, connect your print your on Prem to your cloud, your hybrid multi cloud and try to create new opportunities and new value there. >>Yeah, I mean, I think we'll get to the question, but just that point. I think this Zeri Chen's come on the Cube many times. We're trying to get him to come on lunch today with Features startup, but he's always said on the Q B is a V C at Greylock great firm. Jerry's Cloud genius. He's been there, but he made a point many, many years ago. It's not a winner. Take all the winner. Take most, and the Big Three maybe put four or five in there. We'll take most of the markets here. But I think one of the things that people are missing and aren't talking about Dave is that there's going to be a second tier cloud, large scale model. I don't want to say tear to cloud. It's coming to sound like a sub sub cloud, but a new category of cloud on cloud, right? So meaning if you get a snowflake, did I think this is a tale? Sign to what's coming. VM Ware Cloud is a native has had huge success, mainly because Amazon is essentially enabling them to be successful. So I think is going to be a wave of a more of a channel model of indirect cloud build out where companies like the Cube, potentially for media or others, will build clouds on top of the cloud. So if Google, Microsoft and Amazon, whoever is the first one to really enable that okay, we'll do extremely well because that means you can compete with their scale and create differentiation on top. So what snowflake did is all on Amazon now. They kind of should go to azure because it's, you know, politically correct that have multiple clouds and distribution and business model shifts. But to get that kind of performance they just wrote on Amazon. So there's nothing wrong with that. Because you're getting paid is variable. It's cap ex op X nice categorization. So I think that's the way that we're watching. I think it's super valuable, I think will create some surprises in terms of who might come out of the woodwork on be a leader in a category. Well, >>your timing is perfect, John and we do have some questions in the chat. But before we get to that, I want to bring in Sargi Joe Hall, who's a contributor to to our community. Sargi. Can you hear us? All right, so we got, uh, while >>bringing in Sarpy. Let's go down from the questions. So the first question, Um, we'll still we'll get the student second. The first question. But Ronald ask, Can a vendor in 2021 exist without a hybrid cloud story? Well, story and capabilities. Yes, they could live with. They have to have a story. >>Well, And if they don't own a public cloud? No. No, they absolutely cannot. Uh hey, Sergey. How you doing, man? Good to see you. So, folks, let me let me bring in Sergeant Kohala. He's a He's a cloud architect. He's a practitioner, He's worked in as a technologist. And there's a frequent guest on on the Cube. Good to see you, my friend. Thanks for taking the time with us. >>And good to see you guys to >>us. So we were kind of riffing on the competitive landscape we got. We got so much to talk about this, like, it's a number of questions coming in. Um, but Sargi we wanna talk about you know, what's happening here in Cloud Land? Let's get right into it. I mean, what do you guys see? I mean, we got yesterday. New regime, new inaug inauguration. Do you do you expect public policy? You'll start with you Sargi to have What kind of effect do you think public policy will have on, you know, cloud generally specifically, the big tech companies, the tech lash. Is it gonna be more of the same? Or do you see a big difference coming? >>I think that there will be some changing narrative. I believe on that. is mainly, um, from the regulators side. A lot has happened in one month, right? So people, I think are losing faith in high tech in a certain way. I mean, it doesn't, uh, e think it matters with camp. You belong to left or right kind of thing. Right? But parlor getting booted out from Italy s. I think that was huge. Um, like, how do you know that if a cloud provider will not boot you out? Um, like, what is that line where you draw the line? What are the rules? I think that discussion has to take place. Another thing which has happened in the last 23 months is is the solar winds hack, right? So not us not sort acknowledging that I was Russia and then wish you watching it now, new administration might have a different sort of Boston on that. I think that's huge. I think public public private partnership in security arena will emerge this year. We have to address that. Yeah, I think it's not changing. Uh, >>economics economy >>will change gradually. You know, we're coming out off pandemic. The money is still cheap on debt will not be cheap. for long. I think m and a activity really will pick up. So those are my sort of high level, Uh, >>thank you. I wanna come back to them. And because there's a question that chat about him in a But, John, how do you see it? Do you think Amazon and Google on a slippery slope booting parlor off? I mean, how do they adjudicate between? Well, what's happening in parlor? Uh, anything could happen on clubhouse. Who knows? I mean, can you use a I to find that stuff? >>Well, that's I mean, the Amazons, right? Hiding right there bunkered in right now from that bad, bad situation. Because again, like people we said Amazon, these all three cloud players win in the current environment. Okay, Who wins with the U. S. With the way we are China, Russia, cloud players. Okay, let's face it, that's the reality. So if I wanted to reset the world stage, you know what better way than the, you know, change over the United States economy, put people out of work, make people scared, and then reset the entire global landscape and control all with cash? That's, you know, conspiracy theory. >>So you see the riches, you see the riches, get the rich, get richer. >>Yeah, well, that's well, that's that. That's kind of what's happening, right? So if you start getting into this idea that you can't actually have an app on site because the reason now I'm not gonna I don't know the particular parlor, but apparently there was a reason. But this is dangerous, right? So what? What that's gonna do is and whether it's right or wrong or not, whether political opinion is it means that they were essentially taken offline by people that weren't voted for that. Weren't that when people didn't vote for So that's not a democracy, right? So that's that's a different kind of regime. What it's also going to do is you also have this groundswell of decentralized thinking, right. So you have a whole wave of crypto and decentralized, um, cyber punks out there who want to decentralize it. So all of this stuff in January has created a huge counterculture, and I had predicted this so many times in the Cube. David counterculture is coming and and you already have this kind of counterculture between centralized and decentralized thinking and so I think the Amazon's move is dangerous at a fundamental level. Because if you can't get it, if you can't get buy domain names and you're completely blackballed by by organized players, that's a Mafia, in my opinion. So, uh, and that and it's also fuels the decentralized move because people say, Hey, if that could be done to them, it could be done to me. Just the fact that it could be done will promote a swing in the other direction. I >>mean, independent of of, you know, again, somebody said your political views. I mean Parlor would say, Hey, we're trying to clean this stuff up now. Maybe they didn't do it fast enough, but you think about how new parlor is. You think about the early days of Twitter and Facebook, so they were sort of at a disadvantage. Trying to >>have it was it was partly was what it was. It was a right wing stand up job of standing up something quick. Their security was terrible. If you look at me and Cory Quinn on be great to have him, and he did a great analysis on this, because if you look the lawsuit was just terrible. Security was just a half, asshole. >>Well, and the experience was horrible. I mean, it's not It was not a great app, but But, like you said, it was a quick stew. Hand up, you know, for an agenda. But nonetheless, you know, to start, get to your point earlier. It's like, you know, Are they gonna, you know, shut me down? If I say something that's, you know, out of line, or how do I control that? >>Yeah, I remember, like, 2019, we involved closing sort of remarks. I was there. I was saying that these companies are gonna be too big to fail. And also, they're too big for other nations to do business with. In a way, I think MNCs are running the show worldwide. They're running the government's. They are way. Have seen the proof of that in us this year. Late last year and this year, um, Twitter last night blocked Chinese Ambassador E in us. Um, from there, you know, platform last night and I was like, What? What's going on? So, like, we used to we used to say, like the Chinese company, tech companies are in bed with the Chinese government. Right. Remember that? And now and now, Actually, I think Chinese people can say the same thing about us companies. Uh, it's not a good thing. >>Well, let's >>get some question. >>Let's get some questions from the chat. Yeah. Thank you. One is on M and a subject you mentioned them in a Who do you see is possible emanate targets. I mean, I could throw a couple out there. Um, you know, some of the cdn players, maybe aka my You know, I like I like Hashi Corp. I think they're doing some really interesting things. What do you see? >>Nothing. Hashi Corp. And anybody who's doing things in the periphery is a candidate for many by the big guys, you know, by the hyper scholars and number two tier two or five hyper scholars. Right. Uh, that's why sales forces of the world and stuff like that. Um, some some companies, which I thought there will be a target, Sort of. I mean, they target they're getting too big, because off their evaluations, I think how she Corpuz one, um, >>and >>their bunch in the networking space. Uh, well, Tara, if I say the right that was acquired by at five this week, this week or last week, Actually, last week for $500 million. Um, I know they're founder. So, like I found that, Yeah, there's a lot going on on the on the network side on the anything to do with data. Uh, that those air too hard areas in the cloud arena >>data, data protection, John, any any anything you could adhere. >>And I think I mean, I think ej ej is gonna be where the gaps are. And I think m and a activity is gonna be where again, the bigger too big to fail would agree with you on that one. But we're gonna look at white Spaces and say a white space for Amazon is like a monster space for a start up. Right? So you're gonna have these huge white spaces opportunities, and I think it's gonna be an M and a opportunity big time start ups to get bought in. Given the speed on, I think you're gonna see it around databases and around some of these new service meshes and micro services. I mean, >>they there's a There's a question here, somebody's that dons asking why is Google who has the most pervasive tech infrastructure on the planet. Not at the same level of other to hyper scale is I'll give you my two cents is because it took him a long time to get their heads out of their ads. I wrote a piece of around that a while ago on they just they figured out how to learn the enterprise. I mean, John, you've made this point a number of times, but they just and I got a late start. >>Yeah, they're adding a lot of people. If you look at their who their hiring on the Google Cloud, they're adding a lot of enterprise chops in there. They realized this years ago, and we've talked to many of the top leaders, although Curry and hasn't yet sit down with us. Um, don't know what he's hiding or waiting for, but they're clearly not geared up to chicken Pete. You can see it with some some of the things that they're doing, but I mean competed the level of Amazon, but they have strength and they're playing their strength, but they definitely recognize that they didn't have the enterprise motions and people in the DNA and that David takes time people in the enterprise. It's not for the faint of heart. It's unique details that are different. You can't just, you know, swing the Google playbook and saying We're gonna home The enterprises are text grade. They knew that years ago. So I think you're going to see a good year for Google. I think you'll see a lot of change. Um, they got great people in there. On the product marketing side is Dev Solution Architects, and then the SRE model that they have perfected has been strong. And I think security is an area that they could really had a lot of value it. So, um always been a big fan of their huge network and all the intelligence they have that they could bring to bear on security. >>Yeah, I think Google's problem main problem that to actually there many, but one is that they don't They don't have the boots on the ground as compared to um, Microsoft, especially an Amazon actually had a similar problem, but they had a wide breath off their product portfolio. I always talk about feature proximity in cloud context, like if you're doing one thing. You wanna do another thing? And how do you go get that feature? Do you go to another cloud writer or it's right there where you are. So I think Amazon has the feature proximity and they also have, uh, aske Compared to Google, there's skills gravity. Larger people are trained on AWS. I think Google is trying there. So second problem Google is having is that that they're they're more focused on, I believe, um, on the data science part on their sort of skipping the cool components sort of off the cloud, if you will. The where the workloads needs, you know, basic stuff, right? That's like your compute storage and network. And that has to be well, talk through e think e think they will do good. >>Well, so later today, Paul Dillon sits down with Mids Avery of Google used to be in Oracle. He's with Google now, and he's gonna push him on on the numbers. You know, you're a distant third. Does that matter? And of course, you know, you're just a preview of it's gonna say, Well, no, we don't really pay attention to that stuff. But, John, you said something earlier that. I think Jerry Chen made this comment that, you know, Is it a winner? Take all? No, but it's a winner. Take a lot. You know the number two is going to get a big chunk of the pie. It appears that the markets big enough for three. But do you? Does Google have to really dramatically close the gap on be a much, much closer, you know, to the to the leaders in orderto to compete in this race? Or can they just kind of continue to bump along, siphon off the ad revenue? Put it out there? I mean, I >>definitely can compete. I think that's like Google's in it. Then it they're not. They're not caving, right? >>So But But I wrote I wrote recently that I thought they should even even put mawr oven emphasis on the cloud. I mean, maybe maybe they're already, you know, doubling down triple down. I just I think that is a multi trillion dollar, you know, future for the industry. And, you know, I think Google, believe it or not, could even do more. Now. Maybe there's just so much you could dio. >>There's a lot of challenges with these company, especially Google. They're in Silicon Valley. We have a big Social Justice warrior mentality. Um, there's a big debate going on the in the back channels of the tech scene here, and that is that if you want to be successful in cloud, you have to have a good edge strategy, and that involves surveillance, use of data and pushing the privacy limits. Right? So you know, Google has people within the country that will protest contract because AI is being used for war. Yet we have the most unstable geopolitical seen that I've ever witnessed in my lifetime going on right now. So, um, don't >>you think that's what happened with parlor? I mean, Rob Hope said, Hey, bar is pretty high to kick somebody off your platform. The parlor went over the line, but I would also think that a lot of the employees, whether it's Google AWS as well, said, Hey, why are we supporting you know this and so to your point about social justice, I mean, that's not something. That >>parlor was not just social justice. They were trying to throw the government. That's Rob e. I think they were in there to get selfies and being protesters. But apparently there was evidence from what I heard in some of these clubhouse, uh, private chats. Waas. There was overwhelming evidence on parlor. >>Yeah, but my point is that the employee backlash was also a factor. That's that's all I'm saying. >>Well, we have Google is your Google and you have employees to say we will boycott and walk out if you bid on that jet I contract for instance, right, But Microsoft one from maybe >>so. I mean, that's well, >>I think I think Tom Poole's making a really good point here, which is a Google is an alternative. Thio aws. The last Google cloud next that we were asked at they had is all virtual issue. But I saw a lot of I T practitioners in the audience looking around for an alternative to a W s just seeing, though, we could talk about Mano Cloud or Multi Cloud, and Andy Jassy has his his narrative around, and he's true when somebody goes multiple clouds, they put you know most of their eggs in one basket. Nonetheless, I think you know, Google's got a lot of people interested in, particularly in the analytic side, um, in in an alternative, hedging their bets eso and particularly use cases, so they should be able to do so. I guess my the bottom line here is the markets big enough to have Really? You don't have to be the Jack Welch. I gotta be number one and number two in the market. Is that the conclusion here? >>I think so. But the data gravity and the skills gravity are playing against them. Another problem, which I didn't want a couple of earlier was Google Eyes is that they have to boot out AWS wherever they go. Right? That is a huge challenge. Um, most off the most off the Fortune 2000 companies are already using AWS in one way or another. Right? So they are the multi cloud kind of player. Another one, you know, and just pure purely somebody going 200% Google Cloud. Uh, those cases are kind of pure, if you will. >>I think it's gonna be absolutely multi cloud. I think it's gonna be a time where you looked at the marketplace and you're gonna think in terms of disaster recovery, model of cloud or just fault tolerant capabilities or, you know, look at the parlor, the next parlor. Or what if Amazon wakes up one day and said, Hey, I don't like the cubes commentary on their virtual events, so shut them down. We should have a fail over to Google Cloud should Microsoft and Option. And one of people in Microsoft ecosystem wants to buy services from us. We have toe kind of co locate there. So these are all open questions that are gonna be the that will become certain pretty quickly, which is, you know, can a company diversify their computing An i t. In a way that works. And I think the momentum around Cooper Netease you're seeing as a great connective tissue between, you know, having applications work between clouds. Right? Well, directionally correct, in my opinion, because if I'm a company, why wouldn't I wanna have choice? So >>let's talk about this. The data is mixed on that. I'll share some data, meaty our data with you. About half the companies will say Yeah, we're spreading the wealth around to multiple clouds. Okay, That's one thing will come back to that. About the other half were saying, Yeah, we're predominantly mono cloud we didn't have. The resource is. But what I think going forward is that that what multi cloud really becomes. And I think John, you mentioned Snowflake before. I think that's an indicator of what what true multi cloud is going to look like. And what Snowflake is doing is they're building abstraction, layer across clouds. Ed Walsh would say, I'm standing on the shoulders of Giants, so they're basically following points of presence around the globe and building their own cloud. They call it a data cloud with a global mesh. We'll hear more about that later today, but you sign on to that cloud. So they're saying, Hey, we're gonna build value because so many of Amazon's not gonna build that abstraction layer across multi clouds, at least not in the near term. So that's a really opportunity for >>people. I mean, I don't want to sound like I'm dating myself, but you know the date ourselves, David. I remember back in the eighties, when you had open systems movement, right? The part of the whole Revolution OS I open systems interconnect model. At that time, the networking stacks for S N A. For IBM, decadent for deck we all know that was a proprietary stack and then incomes TCP I p Now os I never really happened on all seven layers, but the bottom layers standardized. Okay, that was huge. So I think if you look at a W s or some of the comments in the chat AWS is could be the s n a. Depends how you're looking at it, right? And you could say they're open. But in a way, they want more Amazon. So Amazon's not out there saying we love multi cloud. Why would they promote multi cloud? They are a one of the clouds they want. >>That's interesting, John. And then subject is a cloud architect. I mean, it's it is not trivial to make You're a data cloud. If you're snowflake, work on AWS work on Google. Work on Azure. Be seamless. I mean, certainly the marketing says that, but technically, that's not trivial. You know, there are latent see issues. Uh, you know, So that's gonna take a while to develop. What? Do your thoughts there? >>I think that multi cloud for for same workload and multi cloud for different workloads are two different things. Like we usually put multiple er in one bucket, right? So I think you're right. If you're trying to do multi cloud for the same workload, that's it. That's Ah, complex, uh, problem to solve architecturally, right. You have to have a common ap ice and common, you know, control playing, if you will. And we don't have that yet, and then we will not have that for a for at least one other couple of years. So, uh, if you if you want to do that, then you have to go to the lower, lowest common denominator in technical sort of stock, if you will. And then you're not leveraging the best of the breed technology off their from different vendors, right? I believe that's a hard problem to solve. And in another thing, is that that that I always say this? I'm always on the death side, you know, developer side, I think, uh, two deaths. Public cloud is a proxy for innovative culture. Right. So there's a catch phrase I have come up with today during shower eso. I think that is true. And then people who are companies who use the best of the breed technologies, they can attract the these developers and developers are the Mazen's off This digital sort of empires, amazingly, is happening there. Right there they are the Mazen's right. They head on the bricks. I think if you don't appeal to developers, if you don't but extensive for, like, force behind educating the market, you can't you can't >>put off. It's the same game Stepping story was seeing some check comments. Uh, guard. She's, uh, linked in friend of mine. She said, Microsoft, If you go back and look at the Microsoft early days to the developer Point they were, they made their phones with developers. They were a software company s Oh, hey, >>forget developers, developers, developers. >>You were if you were in the developer ecosystem, you were treated his gold. You were part of the family. If you were outside that world, you were competitors, and that was ruthless times back then. But they again they had. That was where it was today. Look at where the software defined businesses and starve it, saying it's all about being developer lead in this new way to program, right? So the cloud next Gen Cloud is going to look a lot like next Gen Developer and all the different tools and techniques they're gonna change. So I think, yes, this kind of developer ecosystem will be harnessed, and that's the power source. It's just gonna look different. So, >>Justin, Justin in the chat has a comment. I just want to answer the question about elastic thoughts on elastic. Um, I tell you, elastic has momentum uh, doing doing very well in the market place. Thea Elk Stack is a great alternative that people are looking thio relative to Splunk. Who people complain about the pricing. Of course it's plunks got the easy button, but it is getting increasingly expensive. The problem with elk stack is you know, it's open source. It gets complicated. You got a shard, the databases you gotta manage. It s Oh, that's what Ed Walsh's company chaos searches is all about. But elastic has some riel mo mentum in the marketplace right now. >>Yeah, you know, other things that coming on the chat understands what I was saying about the open systems is kubernetes. I always felt was that is a bad metaphor. But they're with me. That was the TCP I peep In this modern era, C t c p I p created that that the disruptor to the S N A s and the network protocols that were proprietary. So what KUBERNETES is doing is creating a connective tissue between clouds and letting the open source community fill in the gaps in the middle, where kind of way kind of probably a bad analogy. But that's where the disruption is. And if you look at what's happened since Kubernetes was put out there, what it's become kind of de facto and standard in the sense that everyone's rallying around it. Same exact thing happened with TCP was people were trashing it. It is terrible, you know it's not. Of course they were trashed because it was open. So I find that to be very interesting. >>Yeah, that's a good >>analogy. E. Thinks the R C a cable. I used the R C. A cable analogy like the VCRs. When they started, they, every VC had had their own cable, and they will work on Lee with that sort of plan of TV and the R C. A cable came and then now you can put any TV with any VCR, and the VCR industry took off. There's so many examples out there around, uh, standards And how standards can, you know, flair that fire, if you will, on dio for an industry to go sort of wild. And another trend guys I'm seeing is that from the consumer side. And let's talk a little bit on the consuming side. Um, is that the The difference wouldn't be to B and B to C is blood blurred because even the physical products are connected to the end user Like my door lock, the August door lock I didn't just put got get the door lock and forget about that. Like I I value the expedience it gives me or problems that gives me on daily basis. So I'm close to that vendor, right? So So the middle men, uh, middle people are getting removed from from the producer off the technology or the product to the consumer. Even even the sort of big grocery players they have their APs now, uh, how do you buy stuff and how it's delivered and all that stuff that experience matters in that context, I think, um, having, uh, to be able to sell to thes enterprises from the Cloud writer Breuder's. They have to have these case studies or all these sample sort off reference architectures and stuff like that. I think whoever has that mawr pushed that way, they are doing better like that. Amazon is Amazon. Because of that reason, I think they have lot off sort off use cases about on top of them. And they themselves do retail like crazy. Right? So and other things at all s. So I think that's a big trend. >>Great. Great points are being one of things. There's a question in there about from, uh, Yaden. Who says, uh, I like the developer Lead cloud movement, But what is the criticality of the executive audience when educating the marketplace? Um, this comes up a lot in some of my conversations around automation. So automation has been a big wave to automate this automate everything. And then everything is a service has become kind of kind of the the executive suite. Kind of like conversation we need to make everything is a service in our business. You seeing people move to that cloud model. Okay, so the executives think everything is a services business strategy, which it is on some level, but then, when they say Take that hill, do it. Developers. It's not that easy. And this is where a lot of our cube conversations over the past few months have been, especially during the cova with cute virtual. This has come up a lot, Dave this idea, and start being around. It's easy to say everything is a service but will implement it. It's really hard, and I think that's where the developer lead Connection is where the executive have to understand that in order to just say it and do it are two different things. That digital transformation. That's a big part of it. So I think that you're gonna see a lot of education this year around what it means to actually do that and how to implement it. >>I'd like to comment on the as a service and subject. Get your take on it. I mean, I think you're seeing, for instance, with HP Green Lake, Dell's come out with Apex. You know IBM as its utility model. These companies were basically taking a page out of what I what I would call a flawed SAS model. If you look at the SAS players, whether it's salesforce or workday, service now s a P oracle. These models are They're really They're not cloud pricing models. They're they're basically you got to commit to a term one year, two year, three year. We'll give you a discount if you commit to the longer term. But you're locked in on you. You probably pay upfront. Or maybe you pay quarterly. That's not a cloud pricing model. And that's why I mean, they're flawed. You're seeing companies like Data Dog, for example. Snowflake is another one, and they're beginning to price on a consumption basis. And that is, I think, one of the big changes that we're going to see this decade is that true cloud? You know, pay by the drink pricing model and to your point, john toe, actually implement. That is, you're gonna need a whole new layer across your company on it is quite complicated it not even to mention how you compensate salespeople, etcetera. The a p. I s of your product. I mean, it is that, but that is a big sea change that I see coming. Subject your >>thoughts. Yeah, I think like you couldn't see it. And like some things for this big tech exacts are hidden in the plain >>sight, right? >>They don't see it. They they have blind spots, like Look at that. Look at Amazon. They went from Melissa and 200 millisecond building on several s, Right, Right. And then here you are, like you're saying, pay us for the whole year. If you don't use the cloud, you lose it or will pay by month. Poor user and all that stuff like that that those a role models, I think these players will be forced to use that term pricing like poor minute or for a second, poor user. That way, I think the Salesforce moral is hybrid. They're struggling in a way. I think they're trying to bring the platform by doing, you know, acquisition after acquisition to be a platform for other people to build on top off. But they're having a little trouble there because because off there, such pricing and little closeness, if you will. And, uh, again, I'm coming, going, going back to developers like, if you are not appealing to developers who are writing the latest and greatest code and it is open enough, by the way open and open source are two different things that we all know that. So if your platform is not open enough, you will have you know, some problems in closing the deals. >>E. I want to just bring up a question on chat around from Justin didn't fitness. Who says can you touch on the vertical clouds? Has your offering this and great question Great CP announcing Retail cloud inventions IBM Athena Okay, I'm a huge on this point because I think this I'm not saying this for years. Cloud computing is about horizontal scalability and vertical specialization, and that's absolutely clear, and you see all the clouds doing it. The vertical rollouts is where the high fidelity data is, and with machine learning and AI efforts coming out, that's accelerated benefits. There you have tow, have the vertical focus. I think it's super smart that clouds will have some sort of vertical engine, if you will in the clouds and build on top of a control playing. Whether that's data or whatever, this is clearly the winning formula. If you look at all the successful kind of ai implementations, the ones that have access to the most data will get the most value. So, um if you're gonna have a data driven cloud you have tow, have this vertical feeling, Um, in terms of verticals, the data on DSO I think that's super important again, just generally is a strategy. I think Google doing a retail about a super smart because their whole pitches were not Amazon on. Some people say we're not Google, depending on where you look at. So every of these big players, they have dominance in the areas, and that's scarce. Companies and some companies will never go to Amazon for that reason. Or some people never go to Google for other reasons. I know people who are in the ad tech. This is a black and we're not. We're not going to Google. So again, it is what it is. But this idea of vertical specialization relevant in super >>forts, I want to bring to point out to sessions that are going on today on great points. I'm glad you asked that question. One is Alan. As he kicks off at 1 p.m. Eastern time in the transformation track, he's gonna talk a lot about the coming power of ecosystems and and we've talked about this a lot. That that that to compete with Amazon, Google Azure, you've gotta have some kind of specialization and vertical specialization is a good one. But of course, you see in the big Big three also get into that. But so he's talking at one o'clock and then it at 3 36 PM You know this times are strange, but e can explain that later Hillary Hunter is talking about she's the CTO IBM I B M's ah Financial Cloud, which is another really good example of specifying vertical requirements and serving. You know, an audience subject. I think you have some thoughts on this. >>Actually, I lost my thought. E >>think the other piece of that is data. I mean, to the extent that you could build an ecosystem coming back to Alan Nancy's premise around data that >>billions of dollars in >>their day there's billions of dollars and that's the title of the session. But we did the trillion dollar baby post with Jazzy and said Cloud is gonna be a trillion dollars right? >>And and the point of Alan Answer session is he's thinking from an individual firm. Forget the millions that you're gonna save shifting to the cloud on cost. There's billions in ecosystems and operating models. That's >>absolutely the business value. Now going back to my half stack full stack developer, is the business value. I've been talking about this on the clubhouses a lot this past month is for the entrepreneurs out there the the activity in the business value. That's the new the new intellectual property is the business logic, right? So if you could see innovations in how work streams and workflow is gonna be a configured differently, you have now large scale cloud specialization with data, you can move quickly and take territory. That's much different scenario than a decade ago, >>at the point I was trying to make earlier was which I know I remember, is that that having the horizontal sort of features is very important, as compared to having vertical focus. You know, you're you're more healthcare focused like you. You have that sort of needs, if you will, and you and our auto or financials and stuff like that. What Google is trying to do, I think that's it. That's a good thing. Do cook up the reference architectures, but it's a bad thing in a way that you drive drive away some developers who are most of the developers at 80 plus percent, developers are horizontal like you. Look at the look into the psyche of a developer like you move from company to company. And only few developers will say I will stay only in health care, right? So I will only stay in order or something of that, right? So they you have to have these horizontal capabilities which can be applied anywhere on then. On top >>of that, I think that's true. Sorry, but I'll take a little bit different. Take on that. I would say yes, that's true. But remember, remember the old school application developer Someone was just called in Application developer. All they did was develop applications, right? They pick the framework, they did it right? So I think we're going to see more of that is just now mawr of Under the Covers developers. You've got mawr suffer defined networking and software, defined storage servers and cloud kubernetes. And it's kind of like under the hood. But you got your, you know, classic application developer. I think you're gonna see him. A lot of that come back in a way that's like I don't care about anything else. And that's the promise of cloud infrastructure is code. So I think this both. >>Hey, I worked. >>I worked at people solved and and I still today I say into into this context, I say E r P s are the ultimate low code. No code sort of thing is right. And what the problem is, they couldn't evolve. They couldn't make it. Lightweight, right? Eso um I used to write applications with drag and drop, you know, stuff. Right? But But I was miserable as a developer. I didn't Didn't want to be in the applications division off PeopleSoft. I wanted to be on the tools division. There were two divisions in most of these big companies ASAP. Oracle. Uh, like companies that divisions right? One is the cooking up the tools. One is cooking up the applications. The basketball was always gonna go to the tooling. Hey, >>guys, I'm sorry. We're almost out of time. I always wanted to t some of the sections of the day. First of all, we got Holder Mueller coming on at lunch for a power half hour. Um, you'll you'll notice when you go back to the home page. You'll notice that calendar, that linear clock that we talked about that start times are kind of weird like, for instance, an appendix coming on at 1 24. And that's because these air prerecorded assets and rather than having a bunch of dead air, we're just streaming one to the other. So so she's gonna talk about people, process and technology. We got Kathy Southwick, whose uh, Silicon Valley CEO Dan Sheehan was the CEO of Dunkin Brands and and he was actually the c 00 So it's C A CEO connecting the dots to the business. Daniel Dienes is the CEO of you I path. He's coming on a 2:47 p.m. East Coast time one of the hottest companies, probably the fastest growing software company in history. We got a guy from Bain coming on Dave Humphrey, who invested $750 million in Nutanix. He'll explain why and then, ironically, Dheeraj Pandey stew, Minuteman. Our friend interviewed him. That's 3 35. 1 of the sessions are most excited about today is John McD agony at 403 p. M. East Coast time, she's gonna talk about how to fix broken data architectures, really forward thinking stuff. And then that's the So that's the transformation track on the future of cloud track. We start off with the Big Three Milan Thompson Bukovec. At one oclock, she runs a W s storage business. Then I mentioned gig therapy wrath at 1. 30. He runs Azure is analytics. Business is awesome. Paul Dillon then talks about, um, IDs Avery at 1 59. And then our friends to, um, talks about interview Simon Crosby. I think I think that's it. I think we're going on to our next session. All right, so keep it right there. Thanks for watching the Cuban cloud. Uh huh.

(upbeat music) >> Narrator: Live from Boston, MA it's theCUBE covering Red Hat Summit 2019. Brought to you by Red Hat. (upbeat music continues) >> It is so good to have you back with us here on theCUBE as we continue our live coverage here at the BCEC at Red Hat Summit 2019. Glad to have you watching wherever you might be, Eastern Time Zone or maybe out West. Stu Miniman, John Walls here. Our coverage continuing; sixth year we've been at this summit. Eric Kern now joins us here. Both from Lenovo, Eric and Steve Brown. Eric is the Executive Distinguished Engineer. And Steve is the Managing Partner in the Software Business Unit and the DevOps practice leader. So gentlemen good to have you with us on theCUBE. Good to see you today! >> Thanks for having us. >> Thank you. >> No surprise, right, that you're here; long term partnership, very successful get together. First off, I want your ideas or your impressions of what you've hear or what you've seen so far here in the day and a half that we've been underway. And whether it's keynote or maybe one of the side sessions, just what's your first impression of what's goin' on here? >> Yeah, I mean it's great. There's a lot of people here, a lot of activity. I mean we can see the Expo behind us. You know the food is great, lunch is great so- (laughter) >> Rub it in. (laughter continues) Rub it in just a little bit. Okay, so a little bit of news this week with regard to what you're up to. And if you would, I'm not gonna ask you to go terribly deep, but just give us an idea of what some of the headlines are you guys were sending out this week. Steve, why don't you take that? >> Yeah, so this week we announced six new reference designs and solutions, engineered solutions. But pretty excited about OpenShift 4 and certainly Rel8 after a five year I guess pause, if you will, on major releases. So that's exciting. >> So, Eric, why don't we start with building on those partnerships, talk about some of the solutions your talking to customers and some of the latest and greatest? There's a lot of interesting things we're doing; one of the things we've been doing recently is around TruScale. So TruScale is our infrastructure as a service on premise. So one of the things we do with it is we build overall solutions. So there's a number of reference architectures that we talked about with Red Hat. These solutions, think about them as having an overall CapEx price and then we convert that into a OpEx price. Probably one of the neat novel things, and this is kind of the area that I really got into, right, is around how do we build a metering system that doesn't require us to install a bunch of software and can be compatible with everything? So with TruScale what we've done is we've leveraged our what's called our xclarity controller, it's the chip basically on the motherboard, and that xclarity controller has the ability to measure power. And measure power both at the overall input consumption, as well measuring power in the CPU, the memory and the eye out. And we built an infrastructure around that. We can actually tell you exactly what percentage the system is being used and consumed based on that. And we can charge for the overall system on a monthly basis. So we have a portal that's set up for that, whether it be our hardware on its own or our hardware with the Red Hat software installed on top of it. >> So how's that effect the customer relationship then? All the sudden your- whether there was a- not I'd say a dispute, but might of been questions about how much usage am I getting? How am I using this? Why am I being billed as I'm being billed? So on and so forth. Now all the sudden you can just deliver the proof's in the pudding, right? You can say this is exactly what you're doing with this, this is exactly how much you're consuming. And I would assume from a pricing standpoint for that modeling standpoint, you give everybody a lot of comfort, I would think; right? >> You do, right. Not only do they see exactly what they're being charged for, they see exactly some of the usage on their own systems. A lot of times they don't know how well-balanced or unbalanced their systems are. And so we're actually providing real usage data. It's different than what you get in public cloud. It's different in what you get in other solutions where it's virtual allocation. So there's a difference in knowing the physical utilization versus the allocated utilization. What a lot of people do, a lot of companies do when they're renting public cloud infrastructure is they spend a lot of time in automation to actually deallocate. Right, so they're doing all this work just to try to save money. Whereas in the TruScale model, you just run it like you normally run it and you save money because you know, if you're not using it, you're not paying for it. >> John: You don't pay for it. >> Exactly, exactly. >> All right, well Steve, a lot of discussion at the show this week about OpenShift, not least this morning, OpenShift 4 was released. We've had a chance to talk to a number of customers, bring us inside, you know, Lenovo's worked with OpenShift for awhile. Oftentimes we think about the application layers like oh, it's totally divorced, I don't need to think of it. Well, we understand there's integration work that happens there and would love your insight into what is happening at he integration, where it's progressed, and any customer stories that you've got along those lines. >> Well, yeah, we've been doing a lot of work with OpenShift. I would say for an upwards of more than two years. We started with Intel and Red Hat and built a number of Intel Select solutions, reference designs, both bare metal and hyper converged. We are on our fifth edition now of the OpenShift design on Cascade Lake. We're the, I wanna say the pioneers in the industry. We have a center of competency in DevOps with software to really promote software development solutions. And we're excited with OpenShift 4 because of the CoreOS integration as well as the auto-provisioning. Key things, it makes it so much easier to adopt and integrate. >> Any customer deployments? When they come to you, what's the kind of a-ha moment that they have? Is it just the agility that it brings them? Is there anything you can share as to the customers that are actually doing this in the field? >> Well, I like to think the customers get the a-ha when they realize that there is an engineered platform that's been purpose built and they're not coddling software and tools together. It helps with the CI/CD pipeline process templating much more effectively. Overall it's, I think, a lot more streamlined than it was in the earlier editions of OpenShift, especially Open Source. So we're pretty excited with comprehensive business support. I think that businesses feel comfortable. >> Kind of a simple question, but what do you, in terms of what TruScale operates now, what is the- what are you allowing people to do now that they didn't do before? In the latest version here, what exactly is- where's, you think, this improvement? Or where's the new efficiency? What are they getting out of it that would make me, as a customer, have that- if I haven't converted yet, or if I'm perhaps ripe for the taking, what would make me jump? >> Part of it is customers don't want to be managing their infrastructure. And so this there's a big push to public cloud. They just wanna be managing their applications. They just wanna focus on what's paying the bills, right? And paying the bills are providing the IT service is all in the application layer for the most part. What TruScale allows them to do is to have that public cloud kind of management platform. So it's Lenovo premium support behind the scenes; so Lenovo is managing the hardware itself, Lenovo maintains the ownership of the hardware, so they're not even owning the hardware, very similar to public cloud. And they can go and use it on FREM. So they don't have to worry about any security issues with the public cloud. They don't have to worry about any kind of network issues, right, it's all in their data center. It's running just exactly the way they'd run CapEx, but they're running in the way that they have really liked with the public cloud infrastructure. >> So confidence, comfort, security and all that stuff right? >> Eric: There ya go. Yeah. >> Yeah, that's just- I'll pay for that! >> Sure! (laughter) So, we've seen software move heavily towards this model whether it be SaaS or various moving CapEx to OpEx. When I look at infrastructure it's been a little bit of a slower move, especially, I've got some background on the storage side, if you look at storage, it's like oh okay. I'm conditioned as a customer to think about my capacity, my performance, and how I'm tuning everything, and I need to make adjustments, and making changes usually takes a little bit longer. Red Hat's got a lot of software products in the storage space. Help us understand how this fits in and are customers gettin' more comfortable moving from the CapEx to the OpEx for their uses? >> Yeah, good segue. So Ceph and Gluster are some really interesting storage products from Red Hat. And they fit right on our servers, and so we install them; we build big solutions around both of them. I'm actually working on big architecture for another company, for another customer out in Germany. So it's huge stuff cluster. The neat thing about it is our TruScale model allows us to actually sell them on OpEx in a storage product. And what we're measuring is the storage, what I call storage in motion versus the storage at rest. So we see all the different usages of the different servers. The servers are acting as controllers, a multi-tenant controller. And there's a lot of information that's being stored and transmitted through the systems. TruScale's just accumulating all the usage of that. And Steve, maybe you want to talk about some of the software side of it from the storage perspective, but it's really, TruScale fits right in real nicely with the storage side of it. >> I'd actually like to talk about it more comprehensively from the Red Hat software side of it. Anywho, let's talk about how they're already no certification needed. We're looking at all Red Hat applications on TruScale; whether it's OpenShift, or Rel8, Gluster, Ceph, Ansible. So we're really excited because we're not limited in the portfolio. >> Exactly. Exactly. >> Yeah. >> So, Steve, it's interesting, you used to think about, okay, what boxes am I buying, what license I'm doing. If you talk about a real true software world it should be a platform that unifies these things together. So it sounds like you're saying we're getting there. I shouldn't have to think about- give us a little bit, kind of the old way and where customers are seeing it today. >> Yeah, well we're not getting there. We're there. What that allows us to do is to take the reference designs that we have and the testing that we've previously validated with Intel and Red Hat and be able to snap pieces together. So it's just a matter of what's different and unique for the client and the client's situation and their growth pattern. What's great about TruScale is that in this model we can predicatively analyze their consumption forward based on the business growth. So for example, if you're using OpenShift and you start with a small cluster for one or two lines of business, as they adopt DevOps methodologies going from either Waterfall or Agile, we can predicatively analyze the consumption forward that they're gonna need. So they can plan years in advance as they progress. And as such, the other snap-ins, say storage, that they're gonna need for data in motion or data at rest. So it's actually smarter. And what that ends up doing is obviously saving them money, but it saves them time. The typical model is going back to IT and saying we need these severs, we need the storage and the software, and bolt it altogether. And the IT guys are hair on fire running around already. So they can, as long as IT approves it, they can sort of bypass that big, heavy lift. >> So from what you've heard of this week, with Rel8, the big launch last night, a lot of fun, right? >> Steve: Yeah. >> And then OpenShift 4 earlier today talked about- >> Yeah. >> What if there are elements to those two, either one of them, that you find most attractive? Or that really kinda jump off the page to you? Is there anything out there that you're seein' or through the demos that we saw today, or last night even that you think wow, that's cool, that's good, that this is gonna be useful for us? >> OpenShift is one of the things that we're seeing in the industry that's just really enabling the whole DevOps practice. So OpenShift is interesting from the perspective of flexibility, automation, the tooling. Rel8, of course, we've all been waiting for it, I guess for a while now probably. >> Host: Right. >> It's just the next level, the next generation. The Red Hat software, see I'm a big fan of Ceph. I mean I just like Ceph, it's just a neat storage product. It's been around for awhile, but it keeps getting better. It's kinda like the old storage product that first came out with some soft-refined storage. But the whole ecosystem around Red Hat is just very appealing. I actually, Cloudforms is one I think is a little under-utilized today. Cloudforms is a real nice cloud management platform as well. So there's a lot of interesting Red Hat software. Steve, we've done all these reference architectures, are there any ones that stick out to you? I've just been kind of rattling off some of the ones that I like. >> Yeah, I really like the CoreOS integration, 'cause we now see that acquisition really taking shape in a true productization sense, in a practical use sense. I think with Red Hat owning that asset and controlling the development, they can build out features as needed. They're not having to wait on the ecosystem or to spin different cycles for growth. So I think that's my highlight. I've been looking for that. And auto-provisioning as well. I think that's a really key benefit to it, just to make things more smooth and simple. >> Well gentlemen, thanks for the time. >> Guest: Sure. >> Nice to meet you. Look forward to seeing you down the road. We were talkin' about Lenovo, Stu and I were there a couple of years ago, Ashton Kutcher out in San Francisco, so now we get the two of you guys. You're right there with Ashton, right? (laughter) >> That's right. >> Same celebrity! Thanks for sharing the time. Good to see you guys. >> Eric: Thank you. >> Steve: You too. >> Back with more live here at Red Hat Summit 2019, we're in Boston, and you're watching theCube. (electronic music)

>> Announcer: Live, from Las Vegas, it's theCUBE. Covering Dell Technologies World 2018. Brought to you by Dell EMC and its ecosystem partners. >> Well, good afternoon, or good evening if you're watching back in the Eastern Time Zone. Good to have you here live as theCUBE continues our coverage of Dell Technologies World 2018. I'm John Walls, along with Stu Miniman and we now welcome Jeff Boudreau, who's the president and GM of storage at Dell EMC. >> Thank you! >> John: Jeff, good to see you. >> Good to see you guys, thank you for having me. >> Alright, it's been like a solid six hours since you launched your new product. >> That's right. >> The PowerMax. >> What's been, I'm curious, what's been the reaction and what do people want to know from you when they get a little face time? >> Well, the big things I have is one, the reaction's been fantastic since we launched this morning. Obviously Jeff Clarke on stage with my good friend Bob Decrescenzo, PowerMax Bob, now known and understood, and Bob did a great job today announcing the product. The feedback has been phenomenal. People really want to understand, I kind of frame it as, we talk about the future of enterprise storage, and I kind of put some bold statements out there saying it's the fastest storage array, it's the most intelligent storage array, and it's the most resilient storage array in the market today. And I kind of go through that, and a lot of people want to understand a lot around what we've done around NVME as an interface. NVME in the protocol stack and also with the media itself and understand that and truly unleashing the power of doing what I would call NVME right. As you kind of think about where we are and where we want to go with storage class memory, and making sure you unleash the whole value, so that's a big one customers talk to me about. The other big one is around a lot of the ML in the AI. So, we've done a lot of great work. The team's done an amazing job with the OS and the PowerMax operating system, and we do a lot of work with the application hinting, if you will. So we have some technology that we built that actually understands the applications, almost like putting a fingerprint on it, if you will. And then we have algorithms and heuristics within the array that understands the pattern recognition across that and that really understands that. So last year I talked a lot about autonomous storage, this is the realest first step of actually trying to be truly autonomous storage. >> Yeah, Jeff, it's really interesting. The people that have watched the storage industry, there's certain things that have kind of, this is where we are. SCSI has been with us for-- >> Ever. >> Longer than my career. >> Jeff: Mine too. >> You look at NVME and storage class memory and we're starting to get beyond that. I talked with Adnan earlier and saying intelligent storage is something that I've seen lots of product announcements over the other top two intelligent storage! >> Yeah. >> But when you talk about billions of decisions being made by arrays underneath, bring us inside the product team a little bit and how much effort goes into this and the effort. >> The team, number one, is a phenomenal team. I think they're world-class in everything they do and all the products they build, it's been phenomenal. And they've done a ton of work underneath around the algorithms and heuristics. I mean, we've been doing, if you think of our install base and how much data that we store, protect, and secure, at the end of the day we do more than anybody else. So the team's done a lot of work around our data scientists and our engineers have done a lot of work to understand the I/O patterns, heuristics off the drives, the telemetry streams, and then actually build the algorithms to really make sense of that data and provide useful insights. So, it's not easy, to your point, it's a lot of great work by a great team. So, Adnan, I'm glad you had him, because he's one of the key guys to make sure that it all works and comes together. And then, understand the use case of that application tied back to the system is where the magic happens, really connecting that and really putting that forward. >> You know, we talk about faster, and you probably can, maybe you can hear the music, it's got louder. >> It's loud. >> If not faster. How so, and what was your measurement for success there? How did you say, okay, this is the goal, this is what we're shooting for, or did you take technology and say, what can we squeeze out of this? >> Well, it's kind of funny, when we built the architecture, we actually do a lot of prototyping and we do a, actually we do a lot of paperwork up front as we understand the customer requirements, the use cases we're trying to drive, we actually write a lot of that down on paper and say okay, what do we need to do to hit that market need? And then we look at what we need to do from a hardware and software standpoint as we architect the system. And that's what the team really did here. So, what we're looking at is what the customers are looking at, not only for today, but into the future, so as you think about where we are today, and you heard Michael talk about 2020, I've actually been talking a lot about 2030. If you think about IoT, you think about AI, and machine learning and all the sensa data, structured and unstructured, data's exploding. And at the end of the day is, how is our customers going to, it's one thing to store and protect and secure that data, we got to do a lot more than that. And this goes back to how do we make, get in real-time, make it accessible, but also extract the value of that data to provide useful insights back to our customers. They can provide them to their customers, either for better business decisions or more value, or what have you. And that's really where the power comes from. So I've been focusing a lot on the data, and to me it's really about the data, the data explosion that's coming. The customers really understand how big that's going to be, and the period of time, and so what we worked on today, focused on what we're trying to do tomorrow, we want to make sure that we have a clear path to help our customers on that journey. So, going back to some of the performance characteristics that we looked at is not only what we model for today, making sure that we're the best in the industry, best in the market, we also want to look forward saying okay, as data explodes over the next few years, can this technology evolve and support that growth and that data? And a lot of it's going to go back to the machine learning and AI because there's going to be a lot of compute required to actually do a lot of that and provide that intelligence going back. So some big claims I think probably the team talked to you about today, we're 2x anybody in the industry bar-none on this base, so it's ten million IOPS on an 8,000, you're talking 150 gigabytes per second for bandwidth. I mean just the latency and the performance is just phenomenal in its box and it's got so much horsepower behind it. And we also did some creative things around efficiency, as hopefully Adnan and them talked to you guys about it, but we did inline denuke, inline compression, we offload that engine so that way we could have no impact on the data services and really offload on the card, so we don't impact performance for our customers. >> Yeah, Jeff, loved that discussion of data, I think there's been a great trend the last few years talking, it's not just about storing, whether it's structured, unstructured, block, file, object, it's about how businesses can leverage that data, get it in the business. Big in the themes, the keynotes, IT and business, we really really bring it together, maybe look at your storage portfolio, how is that transforming businesses? How is the, not just storage, but data impacting what's going on? >> I mean, to me data is the precious metal, it's the crucial asset, right? You can debate if it's the most important asset for our customers, between their people and their data, you can debate. For me, if you step back, data's the most crucial asset they have, so you've got to treat it as such. To me, it's about what can we do to unleash the power of that data to enable them to be more successful? And so, I think you're dead on, it's not just about infrastructure. Infrastructure's interesting, it's cool, it's modern, we have to make sure that we enable through that way, but it's really about having a data strategy and how they want to do it. So, if you think about having the right data in the right place at the right SLA, this thing's around how you manage the mobility, the infrastructure support and all of the things that you would do to drive that, and I think that's critical. So, we want to make sure we as Dell Technologies and we as Dell EMC, and me as the storage guy, make sure that we unleash the value of that data to enable our customers to make better business decisions to add more value to their businesses, and that's what we're driving, and that's the whole strategy of what we're working on. >> All right, Jeff, talking about PowerMax, >> Jeff: Yeah. >> I've talked to the team about the X2, >> Jeff: Awesome. >> announcement, step back for a second, give us a snapshot of what's happening with the storage portfolio, and you came from what I guess we would call the legacy EMC side. >> Correct. >> Now that we've had more than a year under our belts, between the company together, give us that update on the portfolio. >> Yeah, so we still believe in the power of the portfolio and no ifs, ands, or buts, so I'm not going to shy away from that, in regards to that, brings us a lot of strengths, but it also provides some weaknesses in regards to complexity. And the big thing I think Michael talked about a year ago is we're going to leave no customer behind, and we're completely living up to that. So, you've seen launches recently on Unity, you've seen launches recently on SC, you've seen launches recently on X2 and what have you, and we're going to continue to do that because our customers, we have a large and loyal install base of legacy Dell or legacy EMC customers, which are obviously the most important people, direct and indirect sellers that have some biases or confidence in certain things, and we want to make sure we take care of them. To be clear, simplification is part of our strategy and it will be. So, going from a lot of brands to less than brands, we're absolutely going to do it, and I'm happy to share that in more detail when we have more detail. But we are working through that. But my commitment to the customer is going back to Michael's point, is really two-fold, one is on the data migration and the data mobility, it will be native and it will be seamless to move data from point A to point B. So, I want to be clear, everything will have a next gen, it might not be the same brand or tattoo that they were used to before, it will be a system that meets the market need, the customer requirements and the architecture and the future functions to support that. We'll provide the mobility natively. In addition to that we're going to provide our Loyalty Programs, so not only on the technology side we'll make sure that they're whole, but on the Loyalty Program, so our investment protection that our customers want, need, and demand, and deserve, we're going to provide that as well. So we're going to take care of them on the technology side, but we're also going to take care of them on the business side. But, like I said, I'll share more details when we're here, probably more so next year. >> Right. (laughing) Simple, predictable, profitable, right? >> That's right. >> Keep it simple. >> It's really that simple. >> That's a good formula. Jeff, thanks for being with us. We appreciate the time. >> Awesome, thank you for having me. >> Jeff Boudreau from Storage at Dell EMC. Back with more and we are live here in the Sands at Dell Technologies World 2018. (upbeat music)

>> Announcer: Live from New York City it's The Cube, covering IBM data science for all. Brought to you by IBM. (light digital music) >> Welcome back to data science for all. It's a whole new game. And it is a whole new game. >> Dave Vellante, John Walls here. We've got quite a distinguished panel. So it is a new game-- >> Well we're in the game, I'm just happy to be-- (both laugh) Have a swing at the pitch. >> Well let's what we have here. Five distinguished members of our panel. It'll take me a minute to get through the introductions, but believe me they're worth it. Jennifer Shin joins us. Jennifer's the founder of 8 Path Solutions, the director of the data science of Comcast and part of the faculty at UC Berkeley and NYU. Jennifer, nice to have you with us, we appreciate the time. Joe McKendrick an analyst and contributor of Forbes and ZDNet, Joe, thank you for being here at well. Another ZDNetter next to him, Dion Hinchcliffe, who is a vice president and principal analyst of Constellation Research and also contributes to ZDNet. Good to see you, sir. To the back row, but that doesn't mean anything about the quality of the participation here. Bob Hayes with a killer Batman shirt on by the way, which we'll get to explain in just a little bit. He runs the Business over Broadway. And Joe Caserta, who the founder of Caserta Concepts. Welcome to all of you. Thanks for taking the time to be with us. Jennifer, let me just begin with you. Obviously as a practitioner you're very involved in the industry, you're on the academic side as well. We mentioned Berkeley, NYU, steep experience. So I want you to kind of take your foot in both worlds and tell me about data science. I mean where do we stand now from those two perspectives? How have we evolved to where we are? And how would you describe, I guess the state of data science? >> Yeah so I think that's a really interesting question. There's a lot of changes happening. In part because data science has now become much more established, both in the academic side as well as in industry. So now you see some of the bigger problems coming out. People have managed to have data pipelines set up. But now there are these questions about models and accuracy and data integration. So the really cool stuff from the data science standpoint. We get to get really into the details of the data. And I think on the academic side you now see undergraduate programs, not just graduate programs, but undergraduate programs being involved. UC Berkeley just did a big initiative that they're going to offer data science to undergrads. So that's a huge news for the university. So I think there's a lot of interest from the academic side to continue data science as a major, as a field. But I think in industry one of the difficulties you're now having is businesses are now asking that question of ROI, right? What do I actually get in return in the initial years? So I think there's a lot of work to be done and just a lot of opportunity. It's great because people now understand better with data sciences, but I think data sciences have to really think about that seriously and take it seriously and really think about how am I actually getting a return, or adding a value to the business? >> And there's lot to be said is there not, just in terms of increasing the workforce, the acumen, the training that's required now. It's a still relatively new discipline. So is there a shortage issue? Or is there just a great need? Is the opportunity there? I mean how would you look at that? >> Well I always think there's opportunity to be smart. If you can be smarter, you know it's always better. It gives you advantages in the workplace, it gets you an advantage in academia. The question is, can you actually do the work? The work's really hard, right? You have to learn all these different disciplines, you have to be able to technically understand data. Then you have to understand it conceptually. You have to be able to model with it, you have to be able to explain it. There's a lot of aspects that you're not going to pick up overnight. So I think part of it is endurance. Like are people going to feel motivated enough and dedicate enough time to it to get very good at that skill set. And also of course, you know in terms of industry, will there be enough interest in the long term that there will be a financial motivation. For people to keep staying in the field, right? So I think it's definitely a lot of opportunity. But that's always been there. Like I tell people I think of myself as a scientist and data science happens to be my day job. That's just the job title. But if you are a scientist and you work with data you'll always want to work with data. I think that's just an inherent need. It's kind of a compulsion, you just kind of can't help yourself, but dig a little bit deeper, ask the questions, you can't not think about it. So I think that will always exist. Whether or not it's an industry job in the way that we see it today, and like five years from now, or 10 years from now. I think that's something that's up for debate. >> So all of you have watched the evolution of data and how it effects organizations for a number of years now. If you go back to the days when data warehouse was king, we had a lot of promises about 360 degree views of the customer and how we were going to be more anticipatory in terms and more responsive. In many ways the decision support systems and the data warehousing world didn't live up to those promises. They solved other problems for sure. And so everybody was looking for big data to solve those problems. And they've begun to attack many of them. We talked earlier in The Cube today about fraud detection, it's gotten much, much better. Certainly retargeting of advertising has gotten better. But I wonder if you could comment, you know maybe start with Joe. As to the effect that data and data sciences had on organizations in terms of fulfilling that vision of a 360 degree view of customers and anticipating customer needs. >> So. Data warehousing, I wouldn't say failed. But I think it was unfinished in order to achieve what we need done today. At the time I think it did a pretty good job. I think it was the only place where we were able to collect data from all these different systems, have it in a single place for analytics. The big difference between what I think, between data warehousing and data science is data warehouses were primarily made for the consumer to human beings. To be able to have people look through some tool and be able to analyze data manually. That really doesn't work anymore, there's just too much data to do that. So that's why we need to build a science around it so that we can actually have machines actually doing the analytics for us. And I think that's the biggest stride in the evolution over the past couple of years, that now we're actually able to do that, right? It used to be very, you know you go back to when data warehouses started, you had to be a deep technologist in order to be able to collect the data, write the programs to clean the data. But now you're average causal IT person can do that. Right now I think we're back in data science where you have to be a fairly sophisticated programmer, analyst, scientist, statistician, engineer, in order to do what we need to do, in order to make machines actually understand the data. But I think part of the evolution, we're just in the forefront. We're going to see over the next, not even years, within the next year I think a lot of new innovation where the average person within business and definitely the average person within IT will be able to do as easily say, "What are my sales going to be next year?" As easy as it is to say, "What were my sales last year." Where now it's a big deal. Right now in order to do that you have to build some algorithms, you have to be a specialist on predictive analytics. And I think, you know as the tools mature, as people using data matures, and as the technology ecosystem for data matures, it's going to be easier and more accessible. >> So it's still too hard. (laughs) That's something-- >> Joe C.: Today it is yes. >> You've written about and talked about. >> Yeah no question about it. We see this citizen data scientist. You know we talked about the democratization of data science but the way we talk about analytics and warehousing and all the tools we had before, they generated a lot of insights and views on the information, but they didn't really give us the science part. And that's, I think that what's missing is the forming of the hypothesis, the closing of the loop of. We now have use of this data, but are are changing, are we thinking about it strategically? Are we learning from it and then feeding that back into the process. I think that's the big difference between data science and the analytics side. But, you know just like Google made search available to everyone, not just people who had highly specialized indexers or crawlers. Now we can have tools that make these capabilities available to anyone. You know going back to what Joe said I think the key thing is we now have tools that can look at all the data and ask all the questions. 'Cause we can't possibly do it all ourselves. Our organizations are increasingly awash in data. Which is the life blood of our organizations, but we're not using it, you know this a whole concept of dark data. And so I think the concept, or the promise of opening these tools up for everyone to be able to access those insights and activate them, I think that, you know, that's where it's headed. >> This is kind of where the T shirt comes in right? So Bob if you would, so you've got this Batman shirt on. We talked a little bit about it earlier, but it plays right into what Dion's talking about. About tools and, I don't want to spoil it, but you go ahead (laughs) and tell me about it. >> Right, so. Batman is a super hero, but he doesn't have any supernatural powers, right? He can't fly on his own, he can't become invisible on his own. But the thing is he has the utility belt and he has these tools he can use to help him solve problems. For example he as the bat ring when he's confronted with a building that he wants to get over, right? So he pulls it out and uses that. So as data professionals we have all these tools now that these vendors are making. We have IBM SPSS, we have data science experience. IMB Watson that these data pros can now use it as part of their utility belt and solve problems that they're confronted with. So if you''re ever confronted with like a Churn problem and you have somebody who has access to that data they can put that into IBM Watson, ask a question and it'll tell you what's the key driver of Churn. So it's not that you have to be a superhuman to be a data scientist, but these tools will help you solve certain problems and help your business go forward. >> Joe McKendrick, do you have a comment? >> Does that make the Batmobile the Watson? (everyone laughs) Analogy? >> I was just going to add that, you know all of the billionaires in the world today and none of them decided to become Batman yet. It's very disappointing. >> Yeah. (Joe laughs) >> Go ahead Joe. >> And I just want to add some thoughts to our discussion about what happened with data warehousing. I think it's important to point out as well that data warehousing, as it existed, was fairly successful but for larger companies. Data warehousing is a very expensive proposition it remains a expensive proposition. Something that's in the domain of the Fortune 500. But today's economy is based on a very entrepreneurial model. The Fortune 500s are out there of course it's ever shifting. But you have a lot of smaller companies a lot of people with start ups. You have people within divisions of larger companies that want to innovate and not be tied to the corporate balance sheet. They want to be able to go through, they want to innovate and experiment without having to go through finance and the finance department. So there's all these open source tools available. There's cloud resources as well as open source tools. Hadoop of course being a prime example where you can work with the data and experiment with the data and practice data science at a very low cost. >> Dion mentioned the C word, citizen data scientist last year at the panel. We had a conversation about that. And the data scientists on the panel generally were like, "Stop." Okay, we're not all of a sudden going to turn everybody into data scientists however, what we want to do is get people thinking about data, more focused on data, becoming a data driven organization. I mean as a data scientist I wonder if you could comment on that. >> Well I think so the other side of that is, you know there are also many people who maybe didn't, you know follow through with science, 'cause it's also expensive. A PhD takes a lot of time. And you know if you don't get funding it's a lot of money. And for very little security if you think about how hard it is to get a teaching job that's going to give you enough of a pay off to pay that back. Right, the time that you took off, the investment that you made. So I think the other side of that is by making data more accessible, you allow people who could have been great in science, have an opportunity to be great data scientists. And so I think for me the idea of citizen data scientist, that's where the opportunity is. I think in terms of democratizing data and making it available for everyone, I feel as though it's something similar to the way we didn't really know what KPIs were, maybe 20 years ago. People didn't use it as readily, didn't teach it in schools. I think maybe 10, 20 years from now, some of the things that we're building today from data science, hopefully more people will understand how to use these tools. They'll have a better understanding of working with data and what that means, and just data literacy right? Just being able to use these tools and be able to understand what data's saying and actually what it's not saying. Which is the thing that most people don't think about. But you can also say that data doesn't say anything. There's a lot of noise in it. There's too much noise to be able to say that there is a result. So I think that's the other side of it. So yeah I guess in terms for me, in terms of data a serious data scientist, I think it's a great idea to have that, right? But at the same time of course everyone kind of emphasized you don't want everyone out there going, "I can be a data scientist without education, "without statistics, without math," without understanding of how to implement the process. I've seen a lot of companies implement the same sort of process from 10, 20 years ago just on Hadoop instead of SQL. Right and it's very inefficient. And the only difference is that you can build more tables wrong than they could before. (everyone laughs) Which is I guess >> For less. it's an accomplishment and for less, it's cheaper, yeah. >> It is cheaper. >> Otherwise we're like I'm not a data scientist but I did stay at a Holiday Inn Express last night, right? >> Yeah. (panelists laugh) And there's like a little bit of pride that like they used 2,000, you know they used 2,000 computers to do it. Like a little bit of pride about that, but you know of course maybe not a great way to go. I think 20 years we couldn't do that, right? One computer was already an accomplishment to have that resource. So I think you have to think about the fact that if you're doing it wrong, you're going to just make that mistake bigger, which his also the other side of working with data. >> Sure, Bob. >> Yeah I have a comment about that. I've never liked the term citizen data scientist or citizen scientist. I get the point of it and I think employees within companies can help in the data analytics problem by maybe being a data collector or something. I mean I would never have just somebody become a scientist based on a few classes here she takes. It's like saying like, "Oh I'm going to be a citizen lawyer." And so you come to me with your legal problems, or a citizen surgeon. Like you need training to be good at something. You can't just be good at something just 'cause you want to be. >> John: Joe you wanted to say something too on that. >> Since we're in New York City I'd like to use the analogy of a real scientist versus a data scientist. So real scientist requires tools, right? And the tools are not new, like microscopes and a laboratory and a clean room. And these tools have evolved over years and years, and since we're in New York we could walk within a 10 block radius and buy any of those tools. It doesn't make us a scientist because we use those tools. I think with data, you know making, making the tools evolve and become easier to use, you know like Bob was saying, it doesn't make you a better data scientist, it just makes the data more accessible. You know we can go buy a microscope, we can go buy Hadoop, we can buy any kind of tool in a data ecosystem, but it doesn't really make you a scientist. I'm very involved in the NYU data science program and the Columbia data science program, like these kids are brilliant. You know these kids are not someone who is, you know just trying to run a day to day job, you know in corporate America. I think the people who are running the day to day job in corporate America are going to be the recipients of data science. Just like people who take drugs, right? As a result of a smart data scientist coming up with a formula that can help people, I think we're going to make it easier to distribute the data that can help people with all the new tools. But it doesn't really make it, you know the access to the data and tools available doesn't really make you a better data scientist. Without, like Bob was saying, without better training and education. >> So how-- I'm sorry, how do you then, if it's not for everybody, but yet I'm the user at the end of the day at my company and I've got these reams of data before me, how do you make it make better sense to me then? So that's where machine learning comes in or artificial intelligence and all this stuff. So how at the end of the day, Dion? How do you make it relevant and usable, actionable to somebody who might not be as practiced as you would like? >> I agree with Joe that many of us will be the recipients of data science. Just like you had to be a computer science at one point to develop programs for a computer, now we can get the programs. You don't need to be a computer scientist to get a lot of value out of our IT systems. The same thing's going to happen with data science. There's far more demand for data science than there ever could be produced by, you know having an ivory tower filled with data scientists. Which we need those guys, too, don't get me wrong. But we need to have, productize it and make it available in packages such that it can be consumed. The outputs and even some of the inputs can be provided by mere mortals, whether that's machine learning or artificial intelligence or bots that go off and run the hypotheses and select the algorithms maybe with some human help. We have to productize it. This is a constant of data scientist of service, which is becoming a thing now. It's, "I need this, I need this capability at scale. "I need it fast and I need it cheap." The commoditization of data science is going to happen. >> That goes back to what I was saying about, the recipient also of data science is also machines, right? Because I think the other thing that's happening now in the evolution of data is that, you know the data is, it's so tightly coupled. Back when you were talking about data warehousing you have all the business transactions then you take the data out of those systems, you put them in a warehouse for analysis, right? Maybe they'll make a decision to change that system at some point. Now the analytics platform and the business application is very tightly coupled. They become dependent upon one another. So you know people who are using the applications are now be able to take advantage of the insights of data analytics and data science, just through the app. Which never really existed before. >> I have one comment on that. You were talking about how do you get the end user more involved, well like we said earlier data science is not easy, right? As an end user, I encourage you to take a stats course, just a basic stats course, understanding what a mean is, variability, regression analysis, just basic stuff. So you as an end user can get more, or glean more insight from the reports that you're given, right? If you go to France and don't know French, then people can speak really slowly to you in French, you're not going to get it. You need to understand the language of data to get value from the technology we have available to us. >> Incidentally French is one of the languages that you have the option of learning if you're a mathematicians. So math PhDs are required to learn a second language. France being the country of algebra, that's one of the languages you could actually learn. Anyway tangent. But going back to the point. So statistics courses, definitely encourage it. I teach statistics. And one of the things that I'm finding as I go through the process of teaching it I'm actually bringing in my experience. And by bringing in my experience I'm actually kind of making the students think about the data differently. So the other thing people don't think about is the fact that like statisticians typically were expected to do, you know, just basic sort of tasks. In a sense that they're knowledge is specialized, right? But the day to day operations was they ran some data, you know they ran a test on some data, looked at the results, interpret the results based on what they were taught in school. They didn't develop that model a lot of times they just understand what the tests were saying, especially in the medical field. So when you when think about things like, we have words like population, census. Which is when you take data from every single, you have every single data point versus a sample, which is a subset. It's a very different story now that we're collecting faster than it used to be. It used to be the idea that you could collect information from everyone. Like it happens once every 10 years, we built that in. But nowadays you know, you know here about Facebook, for instance, I think they claimed earlier this year that their data was more accurate than the census data. So now there are these claims being made about which data source is more accurate. And I think the other side of this is now statisticians are expected to know data in a different way than they were before. So it's not just changing as a field in data science, but I think the sciences that are using data are also changing their fields as well. >> Dave: So is sampling dead? >> Well no, because-- >> Should it be? (laughs) >> Well if you're sampling wrong, yes. That's really the question. >> Okay. You know it's been said that the data doesn't lie, people do. Organizations are very political. Oftentimes you know, lies, damned lies and statistics, Benjamin Israeli. Are you seeing a change in the way in which organizations are using data in the context of the politics. So, some strong P&L manager say gets data and crafts it in a way that he or she can advance their agenda. Or they'll maybe attack a data set that is, probably should drive them in a different direction, but might be antithetical to their agenda. Are you seeing data, you know we talked about democratizing data, are you seeing that reduce the politics inside of organizations? >> So you know we've always used data to tell stories at the top level of an organization that's what it's all about. And I still see very much that no matter how much data science or, the access to the truth through looking at the numbers that story telling is still the political filter through which all that data still passes, right? But it's the advent of things like Block Chain, more and more corporate records and corporate information is going to end up in these open and shared repositories where there is not alternate truth. It'll come back to whoever tells the best stories at the end of the day. So I still see the organizations are very political. We are seeing now more open data though. Open data initiatives are a big thing, both in government and in the private sector. It is having an effect, but it's slow and steady. So that's what I see. >> Um, um, go ahead. >> I was just going to say as well. Ultimately I think data driven decision making is a great thing. And it's especially useful at the lower tiers of the organization where you have the routine day to day's decisions that could be automated through machine learning and deep learning. The algorithms can be improved on a constant basis. On the upper levels, you know that's why you pay executives the big bucks in the upper levels to make the strategic decisions. And data can help them, but ultimately, data, IT, technology alone will not create new markets, it will not drive new businesses, it's up to human beings to do that. The technology is the tool to help them make those decisions. But creating businesses, growing businesses, is very much a human activity. And that's something I don't see ever getting replaced. Technology might replace many other parts of the organization, but not that part. >> I tend to be a foolish optimist when it comes to this stuff. >> You do. (laughs) >> I do believe that data will make the world better. I do believe that data doesn't lie people lie. You know I think as we start, I'm already seeing trends in industries, all different industries where, you know conventional wisdom is starting to get trumped by analytics. You know I think it's still up to the human being today to ignore the facts and go with what they think in their gut and sometimes they win, sometimes they lose. But generally if they lose the data will tell them that they should have gone the other way. I think as we start relying more on data and trusting data through artificial intelligence, as we start making our lives a little bit easier, as we start using smart cars for safety, before replacement of humans. AS we start, you know, using data really and analytics and data science really as the bumpers, instead of the vehicle, eventually we're going to start to trust it as the vehicle itself. And then it's going to make lying a little bit harder. >> Okay, so great, excellent. Optimism, I love it. (John laughs) So I'm going to play devil's advocate here a little bit. There's a couple elephant in the room topics that I want to, to explore a little bit. >> Here it comes. >> There was an article today in Wired. And it was called, Why AI is Still Waiting for It's Ethics Transplant. And, I will just read a little segment from there. It says, new ethical frameworks for AI need to move beyond individual responsibility to hold powerful industrial, government and military interests accountable as they design and employ AI. When tech giants build AI products, too often user consent, privacy and transparency are overlooked in favor of frictionless functionality that supports profit driven business models based on aggregate data profiles. This is from Kate Crawford and Meredith Whittaker who founded AI Now. And they're calling for sort of, almost clinical trials on AI, if I could use that analogy. Before you go to market you've got to test the human impact, the social impact. Thoughts. >> And also have the ability for a human to intervene at some point in the process. This goes way back. Is everybody familiar with the name Stanislav Petrov? He's the Soviet officer who back in 1983, it was in the control room, I guess somewhere outside of Moscow in the control room, which detected a nuclear missile attack against the Soviet Union coming out of the United States. Ordinarily I think if this was an entirely AI driven process we wouldn't be sitting here right now talking about it. But this gentlemen looked at what was going on on the screen and, I'm sure he's accountable to his authorities in the Soviet Union. He probably got in a lot of trouble for this, but he decided to ignore the signals, ignore the data coming out of, from the Soviet satellites. And as it turned out, of course he was right. The Soviet satellites were seeing glints of the sun and they were interpreting those glints as missile launches. And I think that's a great example why, you know every situation of course doesn't mean the end of the world, (laughs) it was in this case. But it's a great example why there needs to be a human component, a human ability for human intervention at some point in the process. >> So other thoughts. I mean organizations are driving AI hard for profit. Best minds of our generation are trying to figure out how to get people to click on ads. Jeff Hammerbacher is famous for saying it. >> You can use data for a lot of things, data analytics, you can solve, you can cure cancer. You can make customers click on more ads. It depends on what you're goal is. But, there are ethical considerations we need to think about. When we have data that will have a racial bias against blacks and have them have higher prison sentences or so forth or worse credit scores, so forth. That has an impact on a broad group of people. And as a society we need to address that. And as scientists we need to consider how are we going to fix that problem? Cathy O'Neil in her book, Weapons of Math Destruction, excellent book, I highly recommend that your listeners read that book. And she talks about these issues about if AI, if algorithms have a widespread impact, if they adversely impact protected group. And I forget the last criteria, but like we need to really think about these things as a people, as a country. >> So always think the idea of ethics is interesting. So I had this conversation come up a lot of times when I talk to data scientists. I think as a concept, right as an idea, yes you want things to be ethical. The question I always pose to them is, "Well in the business setting "how are you actually going to do this?" 'Cause I find the most difficult thing working as a data scientist, is to be able to make the day to day decision of when someone says, "I don't like that number," how do you actually get around that. If that's the right data to be showing someone or if that's accurate. And say the business decides, "Well we don't like that number." Many people feel pressured to then change the data, change, or change what the data shows. So I think being able to educate people to be able to find ways to say what the data is saying, but not going past some line where it's a lie, where it's unethical. 'Cause you can also say what data doesn't say. You don't always have to say what the data does say. You can leave it as, "Here's what we do know, "but here's what we don't know." There's a don't know part that many people will omit when they talk about data. So I think, you know especially when it comes to things like AI it's tricky, right? Because I always tell people I don't know everyone thinks AI's going to be so amazing. I started an industry by fixing problems with computers that people didn't realize computers had. For instance when you have a system, a lot of bugs, we all have bug reports that we've probably submitted. I mean really it's no where near the point where it's going to start dominating our lives and taking over all the jobs. Because frankly it's not that advanced. It's still run by people, still fixed by people, still managed by people. I think with ethics, you know a lot of it has to do with the regulations, what the laws say. That's really going to be what's involved in terms of what people are willing to do. A lot of businesses, they want to make money. If there's no rules that says they can't do certain things to make money, then there's no restriction. I think the other thing to think about is we as consumers, like everyday in our lives, we shouldn't separate the idea of data as a business. We think of it as a business person, from our day to day consumer lives. Meaning, yes I work with data. Incidentally I also always opt out of my credit card, you know when they send you that information, they make you actually mail them, like old school mail, snail mail like a document that says, okay I don't want to be part of this data collection process. Which I always do. It's a little bit more work, but I go through that step of doing that. Now if more people did that, perhaps companies would feel more incentivized to pay you for your data, or give you more control of your data. Or at least you know, if a company's going to collect information, I'd want you to be certain processes in place to ensure that it doesn't just get sold, right? For instance if a start up gets acquired what happens with that data they have on you? You agree to give it to start up. But I mean what are the rules on that? So I think we have to really think about the ethics from not just, you know, someone who's going to implement something but as consumers what control we have for our own data. 'Cause that's going to directly impact what businesses can do with our data. >> You know you mentioned data collection. So slightly on that subject. All these great new capabilities we have coming. We talked about what's going to happen with media in the future and what 5G technology's going to do to mobile and these great bandwidth opportunities. The internet of things and the internet of everywhere. And all these great inputs, right? Do we have an arms race like are we keeping up with the capabilities to make sense of all the new data that's going to be coming in? And how do those things square up in this? Because the potential is fantastic, right? But are we keeping up with the ability to make it make sense and to put it to use, Joe? >> So I think data ingestion and data integration is probably one of the biggest challenges. I think, especially as the world is starting to become more dependent on data. I think you know, just because we're dependent on numbers we've come up with GAAP, which is generally accepted accounting principles that can be audited and proven whether it's true or false. I think in our lifetime we will see something similar to that we will we have formal checks and balances of data that we use that can be audited. Getting back to you know what Dave was saying earlier about, I personally would trust a machine that was programmed to do the right thing, than to trust a politician or some leader that may have their own agenda. And I think the other thing about machines is that they are auditable. You know you can look at the code and see exactly what it's doing and how it's doing it. Human beings not so much. So I think getting to the truth, even if the truth isn't the answer that we want, I think is a positive thing. It's something that we can't do today that once we start relying on machines to do we'll be able to get there. >> Yeah I was just going to add that we live in exponential times. And the challenge is that the way that we're structured traditionally as organizations is not allowing us to absorb advances exponentially, it's linear at best. Everyone talks about change management and how are we going to do digital transformation. Evidence shows that technology's forcing the leaders and the laggards apart. There's a few leading organizations that are eating the world and they seem to be somehow rolling out new things. I don't know how Amazon rolls out all this stuff. There's all this artificial intelligence and the IOT devices, Alexa, natural language processing and that's just a fraction, it's just a tip of what they're releasing. So it just shows that there are some organizations that have path found the way. Most of the Fortune 500 from the year 2000 are gone already, right? The disruption is happening. And so we are trying, have to find someway to adopt these new capabilities and deploy them effectively or the writing is on the wall. I spent a lot of time exploring this topic, how are we going to get there and all of us have a lot of hard work is the short answer. >> I read that there's going to be more data, or it was predicted, more data created in this year than in the past, I think it was five, 5,000 years. >> Forever. (laughs) >> And that to mix the statistics that we're analyzing currently less than 1% of the data. To taking those numbers and hear what you're all saying it's like, we're not keeping up, it seems like we're, it's not even linear. I mean that gap is just going to grow and grow and grow. How do we close that? >> There's a guy out there named Chris Dancy, he's known as the human cyborg. He has 700 hundred sensors all over his body. And his theory is that data's not new, having access to the data is new. You know we've always had a blood pressure, we've always had a sugar level. But we were never able to actually capture it in real time before. So now that we can capture and harness it, now we can be smarter about it. So I think that being able to use this information is really incredible like, this is something that over our lifetime we've never had and now we can do it. Which hence the big explosion in data. But I think how we use it and have it governed I think is the challenge right now. It's kind of cowboys and indians out there right now. And without proper governance and without rigorous regulation I think we are going to have some bumps in the road along the way. >> The data's in the oil is the question how are we actually going to operationalize around it? >> Or find it. Go ahead. >> I will say the other side of it is, so if you think about information, we always have the same amount of information right? What we choose to record however, is a different story. Now if you want wanted to know things about the Olympics, but you decide to collect information every day for years instead of just the Olympic year, yes you have a lot of data, but did you need all of that data? For that question about the Olympics, you don't need to collect data during years there are no Olympics, right? Unless of course you're comparing it relative. But I think that's another thing to think about. Just 'cause you collect more data does not mean that data will produce more statistically significant results, it does not mean it'll improve your model. You can be collecting data about your shoe size trying to get information about your hair. I mean it really does depend on what you're trying to measure, what your goals are, and what the data's going to be used for. If you don't factor the real world context into it, then yeah you can collect data, you know an infinite amount of data, but you'll never process it. Because you have no question to ask you're not looking to model anything. There is no universal truth about everything, that just doesn't exist out there. >> I think she's spot on. It comes down to what kind of questions are you trying to ask of your data? You can have one given database that has 100 variables in it, right? And you can ask it five different questions, all valid questions and that data may have those variables that'll tell you what's the best predictor of Churn, what's the best predictor of cancer treatment outcome. And if you can ask the right question of the data you have then that'll give you some insight. Just data for data's sake, that's just hype. We have a lot of data but it may not lead to anything if we don't ask it the right questions. >> Joe. >> I agree but I just want to add one thing. This is where the science in data science comes in. Scientists often will look at data that's already been in existence for years, weather forecasts, weather data, climate change data for example that go back to data charts and so forth going back centuries if that data is available. And they reformat, they reconfigure it, they get new uses out of it. And the potential I see with the data we're collecting is it may not be of use to us today, because we haven't thought of ways to use it, but maybe 10, 20, even 100 years from now someone's going to think of a way to leverage the data, to look at it in new ways and to come up with new ideas. That's just my thought on the science aspect. >> Knowing what you know about data science, why did Facebook miss Russia and the fake news trend? They came out and admitted it. You know, we miss it, why? Could they have, is it because they were focused elsewhere? Could they have solved that problem? (crosstalk) >> It's what you said which is are you asking the right questions and if you're not looking for that problem in exactly the way that it occurred you might not be able to find it. >> I thought the ads were paid in rubles. Shouldn't that be your first clue (panelists laugh) that something's amiss? >> You know red flag, so to speak. >> Yes. >> I mean Bitcoin maybe it could have hidden it. >> Bob: Right, exactly. >> I would think too that what happened last year is actually was the end of an age of optimism. I'll bring up the Soviet Union again, (chuckles). It collapsed back in 1991, 1990, 1991, Russia was reborn in. And think there was a general feeling of optimism in the '90s through the 2000s that Russia is now being well integrated into the world economy as other nations all over the globe, all continents are being integrated into the global economy thanks to technology. And technology is lifting entire continents out of poverty and ensuring more connectedness for people. Across Africa, India, Asia, we're seeing those economies that very different countries than 20 years ago and that extended into Russia as well. Russia is part of the global economy. We're able to communicate as a global, a global network. I think as a result we kind of overlook the dark side that occurred. >> John: Joe? >> Again, the foolish optimist here. But I think that... It shouldn't be the question like how did we miss it? It's do we have the ability now to catch it? And I think without data science without machine learning, without being able to train machines to look for patterns that involve corruption or result in corruption, I think we'd be out of luck. But now we have those tools. And now hopefully, optimistically, by the next election we'll be able to detect these things before they become public. >> It's a loaded question because my premise was Facebook had the ability and the tools and the knowledge and the data science expertise if in fact they wanted to solve that problem, but they were focused on other problems, which is how do I get people to click on ads? >> Right they had the ability to train the machines, but they were giving the machines the wrong training. >> Looking under the wrong rock. >> (laughs) That's right. >> It is easy to play armchair quarterback. Another topic I wanted to ask the panel about is, IBM Watson. You guys spend time in the Valley, I spend time in the Valley. People in the Valley poo-poo Watson. Ah, Google, Facebook, Amazon they've got the best AI. Watson, and some of that's fair criticism. Watson's a heavy lift, very services oriented, you just got to apply it in a very focused. At the same time Google's trying to get you to click on Ads, as is Facebook, Amazon's trying to get you to buy stuff. IBM's trying to solve cancer. Your thoughts on that sort of juxtaposition of the different AI suppliers and there may be others. Oh, nobody wants to touch this one, come on. I told you elephant in the room questions. >> Well I mean you're looking at two different, very different types of organizations. One which is really spent decades in applying technology to business and these other companies are ones that are primarily into the consumer, right? When we talk about things like IBM Watson you're looking at a very different type of solution. You used to be able to buy IT and once you installed it you pretty much could get it to work and store your records or you know, do whatever it is you needed it to do. But these types of tools, like Watson actually tries to learn your business. And it needs to spend time doing that watching the data and having its models tuned. And so you don't get the results right away. And I think that's been kind of the challenge that organizations like IBM has had. Like this is a different type of technology solution, one that has to actually learn first before it can provide value. And so I think you know you have organizations like IBM that are much better at applying technology to business, and then they have the further hurdle of having to try to apply these tools that work in very different ways. There's education too on the side of the buyer. >> I'd have to say that you know I think there's plenty of businesses out there also trying to solve very significant, meaningful problems. You know with Microsoft AI and Google AI and IBM Watson, I think it's not really the tool that matters, like we were saying earlier. A fool with a tool is still a fool. And regardless of who the manufacturer of that tool is. And I think you know having, a thoughtful, intelligent, trained, educated data scientist using any of these tools can be equally effective. >> So do you not see core AI competence and I left out Microsoft, as a strategic advantage for these companies? Is it going to be so ubiquitous and available that virtually anybody can apply it? Or is all the investment in R&D and AI going to pay off for these guys? >> Yeah, so I think there's different levels of AI, right? So there's AI where you can actually improve the model. I remember when I was invited when Watson was kind of first out by IBM to a private, sort of presentation. And my question was, "Okay, so when do I get "to access the corpus?" The corpus being sort of the foundation of NLP, which is natural language processing. So it's what you use as almost like a dictionary. Like how you're actually going to measure things, or things up. And they said, "Oh you can't." "What do you mean I can't?" It's like, "We do that." "So you're telling me as a data scientist "you're expecting me to rely on the fact "that you did it better than me and I should rely on that." I think over the years after that IBM started opening it up and offering different ways of being able to access the corpus and work with that data. But I remember at the first Watson hackathon there was only two corpus available. It was either the travel or medicine. There was no other foundational data available. So I think one of the difficulties was, you know IBM being a little bit more on the forefront of it they kind of had that burden of having to develop these systems and learning kind of the hard way that if you don't have the right models and you don't have the right data and you don't have the right access, that's going to be a huge limiter. I think with things like medical, medical information that's an extremely difficult data to start with. Partly because you know anything that you do find or don't find, the impact is significant. If I'm looking at things like what people clicked on the impact of using that data wrong, it's minimal. You might lose some money. If you do that with healthcare data, if you do that with medical data, people may die, like this is a much more difficult data set to start with. So I think from a scientific standpoint it's great to have any information about a new technology, new process. That's the nice that is that IBM's obviously invested in it and collected information. I think the difficulty there though is just 'cause you have it you can't solve everything. And if feel like from someone who works in technology, I think in general when you appeal to developers you try not to market. And with Watson it's very heavily marketed, which tends to turn off people who are more from the technical side. Because I think they don't like it when it's gimmicky in part because they do the opposite of that. They're always trying to build up the technical components of it. They don't like it when you're trying to convince them that you're selling them something when you could just give them the specs and look at it. So it could be something as simple as communication. But I do think it is valuable to have had a company who leads on the forefront of that and try to do so we can actually learn from what IBM has learned from this process. >> But you're an optimist. (John laughs) All right, good. >> Just one more thought. >> Joe go ahead first. >> Joe: I want to see how Alexa or Siri do on Jeopardy. (panelists laugh) >> All right. Going to go around a final thought, give you a second. Let's just think about like your 12 month crystal ball. In terms of either challenges that need to be met in the near term or opportunities you think will be realized. 12, 18 month horizon. Bob you've got the microphone headed up, so I'll let you lead off and let's just go around. >> I think a big challenge for business, for society is getting people educated on data and analytics. There's a study that was just released I think last month by Service Now, I think, or some vendor, or Click. They found that only 17% of the employees in Europe have the ability to use data in their job. Think about that. >> 17. >> 17. Less than 20%. So these people don't have the ability to understand or use data intelligently to improve their work performance. That says a lot about the state we're in today. And that's Europe. It's probably a lot worse in the United States. So that's a big challenge I think. To educate the masses. >> John: Joe. >> I think we probably have a better chance of improving technology over training people. I think using data needs to be iPhone easy. And I think, you know which means that a lot of innovation is in the years to come. I do think that a keyboard is going to be a thing of the past for the average user. We are going to start using voice a lot more. I think augmented reality is going to be things that becomes a real reality. Where we can hold our phone in front of an object and it will have an overlay of prices where it's available, if it's a person. I think that we will see within an organization holding a camera up to someone and being able to see what is their salary, what sales did they do last year, some key performance indicators. I hope that we are beyond the days of everyone around the world walking around like this and we start actually becoming more social as human beings through augmented reality. I think, it has to happen. I think we're going through kind of foolish times at the moment in order to get to the greater good. And I think the greater good is using technology in a very, very smart way. Which means that you shouldn't have to be, sorry to contradict, but maybe it's good to counterpoint. I don't think you need to have a PhD in SQL to use data. Like I think that's 1990. I think as we evolve it's going to become easier for the average person. Which means people like the brain trust here needs to get smarter and start innovating. I think the innovation around data is really at the tip of the iceberg, we're going to see a lot more of it in the years to come. >> Dion why don't you go ahead, then we'll come down the line here. >> Yeah so I think over that time frame two things are likely to happen. One is somebody's going to crack the consumerization of machine learning and AI, such that it really is available to the masses and we can do much more advanced things than we could. We see the industries tend to reach an inflection point and then there's an explosion. No one's quite cracked the code on how to really bring this to everyone, but somebody will. And that could happen in that time frame. And then the other thing that I think that almost has to happen is that the forces for openness, open data, data sharing, open data initiatives things like Block Chain are going to run headlong into data protection, data privacy, customer privacy laws and regulations that have to come down and protect us. Because the industry's not doing it, the government is stepping in and it's going to re-silo a lot of our data. It's going to make it recede and make it less accessible, making data science harder for a lot of the most meaningful types of activities. Patient data for example is already all locked down. We could do so much more with it, but health start ups are really constrained about what they can do. 'Cause they can't access the data. We can't even access our own health care records, right? So I think that's the challenge is we have to have that battle next to be able to go and take the next step. >> Well I see, with the growth of data a lot of it's coming through IOT, internet of things. I think that's a big source. And we're going to see a lot of innovation. A new types of Ubers or Air BnBs. Uber's so 2013 though, right? We're going to see new companies with new ideas, new innovations, they're going to be looking at the ways this data can be leveraged all this big data. Or data coming in from the IOT can be leveraged. You know there's some examples out there. There's a company for example that is outfitting tools, putting sensors in the tools. Industrial sites can therefore track where the tools are at any given time. This is an expensive, time consuming process, constantly loosing tools, trying to locate tools. Assessing whether the tool's being applied to the production line or the right tool is at the right torque and so forth. With the sensors implanted in these tools, it's now possible to be more efficient. And there's going to be innovations like that. Maybe small start up type things or smaller innovations. We're going to see a lot of new ideas and new types of approaches to handling all this data. There's going to be new business ideas. The next Uber, we may be hearing about it a year from now whatever that may be. And that Uber is going to be applying data, probably IOT type data in some, new innovative way. >> Jennifer, final word. >> Yeah so I think with data, you know it's interesting, right, for one thing I think on of the things that's made data more available and just people we open to the idea, has been start ups. But what's interesting about this is a lot of start ups have been acquired. And a lot of people at start ups that got acquired now these people work at bigger corporations. Which was the way it was maybe 10 years ago, data wasn't available and open, companies kept it very proprietary, you had to sign NDAs. It was like within the last 10 years that open source all of that initiatives became much more popular, much more open, a acceptable sort of way to look at data. I think that what I'm kind of interested in seeing is what people do within the corporate environment. Right, 'cause they have resources. They have funding that start ups don't have. And they have backing, right? Presumably if you're acquired you went in at a higher title in the corporate structure whereas if you had started there you probably wouldn't be at that title at that point. So I think you have an opportunity where people who have done innovative things and have proven that they can build really cool stuff, can now be in that corporate environment. I think part of it's going to be whether or not they can really adjust to sort of the corporate, you know the corporate landscape, the politics of it or the bureaucracy. I think every organization has that. Being able to navigate that is a difficult thing in part 'cause it's a human skill set, it's a people skill, it's a soft skill. It's not the same thing as just being able to code something and sell it. So you know it's going to really come down to people. I think if people can figure out for instance, what people want to buy, what people think, in general that's where the money comes from. You know you make money 'cause someone gave you money. So if you can find a way to look at a data or even look at technology and understand what people are doing, aren't doing, what they're happy about, unhappy about, there's always opportunity in collecting the data in that way and being able to leverage that. So you build cooler things, and offer things that haven't been thought of yet. So it's a very interesting time I think with the corporate resources available if you can do that. You know who knows what we'll have in like a year. >> I'll add one. >> Please. >> The majority of companies in the S&P 500 have a market cap that's greater than their revenue. The reason is 'cause they have IP related to data that's of value. But most of those companies, most companies, the vast majority of companies don't have any way to measure the value of that data. There's no GAAP accounting standard. So they don't understand the value contribution of their data in terms of how it helps them monetize. Not the data itself necessarily, but how it contributes to the monetization of the company. And I think that's a big gap. If you don't understand the value of the data that means you don't understand how to refine it, if data is the new oil and how to protect it and so forth and secure it. So that to me is a big gap that needs to get closed before we can actually say we live in a data driven world. >> So you're saying I've got an asset, I don't know if it's worth this or this. And they're missing that great opportunity. >> So devolve to what I know best. >> Great discussion. Really, really enjoyed the, the time as flown by. Joe if you get that augmented reality thing to work on the salary, point it toward that guy not this guy, okay? (everyone laughs) It's much more impressive if you point it over there. But Joe thank you, Dion, Joe and Jennifer and Batman. We appreciate and Bob Hayes, thanks for being with us. >> Thanks you guys. >> Really enjoyed >> Great stuff. >> the conversation. >> And a reminder coming up a the top of the hour, six o'clock Eastern time, IBMgo.com featuring the live keynote which is being set up just about 50 feet from us right now. Nick Silver is one of the headliners there, John Thomas is well, or rather Rob Thomas. John Thomas we had on earlier on The Cube. But a panel discussion as well coming up at six o'clock on IBMgo.com, six to 7:15. Be sure to join that live stream. That's it from The Cube. We certainly appreciate the time. Glad to have you along here in New York. And until the next time, take care. (bright digital music)

(upbeat music) >> Narrator: Live from New York City, it's the Cube, covering IBM Data Science for All. Brought to you by IMB. >> Welcome back to Data Science for All. It's a whole new game here at IBM's event, two-day event going on, 6:00 tonight the big keynote presentation on IBM.com so be sure to join the festivities there. You can watch it live stream, all that's happening. Right now, we're live here on the Cube, along with Dave Vellente, I'm John Walls and we are joined by John Thomas who is a distinguished engineer and director at IBM. John, thank you for your time, good to see you. >> Same here, John. >> Yeah, pleasure, thanks for being with us here. >> John Thomas: Sure. >> I know, in fact, you just wrote this morning about machine learning, so that's obviously very near and dear to you. Let's talk first off about IBM, >> John Thomas: Sure. >> Not a new concept by any means, but what is new with regard to machine learning in your work? >> Yeah, well, that's a good question, John. Actually, I get that question a lot. Machine learning itself is not new, companies have been doing it for decades, so exactly what is new, right? I actually wrote this in a blog today, this morning. It's really three different things, I call them democratizing machine learning, operationalizing machine learning, and hybrid machine learning, right? And we can talk through each of these if you like. But I would say hybrid machine learning is probably closest to my heart. So let me explain what that is because it's sounds fancy, right? (laughter) >> Right. It's what we need is another hybrid something, right? >> In reality, what it is is let data gravity decide where your data stays and let your performance requirements, your SLA's, dictate where your machine learning models go, right? So what do I mean by that? You might have sensitive data, customer data, which you want to keep on a certain platform, right? Instead of moving data off that platform to do machine learning, bring machine learning to that platform, whether that be the mainframe or specialized appliances or hadoop clusters, you name it, right? Bring machine learning to where the data is. Do the training, building of the model, where that is, but then have complete flexibility in terms of where you deploy that model. As an example, you might choose to build and train your model on premises behind the firewall using very sensitive data, but the model that has been built, you may choose to deploy that into a Cloud environment because you have other applications that need to consume it. That flexibility is what I mean by hybrid. Another example is, especially when you get into so many more complex machine learning, deep learning domains, you need exploration and there is hardware that provides that exploration, right? For example, GPU's provide exploration. Well, you need to have the flexibility to train and build the models on hardware that provides that kind of exploration, but then the model that has been built might go into inside of a CICS mainframe transaction for some second scoring of a credit card transaction as to whether it's fraudulent or not, right? So there's flexibility off peri, on peri, different platforms, this is what I mean by hybrid. >> What is the technical enabler to allow that to happen? Is it just a modern software architecture, microservices, containers, blah, blah, blah? Explain that in more detail. >> Yeah, that's a good question and we're not, you know, it's a couple different things. One is bringing native machine learning to these platforms themselves. So you need native machine learning on the mainframe, in the Cloud, in a hadoop cluster environment, in an appliance, right? So you need the run times, the libraries, the frameworks running native on those platforms. And that is not easy to do that, you know? You've got machine learning running native on ZOS, not even Linux on Z. It's native to ZOS on the mainframe. >> At the very primitive level you're talking about. >> Yeah. >> So you get the performance you need. >> You have the runtime environments there and then what you need is a seamless experience across all of these platforms. You need way to export models, repositories into which you can save models, the same API's to save models into a different repository and then consume from them there. So it's a bit of engineering that IBM is doing to enable this, right? Native capabilities on the platforms, the same API's to talk to repositories and consume from the repositories. >> So the other piece of that architecture is talking a lot of tooling that integrated and native. >> John Thomas: Yes. >> And the tooling, as you know, changes, I feel like daily. There's a new tool out there and everybody gloms onto it, so the architecture has to be able to absorb those. What is the enabler there? >> Yeah, so you actually bring up a very good point. There is a new language, a new framework everyday, right? I mean, we all know that, in the world of machine learning, Python and R and Scala. Frameworks like Spark and TensorFlow, they're table scapes now, you know? You have to support all of these, scikit-learning, you name it, right? Obviously, you need a way to support all these frameworks on the platforms you want to enable, right? And then you need an environment which lets you work with the tools of your choice. So you need an environment like a workbench which can allow you to work in the language, the framework that you are the most comfortable with. And that's what we are doing with data science experience. I don't know if you have thought of this, but data science experience is an enterprise ML platform, right, runs in the Cloud, on prem, on x86 machines, you can have it on a (mumbles) box. The idea here is support for a variety of open languages, frameworks, enable through a collaborative workbench kind of interface. >> And the decision to move, whether it's on-prem or in the Cloud, it's a function of many things, but let's talk about those. I mean, data volume is one. You can't just move your business into the Cloud. It's not going to work that well. >> It's a journey, yeah. >> It's too expensive. But then there's others, there's governance edicts and security edicts, not that the security in the Cloud is any worse, it might just different than what your organization requires, and the Cloud supplier might not support that. It's different Clouds, it's location, etc. When you talked about the data thing being on trend, maybe training a model, and then that model moving to the Cloud, so obviously, it's a lighter weight ... It's not as much-- >> Yeah, yeah, yeah, you're not moving the entire data. Right. >> But I have a concern. I wonder if clients as you about this. Okay, well, it's my data, my data, I'm going to keep behind my firewall. But that data trained that model and I'm really worried that that model is now my IP that's going to seep out into the industry. What do you tell a client? >> Yeah, that's a fair point. Obviously, you still need your security mechanisms, you access control mechanisms, your governance control mechanisms. So you need governance whether you are on the Cloud or on prem. And your encryption mechanisms, your version control mechanisms, your governance mechanisms, all need to be in place, regardless of where you deploy, right? And to your question of how do you decide where the model should go, as I said earlier to John, you know, let data gravity SLA's performance security requirements dictate where the model should go. >> We're talking so much about concepts, right, and theories that you have. Lets roll up our sleeves and get to the nitty-gritty a little bit here and talk about what are people really doing out there? >> John Thomas: Oh yeah, use cases. >> Yeah, just give us an idea for some of the ... Kind of the latest and greatest that you're seeing. >> Lots of very interesting, interesting use cases out there so actually, a part of what IBM calls a data science elite team. We go out and engage with customers on very interesting use cases, right? And we see a lot of these hybrid discussions happen as well. On one end of the spectrum is understanding customers better. So I call this reading the customer's mind. So can you understand what is in the customer's mind and have an interaction with the client without asking a bunch of questions, right? Can you look at his historical data, his browsing behavior, his purchasing behavior, and have an offer that he will really love? Can you really understand him and give him a celebrity experience? That's one class of use cases, right? Another class of use cases is around improving operations, improving your own internal processes. One example is fraud detection, right? I mean, that is a hot topic these days. So how do you, as the credit card is swiped, right, it's just a few milliseconds before that travels through a network and kicks you back in mainframe and a scoring is done to as to whether this should be approved or not. Well, you need to have a prediction of how likely this is to be fraudulent or not in the span of the transaction. Here's another one. I don't know if you call help desks now. I sometimes call them "helpless desks." (laughter) >> Try not to. >> Dave: Hell desks. >> Try not to helpless desks but, you know, for pretty every enterprise that I am talking to, there is a goal to optimize their help desk, their call centers. And call center optimization is good. So as the customer calls in, can you understand the intent of the customer? See, he may start off talking about something, but as the call progresses, the intent might change. Can you understand that? In fact, not just understand, but predict it and intercept with something that the client will love before the conversation takes a bad turn? (laughter) >> You must be listening in on my calls. >> Your calls, must be your calls! >> I meander, I go every which way. >> I game the system and just go really mad and go, let me get you an operator. (laughter) Agent, okay. >> You tow guys, your data is a special case. >> Dave: Yeah right, this guy's pissed. >> We are red-flagged right off the top. >> We're not even analyzing you. >> Day job, forget about, you know. What about things, you know, because they're moving so far out to the edge and now with mobile and that explosion there, and sensor data being what it is and all this is tremendous growth. Tough to manage. >> Dave: It is, it really is. >> I guess, maybe tougher to make sense of it, so how are you helping people make sense of this so they can really filter through and find the data that matters? >> Yeah, this is a lot of things rolled up into that question, right? One is just managing those devices, those endpoints in multiple thousands, tens of thousands, millions of these devices. How would you manage them? Then, are you doing the processing of the data and applying ML and DL right at the edge, or are you bringing the data back behind the firewall or into Cloud and then processing it there? If you are doing image reduction in a car, in a self-driving car, can you allow the latency of data being shipping of an image of a pedestrian jumping in front, do we ship across the Cloud for a deep-learning network to process it and give you an answer - oh, that's a pedestrian? You know, you may not have that latency now. So you may want to do some processing on the edge, so that is another interesting discussion, right? And you need exploration there as well. Another aspect now is, as you said, separating the signal from the noise, you know. It's just really, really coming down to the different industries that we go into, what are the signals that we understand now? Can we build on them and can we re-use them? That is an interesting discussion as well. But, yeah, you're right. With the world of exploding data that we are in, with all these devices, it's very important to have systematic approach to managing your data, cataloging it, understanding where to apply ML, where to apply exploration, governance. All of these things become important. >> I want to ask you about, come back to the use cases for a moment. You talk about celebrity experiences, I put that in sort of a marketing category. Fraud detection's always been one of the favorite, big data use cases, help desks, recommendation engines and so forth. Let's start with the fraud detection. About a year ago, first of all, fraud detection in the last six, seven years, has been getting immensely better, no question. And it's great. However, the number of false positives, about a year ago, it was too many. We're a small company but we buy a lot of equipment and lights and cameras and stuff. The number of false positives that I personally get was overwhelming. >> Yeah. >> They've gone down dramatically. >> Yeah. >> In the last 12 months. Is that just a coincidence, happenstance, or is it getting better? >> No, it's not that the bad guys have gone down in number. It's not that at all, no. (laughter) >> Well, that, I know. >> No, I think there is a lot of sophistication in terms of the algorithms that are available now. In terms of ... If you have tens of thousands of features that you're looking at, how do you collapse that space and how do you do that efficiently, right? There are techniques that are evolving in terms of handing that kind of information. In terms of the actual algorithms, are different types of innovations that are happening in that space. But I think, perhaps, the most important one is that things that use to take weeks or days to train and test, now can be done in days or minutes, right? The exploration that comes from GPU's, for example, allows you to test out different algorithms, different models and say, okay, well, this performs well enough for me to roll it out and try this out, right? It gives you a very quick cycle of innovation. >> The time to value is really compressed. Okay, now let's take one that's not so good. Ad recommendations, the Google ads that pop up. One in a hundred are maybe relevant, if that, right? And they pop up on the screen and they're annoying. I worry that Siri's listening somehow. I talk to my wife about Israel and then next thing I know, I'm getting ads for going to Israel. Is that a coincidence or are they listening? What's happening there? >> I don't know about what Google's doing. I can't comment on that. (laughter) I don't want to comment on that. >> Maybe just from a technology perspective. >> From a technology perspective, this notion of understanding what is in the customer's mind and really getting to a customer segment at one, this is top interest for many, many organizations. Regardless of which industry you are, insurance or banking or retail, doesn't matter, right? And it all comes down to the fundamental principles about how efficiently can you do. Now, can you identify the features that have the most predictive power? This is a level of sophistication in terms of the feature engineering, in terms of collapsing that space of features that I had talked about, and then, how do I actually go to the latest science of this? How do I do the exploratory analysis? How do I actually build and test my machine learning models quickly? Do the tools allow me to be very productive about this? Or do I spend weeks and weeks coding in lower-level formats? Or do I get help, do I get guided interfaces, which guide me through the process, right? And then, the topic of exploration we talk about, right? These things come together and then couple that with cognitive API's. For example, speech to text, the word (mumbles) have gone down dramatically now. So as you talk on the phone, with a very high accuracy, we can understand what is being talked about. Image recognition, the accuracy has gone up dramatically. You can create custom classifiers for industry-specific topics that you want to identify in pictures. Natural language processing, natural language understanding, all of these have evolved in the last few years. And all these come together. So machine learning's not an island. All these things coming together is what makes these dramatic advancements possible. >> Well, John, if you've figured out anything about the past 20 minutes or so, is that Dave and I want ads delivered that matter and we want our help desk questions answered right away. (laugher) so if you can help us with that, you're welcome back on the Cube anytime, okay? >> We will try, John. >> That's all we want, that's all we ask. >> You guys, your calls are still being screened. (laughter) >> John Thomas, thank you for joining us, we appreciate that. >> Thank you. >> Our panel discussion coming up at 4:00 Eastern time. Live here on the Cube, we're in New York City. Be back in a bit. (upbeat music)

>> Narrator: Live from Washington D.C. It's The Cube covering .Conf 2017. Brought to you by Splunk. >> Welcome back to Washington D.C., the Cube continue our coverage here of .Conf2017. It's the Splunk get together here in Washington D.C. We're at the Washington convention center where they have a record crowd, 7,000+ everyone having a splunking good time you might say. Dave Alante, John Walls here and we're joined by a couple of gentlemen who work with TransAlta. Kent Farries on the far left, who's a senior analyist working the security intelligence analytics as well at TransAlta Kent good morning to you sir. I guess good afternoon, we've crossed that threshold haven't we? And Ikenna Nwafor who's a senior information security specialist at TransAlta as well. So good morning to you. >> Thank you good morning to you. >> Kent maybe you could just tee us up a little bit about TransAlta. Tell us a little bit about what core function, what you all are up to and then how the two of you are helping that mission along it's way. >> Sure, TransAlta is a well-respected power generator and wholesale marketer of electricity. It's been in business for over 100 years. We're based out of Calgary, Canada and we have operations in the United States as well as Australia. Myself and Ikenna are part of the security team based out of Calgary and then we also have off shored or outsourced some of the security operations and our function. >> Which I imagine is vast. Right, I mean you've got you know, you're primary mission obviously security, I would assume of the grid, distribution of power. >> Kent: You are correct. >> That's your number one focus. Right, so talk about the complexities of that in general for our audience who may not be familiar with your particular business but you obviously can imagine the nuances and the sensitivities that you have to deal with. >> Kent: So do you want to? >> Ikenna why don't you take that. >> I think they found out that we are in the prior generation business, makes us a critical infrastructure. And that means working and having ties to the grid makes it very critical that we protect our critical information systems from the threat landscape currently in security so it's a vast responsibility for the team, and we have regulatory requirements we need to abide by, things around (inaudible) and compliance requirements so that's really a very daunting task for us to mate with from a security standpoint. >> Right so it's critical infrastructure, that is distributed in it's nature, so it's high value, you're a target. You got to wake up every day knowing that. >> Yeah sure. >> Okay, so maybe take us through sort of your Splunk journey and what role it played kind of the before and after and how has it affected your business? >> I'll take that. So in the mid-2000s, we did security and everything but it wasn't really a key focus of senior manaagement or anything, it wasn't a lot of real breeches, most of the stuff that was going on was a nuisance, right? Out of the marketplace. >> Dave: Kind of hacktivists. >> Yeah, and we dealt with it, a lot of it still wasn't really coming through the internet, it was still coming through other means. So it wasn't at the forefront, even though we tried in say 2006 to make sure that security was at the forefront management wasn't quite ready at that time. Wasn't big breaches or anything. Around 2009 is our first introduction to what we call the SIEM, Security Information Event Management Solution, basically log management. We implemented that in 2009, and then we had that running for about five years until about 2014, but we started to lose some confidence in that tool, it just didn't give us the information that we wanted or needed to properly detect, respond to today's threats. So we stumbled upon Splunk, it took a little while to actually buy it. One of the system engineers tried to sell it to us we said nah, come back later. Nah, no, I don't even know what it is. And then finally I actually spun it up a proof of concept and I go this thing's amazing. Everything I ever thought of doing, I can actually do with this tool. This is wow. So took the POC, sold it to management, come January 2015 we implemented it, we hired the company out of Ontario to help stand it up, and bring all the data in. It was amazing and we had everything we ever wanted. It blew away our previous security information management system. >> So the SIEM fell short, you said because it didn't really give you the information you needed. Was it also a case of it was just too much information? >> It was difficult to use, so we actually went on training when we implemented the original one in 2009. So two weeks of training, down in the U.S., come back, architect still had a consultant help us stand it all up. But we couldn't build the use cases that we really needed. We were happy at the time, just to get log data, but there's no data enrichment or good correlation capabilities or it was super super difficult to implement. You couldn't search something like Splunk Answers, which you can today. I need to Google anything and the answer's out there around Splunk which is just the community's phenomenal. >> So at the time you didn't know what you didn't know and then once you saw Splunk, it sort of changed your vision of what was possible but so you said it was amazing but why is it amazing, what is it about Splunk that the SIEM tools don't do? >> I think to Kent's point, part of the challenge we had with the previous SIEM tool was the fact that it required a whole lot of work to even get a single simple use case in place for our security. Where as when we had Splunk in place, one is onboarding data logs from various sources was really really dead simple. The initial set up was within a day or half a day to basically replicate what we had from our previous SIEM, which was really fast. And then the other thing is Splunk provided a whole lot of flexibility where you really didn't need to go for some two weeks training to actually get going initially. And through the period we've had Splunk, we've seen that there's been a lot of things we've been able to achieve that we couldn't accomplish when we had our previous SIEM. >> Like for example, I mean what's it letting you do now that day to day that you couldn't do before? >> So if you buy a SIEM, typically it's in a vertical. It's serving one purpose. When you implement that it's usually the security team that gets to use it, and you got to bring in all this log data. Your other teams, say in operations or whatever, they want their log data too but they're in a totally different system, with Splunk it's a platform for us. So we bring all the data in, it's consumed by the IT security, it's consumed by dev ops and operations. So the same amount of data that you bring in say from an endpoint, we'll use it for detection forensics type capabilities, but the desktop team can use it as well to see is there application problems, desktop problems. Do I have drivers or something on a desktop that needs to be updated. We can be more proactive and help out the user so for us it's like a fabric. The foundation so once we've got that laid, yep? >> So all these use cases that you're laying out, previously you would have to essentially customize for each use case, is that right? >> Previously we couldn't even do some of them and then the other thing is we would most likely need to engage a third party contractor to assist us with that. Somebody who is a specialist in that field, whereas with Splunk some of the key things that helped us with Splunk is that maybe in the process of responding to a security event. We could think up ideas of we need this information, how do we get it? And on the fly we can easily build up a use case within minutes to get the information we need from Splunk we don't need to consult anyone, we don't need to read up manuals and for instances here we really need information to help us with building up the use cases going to like Kent mentioned earlier, going to Splunk Answers, you most likely get, so there's a broader community with Splunk that really helps with giving you the information you need to help you in your Splunk journey. >> Okay, so it's more intuitive I'm hearing and it's got the data that you need. >> Exactly. >> And so but even if you had an equivalent of Splunk Answers for your previous SIEM tool, you're saying you wouldn't have been able to because it's not flexible enough to architect what you needed? >> Ikenna: Exactly. >> And I'd like to just put a comment in there. I've been in IT for a long time. And I've always wanted to say, build my own database to bring stuff in and do different things, so I'm pretty good at scripting, but I don't want to be designing a full application or whatever. When I saw Splunk and how easy it was to onboard data, I go wow, this is amazing. So when I brought the consultant in and we stood up our original infrastructure, not only did we stand up ES within two weeks, enterprise security, we also onboarded all my custom stuff, like PowerShell scripts, everything else so we brought in acting directory data into Splunk and made it a PVR for us. So we go back in time and look at any one who their manager was and everything that's happened to that account at that exact time and we can correlate that with IP information everything else. As well we have all of our floors are mapped out. We know where you are in any given building or facility. So we were able to do that at a point in time, 'cause there's a PVR. We don't lose that information. And that's data enrichment, and we couldn't do that in the old system. >> So you had a time machine for your machine data. >> Kent: Yeah, it is, absolutely. >> Okay, cool. Now back to your business a little bit, so there's a physical security aspect of what you guys have to worry about as well. And I'm wondering if you could talk about that and how just the sort of attitude you touched on this before, Kent but how the attitudes towards security have changed and evolved over the last decade. Obviously greater awareness. Has that trickled into the lines of business? Or is it still mostly an IT and a security pro problem? >> I'll let Ikenna answer this. >> So really, for us it's been a journey for the last little while around security. And a couple of things we've had over the past few years is spreading the awareness around security across the business and that's really gained traction where it's no longer just the IT security folks talking to the business about what they need to do for security. But also the business getting back to IT security and trying ones they want to implement, setting up solutions trying to figure out okay, what do we do for security? Can you help assist us with something around risk assessment and really over time that has really helped spread that awareness and also we do a whole lot of things around trying to build a security program through performance assesments, that would be useful to identify gaps. And being able to communicate with the stats to senior management, around getting the necessary buy-in to proceed with whatever initiatives we want to run along with from a security standpoint. You want to add to that? >> I think that's good. >> Yeah, I'm sensing that prior to Splunk it was an uphill battle to get management to invest. Because they probably said, alright we're going to throw money at it, what's the result that we're going to get. As you can present metrics to management, it's easier to justify the investments because they're going to be able to see the outcomes, is that fair? >> Yes, definitely. I think prior to Splunk really we had certain sets of metrics but what Splunk has really helped us do is really consolidate all the log sources we have, get the right information and be able to actually provide a holistic view of our security program to senior management and show them across the different business units where we can get value for investment pointing to security. >> And have you evaluated alternatives, I know those competitors, they've bumped up in the past couple of years, have you evaluated those? Or did you at the time? >> Yeah so in 2009, we looked at a few different vendors and we picked a market leader at the time. There's a couple that we liked more than the market leader but they just didn't scale to our size. Back in those days certain vendors would call it events per second or whatever, we did some analysis and go, they just can't scale. That one back in 2009 is now a market leader. It's pretty good, it looks really interesting and everything as well there's about two or three players out there that I think look great from a SIEM perspective, but if you think of us, where we are at a SIEM is a component, but we actually have a platform. And management's bought into the platform, not only a SIEM, they didn't even know what a SIEM really was, before say 2013. And now they just know that we can provide information when they ask for it. If we don't know, we can get the answer within minutes or maybe hours sometimes depending on the complexity of the query, but we have all the information, we have all the PVR, time machine as you mentioned. It's all sitting there. We brought in most of our data, we got a couple little pieces we're still working on, there's different cloud information we're bringing in or other data enrichment. We can tell for example, an ISP anywhere in the world. We can tell our user visited that ISP. Or that attacker came from that ISP. Let's lock that whole ISP out. We have a lot of interesting capabilities where we don't know if we can do that in those other tools. >> So what's your headache of the future? It sounds like Splunk has done a lot to get you up to speed and get you to a very high comfort level now, looking down the road here, what's the next? >> Quickly start and then I think Ikenna wants to speak to this as well, one of the things that we need to do is we're getting better at detecting and responding. We've really focused a lot on prevention to make sure we can prevent what we can. But it's impossible to basically prevent everything, everybody knows that. You see it in the news. So we're trying to get better at detection and response. One of the shortcomings that we've noticed is that we can't always respond as humans fast enough. So we're trying to automate that, get richer information which Splunk allows us to do, so we call them like high fidelity alerts or high confidence alerts. So if we see that, that should never happen in our environment we'll shut that workstation down, disable that account, or cut off that subnet or something like that so it will all be automated. And then us as a team, will come back after the fact and look at it and go oh, yeah that was good. Or oops we made a mistake, sorry about that. And we'll bring the machine back online. >> Yeah, apologize after. >> After, because they move so quickly, or at least what we're seeing, adversaries move fast. >> How about, you want to add to that? >> I think they key, the way we look at our security program is just being on a journey, because the threat landscape changes like by minutes or days really. There's never a point where we'll say we are done. We are fully okay from a security standpoint, so we constantly look at where we need to evolve. A lot of our techs now are looking at cloud services so we are trying to see how we can show cloud services that we use, pool their log information where we can. And I try to actually enhance what we are currently doing. There's really no silver bullet to solving the issue of security so it's really constantly looking at where we can derive efficiencies to help our program. >> I wanted to ask you about pricing. Are you a Splunk cloud customer? You pay a subscription, you have a perpetual license? >> We did the subscription to term. We're evaluating potentially moving to the cloud. It would be near the end of 2018. We're not sure how we're going to go, maybe we'll just put it in say one of the like AWS or Azure instead of maybe going to the cloud offered because personally we like tweaking and doing a couple things under the hood, so there's a little more change control in cloud. At least at the moment, maybe that will change over time. But we like to be able to quickly onboard data, do all this as fast as we can when we need to. >> And you priced, Splunk charged you by the amount of data? >> You pay by the amount of data. >> Okay, so my follow up is, as the amount of data exponentially, as that data curve growth curve kind of grows, reshapes if you will, are you concerned about just the whole pricing model? Does it have to? >> I'll take that one. So the interesting thing about Splunk it's actually disruptive or disruptor or, it can displace technologies within your environment. So we really try to consolidate things down and take out things that aren't needed. So in certain scenarios, we do a lot of vulnerability scanning and all that, we don't necessarily go buy the top top end product and spend a lot of money on that, we might buy something else or even use open source in the future, who knows. Get the information into Splunk and then use Splunk to do all the analysis. So we're paying like one or two percent of what a typical cost would be and that license itself would pay for Splunk. >> So you're getting asset leverage there. >> Yeah. >> It pays for the data growth. >> As well, we're finding other benefits in the environment using predictive analysis for example, we Splunked all of our storage, and I gave that to my boss and I go here ya go, what do ya think? And you can predict it out a quarter, half a year or a year and he was just ready to buy basically a million dollars of hardware and said geez, I don't need to do that. That's pretty cool. >> So you're using Splunk as a capacity planning tool. >> As well, yeah. We use it for many purposes. >> Very interesting. >> That sounds like a good year end bonus to me there, Kent. (laughter) Gentlemen you both came down from Canada, is that right? >> Yes, we did. >> So my apologies for the unseasonably warm weather here, but we have the lights on which is something you're very familiar with, right at TransAlta. Thanks for the time, interesting conversation glad you both could be here with us today. >> Thanks for having us. >> Alright continuing more our coverage here on The Cube for .conf2017, we'll be live here in Washington D.C. Take a little break, back at 1:30 Eastern time, see you then.

okay we're back live in new york city for hadoop world 2011 john furrier its founder SiliconANGLE calm and we have a special walk-in guest tomorrow and allah the vp of engineering co founder of Cloudera who's going to be on at two thirty eastern time on the cube to go more in depth but since we saw her in the hallway we had a quick spot wanted to grab him in here this is the cube our flagship telecast where we go out to the event atop the smartest people and i'm here with my co-host i'm dave vellante Wikibon door welcome back you're a longtime cube alum so appreciate you coming back on and doing a quick drive by here thanks for the nice welcome so you know we go talk to the smart people in the room you're one of the smartest guys that I know and we've been friends for years and it was your my tweet heard around the world by you to find space and we've been sharing the office space at Cloudera a year didn't have you I meant to have you we're going to be trying to find space because you're expanding so fast we have to get in a new home sorry about that but I wanted to really thank you personally appear on live you've enabled SiliconANGLE Wikibon to we figured it out early because of you I mean we had our nose sniffing around the big data area before it's called big data but when we met talked we've been tracking the social web and really it's exploded in an amazing way and I'm just really thankful because I've been had a front-row seat in the trenches with you guys and and it's been amazing so I want to thank you're welcome and that's great to have you on board and so so you you've been evangelizing in the trenches at Yahoo you were a ir a textile partners announcing the hundred million dollar fund which is all great news today but you've been the real spark get cloudy air is one of the 10 others one of them but I know one of the main sparks a co-founder a lots of ginger cuz I'm Rebecca and my co-founder from facebook I mean we both we said this before like we saw the future like an hour companies we saw the future where everybody is gonna go next and now Jeff's gonna be on as well he's now taking this whole date of science thing art yep building out a team you gotta drilled that down with him what do you what do you think about all this I mean like right now how do you feel personally emotionally and looking at the marketplace share with us your yeah I'm very emotional today actually yeah lots of the good news is you heard about the funding news yes million dollars for startups but no but the 14 oh yeah yeah it is more most actually the news was supposed to come out today came out a bit earlier sir day but yeah I'm very very emotional because of that it's a very Testament from very big name investor's of how well we were doing and recognition of how big this wave really is also the hundred million fun from Excel that's also a huge testament and lots of hopefully lots of new innovations or startups will come out of that so I'm very emotional about that but also overwhelmed by the by the the size of this event and how many people are really gravitating towards the technology which shows how much work we still have to do going forward it was very very August of a great a bit scared a bit scared Michaels is a great CEO on stage they're great guy we love Mike just really he's geeky and he's pragmatic Jerry strategist and you got Kirk who's the operator yeah but he showed a slide up at his keynote that showed the evolution of Hadoop yes the core Hadoop and then he showed ya year-by-year and now we got that columns extending and you got new new components coming out take us through that that progression just go back a few years in and walk us through why is this going on so fast and what are the what's the what's the community doing and just yeah and what happened in 2008 it doesn't need was one mr. yeah when we when we started so I mean first 2008 when we started and what he was believing us back then that hey this thing is going to be big like we had the belief because we saw it happen firsthand but many folks were dismissive and no no no this this big data thing is a fat and nobody will care about it and look and behold today it's obviously proving not to be the case in terms of the maturity of the of the platform you're absolutely right i mean the slide that Mike showed should but only thirty percent of the contributions happening today are in the Hadoop core layer and and and and the overall kind of vision there is very system very similar to the operating system right except what this really is it's a data operating system right it's how to operate large amounts of data in a big data center so sorry it's like an operating system for many machines as opposed to Linux which does not bring system for a single machine right so Hadoop when it came out Hadoop is only the colonel it's only that inner layers which if you look at any opening system like windows or linux and so on the core functionality is two things storing files and running applications on top of these files that's what windows does that's what linux does that was loop does at the heart but then to really get an opening system to work you need many ancillary components around it that really make it functional you need libraries in it applications in eat integration IO devices etc etc and that's really what's happening in the hadoop world so started with the core OS layer which is Hadoop HDFS for storage MapReduce for computation but then now all of these other things are showing around that core kernel to really make it a fully functional extensible data opening system I which made a little replay button but let's just put the paws on that because this is kind of an important point in folks out there there's a lot of different and a lot of people and metaphors are used in this business so it's the Linux I want to be it's just like Red Hat right yeah we kind of use that term the business model is talk a little bit about that we just mentioned you know not like Linux just unpack that a little bit deeper for us what's the difference you mentioned Linux is can you replay what you just said that was really so I was actually talking about the similarity the similarity and then i can and then i can talk about the difference the similarity is the heart of Hadoop is a system for storing files which is sdfs and a system for running applications on top of these files which is MapReduce the heart of Linux is the same thing assistant for storing files which is a txt for and a system for scheduling applications on top of these files that's the same heart of Windows and so on the difference though so that's the similarity I got a difference is Linux is made to run on a single note right and when this is made to run on a single note Hadoop is really made to run on many many notes so hadoo bicester cares about taking a data center of servers a rack of servers or a data center of servers and having them look like one big massive mainframe built out of commodity hardware that can store arbitrary amounts of data and run any type of hence the new components like the hives of the world so now so now these new components coming up like high for example I've makes it easier to write queries for Hadoop it's it's a sequel language for writing queries on top of Hadoop so you don't have to go and write it in MapReduce which we call that assembly language of Hadoop so if you write it and MapReduce you will get the most flexibility you will get the most performance but only if you know what you're doing very similar when you do machine code if you do machine cool assembly you will able do anything but you can also shoot yourself in the foot sunbelt is that right the same thing with MapReduce right when you use hive hive abstracts that out for you so your rights equal and then hive takes care of doing all of the plumbing work to get that compulsion to map it is for you so that's hive HBase for example is a very nice system that augments a dupe makes it low latency and makes it makes it support update and insert and delete transactions which are HDFS does not support out of the box so small like a database it's more like my sequel yeah the energy of my sequel to Linux is very similar to hbase to HDFS and what's your take on were from you know your founders had on now yeah on the business model similarities and differences with with redhead yes so actually they are different I mean that the sonority the similarity stops at open source we are both open source right in the sense that the core system is open source is available out there you can look at the source code again the and so on the difference is with redhead red that actually has a license on their bits so there's the source code and then there's the bits so when Red Hat compiles the source code and two bits these bits you cannot deploy them without having a red hat license with us is very different is now we have the source code which is Apache is all in the patchy we compile the source code into a bunch of bits which is our distribution called cdh these bits are one hundred percent open-source 103 can deploy them use them you don't have to face anything the only reason why you would come back and pay us is for Cloudera enterprise which is really when you go operational when become operational a mission-critical cloud enterprise gives you two things first it gives you a proprietary management suite that we built and it's very unique to us nobody in the market has anything close to what we have right now that makes it easier for you to deploy configure monitor provision do capacity planning security management etc for a loop nobody else has anything close what we have right now for that management's that is unique to cloud area and not part of a patchy open source yes it's not part of the vet's office you only get that as a subscriber to cloud era we do have a free version of that that's available for download and it can run up to 15 hours just for you to get up and running quickly yeah and it's really very simple has a very simple installer like you should be able to go fire off that software and say install Hadoop these are one of my servers and would take care of everything else for you it's like having these installers you know when windows came out in the beginning and he had this nice progress bar and you can install applications very easily imagine that now for a cluster of servers right that's ready what this is the other reason why people subscribe to the cloud enterprise in addition to getting this management suite is getting our support services right and support is necessary for any software even if it's free even for hardware think if I give you a free airplane right now just comment just give it here you go here is an airplane right you can run this airplane make money from passengers you still need somebody to maintain their plane for you right you can still go higher your mechanics maybe we'd have a tweetup bummer you can hire your own mechanics to maintain that airplane but we tell you like if you subscribe with us as the mechanics for your airplane the support you will get with us will be way better than anything else and economics of it also would be way better than having your own stuff for doing the maintenance for that airplane okay final question and we got a one-minute because we slid you in real quick we're going to come back for folks armor is going to come back at two-thirty so come back its eastern time and we'll have a more in-depth conversation but just share with the folks watching your view of what's going on in the patchy and you know there's all these kind of weird you know Fudd being thrown around that clutter is not this and that and you guys clearly the leader we talked with Kirk about that we don't need to go into that but just surely this what's going on what's the real deal happening with Apache the code and you have a unique offering which I mean the real deal and I advise people to go look at this blog post that our CEO wrote called by Michaelson road called the community effect and the real deal is there is a very big healthy community developing the source code for Hadoop the core system which is actually fsm MapReduce and all the components around around that core system we at Cloudera employ a very large engineering organization and tactile engineering relation is bigger than many of these other companies in the space that's our engineering is bigger if you look at the whole company itself is much much bigger than any of these other players so we we do a lot of contributions and to the core system and to the projects around it however we are part of the community and we're definitely doing this with the community it's not just a clowder thing for the core platform so that that's the real deal all right yeah so here we are armor that co-founder congratulations great funding hundred L from accel partners who invested in you guys congratulations you're part of the community we all know that just kind of clarifying that for the record and you have a unique differentiator management suite and the enterprise stuff and say expand the experience experience yeah I think a huge differentiation we have is we have been doing this for three years I had over everybody else we have the experience across all the industries that matter so when you come to us we know how to do this in the finance industry in the retail industry and the health industry and the government so that that's something also that so I'll just for the audience out there arm is coming back at two third you're gonna go deeper in today's the highly decorated or a general because there is there a leak oh and thanks for the small extra info he's in the uniform to the cloud era logo yes sir affecting some of those for us to someday great so what you see you again love love our great great friend