>> Announcer: Live, from Silicon Valley, it's theCUBE. Covering Google Cloud Next '17. (electronic music) >> Welcome back to theCUBE's live coverage of Google Next, here from our Palo Alto studio. Happy to welcome to the program a first time guest, Udi Nachmany, who is the Head of Public Cloud at Ubuntu, thank you so much for joining us. >> Thanks for having me, pleasure to be here. >> All right, so I think it goes without saying, anybody that understands the landscape. Oh wait, there's Cloud, there's Linux, and especially Ubuntu, you know that's going to be there. Before we get into some of these, just tell us a little bit about your role there, and inside the company. >> Sure, I've been with Canonical for about three years, and I head up our partnership with the public clouds and the public IS providers as a whole. >> Yeah. >> That includes Google, AWS, Azure, and many, many others. >> So can you just clarify one thing for us, though? >> Yes. >> You just said Canonical, I introduced you as Ubuntu. >> Yes. >> Which is it? How should we be referring to these two? Well, we are very well known for our products. >> Yeah. >> We're best well known our corporate brand and we're very happy with both names. I usually introduce myself as Udi from Ubuntu, >> Yeah. >> Slash Canonical, so we're used to that. >> Totally understand. So public cloud, give us your view on the landscape today. We want to talk specifically about some of the Google stuff, but what's happening, and what are customers to you for public cloud, where does your suite play into that environment? >> Sure, Ubuntu is a very popular OS, and I think probably the most popular, the area where we're most dominant is public cloud, So a large majority of workload's on Google Cloud, Azure, the Linux part of Azure, AWS, and many, many other providers is running on Ubuntu. A lot of high-visibility services actual develop on Ubuntu. And we have responsibility in that. We need to make the Ubuntu experience predictable and optimized for that cloud platform and have people trust that experience, and believe in it. So that's our job on a technical level, and then on the second level, our job is to help users access support and tooling on top of that, to help them with the operational reality. Because what we see, unless you've probably heard it before from Canonical, what we see is it's great that the licensing cost, the cost of software has gone down, that's great news for everyone, however what a lot of people don't realize is that the cost of operations has gone up, it's skyrocketed, right? It's great Kubernetes is open source, but how do you actually spin up a cluster, how do you deal with this architecture, what does it mean for your business? So that's where we critically focus on private and public cloud. >> Yeah, it's funny. I did an interview with Brad Anderson a few years ago, and I'm like, "Customers are complaining "about licensing costs," and he starts ranting, he's like, "Licensing costs? Do you know that licensing is 6% of the overall cost of what you have?" So, look, we understand operations are difficult, so why is that such a strong fit? What do you bring, what customers do you serve that they're choosing you in such a large preponderance? >> I think the two things we do well, one is we're very well-embedded in the industry and in the community, and pretty much where people are developing something exciting, they're developing it on Ubuntu and they're talking to us through the process. We get a really good view of their problems and challenges, as well as our own. And the second thing is we have come up with tools and frameworks to allow a lot of that knowledge to be crowdsourced, right? So a good example is our modeling platform Juju, where you can very easily get from not knowing anything about, for example Kubernetes, into a position where you have a Kubernetes architecture running on a public cloud, like Google, or in another public cloud, or in bare metal, right? So because we tackled that, we assume that somebody's done this before you, somebody's figured this out. Take all that knowledge, encapsulate it in what we call a Charm, and take that Charm and build an architecture on Juju, on the canvas, or through the CLI. >> Okay, maybe could you compare, contrast, Google, of course, has some pretty good chops when it come to Kubernetes, they're really trying to make some of these offerings really as a service, so ya know, what does Google do, what do you do? How do they work together? Are you actually partnering there or are you just in the community just working on things? >> Google is in this in two different ways. One is they have their own managed service GKE, and that's great and I think people who are all in on Google, then that's a probably a good way to go. You get the expertise, and you get the things that you need. Our approach, as always, is cloud-neutral and we do believe in a hybrid world. We are members of the CNCF, we're silver sponsors of the CNCF, we're very well-embedded in the Kubernetes community, and we do ship a pure upstream Kubernetes distribution that we also sell support for. So we work very closely with Google, in general, Google Cloud, on making sure Ubuntu runs well on GCE, and on the other side, we work very closely with the Kubernetes community in that ecosystem, to again, make sure that it becomes very easy to work with that solution. >> Every player that you talk to in the ecosystem gives you a different story when it comes to multi-cloud environments. Google's message tends to be pretty open. I mean, obviously, with what they're doing with Kubernetes and being their position of where they are with customer adoption, they understand that a lot of people that are doing cloud aren't doing it on Google's Cloud, so they want to make it, you can live in both worlds, and we can support it. I listened to Amazon today, they're like, well, the future's going to be, we're all going to be there, we're going to hire another 100,000 people throughout all of Amazon in the US in the next 18 months. And Microsoft is trying to wrap their arms around a lot of their applications, IBM and Google are there, doing their thing. You've got visibility into customers in all of these environments due to your place in the stack. What are you seeing today? How is Google's adoption going? Is one question I have for you. And two, most customers, I would think, are running kind of multi-cloud, if you will, is the term, is that what you see? How many clouds are they doing? What are you seeing, kind of shifts in there, and I know I asked you three different questions there, but maybe you can dig into that and unpack it for us. >> Sure. I think, in terms of what they, at least top three clouds are saying, I think it's more important to look at what they're doing. If you think about the AWS and VMWare announcement, if you think about Azure Stack for Microsoft, I think those are clearly admissions that there is an OnPrem story and there's a hybrid story that they feel they need to address. They might believe in a world where everybody's happy on a public cloud, but they also live in reality. >> We're on a public cloud show, we're not allowed to mitt about OnPrem, right? Next you're going to, like, mention OpenStack. >> Absolutely. And then, in terms of Google, I think the interesting thing Google's doing, Google are clearly in that, even in terms of size and growth, I think they're in that top three league. They are, my impression is they are focused on building the services and the applications that will attract the users, right? So they don't have this blanket approach of you must use this, because this is the best cloud ever. They actually work on making very good, specific solutions, like for big data and for other things, and Kubernetes is a good example, that will attract people and get them into that specific part of Google Cloud platform, and hopefully in the future, using more and more. So I think they have a very interesting more product than approach, in that sense. >> Okay, so. >> I think I answered one question. >> Yeah, you touched on, yes customers have public and OnPrem. >> Yeah. >> Kind of hybrid, if you will. What about public cloud, you know? Most customers have multiple public clouds in your data or are they tending to get most of it on a single cloud, and might having a second one for some other piece? >> Yeah, I think right now, we're seeing, is a lot of a lot of people using perhaps a couple of platforms. Especially if they have certain size, I'm putting things like serenity and data prophesy aside, but just in terms of public cloud users, they might, again, use a specific platform for a specific service, they might use bare metal servers on software, for example, and VMs on the cloud. People are, by and large, the savvy users do understand that a mix is needed, which also plays to our strength, of course, with tools like Juju and Landscape, we allow you to really solve that operational problem, while being really substrate-agnostic, right? And you don't have to necessarily worry about getting logged in to one or the other. The main thing is, you can manage that, and you can focus on your app. >> All right. Udi, what's the top couple of things that customers are coming to you at these shows for? Where do they find themselves engaging with you as opposed to just, ya know, they're the developers, they're loving what you're doing? >> Sure. So the one thing I mentioned before is operations, right? I've heard about big data, I've heard about Kubernetes. What are my options? Do I hire a team? Do I get a consultant? Do I spend six months reading about this? And they're looking for that help, and I think Juju as an open-source tool and conjure-up as a developer tool that's also open-source. Really expand their options in that sense, and make it much more efficient for them to do that. And the second thing I'd say is Ubuntu is obviously very popular on public cloud, it's popular in production, so production workloads, business-critical workloads. And more and more organizations are realizing that they need to think long and hard about what that means in terms of getting the right support for it, in terms of things like security. An example, this week there was a kernel vulnerability in Linus Distros, I don't think it has a name yet, and we have something called the Canonical Livepatch service which patches kernel vulnerabilities, you can guess by the name. Now, people who have that through our support package have not felt a thing through this vulnerability. So I think we'll start to see more and more of these, where people have a lot of machines running on different substrates, and they're really worried about their up time and what a professional support organization can help them do to maintain that up time. >> It's real interesting times, being a company involved in open sourced, involved in open cloud. I want you to react, there was a quote that Vint Cerf gave at the Google event, I was listening, they had a great session Marc Andreessen and Vint Cerf. >> Yeah it was overcrowed. >> Go there. There was actually room if you got in, but I was glad I got up there, and Vint Cerf said, "We have to be careful about fast leading to instability." What's your take on that? I hear, when I go to a lot of these shows it's like, wow, I used to go from 18 months to six months to six weeks for my deployments. And public cloud will just update everything automatically, but that speed, ya know? As you were just talking, security is one of the issues, but there's instability, what's your take on that? And how are customers dealing with this increasing pace of change, which is the only constant that we have in our industry? >> Yeah, that's very true. I think, so from conversations with customers I've had recently. I've had a few where they've been sitting around and really deliberating what they need to do with this public cloud thing that they've heard about. Trying to buy time, eventually might lead to panicking. So a big financial institution that I met, maybe a month ago are trying to move all in to AWS, right? Whether that's a good thing or a bad thing for them, whether it's the right thing for them, I don't think that discussion necessarily took place, it may well be the best thing for them. But it's the kind of, they're rushing in to that decision, because they took so much time to try and understand. On the other hand, you see people who are much more savvy, and understand that in terms of the rate of change, like you said, it's a constant, so you need to take ownership of your architecture. You can't be locked in to one box that solves all your problems. You need to make sure you have the operation agility and you're using the right tooling, to help you stay nimble when the next big thing comes along. Or the next little thing, which is sometimes just as scary. And I think, again, that's where we're very well placed and that's where we can have very interesting conversations. >> Really interesting stuff. Actually, I just published a case study with City, talking about, they use AWS, I would say tactically would be the way to put it. They build, they have a number of locations where they have infrastructure. Speed and agility absolutely something they need as an outcome. Public cloud is a tool that they use at certain times, but not... There are things they were concerned about in how they build their architectures. Want to give you the last word. We see Canonical, Ubuntu at a lot of shows, you're involved in a lot of partnerships. What do we expect to see from your cloud group, kind of over the next six months, what shall we be keeping an eye on? >> I think on the private cloud side we've been doing some great work into the toggle vertical, and I think you'll see us expanding into more verticals, like financial services, where we've had some good early successes. >> Can I ask, is that NFV-related? It was the top discussion point that I had at OpenStacks on it last year was around NFV. Is it that specific or? >> Yeah, that's an element of it, yeah, but it's about, how do I make my privat cloud economically viable as AWS or Google or Azure would be? How do I free myself from that and enable myself to move between the substrates without making that trade off. So I think that's on the private cloud side. And I think you're going to see more and more crossover between the world of platforms and switches and servers and the world of devices, web-connected devices. We just finished MWC in Barcelona last week. I think we're in the top 13 or 14 bars in terms of visibility, way ahead of most other OS platforms. And I think that's because our message resonates, right? It's great to have five million devices out there, but how do you actually ship a security fix? How do you ship an update? How do you ship an app, and how do you commercialize that? When you have that size of fleet. So that's a whole different kind of challenge, which, again, with the approach we have to operations, I think we are already there, in terms of offering the solution. So I think you're going to see a lot of more activity on that front. And in the public cloud, I'd say it's really about continuing to work ever closer with the bigger public clouds so that you have optimized experiences on Ubuntu, on that public cloud, on your public cloud of choice. And you're going to see a lot more focus on support offerings, sold through those clouds, which makes a lot of sense, not everyone wants to buy from another supplier. It's much easier to get all your needs met through one centralized bill. So you're going to see that as well. >> Udi Nachmany, really appreciate you coming to our studio here to help us with our coverage of Google Next 2017. We'll be wrapping up day one of two days of live coverage here from the SiliconANGLE Media Studio in Palo Alto. You're watching theCUBE (electronic music)

(techno music) >> Hey welcome back everybody, Jeff Frick here with The CUBE. We're having a cube conversation in the Palo Alto studio. It's a different kind of format of CUBE. Not in the context of a big show. Got a great guest here lined up who we just had on at a show recently. He's Nathan Trueblood, he's the vice president of product management for DataTorrent. Nathan great to see you. >> Thanks for having me. >> We just had you on The CUBE at Hadoop, or Data Works now, >> That's right. >> not Hadoop Summit anymore. So just a quick follow up on that, we were just talking before we turned the cameras on. You said that was a pretty good show for you guys. >> Yeah it was a really great show. In fact as a software company one of the things you really want to see at shows is a lot of customer flow and a lot of good customer discussions, and that's definitely what happened at Data Works. It was also really good validation for us that everyone was coming and talking to us about what can you do from a real time analytics perspective? So that was also a good strong signal that we're onto something in this marketplace. >> It's interesting, I heard your quote from somewhere, that really the streaming and the real time streaming in the big data space is really grabbing all the attention. Obviously we do Spark Summit. We did Flink Forward. So we're seeing more and more activity around streaming and it's so logical that now that we have the compute horsepower, the storage horsepower, the networking horsepower, to enable something that we couldn't do very effectively before but now it's opening up a whole different way to look at data. >> Yeah it really is and I think as someone who's been working the tech world for a while, I'm always looking for simplifying ways to explain what this means. 'Cause people say streaming and real time and all of that stuff. For us what it really comes down to is the faster I can make decisions or the closer to when something happens I can make a decision, that gives me competitive advantage. And so if you look at the whole big data evolution. It's always been towards how quickly can we analyze this data so that we can respond to what it's telling us? And in many ways that means being more responsive to my customer. So a lot of this came out of course originally from very large scale systems at some of the big internet companies like Yahoo where Hadoop was born. But really it all comes down to if I'm more responsive to my customer, I'm more competitive and I win. And I think what a lot of customers are saying across many different verticals is real time means more responsiveness and that means competitive advantage. >> Right and even we hear all the time moving into a predictive model, and then even to a prescriptive model where you're offloading a lot of the grunt work of the decision making, letting the machine do a lot more of that, and so really it's the higher value stuff that finally gets to the human at the end of the interaction who's got to make a judgment. >> That's exactly right, that's right. And so to me all the buzz about streaming is really representative of just this is now the next evolution of where big data architecture has been going which is towards moving away from a batch oriented world into something where we're making decisions as close to the time of data creation as possible. >> So you've been involved in not only tech for a long time but Hadoop specifically and Big Data specifically. And one of the knocks, I remember that first time I ever heard about Hadoop, is actually from Bill Schmarzo at EMC the dean of Big Data. And I was talking to a friend of it and he goes yeah but what Bill didn't tell you, there's not enough people. You know Hadoop's got all this great promise, there just aren't enough people for all the enterprises at the individual company level to implement this stuff. Huge part of the problem. And now you're at DataTorrent and as we talked before, interesting kind of shift in strategy and going to really an application focus strategy as opposed to more of a platform focus strategy so that you can help people at companies solve problems faster. >> That's right we've definitely focused, especially recently on more of an application strategy. But to kind of peel that back a little bit, you need a platform with all the capabilities that a platform has to be able to deliver large scale operable streaming analytics. But customers aren't looking for platforms, they're looking for please solve my business problem, give me that competitive advantage. I think it's a long standing problem in technology and particularly in Big Data where you build a tremendous platform but there's only a handful of people who know how to actually construct the applications to deliver that value. And I think increasingly in big data but also across all of tech, customers are looking for outcomes now and the way for us to deliver outcomes is to deliver applications that run on our platform. So we've built a tremendous platform and now we are working with customers and delivering applications for that platform so that it takes a lot of the complexity out of the equation for them. And we kind of think of it like if in the past it required sort of an architect level person in order to construct an application on our platform, now we're gearing towards a much larger segment of developers in the enterprise who are tremendously capable but don't have that deep Big Data experience that they need to build an application from scratch. >> And it's pretty interesting too 'cause another theme we see over and over and over and over, especially around the innovation theme is the democratization of the access to the data, the democratization of the tools to access the data so that anyone in the company or a much greater set of individuals inside the company have the opportunity to have a hypothesis, to explore the hypothesis, to come back with solutions. And so by kind of removing this ivory tower, either the data scientists or the super smart engineer who's the only one that has the capability to play with the data and the tools. That's really how you open up innovation is democratizing access and ability to test and try things. >> That's right, to me I look at it very simply, when you have large scale adoption of a technology, usually it comes down to simplifying abstractions of one kind or another. And the big simplifying abstraction really of Big Data is providing the ability to break up a huge amount of data and make some sense of it, using of course large scale distributed computing. The abstraction we're delivering at DataTorrent now is building on all that stuff, on all those layers, we've obscured all of that and now you can download with our software an application that produces an outcome. So for example one of the applications we're shipping shortly is a Omni-Channel credit card fraud prevention application. Now our customers in the past have already constructed applications like this on our platform. But now what we're doing like you said is democratizing access to those kinds of applications by providing an application that works out of the box. And that's a simplifying abstraction. Now truthfully there's still a lot of complexity in there but we are providing the pattern, the foundational application that then the customer can focus on customizing to their particular situation, their integrations, their fraud rules and so forth. And so that just means getting you closer to that outcome much more quickly. >> Watching your video from Data Works, one of the interesting topics you brought up is really speed and how faster, better, cheaper, which is innovative for a little while, becomes the new norm. And as soon as you reset the bar on speed, then they just want it, well can you go faster. So whether you went from a week to a day, a day to an hour, there's just this relentless pressure to be able to get the data, analyze the data, make a decision faster and faster and faster. And you've seen this just changing by leap years right over time. >> Right and I literally started my career in the days of ETL extracting data from tape that was data produced weeks or months ago, down to now we're analyzing data at volumes that were inconceivable and producing insight in less than a second, which is kind of mind boggling. And I think the interesting thing that's happening when we think about speed, and I've had a few discussions with other folks about this, they say well speed really only matters for some very esoteric applications. It's one of the things that people bring up. But no one has ever said well I wish my data was less fresh or my insight was not as current. And so when you start to look at the kinds of customers that want to bring real time data processing and analytics, it turns out that nearly every vertical that we look at has a whole host of applications where if you could bring real time analytics you could be more responsive to what your customer's doing. >> Right right. >> Right and that can be, certainly that's the case in retail, but we see it in industrial automation and IoT. All I think of is IoT is a way to sense what's going on in the world, bring that data in, get insight and take action from it. And so real time analytics is a huge part of that, which you know again, healthcare, insurance, banking, all these different places have used cases. And so what we're aiming to do at DataTorrent is make it easy for the businesses in those different verticals to really get the outcome they're looking for, not produce a platform and say imagine what you could do, but produce an application that actually delivers on a particular problem they have. >> It's funny too the speed equation, you saw it in Flash, remembering to shift gears a little bit into the hardware space right, is people said well it's only super low latency, super high volume transactions, financial services, is the only benefit we're going to get from Flash. >> Right yeah we've had the same knock for real time analytics. >> Same thing right, but as soon as you put it in, there's all these second order impacts, third order impacts that nobody ever thought of, that speed that delivers, that aren't directly tied to that transactional speed, but now enable you because of that transactional speed, to do so many other things that you couldn't even imagine to do and so that's why I think we see this pervasiveness of Flash, why wouldn't you want Flash? I mean why wouldn't you want to go faster? 'Cause there's so much upside. >> Yeah so again all of these innovations in IT come down to how can I be more flexible and more responsive to changing conditions? More responsive to my customer, more flexible when it comes to changing business conditions and so forth. And so now as we start to instrument the world and have technologies like machine learning and artificial intelligence, that all needs to be fed by data that is delivered as quickly as possible and then it can be analyzed to make decisions in real time. >> So I wanted to shift gears a little bit, kind of back to the application strategies. So you said you had the first app that's going to be, (Jeff drowned out by Nathan) >> Yeah so the first application yes it was fraud prevention. That's an important distinction there because the distinction between detection and prevention is the competitive advantage of real time. Because what we deliver in DataTorrent is the ability to process massive amounts of data in very very low time frame. Sub seconds time frames. And so that's the kind of fundamental capability you need in order to do something like respond to some kind of fraud event. And what we see in the market is that fraud is becoming a greater and greater problem. The market itself is expanding. But I think as we see fraud is also evolving in terms of the ways it can take place across e-commerce and point of sale and so forth. And so merchants and processors and everyone in the whole spectrum of that market is facing a massive problem and an evolving problem. And so that's where we're focused in one of our first I would say vertically oriented business applications is it's really easy to be able to take in new sources of data with our application but also to be able to process all that data and then run it through a decision engine to decide if something is fraudulent or not in a short period of time. So you need to be able to take in all that data to be able to make a good decision. And you need to be able to decide quickly if it's going to matter. And you also need to be able to have a really strong model for making decisions so that you avoid things like false positives which are as big a problem as preventing fraud itself if you deliver bad customer experience. And we've all had that experience as well which is your card gets shut down for what you think is a legitimate activity. >> It's just so ironic that false positives are the biggest problem with credit card fraud. >> Yeah it's one of yeah. >> You would think we would be thankful for a false positive but all you hear over and over and over is that false positive and the customer experience. It shows that we're so good at it is the thing that really irks people. >> Well if you think about that, having an application that allows you to make better decisions more quickly and prevent those false positives and take care of fraud is a huge competitive advantage for all the different players in that industry. And it's not just for the credit card companies of course, it's for the whole spectrum of people from the merchant all the way to the bank that are trying to deal with this problem. And so that's why it's one of the applications that we think of as a key example where we see a lot of opportunity. And certainly people that are looking at credit card fraud have been thinking about this problem for a while. But there's the complexity like we were discussing earlier of finding the talent, on being able to deliver these kinds of applications finding the technology that can actually scale to the processing volume. And so by delivering Omni-Channel fraud prevention as a Big Data application, that just puts our customers so much closer to the outcome that they want. And it makes it a lot easier to adopt. >> So as you sit, shift gears a little bit, as your VP of product hat, and there's a huge wide world of opportunity in front of you, we talked about IoT a little bit, obviously fraud, you've talked about Omni-Channel retail. How are you guys going to figure out where you want to go next? How are you prioritizing the world, and as you build up more of these applications is it going to be vertically focused, horizontally focused, what are you thoughts as you start down the application journey? >> So a few thoughts on that. Certainly one of the key indicators for me as a product manager when I look at where to go next and what applications we should build next, it comes down to what signal are the customers giving us? As we mentioned earlier, we built a platform for real time analytics and decision making, and one of the things that we see is broad adoption across a lot of different verticals. So I mentioned industrial IoT and financial services fraud prevention and advertising technology, and, and, and. We have a company that we're working with in GPS geofencing. So the possibilities are pretty interesting. But when it comes to prioritizing those different applications we have to also look at what are the economics involved for the customer and for us. So certainly one of the reasons we chose fraud prevention is that the economics are pretty obvious for our customers. Some of these other things are going to take a little bit longer for the economics to show up when it comes to the applications. So you'll certainly see us focusing on vertically oriented business applications because again the horizontals tend to be more like a platform and it's not close enough to delivering an outcome for a customer. But it's worth noting one of the things we see is that while we will deliver vertically oriented applications that oftentimes switching from one vertical app to another is really not a lot more than changing the kind of data we're analyzing, and changing the decision engine. But the fundamental idea of processing data in a pipeline at very high volume with fault tolerance and low latency, that remains the same in every case. So we see a lot of opportunity essentially as we solve an application in one vertical, to rescan it into another. >> So you can say you're tweaking the dials and tweaking the UDI. >> Tweaking the data and the rules that you apply to that data. So if you think about Omni-Channel fraud prevention, well it's not that big of a leap to look at healthcare fraud or into look at all the other kinds of fraud in different verticals that you might see. >> Do you ever see that you'll potentially break out the algorithm, I forget which one we're at, people are talking about algorithms as a service. Or is that too much of a bit, does there need to be a little bit more packaging? >> No I mean I think there will be cases where we will have an algorithm out of the box that provides some basics for the decisions support. But as we see a huge market springing up around AI and machine learning and machine scoring and all of that, there's a whole industry that's growing up around essentially, we provide you the best way to deliver that algorithm or that decision engine, that you train on your data and so forth. So that's certainly an area where we're looking from a partnership perspective. Where we already today partner with some of the AI vendors for what I would say is some custom applications that customers have deployed. But you'll see more of that in our applications coming up in the future. But as far as algorithms as a service, I think that's already here in the form of being able to query against some kind of AI with a question, you know essentially a model and then getting an answer back. >> Right well Nathan, exciting times, and your Big Data journey continues. >> It certainly does, thanks a lot Jeff. >> Thanks Nathan Trueblood from DataTorrent. I'm Jeff Frick, you're watching The CUBE, we'll see you next time, thanks for watching. (techno music)