>> Announcer: From San Jose, it's theCUBE. (upbeat music) Presenting Big Data, Silicon Valley, brought to you by Silicon Angle Media and its ecosystem partners. >> Welcome back to theCUBE. We are live on our first day of coverage of our event, Big Data SV. This is our tenth Big Data event. We've done five here in Silicon Valley. We also do them in New York City in the fall. We have a great day of coverage. We're next to where the Startup Data conference is going on at Forger Tasting Room and Eatery. Come on down, be part of our audience. We also have a great party tonight where you can network with some of our experts and analysts. And tomorrow morning, we've got a breakfast briefing. I'm Lisa Martin with my co-host, Peter Burris, and we're excited to welcome to theCUBE for the first time the CEO of Kinetica, Paul Appleby. Hey Paul, welcome. >> Hey, thanks, it's great to be here. >> We're excited to have you here, and I saw something marketer, and terms, I grasp onto them. Kinetica is the insight engine for the extreme data economy. What is the extreme data economy, and what are you guys doing to drive insight from it? >> Wow, how do I put that in a snapshot? Let me share with you my thoughts on this because the fundamental principals around data have changed. You know, in the past, our businesses are really validated around data. We reported out how our business performed. We reported to our regulators. Over time, we drove insights from our data. But today, in this kind of extreme data world, in this world of digital business, our businesses need to be powered by data. >> So what are the, let me task this on you, so one of the ways that we think about it is that data has become an asset. >> Paul: Oh yeah. >> It's become an asset. But now, the business has to care for, has to define it, care for it, feed it, continue to invest in it, find new ways of using it. Is that kind of what you're suggesting companies to think about? >> Absolutely what we're saying. I mean, if you think about what Angela Merkel said at the World Economic Forum earlier this year, that she saw data as the raw material of the 21st century. And talking about about Germany fundamentally shifting from being an engineering, manufacturing centric economy to a data centric economy. So this is not just about data powering our businesses, this is about data powering our economies. >> So let me build on that if I may because I think it gets to what, in many respects Kinetica's Core Value proposition is. And that is, is that data is a different type of an asset. Most assets are characterized by, you apply it here, or you apply it there. You can't apply it in both places at the same time. And it's one of the misnomers of the notion of data as fuels. Because fuel is still an asset that has certain specificities, you can't apply it to multiple places. >> Absolutely. >> But data, you can, which means that you can copy it, you can share it. You can combine it in interesting ways. But that means that the ... to use data as an asset, especially given the velocity and the volume that we're talking about, you need new types of technologies that are capable of sustaining the quality of that data while making it possible to share it to all the different applications. Have I got that right? And what does Kinetica do in that regard? >> You absolutely nailed it because what you talked about is a shift from predictability associated with data, to unpredictability. We actually don't know the use cases that we're going to leverage for our data moving forward, but we understand how valuable an asset it is. And I'll give you two examples of that. There's a company here, based in the Bay Area, a really cool company called Liquid Robotics. And they build these autonomous aquatic robots. And they've carried a vast array of senses and now we're collecting data. And of course, that's hugely powerful to oil and gas exploration, to research, to shipping companies, etc. etc. etc. Even homeland security applications. But what they did, they were selling the robots, and what they realized over time is that the value of their business wasn't the robots. It was the data. And that one piece of data has a totally different meaning to a shipping company than it does to a fisheries companies. But they could sell that exact same piece of data to multiple companies. Now, of course, their business has grown on in Scaldon. I think they were acquired by Bowing. But what you're talking about is exactly where Kinetica sits. It's an engine that allows you to deal with the unpredictability of data. Not only the sources of data, but the uses of data, and enables you to do that in real time. >> So Kinetica's technology was actually developed to meet some intelligence needs of the US Army. My dad was a former army ranger airborne. So tell us a little bit about that and kind of the genesis of the technology. >> Yeah, it's a fascinating use case if you think about it, where we're all concerned, globally, about cyber threat. We're all concerned about terrorist threats. But how do you identity terrorist threats in real time? And the only way to do that is to actually consume vast amount of data, whether it's drone footage, or traffic cameras. Whether it's mobile phone data or social data. but the ability to stream all of those sources of data and conduct analytics on that in real time was, really, the genesis of this business. It was a research project with the army and the NSA that was aimed at identifying terrorist threats in real time. >> But at the same time, you not only have to be able to stream all the data in and do analytics on it, you also have to have interfaces and understandable approaches to acquiring the data, because I have a background, some background in that as well, to then be able to target the threat. So you have to be able to get the data in and analyze it, but also get it out to where it needs to be so an action can be taken. >> Yeah, and there are two big issues there. One issue is the inter-offer ability of the platform and the ability for you to not only consume data in real time from multiple sources, but to push that out to a variety of platforms in real time. That's one thing. The other thing is to understand that in this world that we're talking about today, there are multiple personas that want to consume that data, and many of them are not data scientists. They're not IT people, they're business people. They could be executives, or they could be field operatives in the case of intelligence. So you need to be able to push this data out in real time onto platforms that they consume, whether it's via mobile devices or any other device for that matter. >> But you also have to be able to build applications on it, right? >> Yeah, absolutely. >> So how does Kinetica facilitate that process? Because it looks more like a database, which is, which is, it's more than that, but it satisfies some of those conventions so developers have an afinity for it. >> Absolutely, so in the first instance, we provide tools ourselves for people to consume that data and to leverage the power of that data in real time in an incredibly visual way with a geospatial platform. But we also create the ability for a, to interface with really commonly used tools, because the whole idea, if you think about providing some sort of ubiquitous access to the platform, the easiest way to do that is to provide that through tools that people are used to using, whether that's something like Tablo, for example, or Esri, if you want to talk about geospatial data. So the first instance, it's actually providing access, in real time, through platforms that people are used to using. And then, of course, by building our technology in a really, really open framework with a broadly published set of APIs, we're able to support, not only the ability for our customers to build applications on that platform, and it could well be applications associated with autonomous vehicles. It could well be applications associated with Smart City. We're doing some incredible things with some of the bigger cities on the planet and leveraging the power of big data to optimize transportation, for example, in the city of London. It's those sorts of things that we're able to do with the platform. So it's not just about a database platform or an insights engine for dealing with these complex, vast amounts of data, but also the tools that allow you to visualize and utilize that data. >> Turn that data into an action. >> Yeah, because the data is useless until you're doing something with it. And that's really, if you think about the promise of things like smart grid. Collecting all of that data from all of those smart sensors is absolutely useless until you take an action that is meaningful for a consumer or meaningful in terms of the generational consumption of power. >> So Paul, as the CEO, when you're talking to customers, we talk about chief data officer, chief information officer, chief information security officer, there's a lot, data scientist engineers, there's just so many stakeholders that need access to the data. As businesses transform, there's new business models that can come into development if, like you were saying, the data is evaluated and it's meaningful. What are the conversations that you're having, I guess I'm curious, maybe, which personas are the table (Paul laughs) when you're talking about the business values that this technology can deliver? >> Yeah, that's a really, really good question because the truth is, there are multiple personas at the table. Now, we, in the technology industry, are quite often guilty of only talking to the technology personas. But as I've traveled around the world, whether I'm meeting with the world's biggest banks, the world's biggest Telco's, the world's biggest auto manufacturers, the people we meet, more often than not, are the business leaders. And they're looking for ways to solve complex problems. How do you bring the connected card alive? How do you really bring it to life? One car traveling around the city for a full day generates a terabyte of data. So what does that really mean when we start to connect the billions of cars that are in the marketplace in the framework of connected car, and then, ultimately, in a world of autonomous vehicles? So, for us, we're trying to navigate an interesting path. We're dragging the narrative out of just a technology-based narrative speeds and feeds, algorithms, and APIs, into a narrative about, well what does it mean for the pharmaceutical industry, for example? Because when you talk to pharmaceutical executives, the holy grail for the pharma industry is, how do we bring new and compelling medicines to market faster? Because the biggest challenge for them is the cycle times to bring new drugs to market. So we're helping companies like GSK shorten the cycle times to bring drugs to market. So they're the kinds of conversations that we're having. It's really about how we're taking data to power a transformational initiative in retail banking, in retail, in Telco, in pharma, rather than a conversation about the role of technology. Now, we always needs to deal with the technologists. We need to deal with the data scientists and the IT executives, and that's an important part of the conversation. But you would have seen, in recent times, the conversation that we're trying to have is far more of a business conversation. >> So if I can build on that. So do you think, in your experience, and recognizing that you have a data management tool with some other tools that helps people use the data that gets into Kinetica, are we going to see the population of data scientists increase fast enough so our executives don't have to become familiar with this new way of thinking, or are executives going to actually adopt some of these new ways of thinking about the problem from a data risk perspective? I know which way I think. >> Paul: Wow, >> Which way do you think? >> It's a loaded question, but I think if we're going to be in a world where business is powered by data, where our strategy is driven by data, our investment decisions are driven by data, and the new areas of business that we explored to creat new paths to value are driven by data, we have to make data more accessible. And if what you need to get access to the data is a whole team of data scientists, it kind of creates a barrier. I'm not knocking data scientists, but it does create a barrier. >> It limits the aperture. >> Absolutely, because every company I talk to says, "Our biggest challenge is, we can't get access to the data scientists that we need." So a big part of our strategy from the get go was to actually build a platform with all of these personas in mind, so it is built on this standard principle, the common principles of a relational database, that you're built around anti-standard sequel. >> Peter: It's recognizable. >> And it's recognizable, and consistent with the kinds of tools that executives have been using throughout their careers. >> Last question, we've got about 30 seconds left. >> Paul: Oh, okay. >> No pressure. >> You have said Kinetica's plan is to measure the success of the business by your customers' success. >> Absolutely. >> Where are you on that? >> We've begun that journey. I won't say we're there yet. We announced three weeks ago that we created a customer success organization. We've put about 30% of the company's resources into that customer success organization, and that entire team is measured not on revenue, not on project delivered on time, but on value delivered to the customer. So we baseline where the customer is at. We agree what we're looking to achieve with each customer, and we're measuring that team entirely against the delivery of those benefits to the customer. So it's a journey. We're on that journey, but we're committed to it. >> Exciting. Well, Paul, thank you so much for stopping by theCUBE for the first time. You're now a CUBE alumni. >> Oh, thank you, I've had a lot of fun. >> And we want to thank you for watching theCUBE. I'm Lisa Martin, live in San Jose, with Peter Burris. We are at the Forger Tasting Room and Eatery. Super cool place. Come on down, hang out with us today. We've got a cocktail party tonight. Well, you're sure to learn lots of insights from our experts, and tomorrow morning. But stick around, we'll be right back with our next guest after a short break. (CUBE theme music)

>> Narrator: Live from San Jose, it's theCUBE. Presenting big data silicon valley, brought to you be Siliconangle Media and its ecosystem partner. >> Welcome back to theCUBE we are live in San Jose, at Forger Eatery, super cool place. Our first day of our two days of coverage at our event called Big Data SV. Down the street is the Strata Data Conference, and we've got some great guests today that are going to share a lot insight and different perspectives on Big Data. This is our 10th big data event on theCUBE, our fifth in San Jose. We invite you to come on down to Forger Eatery and we also invite you to come down this evening. We've got a party going on and we've got a really cool breakfast presentation on the analysis site in the morning. Our first guest is, needs no introduction to theCUBE, he's a Cube Alumni, Murthy Mathiprakasam, did I get that right? >> Murthy: Absolutely. >> Murthy, awesome, as we're going to call him. The director of product marketing for Informatica, welcome back to theCUBE, it's great to have you back. >> Thanks for having me back, and congratulations on the 10 year anniversary. >> Yeah! So, interesting, exciting news from Informatica in the last two days, tell us about a couple of those big announcements that you guys just released. >> Absolutely, yes. So this has been very exciting year for us lots of, you know product, innovations and announcements, so just this week alone, actually there's one announcement that's probably going out right now as we speak, around API management, so one of the things, probably taking about before we started interviews you know around the trend toward cloud, lots of people doing a lot more data integration and application integration in the cloud space. But they face all the challenges that we've always seen in the data management space. Around developer productivity, and hand coding, just a lot of complexity that organizations have around maintenance. So one of the things at Informatica always brought to every domain that we cover is this ability to kind of abstract the underlying complexity, use a graphical user interface, make things at the logical level instead of the physical level. So we're bringing that entire kind of paradigm to the API management space. That's going to be very exciting, very game changing on the kind of app-to-app integration side of things. Back on the data world of course, which is what we're, you know, mainly talking about here today. We're doing a lot there as well. So we announced kind of a next generation of our data management platforms for the big data world, part of that is also a lot of cloud capabilities. 'Cause again, one of the bigger trends. >> Peter: Have you made a big bet there? >> Absolutely, and I mean this is the investment, return on investments over like 10 years, right? We were started in a kind of cloud game about 10 years ago with our platform as a service offering. So that has been continuously innovated on and we've been engineering, re-imagining that, to now include more of the big data stuff in it too, because more and more people are building data lakes in the cloud. So it's actually quite surprising, you know the rate at which the data lake kind of projects are now either migrating or just starting in the cloud environments. So given that being the trend, we were kind of innovating against that as well. So now our platform is service offerings supports the ability to connect to data sources in the cloud natively. You can do processing and gestion in the cloud. So there's a lot of really cool capabilities, again it's kind of bringing the Informatica ease of use, and kind of acceleration that comes to platform approach to the cloud environment. And there's a whole bunch of other announcements too, I mean I could spend 20 minutes, just on different innovations, but you know bringing artificial intelligence into the platform so we can talk more about that. >> Well I want to connect what you just announced with the whole notion of the data lake, 'cause it's really Informatica strength has always been in between. And it turns out that where a lot the enterprise problems have been. So the data lake has been there, but it's been big, it's been large, it was big data and the whole notion is make this as big as you can and we'll figure out what to do with it later. >> Murthy: Right. >> And now you're doing the API which is just a indication that we're seeing further segmentation and a specificity, a targeting of how we're going to use data, the value that we create out of data and apply it to business problems. But really Informatica strength is been in between. >> Murthy: Absolutely. >> It's been in, knowing where you data is, it's been in helping to build those pipelines and managing those pipelines. How have the investments that you've made over the last few years, made it possible for you to actually deliver an API orientation, that will actually work for Enterprises? >> Yeah, absolutely, and I would actually phrase it as sort of platform orientation, but you're exactly right. So what's happening is, I view this as sort of maturation of a lot of these new technologies. You know Hadoop was a very very, as you were saying kind of experimental technology four or five years ago. And we had customers too who were kind of in that experimental phase. But what's happening now is, big data isn't just a conversation with data engineers and developers, we're talking to CDO's, and Chief Data Officers, and VP's of data infrastructures about using Hadoop for Enterprise scale projects, now the minute you start having a conversation with a Chief Data Officer, you're not just talking about simple tools for ingestion and stuff like that. You're talking about security, you're talking about compliance, you're talking about GDPR if you're in Europe. So there's a whole host of sort of data management challenges, that are now relevant for the big data world, just because the big data world has become main stream. And so this is exactly to your point, where the investments that I think Informatica has been making and bringing our kind of comprehensive platform oriented approach to this space are paying off. Because for Chief Data Officer, they can't really do big data without those features. They can't not deal with security and compliance, they can't not deal with not knowing what the data is. 'Cause they're accountable for knowing what the data is, right? And so, there's a number of things that by virtue of the maturation of the industry, I think that trends are pointing toward, the enterprises kind of going more toward that platform approach. >> On that platform approach Informatica's really one of the only vendors that's talking about that, and delivering it. So that clearly is an area of differentiation. Why do you think that's nascent. This platform approach verses a kind of fit-for-purpose approach. >> Yeah, absolutely. And we should be careful with even the phrase fit-for-purpose too, 'cause I think that word gets thrown around a lot as it's one of those buzz words in the industry. Because it's sort of the positive way of saying incomplete, you know? And so, I think there are vendors who have tried to kind of address, know you one aspect of sort of one feature of the entire problem, that a Chief Data Officer would care about. They might call it fit-for-purpose, but you have to actually solve a problem at the end of the day. The Chief Data Officer's are trying to build enterprise data pipelines. You know you've got raw information from all sorts of data sources, on premise, in the cloud. You need to push that through a process, like at manufacturing process of being able to ingest it, repair it, cleanse it, govern it, secure it, master it, all the stuff has to happen in order to serve all the various communities that a Chief Data Officer has to serve. And so you're either doing all that or you're not. You know, that's the problem, that way we see the problem. And so the platform approach is a way of addressing the comprehensive set of problems that a Chief Data Officer, or these kind of of Data Executives care about, but also do it in a way, that fosters productivity and re-usability. Because the more you sort of build things in a kind of infrastructure level way, as soon as the infrastructure changes you're hosed, right? So you're seeing a lot of this in the industry now too, where somebody built something in Mapreduce three years ago, as soon as Spark came out, they're throwing all that stuff away. And it's not just, you know, major changes like that, even versions of Spark, or versions of Hadoop, can sometimes trigger a need to recode and throw away stuff. And organization can't afford this. When you're talking about 40 to 50% growth in the data overall. The last thing you want to do is make an investment that you're going to end up throwing away. And so, the platform approach to go back to your question, is the sort of most efficient pathway from an investment stand point, that an enterprise can take, to build something now that they can actually reuse and maintain and kind of scale in a very very pragmatic way. >> Well, let me push you on that a little bit. >> Murthy: Yeah. >> 'Cause what we would say is that, the fit-to-purpose is okay so long as you're true about the purpose, and you understand what it means to fit. What a lot of the open source, a lot of companies have done, is they've got a fit-to-purpose but then they do make promises that they say, oh this is fit-to-purpose, but it's really a platform. And as a consequence you get a whole bunch of, you know, duck-like solutions, (laughing) That are, you know, are they swimming, or are they flying, kind of problems. So, I think that what we see clients asking for, and this is one of my questions, what we see clients asking for is, I want to invest in technologies that allow me to sustain my investments, including perhaps some of my mistakes, if they are generating business value. >> Murthy: Right. >> So it's not a rip and replace, that's not what you're suggesting, what you're suggesting I think is, you know, use what you got, if it's creating value continue to use it, and then over time, invest the platform approach that's able to generate additional returns on top of it. Have I got that right? >> Absolutely. So it goes back to flexibility, that's the key word, I think that's kind of on the minds of a lot of Chief Data Officers. I don't want to build something today, that I know I'm going to throw away a year from now. >> Peter: I want to create options for the future. >> Create options. >> When I build them today. >> Exactly. So even the cloud, you're bringing up earlier on, right? Not everybody knows exactly what their cloud strategy is. And it's changing extremely rapidly, right? We had almost, we were seeing very few big data customers in the cloud maybe even a year or two ago? Now we're close to almost 50% of our big data business is people deploying off premise, I mean that's amazing, you know in a period of just a year or two. So Chief Data Officers are having to operate in these extreme kind of high velocity environments. The last thing you want to do is make a bet today, with the knowledge that you're going to end up having to throw away that bet in six months or a year. So the platform approach is sort of like your insurance policy because it enables you to design for today's requirements, but then very very quickly migrate or modify for new requirements that maybe be six months, a year or two down the line. >> On that front, I'd love for you to give us an example of a customer that has maybe in the last year, since you've seen so much velocity, come to you. But also had other technologies and their environment that from a cost perspective, I mean but at Peter's point there's still generating value, business value. How do you help customers that have multiple different products maybe exploring different multi-calibers, how to they come and start working with Informatica and not have to rip out other stuff, but be able to move forward and achieve ROI? >> So, it's really interesting kind of how people think about the whole rip and replace concept. So we actually had a customer dinner last night and I'm sitting next to a guy, and I was kind of asking very similar question. Tell me about your technology landscape, you know where are things going, where have things gone in the past, and he basically said there's a whole portfolio of technologies that they plan to obsolete. 'Cause they just know that, like they're probably, they don't even bother thinking about sustainability, to your point. They just want to use something just to kind of try it out. It's basically like a series of like three month trails of different technologies. And that's probably why we such proliferation of different technologies, 'cause people are just kind of trying stuff out, but it's like, I know I'm going to throw this stuff out. >> Yeah but that's, I mean, let me make sure I got that. 'Cause I want to reconcile a point. That's if they're in pilot and the pilot doesn't work. But the minute it goes into production and values being created they want to be able to sustain that stream of value. >> This is production environment. I'm glad you asked that question. So this is a customer that, and I'll tell you where I'm going to the point. So they've been using Informatica for over four years, for big data which is essentially almost the entire time big data's been around. So the reason this customers making the point is, Informatica's the only technology that is actually sustained precisely for the point that you're bringing up, because their requirements have changed wildly during this time. Even the internal politics of who needs access to data, all of that has changed radically over these four years. But the platform has enabled them to actually make those changes, and it's you know, been able to give them that flexibly. Everything else as far as, you know, developer tools, you know, visualization tools, like every year there's some kind of new thing that sort of comes out. And I don't want to be terribly harsh, there's probably one or two kind of vendors that have also persisted in those other areas. But, the point that they were trying to make to your original point is, is the point about sustainability. Like, at some point to avoid complete and utter chaos, you got to have like some foundation in the data environment. Something actually has to be something you can invest in today, knowing that as these changes internally externally are happening, you can kind of count on it and you can go to cloud you can be on Premise, you can have structured data, unstructured data, you know, for any type of data, any type of user, any type of deployment environment. I need something that I can count on, that's actually existing for four or more years. And that's where Informatica fits in. And meanwhile there's going to be a lot of other tools that, like this guy was saying, they're going to try out for three month or six months and that's great, but they're almost using it with the idea that they're going to throw it away. >> Couple questions here; What are some of the business values that you were, stating like this gentlemen, that you ere talking to last night. What's the industry that's he in and also, are there any like stats or ranges you can give us. Like, reduction in TCO, or new business models opening up. What's the business impact that Informatica is helping these customers achieve. >> Yeah, absolutely, I'll use this example, he's, I can't mention the name of the company but it's an insurance company. >> Lisa: Lot's of data. >> Lots of data, right. Not only do they have a lot of data, but there's a lot of sensitivity around the data. Because basically the only way they grow is by identifying patterns in consumers and they want to look at it if somebody's using car insurance in, maybe it for so long they're ready to get married, they need home insurance, they have these like really really sophisticated models around human behavior. So they know when to go and position new forms of insurance. There's also obviously security government types of issues that are at play as well. So the sensitivity around data is very very important. So for them, the business value is increased revenue, and you know ability to meet kind of regulatory pressure. I think that's generally, I mean every industry has some variant of that. >> Right. >> Cost production, increase revenue, you know meeting regulatory pressures. And so Informatica facilitates that, because instead of having to hire armies of people, and having to change them out maybe every three months or six months 'cause the underlying infrastructures changing, there's this one team, the Informatica team that's actually existed for this entire journey. They just keep changing, used cases, and projects, and new data sets, new deployment models, but the platform is sort of fixed and it's something that they can count on it's robust, it enables that kind of. >> Peter: It's an asset. >> It's an asset that delivers that sustainable value that you were taking about. >> Last question, we've got about a minute left, in terms of delivering value, Informatica not the only game in town, your competitors are kind of going with this MNA partnership approach. What makes Informatica stand out, why should companies consider Informatica? >> So they say like, what there's a quote about it. Imitation is the most sincere from of flattery. Yeah! (laughing) I guess we should feel as a little bit flattered, you know, by what we're seeing in the industry, but why from a customers stand point should they, you know continue to rely on Informatica. I mean we keep pushing the envelope on innovations, right? So, one the other areas that we innovated on is machine learning within the platform, because ultimately if one of the goals of the platform is to eliminate manual labor, a great way to do that is to just not have people doing it in the first place. Have machines doing it. So we can automatically understand the structure of data without any human intervention, right? We can understand if there's a file and it's got costumer names and you know, cost and skews, it must be an order. You don't actually have to say that it's an order. We can infer all this, because of the machine learning them we have. We can give recommendations to people as they're using our platform, if you're using a data set and you work with another person, we can go to you and say hey, maybe this is a data set that you would be interesting in. So those types of recommendations, predictions, discovery, totally changes the economic game for an organization. 'Cause the last thing you want is to have 40 to 50% growth in data translate into 40 to 50% of labor. Like you just can't afford it. It's not sustainable, again, to go back to your original point. The only sustainable approach to managing data for the future, is to have a machine learning based approach and so that's why, to your question, I think just gluing a bunch of stuff together still doesn't actually get to nut of sustainability. You actually have to have, the glue has to have something in it, you know? And in our case it's the machine learning approach that ties everything together that brings a data organization together, so they can actually deliver the maximum business value. >> Literally creates a network of data that delivers business value. >> You got it. >> Well Murthy, Murthy Awesome, thank you so much for coming back to theCUBE. >> Thank you! >> And sharing what's going on the Informatica and what's differentiating you guys. We wish you a great rest of the Strata Conference. >> Awesome, you as well. Thank you. >> Absolutely, we want to thank you for watching theCUBE. I'm Lisa Martin with Peter Burris, we are live in San Jose at the Forger Eatery, come down here and join us, we've got a really cool space, we've got a part-tay tonight, so come join us. And we've got a really interesting breakfast presentation tomorrow morning, stick around and we'll be right back, with our next guest for this short break. (fun upbeat music)