(upbeat music) >> Okay, welcome to theCUBE everyone. This is a CUBE Conversation here in Palo Alto, California in the CUBE studios. I'm John Furrier, the co-founder of SiliconANGLE media, the host of the CUBE. I'm here with Yinglian Xie. She's the co-founder and CEO of data visor, entrepreneur, former Microsoft researcher. Thanks for joining me in CUBE conversation. >> My great pleasure to be here. >> So I'm excited to chat with you because you've got a really hot company, and a very hot space, but also as an entrepreneur, you're out competing against a huge wave of transformation. You've got big clouds out there, you've got IT enterprises moving to some sort of cloud operating model. You have global IOT market, huge security problem. You guys are trying to solve that with Data Visor, your company. So take me through the journey. First take a minute to explain what Data Visor is, and I want to ask you about how you got into this business, how it started. So what does Data Visor do, first give a one minute overview of the company. >> Sure, so Data Visor is a company that uses the AI machinery and big data, trying to detect and prevent a variety of fraud and abuse problems for all these consumer facing enterprises. So our mission is to really leverage these advance technology that you talk about in many of these, and to help these consumer facing enterprises to establish and restore trust to the end users like you and me, like every one of us. >> Yes, cyber security and security in general is a global issue. I mean, spear phishing is just so effective, you just come in and just send someone a LinkedIn message or an email, they click on a link and you're done. There's not much technology. People are struggling with this, but you guys have a unique approach that you taking with Data Visor so I want to dig into it. But first, how did it all start? When you started this company with your co-founder, did you just wake up one day and say, you know what we're going to go solve the security problems for the world. Where did the idea come from and how did it all start? >> So I would say it's probably, if you look at the background of me and my co-founder, it's probably the natural journey to it, because we actually came from a research and academia background. And me spending seven years of my post doc research in Silicon Valley before starting Data Visor, from there when we joined in 2006, actually it was where we kind of just see this parallel computing paradigm. Like Matt Purdue's paper just got published, and all the data is available, we have all these security problems and at that time we were partnering with a number of large consumer facing groups in Microsoft, and to see how we can use this big data to solve some of the challenges that they face in terms of for example the online fraud and abuse. And also we see the industry and was rapidly getting into the digital era where we have billions of users online, so everybody sees this unique challenge of, they have a variety of vulnerabilities they face, they're trying to bring more rich features to users. At the same time, they see new fraud are coming up also very rapidly. So everybody, when they see new fraud, they are trying to have point solutions. Where they say, let's just tackle this, but then afterwards there's another fraud, or another abuse coming up. >> Throw another tool at em. Build another tool. Buy another tool. >> Exactly. Kind of arms race, where they're being reactive, and catching in a cat and mouse game. So we decided, let's just come to see whether we build something different and leverage the AI machine learning, and then we see what this new cull computing, big data infrastructure can do. So let's build something a little bit more proactive, so that we've been in the security area for so long, that we feel something fundamental that can be a game changer. It's only when we don't make assumptions to see what kind of attacks we want to detect. But be a little bit more open to say, let's try to build something more robust, that can have the ability to automatically discover and detect these new type of unknown attacks more proactively. >> Yinglian, I want to talk about that point, about your time at Microsoft. At that time around 2006, I think it's notable because the environment of Microsoft scale was massive. They were powering, the browsers were everywhere, MSN, the online services that Microsoft had were certainly large scale, but they were built on what I would call gen one internet technology. Databases, big large scale. At the time there, the new entrants, Facebook, otherworlds, they were building all their own tech. So you had kind of the new entrant who had a clean sheet of paper, and they built their own large scale. And we know the history of that, those kinds of companies, that were natively at that time. That's the environment that Microsoft had, that a lot of customers today have. They have technologies that have been around, they have to transform very quickly. So when you learned about some of those data collection capabilities at scale of older technologies, and rushing to a new solution, this is a problem that a lot of end user enterprises have. CIOs, cloud architects, data architects, and they've been operating data warehouses for generations. Big fenced off databases, slow, big data lakes turning into swamps. So that's the current situation, how do you guys speak to that? Because this is the number one challenge we see. Is, I have all this data, I've got a data problem. I'm now full of data, I'm being taken advantage of with the fraud. Whether it's spear phishing or some other scams that are going on with email and all this stuff. How do you guys talk to that customer, that environment? >> You definitely very spot on the challenges and problems that we all face. So while we get into the digital era, everybody has this great sense of trying to collect data and story those data. So that has been, the amount of data we collect is tremendous nowadays. The next step everybody was looking at, the big challenge for us, is how to make value of these in a more effective way. And we also talk about a lot about the AI and machine learning, how they can transform some of the way we do things in the past. The analogy we know is how do we go from the manual driving cars to the self driving era of having all the automation intelligence, and making value out of this. So there are still a lot of challenges that you definitely touch upon. First of all, when they have the data there, does that mean we have the data, we have the data in a consistent, consolidated way. Many times, two different divisions, departments collecting data, they're still in silo mode. So how to bring the data together. And second is, we have the data, we have the computing power, how do we bring the algorithm that operate on top of that the framework to have a system that would let algorithm generating values. Like in the fraud detection space, be able to automatically process huge amount of data, and make decisions in real time. Instantly, detecting these new type of attacks. So we find that's a problem beyond the silo of just an IT problem, or just a data science problem, of just a business problem. So many times these three groups still sort of work separately, but in the end we needed the main knowledge, we need building a system, and we need good data architecture to solve them together. So that's where Datavisor is building a solution, the ecosystem to consider all of this. >> Okay, so let's talk about the ecosystem a little bit later. I want to get to the algorithm piece. That seems to be your secret sauce, right? The algorithms? Is that where the action is for you guys? The secret algorithms or is it setup in the environment first? It kind of makes sense, you've got to set the table first, get the data unified or addressable, and then apply software algorithms to them. That's where the AI comes. What's your secret sauce? >> Yeah, so that's a good question. A lot of our customers ask us the same question, is algorithm your secret sauce? And my answer is kind of partially yes, but also at the same time, not completely. Because we're all catching up very rapidly in algorithm, if you look at the new algorithm being published every year. There's a lot of great ideas out there, great algorithm there. So our unique algorithm is the differentiating technology is called unsupervised machine learning. So unsupervised means we don't need to require customers to have historical loss experience, or need to know the training labels of what past attacks look like. So to proactively discover new type of, unknown type attacks and automate it away. So that's what the algorithm part is, and it has its merit. >> And by the way, people want to know about this machine supervised and unsupervised machine learning, go Google search, there's some papers out there. But I think, most people know this, or might not know it, it's really hard to do unsupervised machine learning because supervised you just tell it what to look for, it finds it. Unsupervised is saying be ready for anything, basically. Oversimplifying. >> Exactly, unsupervised means we want it to make decisions without assumptions. And we want to be able to discover those patterns as the attackers evolve and be very adaptive. So that's definitely a great idea out there. I wouldn't say if you Google, like search unsupervised, and you would find in academia there are published articles about it.6 So I wouldn't say it's a completely new concept, it's a concept out there. >> It's been around for a while, but the compute is the value. Because now you have the computation accelerate all those calculations required that used to be stalling it, from 10 years ago. I mean it's been around for a couple decades. AI and machine learning, but it's been computation intensive. >> Very much so, very much so. So if you look at the gap where that keep the academia side of the world algorithm, to where it's working. It is something similar to deep learning requires a lot more computation complexity compared to the past algorithms. >> Yinglian, I've got to ask you, because this comes up and I'll skip back to the reality of the customer. Because I can geek out on this all day long, I love the conversation, and we should certainly do a follow up on Deep Dive with our team. But the reality is customers have been consolidating and outsourcing IT for generations. And just only few years ago did they wake up, and some woke up earlier than others and said, wow I have no intellectual property, I have no competitive advantage, my IT's all outsourced, I am getting killed with requests for top line revenue growth and I'm getting killed with security breaches, and where's my IT staff. So they don't have the luxury of just turning on a machine learning. Hey, give me some machine learning guys, and solve the problem. That's really hard to setup. You've got to kind of build a trajectory with economies of scale in IT. This is a huge problem. How do you work with companies that just say, look I got security problems but I don't have time or the capability to hire machine learning people, because that's an aspiration, that's not viable, not attainable. What do you say to the customers? Can you still work with those customers, are you a good fit for that kind of environment? Talk about that dynamic, because that seems to happen a lot. >> Yeah, so in that area, you really to bring a solution to solve their problem. Like us today, we have a lot of infrastructure capability, platforms where they can leverage. But you definitely talk about the challenge they face. They don't have people to leverage those underlying primitives and build something to immediately address their business challenges. >> Can you build it for them? >> That's where Datavisor is, to provide the platform and the service to the customers. Where we take data in, and tell them directly all the type of attacks they face, in real time. Constantly, all the time. >> I really want to get your opinion on something that I've been talking about publicly lately, and I've been interviewing folks in the industry about it, because if you look at the graphics market around AI, and nvidia has been doing very, very well. They broke into gaming, obviously is the vertical and using the graphics cards for block chain mining. Then nvidia kind of walked into these new markets because they had purpose built processor for floating point and graphic stuff that was very specialized but now becomes very popular. We're seeing the need for something around data, where you want to have agility, but you also want high performance. So people are making trade offs between agility and high performance and if you ask anyone they'll tell you that I'd love to have more performance in data. So there's no nvidia yet has come out and become the nvidia of data. There's no data processing unit out there yet. This is something that we see a need for. So what you're talking about here is customers have all these demands, it's almost like they need a data processing unit. >> What they need is a solution, like you said, when they have a business solution, they're not looking at something like a generic framework or generic paradigm. They're looking at something to tackle the specific need. For example when we talk about fraud prevention, we're talking about rebuilding a service, the ecosystem that combines the data element, combines the algorithm that address their problem right away. So that's where we talk about with your analogy with nvidia, they want something almost like that chip, directly solve their pain point. >> And that's what you guys are kind of doing, because let me see if I get this right. You guys have this kind of horizontal view of data, but you're going very vertically, and specializing on the vertical markets because that's where the need for the acute nature of the algorithms to be successful. Like say, financial services. Am I getting that right? So it's like horizontally scalable data, but very specialized purpose. >> Exactly. So horizontally scalable data, but then really mine the data and view the algorithms that optimize for the detection of these unknown type of fraud in this area. >> Because they're customized, I mean they have certain techniques that the financial guys will use to attack the banks, right? So you had to be really nimble and agile at the application. >> Right, so when we build the algorithm, we have in mind the specific application we need to target. So you don't want to be over general in the sense that it can do anything, but in the end it does nothing super, super well. So if we are solving that particular fraud detection problem, in the end it needs to be, everything needs to be optimized. The integration with data, the algorithm, the output, the integration with the customer, needs to be optimized for the scenario. In the long run, can it be even generalized. You talked about the agility, and the nimbleness to broaden out to other areas. Then they will say, we are taking approach I would love to see nvidia's approach gradually expanding to other verticals. That is something we are looking from the long term perspective. Our view is that we a layer above all the cloud computing, the data layer. We are the layer that is verticalize position and targeted to solve this specific business issues. And we want to do that really well. Solve that problem one at a time. And then leveraging that algorithm, the underlying infrastructure we built to see whether we can expand that to other verticals, other scenarios. >> So you don't get dependent upon the cloud players? You actually will draft off their success. >> So we leverage the cloud computing era aggressively. Who doesn't in this scenario? It definitely brings the scale, the agility, and the flexibility to expand. And there's a lot of great technology there. >> What do you think about the cloud players? When you look at multiple clouds and hybrid cloud is a trend happening right now. What's your opinion of how that's going? That comes up a lot. CIOs number one channel and cloud architects, and then data architects are all kind of working as the new personas we're seeing. How has the cloud and multi cloud or single cloud approach, for your customers, how do you see that evolving? Because we see trends where, for instance, the Department of Defense, probably going to go all in on Amazon. That's the single cloud solution, but it wasn't sourced as a single cloud. So it turns out that Amazon was better for that, versus spreading things around to multiple clouds. So there's a trade off, what's your thoughts on that as a technologist. >> Well you touched upon an interesting point, because actually, our position is multi cloud. Multi cloud as well as, we support even un-permissed deployment. I will talk about the reason why. The cloud is such a big space, and we see different players there. We definitely see different players, because of their historical working with different vendors, as well as their development you definitely see. Actually our position in this space was driven by the customer need. From that, what we saw is customers have these requirements of their favorite cloud environment. And then there's public cloud verses private cloud. We're not completely there to say there's one cloud that rules all. And you also see some very conservative areas, particularly financial services where their security is really their top priority, they're conservative. And from that perspective, they still are having un-permissed solutions. And we have to be considerate of all these different requirements. And also when we look at evolvement, we also see different geographic landscapes have different cloud deployment landscapes as well. And it's a dynamic environment. >> It's a new dynamic. >> It's a new dynamic. >> Especially the global component, the regions. >> Exactly, the regions. And the different regions, and we also have the GDPR, where does the data residence problem. So that also makes it also challenging to say, just deploy your solution on one type of cloud, that's a very rigid model. So definitely from very early days, we basically decide our data decision would be, we are going to support multi cloud very early on. >> And it makes sense, because people don't want to move a lot of data around. They're going to want to have data in multiple clouds, if that's where the app is. Latency in the threats around moving packets from point A to point B are a risk too. Not just latency, but hacks. Alright, great. I'm very impressed with your vision. I'm very impressed with what you guys are going. I think it's very relevant. Talk about the business. Where are you guys at in terms of customers, what kind of customers do you have, how many customers, can you talk about some of the metrics. How many customers you have, what kind of customers, what are they doing with you, what are the successes? Can you lay out some of the use cases? >> So we work with many of the largest enterprises in the world, and so the probably also the ones that face a lot of challenge of these large scale fraud at the same time they are the ones aggressively moving forward in adopting new technology solutions. They are a little bit more the early, pioneering, adopters. So our customer can be in three verticals, today. So we take a vertical approach. The first is those large social commerce, like Sector. And some of our customers, for example Yelp, Pinterest, kind of customers. And there is also the second vertical, is those mobile apps. There's a lot of fraudulence in stores, where these mobile apps are trying acquire users aggressively everywhere, but among the users acquired, those in stores there can be substantial amount that is fraudulent. So those are the separate segment we target. And the third segment, we talked about, and you mentioned the financial area, where traditionally people focus on the risk of control, the fraud detection definitely causes a big problem. Their challenge is when they move from the past existing era to the digital era, going online, and a lot of new attacks start coming up, and definitely a huge challenge problem for them as well. >> So you guys have some great funds, you have some great investors. NEA, New Enterprise Associates and sequoia capital. What's the growth plan for you? What's the goal for the company, what's your growth strategy? What's on your mind now? Hiring obviously, customer, what's the focus? What's the growth plan? >> So our focus is, we've been working with many of these large service providers. We mentioned our large enterprise customers. So globally today, we've already been protecting over a full billing end user accounts in total. So it's a lot of users at this moment, for our next step of growth and so we have two thoughts. A is we want to basically make the service even more scalable, and even more standardized in a sense that we can work with more than just the largest ones and be able to make it convenient, to be integrated with as many consumer facing providers. >> To expand the breadth. >> To expand the breadth, yes, of customers that we work with. The second aspect is, when looking at the fraud detection, we feel traditionally when the fraud market is segmented, we talk about when in the offline world, you would see financial sector fraud very different from somebody working on content. Nowadays, we can consolidate it, so in that area we're trying to build a more wholistic ecosystem. Where the device side of solutions and the analytical solutions can be consolidated together, to make it an ecosystem where we can have both sides of use and be able to provide to our customers different kind of needs. In the past, it was very point solutions. You would see data signal providers, then you would see some algorithm providers, and focusing on a specific type of fraud, and we wanted to make an ecosystem, so that, to your point in the past on the data, we will be able to connect the data, look at the use at account level and be able to detect a variety of types of fraud. As the enterprises are pushing out new features, and new flavors of these types. >> And the ecosystem participants will look like what? Ad networks, data services? Who is in the ecosystem that you want to build? >> Yeah, so that's a great question. In the ecosystem we talk about, for example, cull providers, can be an ecosystem basically. They actually power the computation layer, of all the resource there. We can also partner with data partners. That's another important element, so you're looking at technology data systems all integrated together. At the same time we can also look at the consulting firms that bring a bigger solution to the customers with the fraud being an important component that they want to address with system integrators. And so all these can fit together, and even some of the underlying algorithm solutions in the end can be plucked into the ecosystem to provide different aspects of use and make value out of data. So that different algorithms work together, and become defense area. >> It's like a security first strategy. First we had cloud first, data first, now security first. I mean, got to have the security. Well I really appreciate, we need more algorithms to police the algorithms. Algorithms for algorithms. So maybe that's next for you guys. Well with the business goal in mind we always take an open holistic view. I like you talking about security first, when we look at how to solve that problem more effectively, then we are very open minded to say, what is the best combinations we want to be three ultimately. And that's a single bit of real time, instant decision that is important at that time, because that matters with good users friction, they face whether we can be able to accurately detect attackers. So we are all optimizing for that, and then all the underlying data consolidation piece, the algorithm in combination working with each other, is just to make the barrier high, make it difficult for the attackers, and to make all of us good users easier. >> Well you're doing amazing things, and I think you're right. There's value in that data, new ways to use that data for better security is just the beginning of this new trend. Thanks for coming in and sharing your insights and congratulations on a great start up, and good luck to you and you co-founder. Thanks for sharing. >> Thank you, great to have this conversation. I'm here in theCUBE studios in Palo Alto, I'm John Furrier for CUBE Conversation with hot start up Data Visor Yinglian Xie CEO and co-founder. I'm John Furrier, thanks for watching. (bright music)

>> Narrator: Live from Washington DC, it's the CUBE. Covering AWS Public Sector Summit 2017. Brought to you by Amazon Web Services and its partner Ecosystem. >> Welcome back here on the CUBE, the flagship broadcast of Silicon Angle TV along with John Furrier, I'm John Wallace. We're here at AWS Public Sector Summit 2017, the sixth one in its history. It's grown leaps and bounds and still a great vibe from the show for us. It's been packed all day John. >> It's the new reinvent for the public sector, so size wise it's going to become a behemoth very shortly. Our first conference, multi-year run covering Amazon, thanks to Theresa Carlson for letting us come and really on the front lines here, it's awesome. It's computing right here, edge broadcasting, we're sending the data out there. >> We are, we're extracting the signal from the noise as John always likes to say. Government, educations all being talked about here this week. And with us to talk about that is Max Peterson, he's a general manager at the AWS and Max, thank you for joining us, we appreciate that. >> Thank you for the invitation. >> And I knew we were in trouble with our next guest, cause I said this is John, I'm John, he said, this is Max and I'm Max. I said no you're not, I know better than that. Andre Pienaar who's a founder and chairman of C5 Consulting, Andre, thank you for being here on the CUBE. >> It's great pleasure being here. >> Alright let's just start off first off with core responsibilities and a little bit about C5 too for our audience. First off, if you would Max, tell us a little bit about your portfolio-- >> Sure. >> At AWS and then Andre, we'll switch over to C5. >> I think I might have the best job in the world because I get to work with government customers, educational institutions, nonprofits who are all working to try and improve the lives of citizens, improve the lives of students, improve the lives of teachers and basically improve the lives of people overall. And I do that all around the world. >> That is a good job. Yeah, Andre. >> Max will have to arm wrestle for who has got the best job in the world, because in C5, we have the privilege of investing into fast growing companies that are built on Amazon Cloud and that specializes in cyber security, big data and cloud computing and helps to make the world a safer place. >> I'm willing to say >> Hold on I think we have the best job. >> we both have the best job. >> Now wait a minute, we get to talk to the two of you, are you kidding? >> Yeah, I've got the best, we talk to all the smartest people like you guys and it can't get better than that. >> You're just a sliver of our great day. >> That's awesome, we have established we all have great jobs. >> Andre, so you hit cyber, obviously there is not a hotter topic, certainly in this city that is talked about quite a bit as you're well aware so let's just talk about that space in general and the kinds of things that you look for and why you have this interest and this association with AWS. >> So the AWS cloud platform is a game changer for cyber security. When we started investing in cyber security, and people considered cloud, one of their main concerns was do I move my data into the cloud and will it be secure? Today it's the other way around because of the innovation that AWS has been driving in the cyber security space. People are saying, we feel we are much more secure having the benefit of all innovation on the cloud platform in terms of our cyber security. >> And the investment thesis that you guys go after, just for the record, you're more on the growth side, what stage of investments do you guys do? >> We're a later stage investor so the companies we invest in are typically post revenue but fast growing in visibility and on profitability. >> So hot areas, cyber security, surveillance, smart cities, autonomous vehicles, I mean there's a data problem going on so you see data and super computing coming back into vogue. Back when I was a youngling in college, they called it data processing. The departments and mainframes, data processing and now you have more compute power, edge compute, now you have tons of data, how is all that coming in for and inching in the business models of companies. This is a completely different shift with the cloud. But you still need high performance computing, you still need huge amounts of data science operations, how do companies and governments and public sectors pull up? >> I think just the sheer volume of data that's being generated also by the emerging internet of things necessitates new models for storing and processing and accessing data and also for securing it. When big enterprises and governments think about cyber security, they really think about how do we secure the most valuable data that's in our custody and our stewardship and how do we meet that obligation to the people who have provided that data to us. >> How would you summarize the intrinsic difference between old way, new way? Old way being non-cloud and new way being cloud as we look forward? >> I think that was a pretty good summary right there. New way is cloud, old way is the legacy that people have locked up in their data centers and it's not just the hardware that is the legacy problem, the data is the legacy problem. Because when you have all that information built in silos around government, it makes it impossible to actually implement a digital citizen experience. You as a citizen would like to be able to just ask your question of government and let them sort out what your postal code was, what your benefits information was, right? You can't do that when you've got the data, much less the systems, locked up in a whole bunch of individual departments. >> Well merging of data, sharing data as an ethos and the cyber security world, where there's an ethos of hey, you know, we're going to help each other out because the more data, the more they can get patterns into the analytics which is a sharing culture. That's not really the way it is. I got governance, I got policy issues. >> Well policing is a good example. In the Washington DC area, there are 19 law enforcement agencies with arresting powers and that data is being kept in completely separate silos. Whereas if we're able to integrate and share that data, you will be able to draw some very useful predictive policing conclusions from that which can prevent and detect crime. >> That's a confidence issue and that's where your security point weighs in. Let me get back to what you said about the old way, new way thing. Another bottleneck or barrier, or just hurdle if you will, in cloud growth, has been cultural. Mindset of management and also operational practices, you have a waterfall development cycles or project management versus agile, which is different. That's a different cultural thing so you got all the best intentions in the world, people could raise their hand put stuff in the cloud, but if you can't scale out, you're going to be on this cadence where projects aren't going to get that ROI picture generated so the agility, how are you guys seeing that developing? >> I would tell you the first thing that it takes is leaders and that's what this conference is about. It's about telling the stories of customers who have seen the potential and who are now leaders. It takes something, it takes a spark to start it and the most powerful spark that we've seen, are customer testimonials, who come forward and they explain, hey I was doing this the old way. A lot of times for a cost reason or a new mandate, they have to come up with a new way to invent and they made that selection of the cloud and that's what so often changed the opportunity that they can address. Here's just using that data as an example, transport for London in the UK has a massive amount of data that comes from all of the journey information. They started their journey to the cloud four years ago and it started with the simple premise of I needed to save costs. They saved money and they were able to take that money and reprogram it now to figuring out how do we unlock the data to generate more information for commuters. Finally, they were able to take that learning and start spinning it into how do I actually improve the journey by using machine learning, artificial intelligence and big data techniques? Classic progression along the cloud. Save some money, reinvest the savings and then start delivering new innovation on that point. >> I was going to ask you the use cases. You jumped right in. Andre, can you just chime in and share your opinion on this or anecdotal or story or data around use cases that you see out there that can point to saying, that's game changing that's transformative, that's disruptive. >> Well one of the customer stories that Max referred to that was a real game changer in cyber security was when the CIA said that they were going to adopt the AWS cloud platform. Because people said if US Intelligence community has the confidence to feel secure on AWS cloud, why can't we? AWS have evolved cyber security from being an offering which is on top of the cloud and the responsibility of the client to something which is inside the cloud which involves a whole range of services and I think that's been a complete game changer. >> The CIA deal, Dave Velanto is not here, my partner in crime as well, I call it the shot heard all around the cloud, that was a seminal moment for AWS in chronicling your guys journey over the years but I've been following you guys since the barely birth days and how you've grown up, that was a really critical moment for AWS in the public sector so I want to ask you guys both a question, right now, 2017 here at public sector conference, what's the perception of AWS outside of the ecosystem? Clearly cloud is the new normal, we heard previously, I agree with that. But what's the perception of the viability, the production level? What's the progress part in the minds of the folks? How far are we in that journey cause this is a breakout year, this year. That was the shot heard around the cloud, now there seems to be a breakout year, almost a hockey stick pick up. >> It's another example of how it takes leadership and it was the shot heard round the cloud, what we're seeing though is now many, many people are picking up that lead and using it to their advantage. The National Cyber Security Center in the UK told a story today that's pretty much a direct follow on. They're now describing to their agencies what they should do to be safe on the cloud. They're not giving them a list of rules that they need to try and go check off. It's very much about enabling and it's very much about providing the right guidance and policy. It's unlocking it instead of using security as a blocker in that example. Much more than just that one example, all over the world-- >> But people generally think okay this is now viable. So in terms of the mind of the people out in the trenches, not in the front lines like here, thoughts on your view on the perception of the progress bar on AWS public sector. >> John, one of the best measures of how the AWS cloud is perceived is what's happening in the startup scene. 90% of all startups today get born on the Amazon cloud in the US. 70% of all startups in France gets born in AWS cloud. This is the future voting for cloud and saying this is where we want to be, this is where we can scale this is where we can grow-- >> If you can believe APIs will be the normal operational interface subsystems and data, then you essentially have a holistic distributed cloud, aka computer. That's the vision. So what's the challenge? What do you guys see as the challenge, is it just education, growth? You only have 10,000 people here, it's not like it's 30 yet. >> Well you heard one of the, or you hit on one of the things that's key and that's policy. You really do have to break through the old government bureaucracy and the old government mentality and help set the new policies. Whether it's economic policies that help enable small businesses to launch and use the cloud. Whether it's procurement policies that allow people to actually buy tech and use tech fast, or whether it's the basic policy of the country. The UK now has a policy of being digital native, cloud native. >> The ecosystem's interesting, Andre, you mentioned startup, because I think for me, challenge opportunity is to have Amazon scale up, to handle the tsunami of Ecosystem partners that could be as you said, we just talked to Fugue here. Amazing startup funded by New Enterprise Associates, NEA, they're kicking ass, they're just awesome. You go back 10 years ago, they wouldn't even be considered. >> Absolutely. >> So you've got an opportunity to jam everyone in the marketplace and let it be a free for all, it's kind of like a fun time. >> It's a great time and in the venture capital world, being architect on the Amazon cloud has become a badge of quality. So increasingly venture capital firms are looking for startups that run on the AWS cloud and use them in an innovative way. >> Well on the efficiency on the product side, but also leverage on the capital side. >> Exactly. You need less capital. >> Been a provision of data center, what? >> You need less capital and secondly, also, you can fail much faster and then still have space and time to build it and restart. I think failing faster is something from an investment point of view that is really attractive. >> John: Final question. >> John: Failing faster? >> Failing faster. Because what you don't want are the long drawn out deaths of businesses. Because that's a sure way to destroy value of money. >> I think the other part though is fix faster. >> Fix faster. >> And that's exactly what the cloud does so instead of spending an immense amount of time and energy trying to figuring out precisely what I need to build, I can come up with the basic idea, I can work quick, I can fail fast, but I can fix it fast. >> Alright, well you mentioned the golden time, the golden era, and I think you both have captured it, so I think both your jobs would be up there at the top of the shelf. >> Thank you John. >> You mentioned 19 agencies by the way here in DC that can arrest, I have parking tickets from every one of them. >> Andre: I'm glad they haven't arrested you yet John. >> No, that's the price you pay for living in this city. >> Thanks John and John. >> Max, Andre thank you very much. >> John and John thank you. >> Cheers. >> Back with more here from AWS Public Sector Summit 2017, live, Washington DC, you're watching the CUBE.

(uplifting music) >> Narrator: Partners. Now, here are your hosts, John Furrier and Stu Miniman. >> US Amazon Web Services re:Invent 2016 their annual conference. 32,000 people, record setting number. I'm John Furrier, Stu Miniman co-host in theCUBE for three days of wall-to-wall coverage. Day two, day one of the conference our next guest is Kiran Bhageshpur, who's the CEO and co-founder of Igneous Systems. He was a hot startup in the, I don't want to say storage area, kind of disrupting storage in a new way. Kiran great to see you, thanks for coming on theCUBE. >> Thanks a lot, glad to be here, John. >> So, you're living the dream the cloud dream, it's not a nightmare for you because you're one of the progressive new ways. I want to get your thoughts on Andy Jassy's Keynote because he really lays out the new mindset of the cloud. Your startup that you founded with your team is doing something kind of, I won't say contrarian, some might say contrarian, but contrarians usually become the big winners, like Amazon was a contrarian now they're obviously the winning. So, take a minute to explain what you guys are doing. You're funded by Madrona Ventures and NEA, New Enterprise Associates, great backers, smart. Your track record at Isilon, you know the business. Take a minute to describe what you guys are doing. >> Great, yes I will. So, Igneous Systems was founded to really deliver cloud services to the enterprise data center for data-centric workloads. So what to we mean by that? With cloud services, just like with Amazon, customers don't buy hardware, license software. They do not monitor or manage your infrastructure. They consume it across API and they pay for it by the drip rather than the drink. Similarly, the same case with us but we make that all available within a customer's data center itself. And we focus on sort of data-centric, data heavy workloads. I don't know whether you saw James Hamilton's-- >> Yeah. >> Speech yesterday, but he also talked about the same thing that Mary Meeker talked about earlier this year which is an overwhelming amount of data generated today is machine generated and machine consumed and that's growing really rapidly. And our view is the same techniques that have made Amazon so powerful and so valuable are needed out at the edge or on-premise, close to where users and machines are generating and using the data. So that's kind of what we do. Very much the cloud model taken out to the enterprise data center. So, think of it as a hybrid. >> Kiran, let's talk about storage and where it lives because I think something that many people miss is that cloud typically starts with very compute heavy types of applications and we know that data is tough to move. I mean, Amazon rolled out a truck to show how they move 100 petabyes. And not just to show it, this is a new product they had 'cause customers do want to be able to migrate data and that's really tough and takes a lot of time. You mentioned IoT at the edge, they announced kind of query services on your data up in S3, so what are you hearing from customers? You know, kind of large data from your previous jobs. Where's the data living, where's data being created, where does data need to be worked on and how does that play into what you're doing? >> That's a great question Stu. What we find with customers, especially the one's with large and growing data sets is there is still a challenge of not just how to go store it but how to go process that on the fly. On a camera today or a next generation microscope could produce tens of terabytes of data per hour and that is not stuff that you can move across the internet to the cloud. And so the ask and the call from customers is to be able to go ingest that, curate that, process that locally and the cloud still has a very compelling role to play as a distribution mechanism and for a sharing mechanism of that data. I found it pretty wild that a big part of Andy Jassy's Keynote was for the first time they talked about hybrid and acknowledged the fact that it is the cloud and cloud-like techniques out in the enterprise data center. So, I look at that as hugely validating what we have been talking about which is bringing cloud native paradigms into the enterprise data center. >> Let's talk about that operational model because what you're highlighting and what Jassy pointed out is an operational model now for IT. >> Kiran: Yep. >> How are you guys creating value for customers? And be specific, is it, 'cause the on-prem is not going away, we've talked about this before and certainly VMware sees the cloud but also on-prem too. What is the value for customers? Because now this operational model of on the cloud is there, one way-- >> Yes. >> But how do I get cloud inside my data center? >> The way we do that is, very similar to the cloud operating model, right? So, we sell customers essentially an annual subscription service and that service is delivered using appliances that are purpose-built. Think of it as, like snowball, if you will, that goes into the customers data centers fully managed by our software running in our cloud. So, for a customer point of view, it happens to live within their data center, but they are consuming it pretty much the same way that they would consume a cloud service. That's the value, it's the same tool chains, the same programming paradigms that they are used to with, say, a native OS. But within their data centers at lower latencies addressing the same things that Andy Jassy brought up, which is you need a truck to go move large amounts of data. >> Well, I want to also bring up James Hamilton's presentation. You mentioned that yesterday one of the key points he made was that scaling up for these peak loads like they have on the Friday's, their Prime Friday spikes, they do instantly and elastic is a big deal we know that. His point though was they would have to provision on bare metal or in the data center months in advance to even rationalize what that peak could be which still is an unknown number. So, the scale point and provisioning is a huge headache for customers, so that's why that's relevant. How do you guys answer that claim when you say, "Hey, I need stuff to be done fast, "I don't have time to provision"? How do you guys, do you address that at all? How do you talk to that specific point? >> We take care of the provisioning and the additional expansion and shrinking of capacity within the customer's data center, because just like Amazon monitors their infrastructure users in the data center, we do that for our infrastructure within the customer's data center, and therefore we can react to go scale up or scale down. But then there's another point to the whole thing, which is the interesting thing is the elasticity is much more important for compute as opposed to data. Data just linearly grows, you never throw that stuff away. The things that you captured, the processing is highly elastic and you might want to do some additional processing and burst out and so on. So, that's another aspect of hybrid we see with our customers which is, I want my work flow here, I want to be able to burst out to the public cloud for that peak capacity that I don't want to have infrastructure locally for. >> So Kiran, sorry. So James Hamilton's presentation talks a lot about, just that hyper scale. They claim they've got the most scale and therefore nobody else should do anything because oversimplifying a little bit, but we've got the best price, we've got the whole stack, give you all the solutions. You talk to enterprises. Scale means different things for different applications for what I need to get done, what I have. What does that really mean to you? How does that hybrid piece fit in to the whole scale discussion? >> So, a lot of what we do is really ride on the coattails of the Amazon and the Google and the Microsoft because everyone has access to the same raw components, hard drives and CPUs and so on and so forth. And then the question is how do you go assemble those in a form factor that is appropriate for that particular use case? If you're going to go build a data center that's one level of scale, but if you look at a vast majority of applications and enterprises, their scales are much smaller. So, we literally look at taking a rack of infrastructure which might have, say, 40 servers and a couple of switches in sheet metal and shrinking that to a 4U form factor which has got 60 of our nano servers which has got switches and has got sheet metal. So, it's shrinking the whole thing down. The economy's of scale are still quite compelling because we use the exact same raw materials from the same suppliers to the cloud guys, right? And the real difference in cost is how things are put together and how they are operationalized. In which case, we are much more like Amazon than not. >> The other thing that's really interesting to watch, if you look at Amazon's storage move, is storage is in a silo, they've now got all these services that I can start doing this. How does the enterprise look at that? How does the solution like yours enable us to be able to use our data more? >> I absolutely think there is a palpable need for and desire for those sorts of new paradigms in the enterprise data center too because what you can do with not just storage but with lambda and with a bunch of other advanced services on top of that, what that really does is allows enterprises and customers to just focus on what is differentiated to them. This is the whole low-code, no-code moment, if you will, right, movement, and that's a compelling trend. It is something that we've actively embraced. We've got our architecture enables that on day one and that's kind of the way you're going to go build applications now onwards. >> So will we see lambda functions calling things on your end? >> Stay tuned. I think my, yeah, stay tuned. >> That's a smile, that's a yes. (laughs) Talk about the drivers in your business, 'cause you guys are new, you're a startup. For the folks watching you're making some bets, big bets obviously funded by some pretty big venture capitalists out there. What is your big bet? Is it true private cloud is going to emerge on-premise? Is the bet that cloud adoption with scalable compute and storage is going to be unmanaged or manageless or serverless, what's the big bet? >> So our bet is the cloud is going to win and I mean the cloud paradigm, which means consuming infrastructure by the drip rather than the drink across APIs. Flexibility, agility is going to win. One answer which is very compelling is the public cloud today. We believe that similar patterns will exist on the on-premise world and we believe we are very well positioned to supply that thing. And the infrastructure which shrinks would be very traditional infrastructure and software technology stacks which has really existed in the enterprise data center for the last 20 years. That will shrink and everything will look similar as in highly flexible, highly scalable, very easy to go put things together and you're going to have very similar patterns in both the public cloud and within your data center. >> Our Wikibon research team is looking at the practitioner side of the market. One of the things they're observing is, among a lot of things, is that you're seeing AWS teams come together. We're seeing Accenture was on earlier talking about the same dynamic. That's the pattern that we're seeing is these teams are coming together, some handful of people, the pizza box teams-- >> Yep. >> As Jeff Bezos calls it, growing into fully functional bigger teams. So, depending upon that progression, what's your advice to practitioners? And how do you add value into this momentum of as they scratch their head go, "Okay, we're going to go to the cloud"? So they know that's the mandate. How do you help them and why should they look at your solution and where do you fit into that? >> So one of the things customers and partners tell us is we are a great on-ramp to the cloud if you will. Everybody wants to embrace the new programming patterns, new programming paradigms and many people have taken that big leap and done the full shift in one step. You've heard Finra, you've heard Capital One all of these guys talk, but not everyone is that far out there. So what we sort of become for these folks is a stepping stone. We are on-premise. It allows them to get used to it. They start using the same patterns that can scale there. There can decide what workflows remain local and why and what go there, and that's our view. We very much live in they hybrid world to burst out to the world, bring it back as appropriate. >> Kiran thanks so much for coming on theCUBE, we really appreciate it, we're getting the break but I do want to ask one personal question. You're back in the entrepreneurial zeal again, you've got the startup, you have some capital but you're not loaded with cash, a good amount to achieve what you need to do. What's it like for you right now? I mean, what do you believe in? What's your guiding principles and what's it like to get back on the entrepreneurial treadmill again? >> You know, it's actually quite exhilarating and liberating to be back in a startup environment because it forces you to focus on what is important what is urgent and important at all points in time, and a guiding principle for us is less is more. Let's be driven by customers and do what is required there and then slowly extend that out. And actually, being a startup and not having infinite money to throw like, large legacy players would frees you from trying to do too many things and focus on only what is important and that's really key to success. >> And how are you making the decisions as an executive like, product-wise? Is it more agile, are you guys doubling down? >> Very, very agile, we can move very quickly. Since we are delivering a service, we are continuously updating infrastructure just like Amazon does within their data center so we can turn around very, very quickly. So I'm very impressed the fact that the Amazon rolls out 1,000 new features this year, but I can see how that is possible at scale and that's what we're doing. >> At Isilon you were very successful scaling up that generation of web scale, we saw that with Facebook and the Apples of the world. What's different now than then? Just in the short years between the web scalers dominating to now full Multi-Cloud, Hybrid Cloud cloud. In your mind, what's different about the landscape out there? Share your thoughts. >> I think there's a couple of things. One of them is Isilon was incredible, was a very useful infrastructure, was something that was easy to deploy, but it was still that something you built, you managed, you owned, if you will. The big transition is away from that, from build to consume and not worry about that infrastructure at all. And that is not something that you can retrofit into an existing architecture, you have to start from scratch to go do that. So, that's the biggest number one. Two, second one is just the scale is bigger. You heard Andy Jassy talk about the exobyte moving problem and he commented on the fact that exobytes are not all that rare and he's true because you go back 10 years ago, maybe four companies had an exobyte problem. It's now a lot more than that. And so the scale is two or three orders of magnitude larger than when Isilon was growing up. >> Scales at table stakes and consumption of infrastructure, that's a dev-ops ethos gone mainstream. >> Yes. >> Thanks so much for sharing. We're live here in Las Vegas for Amazon re:Invent. I'm John Furrier, Stu Miniman, we're back with more live coverage, three days of wall-to-wall coverage. theCUBE will be right back. (upbeat electronic music) (relaxing guitar music)